Tag: Mean Business

  • MarTech Interview with Stephen Howard-Sarin, MD of Retail Media, Americas @ Criteo

    MarTech Interview with Stephen Howard-Sarin, MD of Retail Media, Americas @ Criteo

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    Stephen Howard-Sarin, MD of Retail Media, Americas at Criteo discusses the trends dominating the retail media space in this MarTech Series interview:

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    Hi Stephen, take us through your journey in retail media and the biggest learnings you’ve come away with over the years?

    My decade-plus in retail media has centered on building and scaling businesses that sit at the intersection of commerce and advertising. I’ve had the privilege of contributing to billion-dollar retail media businesses at eBay Ads, Walmart Connect, Instacart and now at Criteo. Along the way, I’ve learned that success hinges on three things: making retail data actionable, solving for cultural friction inside the retailer as it learn to “do media”, and always respecting the customer experience. (In retail media, we charge rent on a house we don’t own, so we need to show some respect.) Retail media is still evolving, and I believe the next wave of growth will come from unifying online and offline experiences, advancing measurement and driving innovation through open, transparent ecosystems – all of which I’m excited to help lead at Criteo across the Americas.

    What top myths around retail media would you like to bust for modern advertisers and marketers?

    The biggest myth is your org structure. Brands have in the past spent money based on a separation between “capital-M” marketing dollars and customer-focused trade dollars, and that distinction is getting fuzzier as retail media capabilities match those found in traditional digital. Within the retailer, the divisions of expertise between media, marketing and merchandising are colliding too. Every retail media success story – from the buy-side or the sell-side – is a story of organizational transformation.

    A big myth is that retail media is just about sponsored product listings or bottom-funnel tactics. While that was true early on, today retail media spans a wider funnel, from awareness to conversion, and through channels like video, display and even in-store digital. If you believe that exposure to your product and message compounds over time, then you have to believe that bottom-funnel tactics help brand, and brand-level tactics help sales. The trick is to build measurement and models that capture the influence we all know is happening. The lingering belief that retail media can’t build brand equity and is purely transactional could not be more wrong. (See also: past purchase behavior strongly predicts future purchases.)

    Marketing Technology News: MarTech Interview with Liat Barer, Chief Product Officer @ Odeeo

    How can modern advertisers make more effective use of retail media channels to drive impact?

    Advertisers can maximize impact by adopting a unified measurement framework that evaluates retail media alongside other marketing channels. This ensures campaigns are tied to broader business objectives, from awareness through conversion. It’s really hard to do! Criteo solves a lot of these cross-format measurement within the ads we deliver, which is great, but big brands have more touchpoints than Criteo (of course) and that makes unified measurement harder.

    Creative should match the shopper’s mindset and context, and an omnichannel consistency is crucial to motivating consumers across digital and physical touchpoints.

    By combining these elements, advertisers can create highly relevant and engaging campaigns that resonate throughout the customer journey – marketing tactics that deliver performance measured by the ringing of the cash register.

    In what ways are you seeing AI impact the entire retail media game today in terms of benefits for end users (advertisers) and ad recipients (potential customers)?

    Any judgment on the impact of AI will age worse than fish. Things are moving really quickly, and value is popping up in unexpected places as smart agencies and brands experiment. Criteo is doing a little of that, too.

    For consumers, AI enables more relevant, personalized experiences that feel helpful rather than intrusive. But like good service at a restaurant, these improvements will not be conspicuous. If people can point and say, “Hey, there’s an AI-based ad!” then something is probably wrong.

    For brands and advertisers, AI helps scale performance, improve (or discover) monetization opportunities, scale personalization, and ultimately enhance the shopper experience from product exposure to purchase.

    Some thoughts around the future of retail media: what will dominate this ecoscape?

    For about 100 years, various parts of the customer journey have been “monetized” and these pieces are all coming together under the retail media umbrella. Outside the store, it was advertising controlled by the brand; inside the store, it was merchandising extras controlled by the retailer; and in ecommerce these lines started to cross. The future of retail media is to unify all these customer touchpoints by applying first-party identity and then measuring all of it at the point of sale. All things in-store that have been monetized, whether physical or digital, will become “retail media”. And much of the advertising we experience outside of a store will also become “retail media,” once it is targeted by retailer data and measured by retailer results.

    Five top global brands who have got their retail media game in place before we wrap up.

    Five brands that come to mind are Pepsi, Unilever, Kiberly-Clark, Kenvue, and Meta.

    These brands have distinctive approaches and strategic discipline. They measure the CPMs, CPC and sales rigorously, but they don’t limit their imaginations to historical media channels and metrics. Fundamentally, their rigor and budgets follow the customer and the data. It makes them tough customers, but they also know how to market to humans through a balanced scorecard of KPIs – audience reach, share of voice, ROAS, iROAS, etc. – instead of just managing what they are measuring. Retail media can measure a lot, but that by itself doesn’t make any ad better or any brand more effective.

    Marketing Technology News: Martech for the Visually Fluent Marketer

    Criteo (NASDAQ: CRTO) is a global commerce media company that enables marketers and media owners to drive better commerce outcomes. Its industry leading Commerce Media Platform connects thousands of marketers and media owners to deliver richer consumer experiences from product discovery to purchase. By powering trusted and impactful advertising, Criteo supports an open internet that encourages discovery, innovation, and choice.

    Stephen Howard-Sarin has spent 10+ years’ operating at the intersection of commerce and advertising and is currently the Managing Director of Retail Media Americas at Criteo. He leads a fantastic team that owns retail media partnerships with dozens of retailers and thousands of brands. Previously, he was VP of Retail Media at Instacart and VP of Strategy & Transformation at Walmart, where he architected the company’s expansion in advertising starting in 2017. Stephen earned his programmatic stripes at eBay Advertising before that, as Senior Director of Sales, Marketing & Analytics. Throughout his career, Howard-Sarin has helped build hypergrowth ad businesses adjacent to core commerce.

    Transaction-based first-party data fuels ideal marketing outcomes, but advertising is not a core competency of most commerce companies. There are significant gaps in culture, talent and technology that hamper retailers from becoming great at advertising – and Stephen loves to build bridges across those gaps.

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  • The Six Trends Quickly Reshaping the Payments Industry in 2025

    The Six Trends Quickly Reshaping the Payments Industry in 2025

    There’s never been a more exciting time in the world of payments. Innovation is happening at breakneck speed, open banking is tearing down data silos and partnerships are forming between traditional financial institutions and fintechs on a global scale. Whether you’re a global retail chain, ecommerce brand, or online marketplace, changes in the payment landscape will have a profound impact on your business. 

    Here are six important trends you can’t afford to ignore:

    1. BNPL, Alternative Payment Methods Are Quickly Displacing Cash and Cards

    While credit cards and cash are still the dominant payment method in many regions, digital payments are quickly capturing market share. For instance, digital wallets are expected to account for more than $25 trillion, or 50% of all online and point-of-sale transactions, by 2027.

    The global BNPL market, led by companies like Klarna, Afterpay, PayPal and Affirm, is meanwhile projected to grow from $560.1 billion in 2025 to $911.8 billion by 2030, at a CAGR of 10.2%

    While card- and cash-based transactions aren’t going away, merchants will certainly need to plan for a future in which consumers in certain markets prefer to use alternative payment methods (APMs) to make purchases in-store or online.

    2. Biometrics and Tokenization are Taking Over

    As the risk and cost of fraud continues to rise, businesses are facing a reckoning: embrace higher standards for payment security or risk losing your customers’ trust. One way that the payment industry is tackling this issue is through advanced payment security measures like tokenization and biometric authentication — both of which combine advanced digital security with added convenience for the end user.

    78% of merchants currently enable network or payment tokens, replacing sensitive data with a digital representation (token) that protects that data. That percentage is expected to increase, but many businesses still struggle with implementing tokenization across their entire payment stack. Biometric authentication — through facial recognition, fingerprint ID or retinal scans — are becoming increasingly common, as are digital “passkeys” that replace passwords and multi-factor authentication.

    Adoption of both payment innovations will continue to accelerate with more regulatory oversight and as technology makes them easier to implement and manage.

    3. The Consumerization of B2B Payments Has Arrived

    While consumers have been the primary focus of payment innovation in recent years, businesses also are demanding the same level of fast, frictionless and secure transactions. Paper checks, invoices, ACH and wire transfers are being replaced by more intricate B2B payment frameworks that can help cut costs and increase efficiency.

    Global payouts, for instance, are ripe for disruption, as they represent a core part of a modern business. Facilitating faster payments to vendors, suppliers and customers can not only help businesses save time and money but also improve relationships with their more important business partners.

    Similarly, businesses are increasingly looking for solutions to help simplify routine payment processes like reconciliation, reporting and payment routing — all of which require considerable resources.

    4. The Relevance of Payment Orchestration Continues to Increase

    While payment orchestration was once a purely technical tool designed to help businesses integrate multiple payment providers, it is quickly evolving into an important part of revenue growth. 84% of businesses leveraging payment orchestration have said it led to a better customer experience, while 76% said it improved customer acquisition.

    Most importantly, payment orchestration is helping businesses stay ahead of changes within the payment landscape, whether that’s integrating new payment methods, optimizing routing across regions and providers, tackling false declines and payment fraud or implementing the latest payments technology.

    5. Payments Fragmentation Means Localization is the Key to Global Expansion

    The payments landscape is becoming increasingly fragmented, as payment method preference and usage varies widely by geography. The digital wallets and real-time payment apps used by U.S. consumers are not the same as the ones used by consumers in Latin America or APAC, and merchants that ignore local payment preferences risk losing market share to competitors.

    But localization isn’t just about payment method integration. There are a host of geo-specific regulations, privacy laws and currency rate dynamics that businesses need to navigate as they develop a localized payment strategy. The key to success will be localizing at scale, bringing a global approach to local ecommerce markets.

    6. Poor UX Translates into Lost Revenue

    Merchants are losing hundreds of billions of sales each year due to cart abandonment. In some cases, pricing or another issue with the product or service is to blame; however, in many instances, friction within the checkout experience is the primary reason for a shopper to abandon a purchase.

    Minimizing cart abandonment should be a top priority for global businesses, as it is one of the most effective ways to recover lost revenue. According to research, preventing cart abandonment at checkout requires businesses to focus on three things:

    • Streamlining the checkout process: 21% of all cart abandonments are due to a checkout process that consumers believe is too complicated.
    • Offering preferred payment methods: 61% of shoppers say lack of preferred payment methods is a key reason why they abandoned a purchase.
    • Minimizing payment declines: Payment authorization issues account for nearly 10% of all ecommerce losses.

    Carol Grunberg is the Chief Business Officer at Yuno, a leading payments orchestration platform, serving a global customer base across 200 countries. With deep experience in the global payments and commerce industry gained through senior roles at Citi, AntGroup/Alipay, Google and Northwestern Mutual, Grunberg is passionate about transforming global commerce by revolutionizing the payments ecosystem and helping democratize finance worldwide. As CBO of Yuno, she leverages her expertise in new business development, client experience and strategy to expand Yuno’s reach and impact across markets and industries.

  • MarTech Series’s Marketing Technology Highlights of The Week Featuring Simon Data, Birdeye, AtData and more in martech!

    MarTech Series’s Marketing Technology Highlights of The Week Featuring Simon Data, Birdeye, AtData and more in martech!

    Grab the latest updates from the global martech industry, from Birdeye’s new social AI agent to Simon Data’s new composable AI agents from this week’s martech highlights by MarTech Series:

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    Marketing and Marketing Tech Quote-of-the-Week!

    On one hand, gaming is an incredible ground for marketers to reach basically everyone, but on the other hand, it poses a big challenge in curation and real understanding of which game is the most relevant or how to target the right users within these games. Games, and especially mobile casual games, are unusually not built on user registrations and collection of data, as they rightfully want to keep their experience easy and fast for the users, but that poses a lot of challenges for brands who are used to knowing a lot about the users from other channels.

    Liat Barer, Chief Product Officer @ Odeeo

    Top MarTech News of The Week –  23rd to 27th June, 2025

    Top MarTech Articles on CTV, Content Marketing, Martech Optimization and more!

    MarTech Q&A of The Week

    Read More

    Brands looking to gain more traction in today’s ecosystem should keep one thing top of mind: quality. The quality of where the ads are appearing and the quality of the viewer. Real users who intentionally visit a site are much more valuable than accidental clicks from someone who’s just trying to navigate somewhere else. Engaged audiences are what drive outcomes.

    Kurt Donnell, CEO @ Freestar

    Missed The Latest Episode of The SalesStar Podcast? Have a quick listen here!

    Episode 229: The Future Of Digital Customer Journeys with Monica Ho, CMO at SOCi

    Episode 228: Gamification for Better Sales Orientation with SalesScreen CEO – Sindre Haaland

    Episode 227: Revenue Generation and RevTech Trends: with Latane Conant, CRO at 6sense

  • Martech for the Visually Fluent Marketer

    Martech for the Visually Fluent Marketer

    The realm of marketing technology, or martech, has historically belonged to those with analytical inclinations. You, the conventional martech user, probably excel with spreadsheets, dashboards, and the complex interplay of KPIs. Your attention is centered on data, optimization, and measurable outcomes.

    A new type of marketer is arising, one who focuses not on figures, but on hues, designs, structures, and emotional impact. This change is significant, prompting martech to advance past simple analysis and harness the strength of creation.

    The Traditional Martech User: A Data-Driven Perspective

    For years, martech platforms have predominantly served individuals who understand data terminology. You probably have dedicated numerous hours analyzing campaigns, recognizing patterns, and enhancing strategies based on concrete data. This analytical method has proven to be highly effective, enhancing efficiency and showcasing evident ROI.

    However, this traditional focus presents certain limitations:

    • Reliance on Quantitative Metrics:

    Success is often defined solely by conversion rates, click-throughs, and cost per acquisition.

    • Emphasis on Post-Campaign Analysis:

    Most tools excel at reporting on what has happened, rather than facilitating what will happen creatively.

    • Structured Data Inputs:

    You’re accustomed to inputting defined data sets, rather than working with fluid, visual concepts.

    • Optimization as the Primary Goal:

    The core objective has often been to incrementally improve existing campaigns rather than conceptualize entirely new ones.

    Unlocking Visual Intelligence in Marketing

    Picture a marketer who envisions campaigns not via a sequence of rational steps, but through a striking mental picture. These visually skilled marketers focus on visual attractiveness, emotional resonance, and narrative branding. They instinctively grasp how a certain color scheme elicits an emotion, or how a specific arrangement affects interpretation. Their approach focuses more on creating experiences than on analyzing figures.

    This shift in thinking matters immensely because martech, as it stands, was largely built for analysis. It was designed to help you understand performance, segment audiences, and optimize workflows. Now, it must adapt to support genuine creative ideation and execution.

    Where Traditional Martech Falls Short for Visual Thinkers?

    Traditional martech tools, while powerful for data analysis, often fall short for the visually fluent. They lack the intuitive interfaces and creative functionalities needed to translate abstract visual concepts into tangible marketing assets.

    Consider these common pain points:

    • Lack of Intuitive Visual Interfaces:

    Most platforms prioritize data displays over drag-and-drop design or real-time visual collaboration.

    • Disconnected Creative Workflows:

    Visual assets are often created in external tools, then painstakingly imported and integrated into martech platforms.

    • Limited Experiential Design Support:

    There’s little direct support for crafting immersive brand experiences or interactive content.

    • Analysis Over Creation:

    The emphasis remains on dissecting past performance rather than fostering new visual ideas and rapid prototyping.

    Marketing Technology News: MarTech Interview with Liat Barer, Chief Product Officer @ Odeeo

    Empowering Creativity with Visual AI

    The emergence of promptless, visual AI tools is revolutionary for the visually fluent marketer. You no longer need to translate your visual ideas into text prompts; the AI understands your intent through images and forms.

    This new generation of martech offers exciting possibilities:

    • Direct Visual Communication with AI:

    You can upload an image or sketch, and the AI generates variations or related content.

    • Rapid Prototyping of Visual Concepts:

    Ideate and iterate on visual campaigns far more quickly than ever before.

    • Personalized Visual Content Generation:

    AI can adapt imagery and design elements to specific audience segments in real-time.

    • Automated Asset Creation:

    From banner ads to social media graphics, visual AI can significantly accelerate content production.

    Practical Use Cases for Visual-First Martech

    The practical applications of visual-first martech are vast and transformative. These tools empower marketers to move from conceptualization to execution with unprecedented speed and precision, ultimately improving the overall impact of your martech stack.

    Here are a few compelling examples:

    • Dynamic Ad Creative Generation:

    AI can generate multiple ad variations based on visual themes, ensuring hyper-personalization.

    • Automated Social Media Content:

    Quickly produce visually engaging posts, stories, and reels tailored to different platforms.

    • Website Personalization Through Imagery:

    Dynamically change website visuals based on user behavior and preferences.

    • Interactive Content Development:

    Create engaging quizzes, polls, and interactive experiences with minimal coding.

    • Brand Guideline Enforcement:

    AI can ensure all generated visuals adhere strictly to brand aesthetics and identity.

    • Enhanced Email Marketing Visuals:

    Design visually striking email templates that capture attention and drive engagement.

    Strategic Implications for Martech Product Teams

    The rise of visually fluent marketers demands a fundamental reconsideration of martech development priorities. Product teams must recognize that visual thinking represents an equally valid cognitive approach deserving dedicated tools.

    This strategic shift means:

    • Incorporating visual interfaces that allow browsing and manipulation of assets rather than text-based navigation
    • Developing approval workflows that accommodate visual feedback mechanisms
    • Creating analytics dashboards that present data visually rather than numerically
    • Building integration points with design tools that maintain visual fidelity

    Martech providers who address these needs position themselves at the forefront of marketing’s evolution toward visual-first communication.

    Martech’s Visually Driven Future

    The rise of the visual-first martech era doesn’t eliminate the importance of analytics; it expands who gets to lead the conversation. Data will always be crucial for understanding performance and informing strategy.

    However, the initial spark, the compelling narrative, and the emotional connection will increasingly originate from those who think in images before words. This evolution signifies a broader, more inclusive future for martech.

    The next frontier of martech will be shaped by those who can translate abstract visual concepts into impactful, data-driven marketing realities.

    Marketing Technology News: Breaking The Martech Gridlock: How Do Aggregator Ecosystems Unlock Seamless Integration?

  • Ikea Cuts Restaurant Prices 50% to Help Mitigate Cost-of-Living Pressures

    Ikea Cuts Restaurant Prices 50% to Help Mitigate Cost-of-Living Pressures

    In 14 countries around the world, Ingka Group, the largest owner of Ikea stores, is cutting the price of restaurant meals in half Monday through Friday, with kids eating free. The move is part of the retailer’s ongoing investment in the in-store experience, including the introduction of new dishes inspired by Asian flavors, and is designed to help customers stretch their budgets.

    “Food has always been very important for Ikea, and we wanted to enable even more people to enjoy our restaurant offer while exploring our home furnishing range,” said Tolga Öncü, Ingka Retail Manager (COO) at Ikea Retail (Ingka Group) in a statement. “Securing the lowest possible price for our products is always our utmost goal, and this is even more important in today’s times of economic uncertainties and cost-of-living pressures.”

    As an example, in France, the cost of a lunch for a family of four, which includes two hot meals with meatballs for adults and two kids’ meals, will drop from €19.9 to €6.96, and all restaurant guests will receive a five-Euro voucher for use in-store.

    The price cuts, which will be activated at different times in different markets, will be applied in Austria, Canada, China, Denmark, France, Germany, Italy, the Netherlands, Poland, Portugal, South Korea, Sweden, Switzerland and the UK.

  • From Hidden Gems to Bestsellers: How Machine Learning is Reshaping Online Retail Marketplaces

    From Hidden Gems to Bestsellers: How Machine Learning is Reshaping Online Retail Marketplaces

    The evolution of ecommerce has seen traditional retailers such as Walmart, Target and Best Buy embrace the marketplace model by listing products from third-party sellers, while marketplaces like Amazon have introduced first-party inventory alongside third-party products. This shift allows retailers to expand their catalog without the burden of holding excess stock, but creates new challenges for product discovery and visibility.

    The key to addressing these challenges lies in machine learning (ML)-powered retail media solutions, which optimize product visibility, enhance the shopping experience and create a win-win-win scenario for sellers, buyers and platforms alike.

    How Marketplaces Took Over Retail — and What’s Next

    Traditionally, retailers have operated on a direct-to-consumer model, purchasing inventory, storing it and managing fulfillment. This approach involved significant risk, as retailers have to predict demand and manage logistics efficiently and accurately. By contrast, the marketplace model allows retailers to offer a much broader selection of products without assuming inventory risk. By enabling third-party sellers to list their products, retailers can expand their product assortment, diversify revenue streams and increase customer engagement.

    This shift has created an intricate and interconnected marketplace ecosystem. Walmart.com now operates as a hybrid marketplace where Walmart stocks some products and others come from third-party sellers, adding further complexity to the marketplace landscape. The sheer volume of products on these platforms creates a critical challenge: how to surface the right products to the right customers.

    Cracking the Code: How Machine Learning Connects Shoppers to the Right Products

    Marketplaces must solve the problem of connecting buyers with the most relevant products amid millions, if not billions, of available listings. ML-powered algorithms analyze vast amounts of customer data to determine which products should be displayed, optimizing for relevance and conversion rates.

    These algorithms rely on customer signals such as browsing history, purchase behavior, trending products, price sensitivity and even contextual factors like time of day, device type and weather. By processing these inputs, ML models can make data-driven recommendations that improve both customer satisfaction and seller success.

    For instance, ML enables marketplaces to identify emerging trends and surface new products that customers may not have explicitly searched for but are likely to be interested in. This ensures that buyers have access to a curated selection of items that align with their preferences, while sellers benefit from increased visibility and sales opportunities.

    Breaking the Ice: How New Sellers can Win in a Data-Driven Marketplace

    One significant challenge in ML-powered product recommendations is the “cold start problem.” When a new seller or product enters the marketplace, there is little to no historical data to inform recommendations, but in this day and age, personalization matters more than ever. McKinsey research shows that 71% of consumers expect companies to deliver personalized interactions, and 76% get frustrated when this doesn’t happen. Without sufficient behavioral insights, it is difficult for the marketplace’s algorithms to determine which customers would be most interested in these new offerings, which ultimately creates hurdles for personalization.

    Advertising plays a crucial role in overcoming this challenge. Sellers can invest in ML-driven sponsored listings through retail media networks — advertising ecosystems within retailers’ digital storefronts — to boost their products’ visibility. By bidding on ad placements, new suppliers can generate initial engagement, which in turn feeds the organic recommendation algorithms with data. Once customers start interacting with the product, organic visibility increases as the algorithm refines its understanding of which audiences are most likely to convert. In this way, advertising acts as a tiebreaker in determining product exposure, allowing new and established sellers alike to compete effectively.

    A Triple Win: AI-Powered Marketplaces Benefit Everyone

    The integration of ML-powered retail media solutions benefits all marketplace stakeholders:

    • Buyers: Personalized recommendations enhance the shopping experience by presenting relevant products, reducing the time spent searching for items and increasing overall satisfaction.
    • Sellers: Increased visibility, particularly for new products, drives higher conversion rates and sales, helping sellers reach their target audiences more effectively.
    • Marketplaces: Optimized product discovery leads to higher engagement, better conversion rates and additional advertising revenue, reinforcing the overall platform’s success.

    A compelling example of this dynamic can be seen in how ML-powered advertising helps surface products that might otherwise be overlooked. StockX, for example, has historically prioritized listings based on hype-driven demand, such as limited-edition sneakers or streetwear. However, this approach can make it challenging for sellers offering high-quality but less trend-driven products — like running shoes from well-known brands — to reach potential buyers. One independent retailer, operating a specialty running shoe store in suburban Chicago, faced this exact challenge. Despite carrying inventory that aligned with the interests of performance-running-focused customers, the platform’s organic discovery algorithms tended to favor more high-profile releases. By leveraging ML-driven advertising, the retailer was able to showcase its products to the right audience, dramatically increasing sales.

    When done correctly, machine learning and advertising work together to connect buyers with relevant products they may not have otherwise discovered, benefiting both sellers and shoppers alike. For StockX, the partnership with Moloco, an AI-driven advertising platform with proven ML models, has demonstrated outsized results, by combining our unique datasets with Moloco’s models to enhance our in-house advertising tech stack.

    The Marketplace Revolution is Just Beginning

    As marketplaces continue to grow in complexity, machine learning emerges as the key driver of an optimized, scalable, and profitable ecosystem. By leveraging ML-powered solutions, retailers can create a more dynamic shopping experience that benefits buyers, sellers and platforms alike. From solving the cold start problem to refining personalized recommendations, machine learning ensures that the right products reach the right customers at the right time. In an increasingly interconnected marketplace landscape, this approach is not just a competitive advantage — it’s a necessity.


    Tim O’Malley is VP of Product and Engineering at StockX, where he leads a 90-person global team across product, engineering, Design, data science and AI. His work spans buyer experience, payments and advertising, driving marketplace conversion, FinTech innovation and monetization. Previously, O’Malley was Chief Product Officer at Delivery.com, which acquired his startup, Brinkmat. He began his career as a Software Engineer at Goldman Sachs before finding his passion in tech leadership.

  • PwC Back-to-School Survey: To Stretch Their Budgets, 37% of Parents will Only Buy Items on Sale

    PwC Back-to-School Survey: To Stretch Their Budgets, 37% of Parents will Only Buy Items on Sale

    A back-to-school (BTS) spending survey conducted in May by PwC has some good news for retailers: shoppers are not planning to curb their BTS spending plans — but they are looking at ways to get the most bang for their buck. Consumers’ most popular money-saving tactic will be to only buy items that are on sale, chosen by 37% of respondents, with the same percentage planning to save by shopping early in the season.

    Despite these penny-pinching tactics, nearly three in four consumers expect to spend the same or more as usual on BTS shopping this fall, with more than one in three anticipating they will spend more than they did in 2024.

    Parents Most Likely to Cut Back on Tech Spending This Year

    Technology will make up a large portion of these BTS budgets: 25% of parents plan to spend $500 or more on tech purchases this season. Apparel and shoes also will claim a large share: 27% of respondents will spend $101 to $250 on these items, with 29% spending $251 to $500.

    However, these buying intentions don’t mean that tech brands can rest easy. The survey asked respondents to identify the broad BTS shopping categories where budget-conscious consumers will limit their purchases. Technology was the top choice, at 44%, followed by clothing and shoes at 40%. As might be expected, more basic BTS purchases — school supplies, books and educational materials — will be spared from cutbacks, with just 30% saying they’ll limit spending on school supplies and even fewer, 26%, saying they will skimp on books.

    AI Emerges as Online Deal-Seeking Tool

    In another sign of consumers’ growing comfort with AI-powered tools, 20% plan to use them to find online deals for BTS items. This percentage is likely to keep climbing, so retailers will need to enhance their digital channels and search capabilities for AI-powered discovery if they want to claim their share of spending.

    How Income and Demographics Affect BTS Shopping Choices

    The PwC survey also tracked shopping preferences by generation. Despite their reputation as store-hating digital natives, Gen Z parents are actually more likely than millennial and Gen X customers to shop exclusively in-store. The finding supports other PwC data and suggests that Gen Z may be driving a brick-and-mortar renaissance fueled by tactile experiences and brand engagement.

    Most shoppers will use a combination of online and brick-and-mortar shopping for the BTS season, although millennial (71%) and Gen X parents (73%) are significantly more online-oriented than Gen Z (57%) and boomers (54%). These latter two groups are more likely to support a store-only approach, chosen by 27% of Gen Z and 30% of boomer parents.

    The online-offline split also occurs between different income groups. Households earning $75,000 or more are nearly twice as likely to shop exclusively online, at 14%, compared to the 8% of those earning less than $75,000 annually. Families earning less than the median household income are almost twice as likely to shop exclusively in-store, at 30% compared to richer households’ 17%.

    The survey of 1,198 parents was conducted by PwC between May 6-8, 2025.

  • Breaking The Martech Gridlock: How Do Aggregator Ecosystems Unlock Seamless Integration?

    Breaking The Martech Gridlock: How Do Aggregator Ecosystems Unlock Seamless Integration?

    The modern marketing environment is a wide ecology of technical tools, each promising to improve process efficiency, increase customer interaction, and deliver measurable outcomes. However, the development of Martech solutions has unintentionally introduced a new challenge: the Martech Integration Crisis. Marketing leaders are battling to establish seamless data flow and operational cohesiveness as their Martech stacks become increasingly sophisticated. This situation is more than just a technical issue; it’s a strategic hindrance to marketing success and, ultimately, corporate growth.

    The issue’s root is the inherent conflict between strict all-in-one suites and disjointed best-of-breed alternatives. While all-in-one systems have the advantage of integrated functionality, they frequently lack the specific capabilities and flexibility of best-in-class solutions. In contrast, best-of-breed solutions, while excellent in specific areas, generate data silos and integration nightmares when deployed in isolation. This dilemma forces marketing leaders to choose between limited functionality and fragmented data.

    Integration remains a major challenge for marketing executives, consistently ranking among their top concerns. The inability to seamlessly connect disparate Martech tools leads to data inconsistencies, duplicated efforts, and a lack of a unified customer view. This fragmented landscape impedes the ability to deliver personalized experiences, optimize marketing campaigns, and make data-driven decisions. As a result, marketing teams are frequently bogged down in manual data reconciliation and troubleshooting, diverting valuable resources away from strategic projects.

    Amidst this integration crisis, aggregator ecosystems are emerging as a promising middle ground. These platforms aim to bridge the gap between all-in-one suites and best-of-breed solutions by providing a centralized hub for connecting and managing various Martech tools. By offering pre-built integrations and standardized data formats, aggregator ecosystems simplify the integration process, enabling marketing teams to build cohesive Martech stacks without sacrificing flexibility or functionality.

    The Martech Sprawl Dilemma: Too Many Tools, Not Enough Cohesion

    The rapid development of the Martech landscape has resulted in an unparalleled abundance of marketing solutions. Marketing teams, motivated by a desire to improve their capabilities and stay ahead of the competition, are continually adding new solutions to their arsenal. This boom of Martech solutions, while initially beneficial, has unintentionally generated the Martech Sprawl Dilemma: too many tools, insufficient cohesiveness.

    The Struggle Between Rigid All-in-One Suites and Disconnected Best-of-Breed Solutions

    Marketing leaders face a difficult choice when selecting Martech solutions. On one side, all-in-one suites promise seamless integration and ease of use but often lack the depth and innovation of best-of-breed solutions. On the other hand, best-of-breed approaches provide superior functionality for specific needs but require significant effort to integrate, leading to fragmented data and operational complexity.

    This ongoing tug-of-war forces marketing teams to compromise—either settling for a monolithic solution that may not fully meet their needs or struggling with an assortment of highly specialized but disconnected tools.

    Introduction to Aggregator Ecosystems as a Promising Middle Ground

    Aggregator ecosystems have emerged as a potential solution to the Martech integration crisis. These platforms act as intermediaries, providing pre-built integrations, centralized data management, and workflow automation without requiring marketers to choose between rigid all-in-one suites and disconnected best-of-breed tools. By leveraging aggregator ecosystems, marketing teams can achieve both flexibility and cohesion, improving efficiency and customer experience.

    The Explosion of Martech Solutions—Why Marketing Teams Keep Adding More Tools

    The Martech ecosystem has expanded dramatically over the last decade, with dozens of solutions available in categories such as email marketing, social media management, customer data platforms (CDPs), and automation. This increase is fueled by:

    • Specialization: As new technologies handle particular marketing concerns, teams may be tempted to adopt specialized tools.
    • Innovation: AI, machine learning, and real-time data are constantly pushing marketing capabilities to new heights.
    • Competitive Pressure: Companies invest in new tools to meet changing customer expectations and industry trends.

    While these factors promote innovation, they also contribute to Martech sprawl, which occurs when marketing teams accumulate an excessive number of tools, resulting in fragmented systems and operational inefficiencies.

    The Unintended Consequences: Data Silos, Inefficiencies, and Operational Bottlenecks

    As Martech stacks expand, the unintended consequences of tool proliferation become increasingly apparent. While marketing teams adopt new technologies to enhance capabilities, the lack of cohesion between tools often results in significant challenges that hinder efficiency and impact overall effectiveness.

    a) Data Silos: The Barrier to a Unified Customer View

    One of the most critical issues in fragmented Martech stacks is the creation of data silos. Different tools collect and store data in isolated environments, making it difficult to achieve a holistic customer view. For example:

    • CRM platforms house sales interactions, while email marketing tools track engagement separately.
    • Social media analytics exist independently from website tracking and customer service data.
    • Ad platforms store their insights, limiting visibility across other marketing channels.

    Without seamless integration, marketers struggle to gain a single source of truth, leading to disconnected customer experiences and inefficient targeting strategies.

    b) Redundant Costs: Paying for Overlapping Features

    The lack of integration across Martech solutions often results in redundant capabilities. Companies end up subscribing to multiple tools with overlapping functionalities—such as separate analytics platforms, automation solutions, and personalization engines—without fully utilizing each tool’s potential. This inefficiency inflates costs, wasting marketing budgets on unnecessary software.

    c) Inefficient Workflows: Wasting Time on Manual Processes

    With many fragmented tools, marketers waste a lot of time traveling between platforms, manually exporting and importing data, and debugging integration issues. Instead of focusing on strategy and execution, they are hindered by inefficiencies. This hinders campaign execution and limits agility in responding to market movements.

    d) Integration Nightmares: IT Dependence Delays Innovation

    Each new Martech product frequently necessitates considerable IT support for integration, delaying adoption and reducing marketing agility. When solutions do not interact seamlessly, firms must rely on sophisticated middleware, custom APIs, or manual data transfers, which slows campaign and new initiative time-to-market significantly.

    These issues ultimately diminish the effectiveness of marketing initiatives, resulting in missed opportunities and a low return on Martech investments. Without a uniform, efficient, and scalable approach, firms struggle to leverage the value of their Martech stacks, making integration a major concern for marketing leaders seeking to drive development and innovation.

    Why Traditional Integration Approaches (APIs, Middleware, Native Connectors) Fall Short

    To address the integration challenge, marketing teams have traditionally relied on three main approaches:

    a) APIs (Application Programming Interfaces): A Double-Edged Sword

    APIs are the foundation of data transmission amongst Martech solutions, enabling diverse systems to communicate. While they provide a considerable degree of freedom, they provide substantial challenges:

    • Technical complexity: Implementing and maintaining APIs necessitates specialist knowledge, making them time-consuming for non-technical marketing teams.
    • Maintenance Overhead: As software upgrades are released, APIs must be continually maintained to ensure compatibility.
    • Security Concerns: Improper API administration might expose data vulnerabilities, hence raising cybersecurity threats.

    b) Middleware Solutions: Adding Another Layer of Complexity

    Middleware systems, such as Integration Platform as a Service (iPaaS) solutions, serve as bridges between Martech tools, allowing for smooth data flow. These platforms include tools for connecting dissimilar systems, such as MuleSoft and Workato. However, middleware solutions bring some challenges:

    • Additional Costs: Many middleware systems require separate license and operations charges, which raises overall Martech costs.
    • Management Complexity: Rather than easing Martech integration, middleware frequently adds a layer of technical complexity that necessitates IT control.
    • Performance bottlenecks: Middleware solutions can slow down data processing, causing delays in insights and campaign execution.

    c) Native Connectors: Convenience with Constraints

    Many Martech providers offer pre-built native connectors that link with major platforms, including CRM, email automation, and analytics tools. While these integrations are helpful, they frequently have limitations:

    • Limited Scope: Native connectors typically only handle basic use cases, failing to meet specific business requirements.
    • Lack of Deep Customization: Marketers frequently require advanced automation and workflow customization, which native integrations do not provide.
    • Vulnerability to Vendor Changes: If a vendor discontinues or changes an integration, marketing teams may experience disruption.

    Why Do Traditional Integration Methods Fall Short?

    Despite these approaches, traditional integration methods struggle to keep up with the evolving demands of modern marketing. Key limitations include:

    a) Scalability Issues: The Growing Burden of Maintenance

    As Martech stacks grow, maintaining APIs and middleware solutions becomes increasingly difficult. Each additional tool necessitates new integration efforts, which strain IT resources and create potential points of failure.

    b) Customization Limitations: One-Size-Fits-All Doesn’t Work

    Off-the-shelf integrations rarely support the unique workflows and automation needs of individual businesses. Custom development is often required, adding costs and time to implementation.

    c) Lack of Real-Time Syncing: A Major Bottleneck

    Many traditional integrations rely on batch processing instead of real-time data changes. This yields:

    • Outdated insights: Marketers make decisions based on obsolete data, which reduces campaign efficacy.
    • Slow Response Times: Customer contacts are delayed, reducing personalization and engagement.

    To address these problems, businesses are increasingly looking into aggregator ecosystems and API-first designs as a more scalable and adaptable approach to Martech integration.

    Moving Forward: Towards Smarter Martech Integration

    The Martech sector is at a crossroads, and marketing professionals must reconsider how they approach integration. Emerging aggregator ecosystems, AI-powered integration solutions, and low-code/no-code platforms all provide interesting alternatives. Companies may combat Martech sprawl by focusing on interoperability and scalability, resulting in more coherent, efficient marketing processes.

    In the following sections, we will look at innovative ways to integrate Martech and real-world case studies of organizations that have successfully navigated the integration difficulty.

    The Rise of Aggregator Ecosystems: A Smarter Alternative to Suite vs. Best-of-Breed

    Aggregator ecosystems are platforms that bridge the gap between various Martech solutions, allowing for smooth data sharing, process automation, and interoperability. Unlike traditional middleware, which frequently necessitates substantial development, aggregator ecosystems offer pre-built connectors that ease Martech stack communication.

    How do They Balance Flexibility with Interoperability?

    Aggregator ecosystems strike a balance between the control and flexibility of best-of-breed solutions and the interoperability of all-in-one suites. They allow marketing teams to:

    • Unify Data: By centralizing data from multiple sources, these platforms eliminate silos and improve decision-making.
    • Streamline Workflows: Pre-built automation and integration templates enhance operational efficiency.
    • Reduce IT Dependency: Many aggregator platforms offer no-code or low-code integration capabilities, empowering marketing teams to manage their technology.

    Examples of Martech Aggregators in Action

    Several Martech aggregators have gained prominence for their ability to connect disparate tools:

    • Segment:

    Segment, a customer data platform (CDP), excels in combining data from several sources – web analytics, mobile apps, CRM systems, and more – to create a cohesive customer profile. Segment eliminates silos by centralizing this data, giving marketing teams a single source of truth.

    This single profile enables marketers to personalize cross-channel experiences, improve campaigns, and acquire a comprehensive picture of client behavior. Segment’s strength is its ability to standardize data, making it easily accessible for analysis and activation across multiple downstream tools.

    • Zapier:

    Zapier specializes in workflow automation, integrating thousands of apps with simple “Zaps” – automated workflows activated by certain circumstances. This software enables marketers to automate repetitive processes like data syncing across platforms, delivering tailored emails, and making social media posts.

    Zapier’s user-friendly interface and vast app library make it accessible to marketers of various technical abilities, allowing them to create unique integrations without substantial coding. Its capability to initiate actions based on events in several applications significantly lowers manual labor and enhances productivity.

    • mParticle:

    mParticle is a data aggregation platform that focuses on real-time data connectivity and audience segmentation. It excels at gathering and integrating data from mobile apps, websites, and other digital touchpoints, allowing marketers to construct dynamic audience groups based on real-time activity. mParticle’s emphasis on data governance and privacy ensures data integrity and security.

    Its capacity to offer real-time audience activation enables advertisers to personalize experiences and deliver targeted messages at the point of engagement, hence increasing the effect. By using these aggregator ecosystems, firms can create scalable and efficient Martech stacks, encouraging a coherent and data-driven marketing approach.

    Breaking the Walled Garden: Why Open Ecosystems Are the Future

    Many big martech companies, such as Salesforce, Adobe, and HubSpot, provide substantial feature sets within their closed ecosystems. These all-in-one suites claim seamless integration of respective products, potentially simplifying setup and maintenance. However, this approach has fundamental constraints that prevent adaptability and innovation.

    a) Lack of Flexibility

    Closed ecosystems limit the tools and features a corporation can employ. Marketers are frequently obliged to modify their workflows to accommodate the suite’s capabilities rather than the other way around. This lack of adaptability may limit an organization’s ability to tailor its Martech stack to unique business requirements.

    b) Slower Innovation

    Closed Martech suite vendors have control over the product roadmap, which means users must wait for new features and integrations. Because these organizations prioritize their solutions, they may take longer to adopt evolving technology than best-of-breed vendors who focus on specific advances.

    c) Higher Costs

    While closed-suite suppliers frequently offer bundled pricing, firms may wind up paying for unneeded functionality while passing up best-in-class options accessible outside the ecosystem. Furthermore, licensing and renewal fees are often greater, particularly when vendors charge extra for premium integrations or enhanced features.

    d) Limited Cross-Platform Compatibility

    Most closed ecosystems have limited or restricted interactions with third-party technologies, making it challenging for enterprises to consolidate their Martech stack. This restriction can lead to inefficiencies because teams must discover workarounds to link tools that are not natively supported.

    The Risks of Vendor Lock-In and Restricted Innovation

    As technology evolves at an unprecedented pace, the risks of vendor lock-in become increasingly apparent, threatening to constrain a company’s ability to adapt and innovate.

    a) Dependence on a Single Vendor

    Vendor lock-in occurs when businesses become heavily reliant on a single provider, making it difficult and costly to switch. Once an organization has built its marketing processes around a closed ecosystem, migrating to another platform often requires significant time, effort, and financial investment.

    b) Barriers to Experimentation

    Marketing teams need the freedom to experiment with new tools and technologies to stay competitive. However, when a company is locked into a closed ecosystem, it may be unable to test and integrate best-of-breed solutions without complex workarounds or additional costs.

    c) Data Silos and Interoperability Challenges

    One of the most significant concerns with closed ecosystems is the formation of data silos. Because proprietary platforms frequently limit data sharing, firms struggle to integrate customer insights across numerous tools. This fragmentation results in:

    • Inconsistent customer data across channels.
    • Reduced personalization and targeting precision.
    • Difficulties with measuring cross-platform campaign performance.

    Without open data exchange, organizations face operational inefficiencies and restricted visibility into their customers’ journeys.

    d) Stifling Innovation

    Closed ecosystems promote internal products over external advances. As a result, firms who use these platforms risk missing out on cutting-edge advances in AI, machine learning, automation, and predictive analytics. In contrast, open ecosystems enable businesses to integrate and experiment with new technologies as they become available.

    Why do Marketers Demand Openness, Interoperability, and Modularity?

    Marketing teams work in a fast-evolving digital context. Businesses must change rapidly as new channels, tools, and client expectations arise regularly. Open ecosystems enable marketers to replace or upgrade particular technologies without redesigning their entire stack, ensuring they remain flexible and responsive.

    a) Seamless Data Flow Across Platforms

    To develop a cohesive customer experience, marketing teams require real-time data across several platforms. Open ecosystems foster interoperability, allowing firms to:

    • Consolidate customer data from various sources.
    • Improve your decision-making by leveraging insights from several tools.
    • Workflows can be automated without requiring any technological knowledge.

    b) Customization and Best-of-Breed Selection

    Marketers are increasingly embracing a best-of-breed strategy, selecting the most successful solutions for each purpose (for example, customer data platforms, AI-driven analytics, or omnichannel automation). Open ecosystems allow firms to create a Martech stack that is tailored to their requirements rather than opting for a one-size-fits-all solution.

    c) Lower Costs and Greater ROI

    With an open, modular Martech stack, companies can optimize their spending by only investing in the tools they need. This flexibility prevents unnecessary costs associated with bundled, closed-suite solutions while maximizing the value of existing investments.

    The Shift Toward API-First, Composable Architectures for Martech

    As marketing technology advances, firms are shifting from rigid, monolithic software suites to more flexible, API-first, composable designs. Traditional Martech systems frequently have constraints, such as vendor lock-in, feature bloat, and slow innovation cycles. In contrast, an API-first approach allows marketing teams to create a personalized, scalable, and future-proof tech stack by integrating best-of-breed solutions via modular APIs.

    What Is an API-First, Composable Architecture?

    A composable Martech’s architecture is based on modular components that connect smoothly via Application Programming Interfaces (APIs). Instead of relying on a single vendor suite, firms can choose specialized solutions that meet their requirements while maintaining seamless compatibility.

    This strategy allows marketing teams to dynamically create and adjust their technology stack, avoiding the need for pre-built connectors or middleware. API-first platforms are designed with integration as a guiding concept, enabling real-time data interchange, improved automation, and increased analytics without the need for ongoing IT involvement.

    Key Benefits of API-First Martech Ecosystems

    The key benefits of API-first Martech Ecosystems are given below.

    a) Scalability: Adapt and Expand with Ease

    An API-first approach allows businesses to scale their Martech stack efficiently. New tools can be added or removed as needed without disrupting existing workflows. This agility is crucial for growing companies or those looking to experiment with new marketing technologies.

    b) Real-Time Data Sharing: Eliminate Silos

    One of the most significant issues in Martech is fragmented data. APIs offer easy, real-time data interchange between platforms, ensuring that marketing teams have access to current customer insights. This interconnectedness breaks down data silos and improves personalization efforts across channels.

    c) Faster Innovation: Stay Ahead of the Curve

    Waiting for a monolithic suite vendor to create a feature can stifle marketing innovation. With an API-first environment, organizations can immediately integrate emerging technologies (such as AI-powered analytics or chatbot automation) without having to wait for proprietary updates. This maintains firms at the cutting edge of Martech innovations.

    d) Lower Technical Barriers: No-Code and Low-Code Integrations

    Modern APIs are increasingly designed for no-code or low-code integrations, allowing marketing teams to connect platforms without deep technical expertise. Tools like Zapier and Workato enable automation and integration without IT support, making Martech management more accessible.

    e) Cost Efficiency: Reduce Redundant Investments

    Rather than paying for bundled features in an all-in-one suite, businesses can select only the tools they need. API-first architectures eliminate redundant software costs and allow organizations to invest in the best solutions for each marketing function.

    Examples of API-First, Open Martech Platforms

    Several Martech companies are at the forefront of the API-first movement, prioritizing modularity, openness, and seamless integrations:

    a) Segment (Customer Data Platform)

    Segment acts as a customer data hub, aggregating and standardizing data across various touchpoints. Its API-driven infrastructure allows businesses to connect customer insights with analytics, email marketing, and personalization tools without relying on a single vendor.

    b) Zapier (Automation Tool)

    Zapier enables no-code workflow automation, connecting thousands of applications. Marketers can automate repetitive tasks (e.g., syncing leads between a CRM and email platform) without writing a single line of code, improving efficiency and reducing manual work.

    c) Twilio (Communications API)

    Twilio provides programmable APIs for messaging, voice, and video, allowing businesses to create personalized, omnichannel customer experiences. It integrates seamlessly with chatbots, email, and customer support systems.

    d) Snowflake (Data Cloud)

    Snowflake facilitates real-time data sharing between Martech and analytics tools. Its cloud-based architecture allows companies to store, analyze, and share data seamlessly, improving decision-making and marketing performance.

    The transition to API-first, composable architectures heralds a new age in marketing technology, one in which firms are no longer bound by vendor constraints. Organizations that embrace an open environment can gain better agility, improve consumer experiences, and drive marketing innovation. The Martech landscape will continue to change, and those who take a modular, API-driven strategy will be better positioned to compete in this dynamic climate.

    As marketing becomes more data-driven, businesses require tools that enable them to collect, evaluate, and act on insights without restriction. The shift to API-first, composable architectures defines Martech’s future, with an emphasis on openness, interoperability, and flexibility.

    Key Takeaways:

    • Closed ecosystems limit innovation, increase costs, and create vendor lock-in.
    • Open ecosystems enable seamless data flow, best-of-breed tool selection, and greater agility.
    • API-first, composable architectures are the future, allowing marketing teams to build custom Martech stacks that evolve with their needs.

    By embracing open ecosystems, marketers can future-proof their Martech investments, stay ahead of industry trends, and deliver better customer experiences.

    Marketing Technology News: MarTech Interview with Liat Barer, Chief Product Officer @ Odeeo

    The Limitations of All-in-One Suites: How All-in-One Platforms Struggle to Keep Pace with Specialized Best-in-Class Tools

    All-in-one Martech suites guarantee a unified experience by combining different technologies on a single platform. Salesforce, Adobe, and HubSpot position their products as entire ecosystems that include everything from customer relationship management (CRM) and email marketing to automation and analytics. While this strategy is convenient, it has severe limits that impede marketing teams’ agility and inventiveness.

    a) Lack of Specialization

    Best-in-class tools are built with a singular focus on excelling in a specific area. For example:

    • Marketo and Pardot specialize in marketing automation.
    • Segment excels in customer data management.
    • Braze and Iterable offer cutting-edge personalization and cross-channel engagement.

    In contrast, all-in-one suites aim to cover numerous functions, frequently resulting in jack-of-all-trades, master-of-none solutions. Their capabilities may be extensive, but they frequently lack the depth, flexibility, and creativity of dedicated, best-in-class platforms.

    b) Slow Adoption of Emerging Technologies

    Because all-in-one suites must maintain and update a wide range of functionality, they frequently fail to innovate as quickly as specialized tools. Best-in-class vendors frequently pioneer the introduction of emerging technologies such as AI-driven personalization, predictive analytics, and real-time consumer engagement. Marketers who use all-in-one platforms may have to wait months, if not years, for equivalent features to be included in their suite.

    c) Limited Customization and Agility

    Marketing teams increasingly require tailored solutions to fit their unique workflows. However, all-in-one suites impose rigid structures that limit customization. Unlike API-first platforms that allow businesses to mix and match tools, monolithic suites often force users into pre-defined workflows that may not align with their specific needs.

    The Problem of “Feature Bloat”—Too Many Underused Capabilities

    All-in-one suites compete by adding more features to attract a broad range of customers. While this might seem beneficial, it often leads to feature bloat, where platforms become cluttered with tools that go largely unused.

    a) The Hidden Cost of Feature Bloat

    Not only does feature bloat increase complexity, but it also raises expenses. Vendors justify higher prices with comprehensive feature sets, even if marketing teams only employ a subset of the available products. As a result, firms pay for unneeded capabilities while still needing to integrate additional best-in-class products to close important gaps.

    b) User Experience and Productivity Challenges

    Too many features can lead to clunky user interfaces, complex workflows, and steep learning curves. Instead of simplifying marketing operations, feature-heavy suites often:

    • Require extensive training and onboarding.
    • This leads to frustration among teams trying to navigate bloated dashboards.
    • Slow down campaign execution due to overly complicated workflows.

    Marketing teams need efficiency and usability, but overloaded platforms often get in the way of productivity rather than enhancing it.

    c)  Underutilization of Capabilities

    According to research, firms seldom use all of the functions available on all-in-one systems. Organizations often focus on:

    • A few essential functions and ignore others.
    • Struggle to incorporate advanced features into their workflows.
    • Continue to rely on external tools to close performance gaps.

    Finally, the promise of an all-in-one solution frequently falls short, prompting marketing teams to consider if the added complexity and cost are worthwhile.

    Why Marketing Teams Often Outgrow Monolithic Suites?

    As marketing strategies become more data-driven, AI-powered, and customer-centric, many teams outgrow traditional all-in-one suites and look for more flexible alternatives.

    The Growing Demand for Open and Modular Ecosystems

    Rather than being tied to a single vendor, marketing leaders increasingly choose composable, API-first ecosystems that enable them to:

    • Choose best-of-breed solutions that are tailored to their requirements.
    • Scale their Martech stack without regard to vendor limits.
    • Replace old tools without completely revamping the system.

    The Rise of Hybrid Martech Stacks

    Many organizations now adopt a hybrid approach, using an all-in-one suite as a foundation while integrating specialized tools for advanced capabilities. For example:

    • A company may use HubSpot for CRM but integrate Segment for customer data management.
    • Marketers may rely on Adobe’s suite for content but use Braze for customer engagement.
    • Businesses might use Salesforce for sales automation but Zapier for workflow automation.

    This approach allows teams to balance the stability of a core platform with the flexibility of specialized tools, ensuring they can adapt to evolving marketing trends.

    The shift away from monolithic Martech suites is already underway. Marketers now demand:

    • Interoperability between platforms to break down data silos.
    • Customizability to tailor their stacks to specific business needs.
    • Rapid innovation from specialized vendors that push Martech forward.

    By embracing open ecosystems and API-first architectures, marketing teams can future-proof their tech stacks and stay ahead of the competition.

    Key Takeaways:

    • All-in-one suites struggle to keep up with best-in-class innovation.
    • Feature bloat leads to unnecessary costs and operational inefficiencies.
    • Marketing teams often outgrow monolithic platforms, leading to a shift toward open, modular solutions.
    • Composable Martech stacks offer greater agility, scalability, and access to emerging technologies.

    As marketing continues to evolve, businesses must prioritize flexibility, specialization, and innovation over the perceived convenience of an all-in-one suite.

    How Aggregator Ecosystems Offer a More Modular and Adaptive Martech Stack

    The rapid expansion of the Martech landscape has left marketing teams with a difficult choice: Opt for an all-in-one suite with rigid structures or piece together a best-of-breed stack with costly and complex integrations. Aggregator ecosystems offer a promising third option—providing a modular, adaptive, and scalable approach that balances flexibility with ease of integration.

    The Benefits of an Aggregator-Driven Approach

    Aggregator ecosystems act as intermediaries that simplify Martech integration by enabling different tools to communicate seamlessly. These platforms—such as Segment, mParticle, and Zapier—serve as connective tissue, eliminating the friction associated with integrating disparate tools.

    a) Flexibility – Choosing the Right Tools Without Integration Headaches

    One of the primary benefits of an aggregator ecosystem is the flexibility to mix and match best-in-class solutions without the need for proprietary integrations. Marketers can:

    • Choose the best solutions for email marketing, automation, CRM, and analytics.
    • Integrate new solutions without disrupting current workflows.
    • Instead of being tied to a single vendor’s roadmap, they can adjust their stack as their needs change.

    For example, a company that uses HubSpot for CRM, Braze for customer engagement, and Snowflake for data analytics can use an aggregator such as Segment to build a centralized customer data pipeline. Rather than pushing a single suite to manage everything, they can use specialized tools for each purpose, eliminating data silos.

    b) Scalability – Adapting the Stack as Business Needs Evolve

    Traditional Martech suites often struggle to scale because they impose predefined structures that may not accommodate a growing or changing business. Aggregator ecosystems, on the other hand, allow companies to:

    • Start small and expand their Martech stack incrementally.
    • Replace outdated tools without reconfiguring the entire system.
    • Integrate new technologies as they emerge, ensuring long-term adaptability.

    For instance, a startup may initially use Google Analytics, MailChimp, and HubSpot, but as they scale, they might need Amplitude for product analytics and Iterable for advanced customer engagement. Instead of rebuilding their tech stack, they can integrate these tools seamlessly through an aggregator like mParticle.

    c) Cost Efficiency – Avoiding Redundant Capabilities

    All-in-one suites frequently include several features, many of which are underutilized but add to the expense. Aggregator ecosystems enable firms to save unnecessary spending by:

    • Paying for only the tools they require.
    • Reducing software costs by deleting superfluous features.
    • Saving on integration costs. As aggregators automate data flows between platforms, it reduces integration costs.

    Zapier is an excellent illustration of how businesses can link thousands of applications without the need for expensive middleware or engineering personnel. Rather than investing in a full-fledged automation suite, companies can utilize Zapier to integrate their existing technologies for a fraction of the cost.

    Case Studies: Companies Leveraging Aggregator Models Successfully

    Let us look at sme case studies where some well-known brands  have leveraged aggregator models successfully:

    a) Case Study 1: Airbnb – Centralizing Customer Data with Segment

    Airbnb faced a data fragmentation problem across multiple marketing, sales, and product analytics platforms. Rather than relying on a single suite, they implemented Segment as a data hub that:

    • Aggregate customer interactions across different channels.
    • Routes data to analytics tools like Google BigQuery and Amplitude.
    • Enables personalized marketing campaigns through platforms like Braze and Iterable.

    By using Segment as an aggregator, Airbnb streamlined data flows while maintaining the flexibility to adopt best-in-class tools.

    b) Case Study 2: IBM – Enhancing Data Integration with mParticle

    IBM needed to unify data from various touchpoints while maintaining compliance with strict security protocols. They turned to mParticle, which allowed them to:

    • Integrate data from mobile apps, websites, and customer support systems.
    • Create a unified customer view across multiple tools.
    • Maintain security compliance while enabling real-time personalization.

    mParticle’s aggregator model helped IBM avoid costly data migrations while enhancing customer intelligence.

    c) Case Study 3: A Fast-Growing E-Commerce Brand Using Zapier

    An e-commerce company running on Shopify, Klaviyo, and Facebook Ads wanted to automate workflows without hiring a full IT team. By leveraging Zapier, they:

    • Automated lead syncing between Shopify and Klaviyo.
    • Streamlined ad campaign reporting by integrating Facebook Ads with Google Sheets.
    • Set up real-time Slack alerts for high-value customer purchases.

    With Zapier, they avoided investing in an enterprise automation suite while still achieving advanced workflow automation.

    Key Principles of a Successful Aggregator-Driven Martech Stack

    Let us look at some key principles of a successful aggregator-driven Martech stack:

    a) API-First and Composable Architecture – Ensuring Seamless Interoperability

    One of the foundational principles of a successful aggregator-driven Martech stack is adopting an API-first and composable architecture. This approach ensures that all components within the stack communicate seamlessly, allowing businesses to integrate various best-of-breed solutions without complex workarounds. Unlike monolithic all-in-one suites, a composable architecture prioritizes modularity, enabling organizations to assemble and reconfigure their Martech stack as needed.

    APIs (Application Programming Interfaces) play a critical role in this setup by serving as bridges between different tools. Modern API-first platforms offer RESTful or GraphQL-based interfaces that facilitate secure, scalable, and real-time data exchange. Additionally, API-first solutions often include extensive developer documentation, SDKs (Software Development Kits), and pre-built connectors, reducing the need for custom development and IT dependency.

    The flexibility of an API-first architecture also supports the growing need for personalization and automation in marketing. By integrating customer relationship management (CRM) tools, advertising platforms, analytics engines, and content management systems through APIs, businesses can create a truly interconnected ecosystem that drives efficiency and innovation.

    b) Data-Centric Approach – Aggregators as the Data Layer of Martech

    A successful Martech stack must be data-centric, leveraging aggregators to serve as the primary data layer. Data aggregators function as the connective tissue between disparate tools, ensuring that data is harmonized, de-duplicated, and enriched before being distributed across platforms.

    By utilizing data aggregators like Segment, mParticle, or Tealium, marketing teams can consolidate customer interactions from multiple touchpoints, creating a single, unified customer profile. This centralized approach eliminates data silos, improves audience segmentation, and enables more accurate analytics and predictive modeling.

    Furthermore, a data-centric, aggregator-driven Martech stack provides real-time data synchronization, ensuring that insights are instantly available for campaign optimization. This is particularly important for dynamic personalization, where marketing messages need to be adapted based on recent customer behavior.

    AI and Automation for Smart Routing – How AI-Powered Aggregators Optimize Workflows

    AI and automation are integral to maximizing the efficiency of an aggregator-driven Martech stack. AI-powered aggregators utilize machine learning algorithms to intelligently route data and automate workflows, reducing manual intervention and minimizing errors.

    For instance, AI can analyze customer behavior across multiple channels and automatically route leads to the most appropriate sales or marketing system. Predictive analytics can help prioritize high-value prospects while chatbots and automated messaging platforms ensure timely and relevant customer engagement.

    In addition, AI-driven aggregators facilitate advanced attribution modeling, enabling marketers to understand the impact of different touchpoints on customer conversion rates. This data-driven decision-making process ensures that marketing budgets are allocated efficiently and campaigns are optimized for maximum ROI.

    By integrating AI-powered tools such as Clearbit (for data enrichment), Drift (for conversational marketing), or HubSpot’s AI-driven CRM capabilities, businesses can create a highly adaptive Martech ecosystem that continuously refines its processes based on real-time insights.

    Vendor-Agnostic Strategy – Avoiding Dependence on a Single Provider

    A vendor-agnostic approach is another key principle of a successful aggregator-driven Martech stack. Traditional all-in-one suites often lock businesses into proprietary ecosystems, limiting their ability to adopt emerging technologies. By contrast, a vendor-agnostic strategy ensures that companies are not overly dependent on a single provider and can swap out or integrate new tools as their needs evolve.

    This flexibility is crucial for maintaining a competitive edge in a rapidly changing digital landscape. With a vendor-agnostic Martech stack, businesses can select the best tools for their specific use cases, avoiding redundant features and reducing overall costs. It also mitigates risks associated with vendor instability, such as product discontinuation, pricing changes, or declining innovation.

    To successfully implement a vendor-agnostic strategy, businesses should prioritize Martech platforms that offer open APIs, extensive integration capabilities, and adherence to industry standards like OAuth for authentication and Webhooks for real-time event notifications.

    Hence, the shift towards aggregator-driven Martech stacks represents a paradigm shift in how businesses approach marketing technology. By embracing API-first and composable architectures, ensuring data centralization through aggregators, leveraging AI for smart data routing, and maintaining a vendor-agnostic strategy, companies can create a future-proof Martech ecosystem.

    This approach not only enhances operational efficiency but also enables greater agility, innovation, and customer-centric marketing strategies. Organizations that adopt these key principles will be better positioned to navigate the complexities of modern marketing and drive sustained business growth.

    How to Transition from a Fragmented Stack to an Aggregator Model

    To transition from a fragmented stack to an aggregator model, the following steps should be implemented:

    a) Step 1: Audit Existing Martech Tools and Integrations

    The first step in transitioning to an aggregator model is conducting a comprehensive audit of your existing Martech stack. Identify all tools currently in use, assess their integrations, and determine their effectiveness. This audit helps uncover redundant functionalities, inefficiencies, and integration gaps that may be hindering performance.

    b) Step 2: Identify Core Capabilities vs. Redundant Features

    Once the audit is complete, distinguish between essential Martech capabilities and redundant or underutilized features. This step ensures that your aggregator-driven stack is built on necessary tools while eliminating excess software that adds complexity without delivering value.

    c) Step 3: Choose an Aggregator Platform That Fits Your Needs

    Selecting the right aggregator platform is crucial for seamless integration. Evaluate different aggregator solutions such as customer data platforms (CDPs), integration platforms as a service (iPaaS), or workflow automation tools that align with your business objectives. Consider factors such as scalability, ease of use, and API compatibility.

    d) Step 4: Develop an Integration Strategy Based on Business Goals

    Create a clear integration strategy that aligns with your organization’s marketing and business goals. Define data flow requirements, establish security protocols, and outline how different tools will interact within the aggregator framework. This strategic planning ensures a smooth transition and minimizes disruptions.

    e) Step 5: Pilot-Test the New Model Before Full Deployment

    Before fully implementing the aggregator model, conduct a pilot test with a subset of your Martech stack. Monitor performance, assess data synchronization accuracy, and gather feedback from users. Address any technical challenges and refine integrations before rolling out the new model across the entire organization.

    By following this structured roadmap, businesses can successfully transition from a fragmented Martech stack to a streamlined, aggregator-driven ecosystem that enhances efficiency, flexibility, and marketing effectiveness.

    The Role of AI, Automation, and Predictive Analytics in Optimizing Integrations

    As Martech stacks become more complex, AI-driven integration platforms are emerging as essential tools for optimizing connections between different solutions. These intelligent platforms help businesses streamline operations, enhance decision-making, and improve customer experiences through automation and predictive analytics.

    AI-Driven Integration Platforms: Smarter, Faster Martech Connections

    AI-driven integration platforms leverage machine learning to automate and optimize data flow between tools. Unlike traditional integration methods that require manual setup and maintenance, these platforms intelligently map data, detect anomalies, and ensure seamless interoperability. By using AI to recognize patterns and recommend optimal workflows, businesses can reduce errors and improve efficiency across their Martech stack.

    For example, AI-powered integration solutions like Tray.io and Workato automatically adjust data mappings and workflows based on historical trends, reducing the need for manual intervention. These platforms ensure that marketing teams spend less time troubleshooting integration issues and more time executing campaigns.

    Automation: Reducing Manual Data Synchronization

    One of the biggest challenges of Martech integration is ensuring real-time data synchronization across different platforms. Manual data transfers between tools can lead to delays, errors, and inefficiencies. AI-driven automation eliminates these bottlenecks by:

    • Enabling real-time data synchronization: AI-powered automation ensures that customer data updates instantly across all connected tools, improving personalization and customer engagement.
    • Minimizing data entry errors: Automated workflows prevent human errors in data input, leading to more accurate insights.
    • Enhancing operational efficiency: By automating repetitive tasks like lead scoring, email triggers, and customer segmentation, marketing teams can focus on strategy and innovation instead of routine data management.

    Predictive Analytics: Workflow Orchestration and Personalization

    Predictive analytics is revolutionizing how Martech integrations operate. By analyzing historical data and user behavior, AI-powered platforms can:

    • Optimize workflow orchestration: AI-driven analytics can identify inefficiencies in existing workflows and suggest improvements, ensuring that marketing operations run smoothly.
    • Enhance personalization efforts: Predictive models analyze customer behavior to trigger personalized campaigns at the right time, increasing engagement and conversions.
    • Improve campaign performance: AI-powered insights help marketers predict which strategies will yield the best results, allowing them to allocate resources more effectively.

    AI, automation, and predictive analytics are reshaping Martech integrations by making them smarter, faster, and more efficient. As these technologies continue to evolve, businesses will benefit from reduced integration complexity, improved campaign performance, and a more agile marketing stack. Companies that embrace AI-driven integration will gain a competitive edge in an increasingly data-driven landscape.

    The Future of Martech Integration: What Comes Next?

    The Martech landscape is transforming, driven by the rapid evolution of AI, automation, and seamless interoperability. As businesses strive for greater efficiency and agility, the future of Martech integration will focus on AI-native ecosystems, hyperautomation, and user-friendly integration solutions. Here’s what lies ahead.

    a) The Move Toward AI-Native Martech Ecosystems

    The next phase of Martech integration will be characterized by AI-native platforms that inherently support intelligent automation and decision-making. These ecosystems will not just facilitate integration but also actively optimize data flow, detect inefficiencies, and recommend strategic actions.

    AI-native Martech solutions will:

    • Proactively manage integrations by identifying and resolving issues before they impact operations.
    • Enable dynamic data synchronization, ensuring that customer data is always accurate and up to date.
    • Enhance personalization efforts by leveraging AI-driven insights for hyper-targeted campaigns.

    As AI becomes an integral part of Martech ecosystems, businesses will experience faster decision-making and more intuitive customer interactions.

    b) Hyperautomation in Integration: Less Human Intervention, More AI-Driven Decisions

    Hyperautomation—the use of AI and machine learning to automate complex workflows—will play a critical role in Martech integration. Rather than relying on manual processes and IT-heavy integration efforts, hyperautomation will enable businesses to:

    • Automate end-to-end marketing workflows without the need for constant human intervention.
    • Optimize campaign execution in real time based on data-driven insights.
    • Reduce reliance on IT teams, allowing marketers to make changes quickly and efficiently.

    By embracing hyperautomation, companies can eliminate redundant processes, reduce integration friction, and improve operational efficiency.

    c) The Rise of No-Code and Low-Code Integration Solutions

    As Martech stacks grow more complex, the demand for no-code and low-code integration solutions will continue to rise. These platforms empower marketing teams to manage integrations without extensive technical expertise, making Martech more accessible and adaptable.

    Key benefits of no-code and low-code integration include:

    • Faster deployment of new tools, reducing the time to market for innovative campaigns.
    • Greater flexibility, allowing teams to customize their stack without developer support.
    • Cost savings, as businesses reduce dependency on expensive IT resources.

    Popular no-code and low-code platforms like Zapier, Tray.io, and Workato are already making integration more seamless, and their capabilities will only expand in the coming years.

    Predictions for Martech Stacks in the Next Five Years

    Looking ahead, Martech integration will continue evolving to support more agile and data-driven marketing strategies. Key trends include:

    • The dominance of API-first, composable architectures allows businesses to build customized, interoperable Martech stacks.
    • Deeper AI-driven analytics, with Martech platforms offering predictive insights and automated optimization.
    • Increased interoperability across industries, as businesses demand greater connectivity between Martech, salestech, and customer experience solutions.
    • A shift toward vendor-neutral ecosystems, reducing dependency on single-suite providers and encouraging a best-of-breed approach.

    Final Thoughts

    Martech stacks have evolved into intricate ecosystems, with marketing teams relying on dozens—sometimes hundreds—of tools to execute campaigns, manage customer data, and analyze performance. From CRM systems and email marketing platforms to analytics suites and AI-driven personalization engines, the average Martech stack continues to expand. While these tools offer specialized capabilities, their sheer number has led to a tangled web of disconnected technologies, creating inefficiencies and hindering marketing performance.

    The Martech landscape is at a crossroads. The long-standing debate between all-in-one suites and best-of-breed solutions has exposed fundamental challenges in integration, efficiency, and adaptability. While suites promise cohesion, they often fall short in innovation and flexibility. Conversely, best-of-breed approaches enable specialization but create fragmented ecosystems riddled with data silos and operational inefficiencies. This ongoing struggle has left marketers searching for a better way to optimize their tech stacks without sacrificing functionality or agility.

    Aggregator ecosystems have emerged as the solution to this gridlock, bridging the gap between the rigidity of monolithic platforms and the disjointed nature of best-of-breed tools. By acting as intermediaries, aggregators provide seamless data flow and interoperability, allowing marketers to leverage the strengths of specialized tools while maintaining an integrated and cohesive system. Companies that embrace an aggregator-driven approach gain the benefits of flexibility, scalability, and cost efficiency, ensuring their Martech stacks evolve in tandem with business needs and technological advancements.

    The key to unlocking Martech’s full potential lies in embracing API-first, composable architectures. These frameworks enable seamless integration, ensuring that data moves freely across the stack and empowering marketers with real-time insights and automation capabilities. AI and predictive analytics further enhance this ecosystem, optimizing workflows and enabling intelligent decision-making without the manual burdens of traditional integration methods. As Martech continues to evolve, hyperautomation and low-code/no-code solutions will play a critical role in simplifying integrations, reducing reliance on IT, and accelerating marketing innovation.

    To future-proof the Martech stacks, organizations must adopt a mindset that prioritizes openness, modularity, and adaptability. Investing in AI-native platforms and aggregator ecosystems will ensure they remain agile in an ever-changing digital landscape. The future of Martech is not about choosing between suites or standalone tools—it’s about orchestrating a connected, intelligent, and frictionless ecosystem that drives maximum marketing performance and business growth.

    By breaking the integration gridlock and embracing an AI-driven, flexible approach, businesses can unlock the true potential of Martech, ensuring they stay ahead of the curve and continue delivering exceptional customer experiences in an increasingly competitive landscape.

    Marketing Technology News: Effectively Connecting Your MarTech And Email Marketing Processes

  • Edited Capital Acquires Enterprise Loyalty Platform Annex Cloud

    Edited Capital Acquires Enterprise Loyalty Platform Annex Cloud

    Investment Supports Expansion in Customer Retention Technology as Enterprises Shift Focus to Lifetime Value

    DENVER, COLORADO — June 24, 2025 /EINPresswire.com/ — Edited Capital, a private equity firm focused on B2B tech buyouts, today announced the acquisition of Annex Cloud, a leading enterprise customer loyalty and engagement platform. The partnership underscores Annex Cloud’s leadership position in the rapidly expanding enterprise customer retention market, while demonstrating Edited Capital’s growing reputation as a strategic partner for scaling B2B technology companies.

    “We are excited to embark on this new chapter with Edited Capital,” said Steve Scribner, CFO and Interim CEO of Annex Cloud. “This partnership will enable us to accelerate our growth, expand our offerings, and continue delivering exceptional value to our customers.”

    Annex Cloud empowers global enterprises to move forward faster and become beloved brands through its cutting-edge Loyalty Experience Platform™. The company serves over 60 enterprise clients across retail, consumer goods, and healthcare sectors, with loyalty programs running in dozens of countries worldwide. Combining advanced technology with expert-led strategy, Annex Cloud’s enterprise-grade SaaS solution enables brands to deliver personalized omnichannel experiences at scale.

    The acquisition is the first investment for Edited Capital’s Fund III, which targets high-quality B2B software companies positioned for sustainable growth. Edited Capital specializes in partnering with promising technology companies that have moved beyond traditional venture funding pathways, providing operational expertise and strategic guidance to unlock value and drive sustainable growth.

    “Annex Cloud has demonstrated impressive innovation and commitment to excellence in the customer loyalty space,” said Ingrid Alongi, Managing Partner at Edited Capital. “Customer retention technology represents a growing market where enterprises increasingly prioritize retention and lifetime value optimization. We look forward to working together to realize the company’s full potential.”

    Edited Capital plans to work closely with Annex Cloud’s management team to drive organic growth initiatives and explore expansion opportunities. The firm’s operational approach focuses on leveraging proven methodologies to enhance platform capabilities and market positioning while maintaining the company’s commitment to innovation and client success.

    About Annex Cloud

    With over 125 integrations and a highly configurable platform, Annex Cloud equips marketers to rapidly deploy and adapt loyalty campaigns. The company maintains a co-sell relationship with Microsoft and strategic partnerships with Adobe, Braze, Zeta Global, and Klaviyo.
    For more information, visit annexcloud.com.

    About Edited Capital

    Edited Capital is a Denver-based private equity firm that’s pioneering a new asset class in technology investing. The firm’s investment approach focuses on partnering with small tech companies that have previously secured venture funding through Series A but are seeking alternative pathways for continued growth. With a proven track record of operational excellence and strategic value creation, Edited Capital provides the expertise and resources to chart a new path to success.

    For more information, visit editedcapital.com.

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    Edited Capital
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    The post Edited Capital Acquires Enterprise Loyalty Platform Annex Cloud appeared first on The Wise Marketer.

  • How ‘Process Mining’ Refines Ikea’s Rich, Plentiful Data so it Can be Put to Use

    How ‘Process Mining’ Refines Ikea’s Rich, Plentiful Data so it Can be Put to Use

    Last year, 650 million people across 30 countries visited an Ikea store, and the company’s website attracted more than 4 billion visitors. Those are staggering numbers that would make any retailer crow, but when an organization of that size decides it needs to streamline processes, those numbers suddenly become daunting. That is, however, exactly what Ikea decided needed to happen, and to do it the company turned to…more numbers.

    Four years ago, Ikea teamed up with Celonis and began an undertaking referred to as “process mining.” The initial goal was relatively modest: to align the company’s finance systems and processes more accurately with what was happening in sales channels. However, the ultimate result has been a whole new way of thinking about omnichannel execution and communication across the organization.

    Tim Hills, Process and Data Insights Development Manager at Ingka Services, a division of Ikea owner Ingka Group.
    Tim Hills, Process and Data Insights Development Manager at Ingka Services, a division of Ikea owner Ingka Group.

    “What process mining really offers you is an industrialization of doing a time-and-motion study,” explained Tim Hills, Process and Data Insights Development Manager at Ingka Services, a division of Ikea owner Ingka Group. “Rather than the more traditional standing there with a stopwatch and clipboard and timing how long it takes people to do things, we pull all of that information out of our systems. Almost everything that we do leaves a digital trace throughout our solution landscape, and that’s what we’re bringing in and representing. The data tells you what, the people tell you why and data-enriched people tell you how to become better,” he said in an interview with Retail TouchPoints.

    How Ikea is ‘Modernizing Our Core DNA’

    The results across the organization have been far-reaching — everything from removing organizational silos to improving distinct moments in the customer journey. And so, while the project started with one particular use case, it is now becoming a way of life at Ikea.

    “Our founder Ingvar Kamprad [which, by the way, is where the name Ingka comes from, a combination of the beginnings of his name and surname] released a book in 1976 called The Testament of a Furniture Dealer, and when you start looking into some of the things in there, all of these concepts around continuous improvements are baked into who and what we are as a business,” said Hills. “So really this is a case of modernizing our core DNA. That isn’t a project that you run and then it closes; it’s a project that you get going, and it then becomes part of the everyday.”

    The Process Mining Journey

    Here’s how it works:

    1. Get the data: The first step is to gather all the available data from sources across the organization and “provision” it, that is, make it centrally accessible to various systems and users at the company. Ikea was aided in this effort by the fact that it had already been going through a years-long digital transformation that included extensive data cleaning and collation.

    2. Understand the data (and maybe get more of it): Individual experts then come together to analyze the data and determine what it’s saying. Ikea initially focused on three core systems: selling, fulfillment and after-sales. “We started there with that red thread, and then by being able to connect those together and looking at a couple of use cases and a couple of things that popped out to us, [we would see that] maybe there’s some additional transparency we need in a couple of other areas,” said Hills. “So we would then bring in some more information to enrich what we were seeing and [come to some conclusions].”

    3. Put the data to work: Hills and his team then draw on investigative and process improvement methodologies such as Lean Six Sigma to draw a line from a pain point to the root cause and then move toward solutions. They then develop a delivery plan for the solution and roll it out for adoption. “It’s great to have all sorts of analyses and pretty dashboards and so on, but unless it makes a difference to a customer and a co-worker, it hasn’t actually changed anything,” said Hills. “There’s been a real journey of delivering that methodology, putting that in place with global folks here in [headquarters] and out in the country units, because that’s where things really need to change to actually make a difference.”

    How it Started and Where it Goes from Here

    The first use case that Ikea tackled began with a dissection of the “order-to-cash” process, but even that term represents a shift in how the company operates. “Order-to-cash” essentially describes the lifecycle of a consumer purchase, from the moment they select a product to when they pay. That phrasing is drawn from the American Process and Quality Council’s Process Classification Framework, which has defined a whole set of these terms that describe business processes end to end. Other examples include “source-to-pay” and “hire-to-retire.”

    “One of the things that we really needed and were able to facilitate through our work with Celonis is to have a common language with which to interact with each other to actually have this conversation,” explained Hills. “Often, if you were to get a salesperson, a marketing person, a logistics person, a finance person and an IT person in the same room, it would be like a meeting at the United Nations when all the translators have gone for a coffee break. Everyone’s saying the right things in their own languages, but no one’s really connecting. This gives us that that unbiased, unfiltered language with which to talk with each other,”

    The primary aim of the initial process mining effort was to define every single step of the order-to-cash process from a financial perspective, in order to ensure compliance across reporting, ESG, taxation and more. But because at Ikea the finance division also includes business “steering and navigation” functions, future improvements also naturally surface throughout this process

    One such example was recurring instances in certain locations of click-and-collect cancellations. In looking through the data, Hills and his team realized there was a notable difference in cancellation rates between locations that offered specific appointment windows for pickup versus any time on a specific day.

    Changing the process in either direction would have huge implications for employees, who would have to pick orders either throughout the day (in time for those appointment windows) or all at once in the morning, and those staffing concerns had to then be weighed against potential upsides like reductions in returns and cancellations.

    But in looking more deeply at the data and speaking to the folks on the ground, the team realized that the root problem wasn’t really the timeframes being offered for pickup but rather how those options were being communicated to customers. “Having that full end-to-end perspective, and that way to communicate and interact with each other, to come to these conclusions was absolutely differentiating,” said Hills.

    Ikea’s Infinite Game

    As the team’s approach to process mining matures, new areas for improvement are being surfaced, and in multiple ways. Sometimes suggestions come from the workforce, both corporate and store-based. The team also is using AI and machine learning statistical analysis tools with Celonis to discover specific pain points that are hindering optimization.

    “Every single day, every single graph we look at surprises us in some way, shape or form,” said Hills. “In some cases [it’s something you] thought that might be true and now there’s proof. In other cases, you notice there is a lot of variety in outcomes from country to country or from store to store within a country. The transparency that offers us is to then bring together people with ridiculous amounts of experience, who can say, ‘I know why that’s happening,’ and then for other people around them to then say, ‘Oh, if that’s happening for you, that’s why this is happening for me, which is also why that’s happening.’ Click, click, click, click, click, and all of a sudden, the outcome and that growth in communal knowledge just explodes.

    “And then as we look to improve and change things, we can also track the impact on customer experience, customer expectation and customer perception, which has been really valuable for us,” he added. “It’s not where you start, it’s where you finish. Anyone can raise a flag to say, ‘There’s an issue.’ That’s one thing. But then if everybody rallies around it, makes it a focus to improve and ultimately you reach that improvement, that’s the thing that, for me, is most interesting.”

    This use of data as the starting point for these conversations has helped Ikea become more evidence-based in its approach to process improvements, so that “we make sure the anecdote is really relevant to reality,” said Hills. Another benefit is that it makes improvements easier to measure.

    In the case of click-and-collect, as improvements were rolled out, baselines were set and specific metrics like cancellation rates and co-worker hours were meticulously tracked to ensure that the changes were having the desired effect. “For each one of the use cases we have a dashboard that tracks it over time, and then we bring [all of those] into a consolidated value realization tracker [to measure the overall benefit of the process mining initiative],” explained Hills.

    Ultimately, though, it’s a process that will never be complete: “It’s a little bit like Simon Sinek and starting with ‘why’ in The Infinite Game,” said Hills. “Retail is never done; you can’t complete retail. Our customer expectations will continually change. Our business model will continually change — the way in which we realize our offer, the products that we sell, the services that we sell. At the moment, this is all much more accelerated whilst we’re getting up to speed. But I think soon there will be a transition to this being business as usual and continuous improvement just being part of the day to day.”