Anastasia Braitsik is a renowned strategist in the realm of digital marketing and retail media, currently leading global initiatives in SEO and data analytics. In a landscape where Amazon’s ecosystem has become increasingly complex, her insights help brands navigate the transition from simple ad-spend tracking to high-level customer lifecycle management. She has been a vocal proponent of leveraging data clean rooms to solve the transparency issues that long plagued last-touch attribution models. We explore the transformative impact of the Amazon Marketing Cloud (AMC), focusing on how the shift from a restrictive 13-month lookback to a comprehensive five-year dataset is redefining success for modern advertisers in 2026. Our conversation delves into the strategic implementation of long-term metrics like customer lifetime value, the nuance of re-acquiring lapsed shoppers, and the integration of first-party data to create a holistic view of the consumer journey.
Historically, many vendors have struggled to calculate customer lifetime value beyond a one-year window. How does the current availability of five-year retail purchase data change the way a brand calculates the actual profitability of their advertising spend?
The shift from a 13-month rolling window to a full 60-month dataset is arguably the most significant development we have seen in retail media analytics recently. Previously, many vendors were essentially flying blind, forced to make massive budget decisions based on a very narrow slice of time that failed to capture the true tail of customer value. By looking at a five-year horizon, brands can finally understand the long-term profitability of their Customer Acquisition Cost (CAC) all the way down to the specific product level. For instance, in the CPG sector, we often see brands that are willing to take a calculated loss on an initial lower-priced SKU purchase because the data now proves those shoppers eventually graduate to high-margin multi-packs over the following three or four years. This level of visibility allows for a much more aggressive and confident advertising allocation, moving away from short-term ROAS targets toward a strategy focused on total brand equity and sustained revenue.
For categories with long repurchase cycles, like consumer electronics, the previous 13-month limit often skewed the perception of customer acquisition. Can you describe how the flexibility of a 60-month window allows for a more authentic new-to-brand assessment?
In categories like consumer electronics or luxury goods, a 13-month lookback was fundamentally flawed because a consumer might only buy a new camera or a high-end appliance every two or three years. Under the old system, if a loyal customer returned to buy an upgraded model after 14 months, they were incorrectly flagged as a “new-to-brand” shopper, which gave advertisers a false sense of their acquisition efficiency. With the 60-month shopping dataset now accessible via AMC, we can distinguish between a truly fresh customer and a returning fan of the brand with incredible precision. This flexibility is vital because the retail purchase dataset allows us to customize that window; while five years is a massive amount of data, a brand might find that a three-year window is the “sweet spot” for their specific product lifecycle. Having that 60-month ceiling ensures we never lose sight of a customer’s history, providing a true-to-life new-to-brand rate that reflects actual market penetration.
Identifying the ideal moment to retarget a customer is a science in itself. How are brands now leveraging expanded lookback windows to create sophisticated audience segments across platforms like Prime Video and the display network?
The ability to look back five years has completely revolutionized how we approach repeat purchase windows and retargeting cycles. Before this expanded access, if a brand’s ideal repurchase cycle was 15 or 18 months, they simply couldn’t build an automated audience in AMC to capture those people—the data would “forget” the shopper before they were ready to buy again. Now, brands are creating and activating audiences that target shoppers within very specific post-purchase windows across the entire Amazon ecosystem, including Prime Video, Twitch, and the broader display network. It’s no longer just about showing an ad to someone who bought a product last month; it’s about knowing that a consumer who bought a specific item two years ago is now in the prime window for an upgrade or a refill. This capability extends our reach into premium inventory like live sports on Prime Video, ensuring that we are present with the right message at the exact moment a long-term customer is likely to re-engage.
We often talk about “gateway products” as the entry point for a consumer’s journey. What specific patterns are brands seeing now that they can track a shopper’s evolution from a low-cost SKU to high-value multipacks over several years?
The concept of the “gateway product” has moved from a theoretical marketing idea to a data-driven certainty thanks to the 2026 reporting environment. By analyzing the customer lifecycle across different SKUs over several years, brands can identify exactly which products serve as the most effective “hooks” for long-term loyalty. In the CPG space, for example, we might find that a single-unit trial size is the primary entry point, but the customers who start there are 40% more likely to move to bulk purchases within the second year compared to those who start with a mid-tier product. This insight drives critical decisions regarding which products should receive the heaviest promotional support or which should be featured in net-new shopper acquisition campaigns. It allows us to treat the initial purchase not as the end goal, but as the beginning of a multi-year migration toward higher-priced, more profitable SKUs.
Winning back a customer who hasn’t made a purchase in over a year used to be a shot in the dark. In what ways does the 2026 data environment enable brands to specifically target high-value lapsed customers based on their historical importance?
The tragedy of the old 13-month limit was that once a customer crossed that one-year threshold without a purchase, they became invisible to the brand’s remarketing efforts, regardless of how much they had spent previously. Now, we have a robust set of tools to reacquire these lapsed customers by using win-back strategies fueled by up to five years of historical data. We can now segment audiences based on a minimum lifetime value threshold, meaning a brand can choose to spend its reacquisition budget specifically on people who were historically “big spenders” but haven’t bought anything in the last 18 to 24 months. By using AMC’s advanced segmentation, we can customize these lookback windows and focus our efforts on those who previously purchased specific “hero” products. This turns what was once a “lost” segment into a high-potential audience that can be re-engaged across Amazon’s expanding inventory, from Whole Foods to third-party supplies like Roku and Netflix.
Integrating first-party data with Amazon’s ecosystem is often touted but rarely executed well. How are privacy-safe shopper IDs allowing brands to bridge the gap between their direct-to-consumer websites and Amazon’s retail data?
One of the most powerful, yet often underutilized, features of the Amazon Marketing Cloud is the ability to securely join a brand’s own first-party data with Amazon’s rich shopping datasets. By uploading hashed email lists or purchase history from a direct-to-consumer (DTC) website, brands can use privacy-safe shopper IDs to see how their various channels are interacting. For the first time, a brand can truly understand the cross-platform lifecycle: for instance, seeing that a customer who initially discovered the brand via an Amazon DSP ad eventually became a high-value subscriber on the brand’s own website. This holistic view helps eliminate the silos between “Amazon sales” and “DTC sales,” allowing marketers to see how Amazon’s massive reach affects long-term loyalty on their own platforms and vice versa. It’s a level of multi-channel attribution that was previously impossible, providing a clear picture of how different touchpoints contribute to the overall five-year customer journey.
Amazon has moved toward making these complex datasets more accessible to brands that might lack deep SQL expertise. How has the introduction of pre-built queries and agentic functionality shifted the workload for marketing teams?
There has been a dramatic democratization of data within the AMC environment over the last year. Historically, accessing these deep insights required a dedicated data scientist or a marketing manager with significant SQL expertise, which created a barrier for many mid-sized brands. However, we’ve shifted into an era where Amazon is providing a comprehensive library of pre-built queries and audiences, making the “five-year lookback” accessible to almost anyone with a seat in the console. Furthermore, the introduction of agentic functionality via the Amazon Ads Agent has streamlined the process, allowing teams to generate complex reports and activate audiences using more intuitive, natural-language-driven interfaces. This shift means that marketing teams can spend less time wrestling with code and more time interpreting the “why” behind the data, focusing on the creative and strategic adjustments needed to improve performance across the 75,000+ marketers now utilizing these advanced tools.
What is your forecast for the evolution of AMC and its role in cross-channel retail strategy?
I believe we are moving toward a future where the distinction between “retail media” and “brand media” completely disappears, with AMC serving as the central nervous system for all advertising decisions. As Amazon continues to expand its reach into third-party inventory like Roku and Netflix, and deepens its integration with live sports such as the NBA and NFL, the five-year purchase dataset will become the gold standard for measuring the true impact of top-of-funnel awareness. From 2026 to 2028, I expect we will see even more automation where AI agents don’t just pull the data, but proactively suggest budget reallocations based on real-time shifts in lifetime value and retention rates. Brands that fail to adopt this “long-view” mentality will find themselves unable to compete with those who are optimizing for a five-year customer relationship rather than a 30-day click. The era of guessing the impact of your display ads is over; the era of precision-engineered customer lifecycles is officially here.
