Dive into the world of retail media and data-driven marketing with Milena Traikovich, a seasoned Demand Gen expert who has dedicated her career to helping businesses craft impactful campaigns that nurture high-quality leads. With a deep background in analytics, performance optimization, and lead generation, Milena offers invaluable insights into how retail data can transform marketing strategies. In this engaging conversation, we explore the intricacies of creating a seamless full-funnel impact, the power of retail data in tackling complex brand challenges, the importance of tailored metrics for campaign success, and the art of balancing brand awareness with conversion across various touchpoints.
How do you define “full-funnel impact” in the realm of retail media, and why does it matter so much to brands?
Full-funnel impact, to me, means creating a cohesive journey for consumers, from the moment they first encounter a brand to the point where they make a purchase—and even beyond, into loyalty. It’s about ensuring that every interaction, whether it’s building awareness through an ad or driving a sale with a promotion, works together toward a common goal. For brands, especially in the consumer packaged goods space, this is crucial because their challenges don’t fit neatly into one part of the funnel. They might need to retain loyal customers while also launching a new product, and a fragmented approach just won’t cut it. A unified strategy powered by data ensures they’re not missing opportunities at any stage.
What makes connecting upper-funnel brand awareness with lower-funnel sales activation such a game-changer for today’s marketing strategies?
It’s a game-changer because it bridges the gap between long-term brand building and immediate sales results. Upper-funnel efforts, like storytelling through video ads, plant the seed of recognition and trust, while lower-funnel tactics, like targeted promotions, push for action. When these are disconnected, you risk losing potential customers who aren’t ready to buy yet or failing to capitalize on those who are. By linking them, brands can guide consumers smoothly through their decision-making process, especially in competitive markets where every touchpoint counts. Data plays a huge role here, letting us track and optimize that journey in real time.
How do the unique challenges faced by consumer packaged goods brands highlight the need for a comprehensive full-funnel approach?
Consumer packaged goods brands face a web of challenges that demand a full-funnel approach. They’re often juggling goals like retaining loyal shoppers, introducing new products without eating into existing sales, or even changing packaging to appeal to different demographics. These aren’t isolated problems—they overlap and influence each other. For example, launching a new flavor might require upper-funnel awareness to attract trial users, mid-funnel education to explain its value, and lower-funnel incentives to close the sale. Without a holistic strategy, you might solve one issue but create another, like losing core customers. A full-funnel approach, backed by solid data, helps balance these competing priorities.
In what ways can retail data help brands overcome specific obstacles like retaining customers or launching new products successfully?
Retail data is a goldmine for tackling these obstacles because it provides granular insights into shopper behavior. For customer retention, data can identify who’s at risk of lapsing by analyzing purchase frequency or basket trends, allowing brands to target them with personalized offers or reminders. When launching new products, retail data helps pinpoint which households are most likely to try something new based on past buying patterns, so you’re not wasting effort on uninterested segments. It also helps avoid cannibalization by showing how a new item might impact existing sales. Essentially, it turns guesswork into precision, letting brands act with confidence.
Can you share how retail data can be used to uncover opportunities within different household segments for targeted campaigns?
Absolutely. Retail data lets us segment households based on a variety of factors—purchase history, shopping frequency, category preferences, even life stage indicators like family size. For instance, we can identify households that frequently buy a certain category but haven’t tried a specific brand, or those who’ve lapsed and need a nudge. From there, we craft campaigns tailored to their needs, whether it’s a discount for lapsed buyers or a bundled offer for frequent shoppers. This level of targeting ensures the message resonates, and we’re not just blasting out generic ads hoping something sticks. It’s about relevance at scale.
Why is it beneficial to have a unified team handling consumer insights, retail media, and loyalty marketing when supporting brands?
Having a unified team is a massive advantage because it eliminates silos and ensures everyone’s working from the same playbook. When consumer insights, retail media, and loyalty marketing are aligned, you get a 360-degree view of the shopper. Insights inform media strategies, media drives loyalty engagement, and loyalty data loops back to refine insights. This cohesion means brands can reach shoppers at the exact right moment with a consistent message. For example, if insights show a segment is price-sensitive, the media team can prioritize discount-focused ads, and loyalty can reinforce that with tailored rewards. It’s a seamless experience for the consumer and more effective for the brand.
When evaluating full-funnel impact, why do you consider household penetration a key metric at the upper level?
Household penetration is critical at the upper level because it measures how widely a brand is reaching and resonating with potential buyers. It’s not just about raw numbers—it’s about expanding your footprint into new homes, which is often the first step toward growth. If you’re not getting into more households, your awareness efforts aren’t working, no matter how creative they are. It sets the stage for everything else; without penetration, you’ve got no one to convert or retain. It’s a foundational metric that tells us if the brand’s story is cutting through the noise.
At the lower funnel, how do incremental sales and incremental return on investment differ from standard metrics, and why are they more meaningful?
Incremental sales and incremental return on investment, or iROAS, focus on the actual impact of your marketing efforts by isolating what wouldn’t have happened without the campaign. Standard ROAS might look at total sales tied to an ad, but it often includes organic purchases—sales that would’ve occurred anyway. Incremental metrics use test and control groups to strip that out, so you’re only measuring the true lift from your media. This matters more because it shows the real value of your investment. If you’re not looking at incrementality, you might overestimate your campaign’s success and misallocate budget.
How do you determine which metrics to prioritize for different stages of a media campaign rather than relying on a single one?
Choosing metrics is about aligning them with the specific goal of each campaign stage. At the top of the funnel, I’d prioritize reach and household penetration to gauge awareness. In the middle, engagement metrics like click-through rates or time spent with content show if we’re building consideration. At the bottom, it’s all about conversion—incremental sales, iROAS, or even basket size if we’re pushing bundles. The key is to let each part of the funnel do what it does best and measure accordingly. A single metric can’t capture the full story; it’s like judging a book by one chapter. You need a blend to see the whole picture.
How does retail data enable different media touchpoints, like connected TV or social media, to collaborate in driving both brand lift and conversion?
Retail data acts as the glue that ties touchpoints together. It allows us to map out a consumer’s journey across platforms by linking behaviors—like who’s seen a connected TV ad or engaged on social media—to actual purchase data. For instance, we can target households on CTV to build brand lift with a compelling story, then follow up with a social media ad offering a trial discount to drive conversion. Because it’s the same data backbone, we ensure consistency in messaging and avoid overlap or fatigue. It’s about orchestrating touchpoints to complement each other, using data to assign roles based on where they’re most effective.
Can you walk us through an example of using retail data to target a specific group, like lapsed buyers, and convert them back to the brand?
Sure, let’s say we’re working with a snack brand. Using retail data, we identify households that bought the brand regularly six months ago but haven’t since—they’re our lapsed buyers. We analyze their shopping patterns to understand why they might’ve stopped; maybe they switched to a competitor or cut back on snacks altogether. Then, we target them with a connected TV ad to rekindle brand affinity, showcasing a new flavor or a feel-good story. Simultaneously, we hit them with a digital coupon on a platform they frequent, tied to their purchase history. Finally, we track in-store sales to confirm conversion. The data lets us personalize the approach and measure the exact impact, turning lapsed buyers into active ones again.
Why do you believe it’s impractical to expect every media touchpoint to achieve both brand awareness and sales conversion simultaneously?
It’s impractical because each touchpoint has a unique strength, and forcing them to do everything dilutes their impact. For example, a connected TV ad is fantastic for emotional storytelling and awareness—it’s hard to beat a 30-second spot for brand lift. But expecting it to drive an immediate sale is a stretch; consumers aren’t usually clicking to buy from their couch. Conversely, a targeted email with a coupon is great for conversion but might not build long-term affinity on its own. When you try to make one touchpoint do it all, you often compromise on both fronts. It’s better to let each play to its strengths and work together as a team, guided by data to ensure the overall journey achieves both goals.
Looking ahead, what is your forecast for the role of retail data in shaping the future of marketing strategies?
I think retail data will become the cornerstone of marketing strategies in the coming years, even more than it is now. As privacy regulations tighten and traditional tracking methods like cookies fade, first-party retail data will be the key to understanding and reaching consumers in a compliant way. We’ll see it drive hyper-personalization at scale, with AI and machine learning unlocking deeper insights into shopper behavior. I also expect retail media networks to grow, blending online and in-store data to create seamless omnichannel experiences. The future is about using this data not just to react to trends, but to predict and shape them, giving brands a real competitive edge.
