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In a world where brand relevance is easily lost and where customers are often hidden behind a screen rather than in a store, it can be difficult to establish intimacy with consumers. As a result, we’ve had to study what online behaviors, actions and shopping habits mean in order to better understand and connect with consumers.

Enabled by technology, goPuff ’s Consumer + Brand Insights practice has been able to capture shopper behavior at speed, process it at scale and extract patterns in behavior that would have otherwise been missed or misinterpreted. Leveraging data like basket starters, impulse vs. intent purchases, and session times against larger patterns of spend, region and in-app journey, we can identify future behaviors and create a truly personalized experience that builds a deeper relationship with consumers. With consumer habits changing rapidly and dramatically, the need for current and effective data is more important than ever.

Browsing Habits vs. Category Affinities: Effective product placement is one of the pillars of a successful retailer —digital or brick & mortar.

While some of the same principles are shared, such as where to place best sellers and where to place impulse products, others like customization through data are reserved for the digital retailers. These data-driven adjustments are a win for both the consumer and the retailers—the consumers’ shopping experience becomes easier and more relevant, while the retailer’s in-app inventory is maximized with conversion increasing.

Category affinity data can greatly reduce any sort of navigation friction while also intelligently recommending popular product pairings, increasing consumer satisfaction. 

This information comes to fruition in the form of cross-category bundles and recommended products. Imagine not needing to walk down multiple store aisles to collect all your tailgating goods, but instead, fan-favorite combinations are on full display in one place. Alternatively, imagine approaching the checkout line and the products at the register change based on what’s in your basket.

As category affinity data is studied to a deeper level, it can be a powerful tool in discovery and exposure. For example, if most chocolate bar shoppers also purchase energy drinks, digital platforms have the ability to identify a set of energy drink shoppers not currently purchasing chocolate bars and suggest the pairing. To get one level deeper, digital retailers can target energy drink shoppers who typically buy sweet items but not chocolate bars, and suggest the pairing. This level of granularity not only gives consumers the benefit of being understood in habits and affinities, but gives retailers a new way to promote their assortments.

• Intent vs. Browsing: At goPuff, we have found that as consumers mature on the platform, they rely on the search function more, with high-volume users increasingly using the search feature because of their clearer idea of the products carried, and therefore begin with a more solidified idea of what they came to buy. However, new consumers often engage with the function later in their journey, as their initial desire is to explore on their own.

Utilizing the search bar is an inherent show of intent. However, the search term itself can fall on a spectrum between intent and browse. Take water and soda product classes, for example. While “water” remains browse-heavy, even within search, “soda” searches show strong intent. Popular search terms for water tend to be types rather than brands: customers search terms like “sparkling” or “seltzer” more frequently than sparkling water brand names. On the other hand, brand name searches, like” sprite”, are far more popular than terms like “soda” or “pop”.

• Basket Data & Deeper Connections: At goPuff, we have an intimate relationship with our consumers because we are delivering goods for immediate use. With this connection, we gain a deeper understanding of who our consumers are and what they’re doing.

Any retailer can provide a list of their Diet Coke drinkers, but we know the importance of going further, and tying such purchases to activities or current events that unlock cohorts of targetable audiences beyond purchase history. • Purchase Patterns & Predictive Strategies: The more consumers purchase, the better we get to know them. The knowledge goes beyond what or how they’re ordering today, and more on how a purchase is different from the week’s before. Once behavioral changes are observed, predicting future purchases based on what consumers before them have done becomes easy.

With constant changes in the CPG industry, shopper behaviors will continue to make large transitions. The pandemic introduced functional shopping from home via delivery and subscriptions at greater rates than before. Meanwhile, digital retailers are getting smarter about creating powerful experiences online, and consumers are starting to separate emotional shopping (i.e., the fun of browsing a store on a weekend) from functional shopping (order online and spend time doing something more enjoyable).

Simply put, retailers need to continue watching how shoppers want to engage with their platforms and leveraging data to accommodate the experience.

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