Data-Driven Precision: Navigating the Future of Personalization in CPG and Retail

Personalization has never been more important for consumer brands and retailers. According to McKinsey, companies that excel at personalization generate 40 percent more revenue from those activities than average players. However, many marketing leaders report ongoing challenges in delivering personalized experiences.

The challenges and opportunities for getting personalization right (or wrong) all hinge on data. While the concept of data-driven personalization isn’t new, at LatentView we're watching three trends that are poised to shape how brands provide customers with more personalized experiences in 2024 and beyond.

Trends and Future Challenges in Data-Driven Personalization

Increasingly Personalized Upper Funnel Experiences

With the launch of Prime Video Ads in January, brands can now connect Amazon.com's massive retail media network, full of customer demographic and behavioral data, to their TV advertising campaigns.

Streaming ads will get much more sophisticated in the year ahead. This goes beyond targeting ads by location. Streaming ads will now be targeted by age, gender, shopping history, etc. It won’t be long before brands are surfacing ads to consumers that have abandoned one of their products in their cart.

Recommendations Based on All Brand Interactions

In order to continue to improve personalized marketing campaigns, brands should take every customer interaction into account.

For example, consider a rental marketplace like Airbnb or VRBO. A customer leaves a negative review on a property they rented, citing that it was unclean. Using that feedback, Airbnb or the like can recommend properties with exceptional cleanliness ratings, turning a customer’s bad experience into a positive one.

In retail to date, existing machine learning-powered recommendation models suggest products based on other customers’ purchasing behaviors. For example, “customers who have purchased X have also purchased Y.” With better data, retailers are now beginning to offer more tailored recommendations. In practice, this looks like, “Customers who have shown interest in A have also shown interest in B based on their viewing history, search behavior, carts and wish lists, etc.”

Increasing Importance of Personalization to Drive Customer Loyalty

As customer acquisition costs have soared over the years, retaining existing customers, especially in categories with high repeat purchase rates, has become increasingly important.

Chewy.com is perhaps one of the best examples of brand personalization at scale with the goal of building customer loyalty. The pet brand will send gifts and paintings of your pet on their birthday, as well as condolences when they pass. Customers have been wowed for years by these deeply empathetic practices, and they remain fiercely loyal to Chewy as a result.

However, with emerging opportunities, change is coming, even for digitally native brands. Marketers will need to adapt to the death of the third-party cookie, expected to phase out by the end of the year. Increasingly, both CPG and retail brands are turning to their own zero- and first-party data to reimagine personalization and targeting.

Navigating the Road Ahead

For most brands with a multichannel strategy, data is distributed across various different e-commerce marketplaces — in large purchase orders from major retailers like Walmart, or living within their own database via their direct-to-consumer stores.

Gone are the days when personalization happens at or near the point of sale. To support evolving customer demands, brands must communicate at multiple tiers of the sales funnel and in a way that resonates with each customer, ultimately requiring an advanced data strategy to support personalization at scale. But leveraging such vast amounts of data requires preparedness. The following three practices will help digital brands navigate these data challenges and seize opportunities for greater personalization:

1. Rethink your data strategy.

The very first step is to collect and align these different data sources, while ensuring data quality so that each different dataset can effectively “talk” to each other, creating a framework that can evolve with the growing demands of the business and customer satisfaction.

Once the data is collected, connected and visualized in a privacy-compliant way, the real personalization work can begin along several data dimensions:

• Communication style: With so many avenues to engage users (email, SMS, push notification, etc.) it can be challenging to know what to say and when. User preferences will differ, even slightly, and adjusting how you communicate can lead to more engaged customers and increased return on investment. Tune into user preferences and past purchasing behavior to understand how they respond best.
• Browsing behavior: Browsing behavior can be a major unlock for brands looking to understand customer motivations, especially on their own website where they're logged in. By scrutinizing the duration spent on particular pages, marketers gain an understanding of the content or products that resonate most with individual users. For instance, an Amazon brand might be able to drive a purchase by retargeting a customer who added a product to cart by offering a personalized discount.
• Purchase history: Purchase history is essential for tailored marketing strategies. Looking at what someone has purchased in the past and the frequency of purchase can unlock timing for marketing messaging. For example, if a customer frequently purchases dog food on a monthly basis, the brand can encourage them to subscribe and save for longer durations, or if they miss a regular purchase, reach out shortly thereafter and remind them to buy.

2. Leverage generative AI.

Artificial intelligence (AI) and machine learning (ML) play a pivotal role in extracting meaningful insights from vast datasets — especially when defining the “next-best action” for the customer. Next-best action uses data to derive insights from marketing, sales, customer service, and other departments to predict the next action brands should take in order to trigger a conversion.

For example, one of the most persistent issues in e-commerce is cart abandonment. In one aggregate study, the estimated average cart abandonment rate was 70 percent. Brands can use AI to send personalized messages to these shoppers to encourage a purchase.

Next-best action is applicable across the sales funnel. Not only can abandoned carts trigger an email reminder, but first-time customers can receive follow-up incentives based on how they subsequently interacted with the brand. For example, did they follow the brand on social media or send a product link to a friend? Furthermore, with the rise of generative AI, brands can now automate the delivery of next-best action content to consumers based on where they are in the sales funnel. All of this data is valuable in converting marketing leads into sales.

One case study in Econsultancy showed that an agency was able to send out automated email campaigns to consumers who had abandoned their carts, offering them reduced prices, which resulted in a 29 percent success rate in closing a sale. In Germany, one car manufacturer saw a tenfold increase in email clickthrough rates going from 600 to 6,000 clicks out of 10,000 emails using a personalized marketing campaign.

3. Translate personalization from online to in-store.

As brands have shifted towards first- and zero-party data, they’re increasingly getting creative to drive personalized experiences and products for their customers.

One such example is the hair care brand Function of Beauty. The entire brand and product portfolio is built on zero-party customer data. What does this mean? Shoppers take a quiz on the Function of Beauty website, which then crafts a personalized hair care formula on their behalf.

This type of hyperpersonalization is really only possible in DTC, but as Function of Beauty expands into wholesale, it’s taking this personalized approach to each individual retailer. For example, when it expanded into Sephora, it developed an exclusive line of products that catered specifically to Sephora’s customer demographics and more premium appeal.

Personalization, in this sense, can go much further than just customer interactions; it becomes a retailer-by-retailer approach, where you customize your assortment to meet each retailer's customers where they are.

Final Thoughts

As consumers continue to demand more personalized experiences, brands will be challenged to rethink their data strategy and prepare for a future where one-to-one personalization is central to the marketing strategy.

The days of running massive TV ads with limited visibility into attribution are at an end. Customized content for specific segments will be the path forward.

Aaditya Raghavendran is the head of retail at LatentView, a global leader in advanced data analytics.