6 valuable types of shopper insights that today’s retailers can offer brands

Retailers are becoming more optimistic about their near-term trading outlook after a “challenging winter”.

Savvy brick-and-mortar retailer leaders are constantly looking for ways to strengthen their relationships with brands, and first-party data about shopping patterns represents a major opportunity.

Since the 1950s, brands have been thirsty for data about their audiences’ preferences and behaviour, as well as insights into the success (or failure) of their promotional campaigns. In the era when TV ads, billboards, and magazine displays were the top messaging channels, gathering this kind of information was a highly speculative and inaccessible discipline, as it often involved surveys and focus groups that were executed by expensive research service companies.

The arrival of digital media, ecommerce and trackable ads changed the playing field. It opened up a treasure chest of available shopper insights, which only sharpened brand appetites for data and raised the standards for highly customised and targeted ads. In this sense, the impending loss of ad tracking via third-party cookies has been especially traumatic for brands.

As a result, brands are eager for retailers’ (and publishers’) first-party data from sources like point of sale (POS) data, footfall data, and loyalty program data. According to Grocery Doppio’s State of Digital Grocery report, over three-quarters of marketing executives agree that loyalty program data is a key differentiator in the quest to understand shopper intentionality.

Retailers who want to strengthen their relationships with brands (and which don’t?) should take advantage of this appetite and deliver valuable insights from their proprietary data. Here are five specific types of data that retailers have and that brands will value.

1.   Final basket data

Final basket data, which is best acquired from your POS system, reveals which products are typically bought together, which can help brands optimise their merchandising strategies and marketing campaigns. Connecting your POS directly with your CRM makes it possible to match POS data with loyalty program data.

Adding shopper frequency data gives further insight into preferences for packaging sizes — for example, do shoppers prefer to buy smaller packages of pasta every week, or large packs every month?

Retail Velocity is a useful tool that collects and analyses information from store POS systems and other sources, producing valuable insights for CPG brands through a collaborative interface.

2.   Conversion rate data

In-store retail media networks (RMN) are rising massively and quickly, thanks to brand appetite for shopper insights. Unlike in the past, when brands might place signs around the store and hope for the best, in-store retail media can reveal which targeted promotions drive the highest conversions.

Grocery Doppio found that 93% of marketing executives believe that the loss of third-party cookies is a significant driver for media monetization, and McKinsey reports that 73% of advertisers expect to spend more on RMNs in the next 12 months. Brands are eager to learn which creative is the most successful, the relative impact of video ads compared to other formats, which types of offers drive the biggest uplift in purchase, and more.

Shopic’s retail media engine delivers all these insights and more. It tracks redemptions of every personalised promotional offer that appears on the company’s network of smart cart screens. The platform can run conversion rate reports that reveal what types of offers resonate most.

3.   Returns data

Data from customer service channels can be a rich source of insights about customer attitudes towards brands and styles. Looking only at what sells means ignoring buyer remorse, and the negative sentiment that results.

For example, which products are returned the most often? Which sizes and models receive the most complaints, and what are the most common issues that customers raise?

Returns analytics like Loop can crunch data from email, phone, chat, or in-app messaging to show trends among complaints and communications about different products and brands. This is particularly useful for brands in the fashion, lifestyle, or interior decorating verticals, who can then adjust their products or marketing messaging accordingly.

4.   Trending products data

It’s highly useful for brand strategists to understand what products are drawing the most attention. Is the “hard seltzer” category, which went big in the US in 2021, starting to pick up in the UK, or is it all hype?

Brand leaders also want to know which products are preferred by shoppers within a specific category. Customer preferences about packaging sizes and types — like if they prefer tomato sauce in boxes or jars — are also very valuable. Brands likely know which variants sell the most among their own product lines, but category-wide insights can provide useful benchmarks.

Shopic’s smart cart screens can capture this data and more. With Shopic, retailers can run analytics to show brands rich insights like new product categories that are surging in demand, specific products that shoppers commonly add to carts but subsequently remove, and how often customers change their minds between two similar brands.

5.   Store placement data

Foot traffic sensors can map the customer journey around the shop, showing which shelf displays draw the most attention and which locations drive the most sales. The order in which shoppers visit different sections can help refine the way brands plan their ads — for example, should you suggest butter in the bread section, or bread in the dairy section?

Advertisers also benefit from a greater awareness of which aisles see the most lingering. If there’s no media causing shoppers to pause, this could be a sign of uncertainty over purchase decisions which reveals an opportunity for a well-targeted ad.

FastSensor uses radio frequency and AI-derived heat mapping to track foot traffic as people move around a store. It uses this information to chart the customer journey and deliver actionable analytics through a digital dashboard.

6.   Brand Tracking Data

Brand tracking provides crucial insights into a brand’s health by monitoring changes in customer perceptions and the impact of marketing initiatives. It helps identify whether the brand is gaining or losing favour and how new product introductions or campaigns affect market share and customer loyalty. Moreover, shifts in competitor brands’ perceptions can be instrumental in identifying market opportunities and potential threats, allowing retailers and brands to strategise accordingly.

Instead of direct engagement with tools, working with a specialised brand tracking agency can be an effective approach. These agencies offer comprehensive brand tracking services and can deliver granular real-time insights into brand performance and consumer sentiment. They monitor various aspects, such as brand awareness, consideration, preference, and loyalty. Integrating brand tracking data with other data types, such as store placement data, can reveal deeper insights. This allows retailers to help brands navigate the complex retail environment, strengthening their relationships.

Brands are eager for the data that retailers possess

Brands are salivating for meaningful, useful insights derived from solid and trustworthy first-party data, and retailers are uniquely positioned to provide them. With the right tools, shops can capture these signals and turn them into insights that help to cement their relationships with brands.