by Jorge Romero, Category & Store Solutions Consulting Director, dunnhumby
Grocery retail is a business of choices. In a physical environment, retailers need to decide: Which products will customers see on the shelf? In which stores? With what type of promotional emphasis? And ultimately, at what price? The famous 4Ps. Even in digital, all this remains true. The online shelf may in theory be infinite, but alas the customer’s time and product inventory costs are not. Therefore, retail continues to be a curator for the best options to satisfy customers’ needs and facilitate their choices.
Business digitisation has accelerated more than ever over the last two years, with retailers from all sectors realising that it is not enough to simply make the product available to customers. There is now a need to not just measure what happens at the point of sale, but also to understand the impact of each product on the entire business and customers alike. customers must be at the center of all decision making that takes place.
One of the industry’s priorities now is to optimise assortment, not only due to recent supply chain pressures but also because of its crucial importance to customers as evidenced by our Retailer Preference Index. Each product category can influence a customer’s behaviour in different ways, and each customer can and will react differently to the various trade levers.
At dunnhumby, we understand the importance of the most predominant levers for customers, the most strategic being price, promotions, variety, innovation and private label. And the way we gain this understanding is mainly via transactional data, because customers as we know will only sometimes do what they say they will do.
When retailers maximise their use of transactional data from real customers, there are two main benefits: competitiveness and innovation (and ultimately a Customer First approach).
Data as an engine of competitiveness
By understanding customer behaviour and the factors that drive purchase decisions, it is possible to determine the optimal mix for each store and the space dedicated to promotions for each product. For example, if customers find product variety to be important, the retailer needs to take a widely different approach than when price or promotion is more relevant to customers in a given category in a given banner.
When these analytics are combined with a granular view of the customers, it becomes possible to optimise the business strategy and as a result invest in the correct sales lever. Once the retailer knows which categories really matter in the customer’s decision about where to shop, they can take actions to keep the customer engaged and satisfied with the overall store experience. At the same time, levers can be successfully used to encourage customers to purchase additional products and engage more with a category, hence increasing the average value or number of items in the basket, and even operating margins.
In the search to increase competitiveness, discounts are not the only tool available, as each customer exhibits different behaviours across categories. By analysing customer data, we can identify the levers that make the most sense for each customer and each category. In some cases, positioning an innovation or introducing a private brand product can be more efficient than a pure discount where a retailer’s private brand resonates with customers.
The retailer who understands the role of each lever comes to understand the DNA of each category and its strategic role in the relationship with the customer. This helps the retailer focus their efforts on where they can impact sales the most. After all, the retailer needs to show that it is better than the competition only where it matters to a customer because that is precisely where and how it will affect their experience and purchase decision.
Data as the basis for innovation in categories
Once a retailer manages to be competitive in the categories that matter most to the customer and manages the relationship from the most appropriate levers, it becomes possible to deepen the strategy. That’s when the use of data-driven innovation tactics comes into play for brands and retailers.
In this aspect, both the supplier and retailer need to make decisions and prioritise where and how to innovate: in the product formula, in packaging, in communication or at the time of purchase. A brand that is not able to make the right choices may lose space to competitors who are more assertive, while the retailer may fail to offer an item valued by customers – which may in turn cause a significant impact on competitiveness.
Therefore, retailers and brands must work as partners, analysing data and building knowledge together to generate sales opportunities on both ends whilst keeping the customer at the center of everything they do. This can be done at all stages of innovation development:
- Prioritisation: establishing where to focus product development efforts
- Ideation: identifying customer needs not being satisfied by existing products
- Measurement: evaluating the product’s performance and its retail execution
In Prioritisation, concepts such as category roles and the importance of leveraging innovation can be used to understand the level of assertiveness that a retailer will need to pursue to achieve its strategic goals in each category. Thus, in “Win” categories (those that matter to customers and business performance alike) with High Innovation importance for customers, the retailer will want to achieve launch prominence and to create a buzz in the market to ensure that customers buy and try the product in their stores rather than do so at a competitor’s location. In a less strategic category with Low Innovation importance, the retailer will not actively seek to highlight a product launch or invest heavily in Innovation.
In the Ideation stage, the focus must be on understanding the customer’s shopping behaviour and their expectations. At this stage, some relevant concepts can be leveraged:
- Level of maturity or category development, linked to the three potential growth sources of any retail category known as: customer Penetration, Frequency of Purchase, and Spend per customer
- Customer profile, which consists of understanding the behaviour of different customer segments to find opportunities for product development and communications
- Consumption trends, which can be identified by integrating different product attributes to the data already collected in historical databases
- Customer decision trees, where a customer’s needs within the category can be identified, such as those that have emerged recently and those not being met
Finally, during Measurement, the focus should be on evaluating the impacts that a new product launch may have on a category’s holistic metrics. These include a customer’s trial and repeat purchase behaviours as well as comparing the product’s performance with other similar category launches in order to get a representative business perspective.
Understanding the behaviours of customers, their purchase drivers and what they expect from each category and brand is essential for retailers and brands to obtain the expected results from their commercial activities. By analysing real data on customer behaviour, brands gain the knowledge they need to increase their competitiveness and encourage innovation where it matters the most to customers. And, thus, improve results and increase customer loyalty as a result of a more Customer First approach.
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