RETAILERS USING OUR DATA
1.Strategic assortment planning
A top 20 global supermarket chain uses Shopper Intelligence across their business. One example is that their central range and space planning team sets the “top-down” criteria for how much branded assortment each category in the business needs to carry, to meet shopper’s expectations. One key input to this is the unique data we collect across all categories on whether a shopper plans to buy a brand (or number of brands) and their willingness to switch if their preferred item is unavailable. This helps them decide objectively where to allocate precious in-store space, whilst maintaining shopper satisfaction.
2. Exploiting a premium Private Label segment better
A leading retailer was puzzled about poor sales of their premium own label, and questioned its role in their range. SI data proved that whilst their shoppers had equal interest in premium and innovation in that category compared to competitor stores, they were not noticing this range at shelf, and in fact rated this retailer below par on this criteria. Having identified the “shelf impact” problem, the range packaging was re-designed, instore merchandising was adjusted and sales doubled. Rather than a first instinct to delist a poor performer, SI shopper data showed a longer term and more profitable way forward.
3. Deciding the best use of gondola ends
This grocery retailer had pursued the policy of multiple brands on a single gondola end to maximise shopper appeal and revenue. This was at odds with competitors and also the desire of key impulse vendors. To resolve the somewhat subjective debate, Shopper Intelligence was used by a “working party” including 3 vendors and the retailer’s central planning team. Evidence in the program showed that whilst this retailers shoppers were particularly responsive to impulse propositions they were finding the store harder to shop and showed lower satisfaction. This was sufficient to prompt a trial of single brand gondola ends, and the results of this have led to a roll out based on the bottom line impact.
4. Driving shopper orientation across a business – whilst saving research dollars
A well known European supermarket retailer used SI to replace a range of historic internal research protocols. They now access the SI database for strategic planning, decisions on promotions and marketing across the store (eg Events), and each buyer uses a digest of shopper facts to inform their thinking. As well as saving money versus previous methods, and now having access to far more category by category depth of insights, the key outcome is best expressed in the words of the head of insights: “there isnt a day when someone somewhere in our business isn’t using Shopper Intelligence data to help make a better decision”
SUPPLIERS USING OUR DATA
1. Improving promotional effectiveness
A global multinational in the personal care sector was debating with a major customer the way a key brand was being used on frequent half price promotions – damaging brand and retailer profitability. The buyer was insistent on this strategy as a tool to drive traffic to the department. SI data showed that the category was not in fact well suited to decision early in the shopping trip and wasn’t achieving the traffic goals being assumed. The “killer chart” in the analysis split shoppers into those buying for themselves and for others (eg mum buying for teenagers) and demonstrated that the current strategy was not meeting the needs of a significant proportion of category buyers. For the first time the retailer agreed to undertake a complete review of promotional strategy, unlocking fresh a dialogue about win-win change.
2. Shopper led listing arguments
A specialist manufacturer in Vegetarian Frozen Food saw significant de-listings in a major supermarket because many of these lines did not meet overall Frozen department requirements for rate of sale. It was only when they could objectively demonstrate that “choice” was of critical importance to vegetarian shoppers (and that this category ranked number 1 in the whole store on this factor) that the buyer understood how critical this particular shopper need is (and the risk of losing that basket altogether), and relisted most of the lost lines resulting in a net £1m sales gain for the supplier.
3. Increasing merchandising investment ROI
A confectionery manufacturer historically invested heavily in Gondola end and Free Standing Display activities of all kinds. Shopper Intelligence’s new “Store Impact” automated observation system measured the true conversion rates from confectionery displays in various in store locations and proved that one location type achieved nearly double the conversion of all the others. Indeed, we showed one frequent location of activity achieved near zero conversion. The supplier briefed their field force on this learning and re-allocated budget to the preferred kind of display and have quoted us a figure of $75m in annualized sales improvements.
4. Pricing strategy
For many years, a North American frozen food brand had been expected to promote on their major customer’s promotional flyer at a deep discount price. Comparison within the SI database for that store showed that this brand was not actually a “Known Price” item compared to many others, and whilst suitable for traffic driving activity there as no need to hit the historic promotional price. This objective data supported the slightly risky re-pricing agreement. The revised higher flyer price led to $400k of margin upside to the retailer with no loss of sales. In return the retailer reduced their demands for promotional support from the supplier.
5. Overcoming retailer “top down” barriers
A confectionery company was told that their key retailer was prioritizing health and wellness messages so promotional space for their category would be cut back. Through across store comparisons in the SI data, the supplier was able to demonstrate the power of confectionery in driving unplanned purchases better than any other options and enabled them to place an authoritative dollar value on that incremental capability (ie the net loss to the store). The quantification of the profit hit of the proposed shift in strategy aligned to objective shopper fact partly reversed the previous decision, “rescuing” substantial revenue and profit.