Promotional efficiency and ROI case study

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Maximising promotional efficiency and ROI with shopper evidence

CASE STUDY

Maximising promotional efficiency and ROI with shopper evidence

A hygiene products manufacturer was struggling with a retailer rule that limited promotions to one SKU at a time. Shopper Intelligence showed that the approach did not match how shoppers bought the category, helping the supplier make the case for full range offers and a more effective promotional strategy.

Full range

Offer scope changed from one SKU to the full range.

ROI up

Promotional efficiency and profitability improved.

Stock down

Remaining inventory was reduced after promotion.

At A Glance

Market

France

Client Type

FMCG hygiene products manufacturer

Retail Context

Major hypermarket retailer

Business Issue

Poor promotional efficiency, weak uplift and remaining stock damaging profitability

Shopper Intelligence Role

Understanding shopper behaviour and assessing retailer promotional performance

Outcome

Promotional efficiency and profitability improved, remaining inventory fell and the retailer supplier relationship improved

Source

Shopper Intelligence France promotional efficiency case study

The Business Context

A major French hypermarket retailer required promotional activity to focus on one SKU within a range at a time.

For the supplier, this was creating poor sales and market share uplift, high remaining inventory and repeated tension after campaigns. Other retailers allowed full range promotions and were delivering stronger ROI.

The Challenge

The supplier needed to challenge the retailer's one SKU policy without turning the discussion into belief against belief.

The key question was whether a single SKU offer reflected how shoppers actually bought the category, or whether a full range promotion would better match shopper needs and improve commercial performance.

What Shopper Intelligence proved

Shopper Intelligence showed that shoppers were more likely to change brand than change the format or product type they wanted. If the promoted SKU did not match the format or size they intended to buy, the offer was less relevant.

The retailer was also underperforming on promotional satisfaction compared with the category Best in Class, showing that the issue extended beyond the supplier's commercial concern.

The evidence supported a full range promotional mechanic because it gave shoppers a better chance of finding the product type or format they intended to buy.

How the insight was used

The supplier used Shopper Intelligence evidence to introduce more flexibility into the retailer conversation.

The recommendation was specific to this category: rather than applying a one size fits all rule, the retailer agreed to test a full range promotion for the next campaign.

The Result

The full range promotion delivered stronger efficiency and profitability for the supplier and reduced the issue of unsold products.

The supplier moved from single SKU offers to full range promotional activity, remaining inventory fell, and the retailer eventually moved away from its one size fits all promotional rule for the category.

The Takeaway

This case shows how Shopper Intelligence can help challenge generic commercial assumptions with category specific shopper evidence. By aligning the promotional mechanic more closely with how shoppers actually buy, the supplier improved promotional efficiency, reduced remaining stock and moved the retailer relationship towards a more constructive approach.

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By comparing their performance, as perceived by their customers, with that of the Best in Class in the category, we convinced the retailer to abandon a long held belief and instead communicate our promotional offers as applying to all products in our range, rather than just one. This very simple demonstration, based on objective and quantified shopper data, has radically increased the effectiveness and profitability of our promotional investment within the retailer. Shopper Intelligence data is very powerful, valued and accepted by retailers because - unlike panel data - suppliers cannot rig the figures to always show only the positive side.

- Trade Marketing Manager

Decision Hierarchy

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