Category: Performance | Solution: DataMa Compare | Type : Recurring 
Tags: #Price#Mix Effect #Valorisation

 

In times of high inflation, it is particularly important to understand how much your value sales trend is linked to volume and price changes… everytime that you have new sales data available.

Having a precise understanding of the valorisation trend is indeed key to make the right business decisions, for instance :

• If you lost volume market share (whilst your activation plan was as strong as can be), you should probably make corrective actions quickly
• If your competitors managed to pass higher price increases than you, they will protect their unit margin better and maybe reduce their marketing investment less than you
• If consumers are switching to lower-end products, this will impact your margin, so you need to take actions to make them switch back to more premium ones

What is complex is that price change can result from multiple factors occuring at the same time :

• Retailers have passed shelf price increases on your products
• Retailers have discounted more or less your products
• Consumers have bought more (or less) your products on promotion
• Consumers have bought more (or less) low-end or premium products
• Consumers have bought your products at cheaper or more expensive retailers

The first 2 points are what we could call the « performance » effect whilst the last 3 account for the « mix » effect.

We will look below at an example which highlights how mix effect can impact pricing trend and decision making.

Units growth contributes to half of the value sales trend

In September, a manufacturer has gained 7.7% of value sales or 700K€/£, the main driver being a 5% units increase, which contributed to 436K€/£ of additional value sales.

There are 2 other drivers contributing to value sales growth

• Promotional weight is down but has a small impact
• The average promoted price is up 18.6%, which generated an additional 377K€/£

At the opposite, the average shelf price is down -1.9%, driving value sales down. Does that mean that retailers have lowered this Manufacturer’s shelf prices ? At that stage we cannot conclude anything.

 

A promo price increase due to both a brand and retailer mix effect

The average promoted price of this Manufacturer is strongly up +18.6%, but the reality is not that the depth of discount went strongly down.

Indeed, only 10% of the promo price increase is due to an increase of the promoted price of the brands.

The remaining 90% are due to a strong Brand X Retailer mix change in promoted unit sales :

• RETAILER C_BRAND Y (which Promo Price is generally higher than average) has increased in the mix of units by +3.7pts
• RETAILER C_BRAND W (which Promo Price is generally lower than average) has decreased in the mix of units by -5.6pts

In this case, consumers decided to shop brands W and Y at the more expensive retailer C, who maybe attracted them thanks to deep discounts on other categories, or because there was an appealing in-store activation on these 2 brands.

 

A shelf price decrease due to a strong brand mix effect

The average shelf price of this Manufacturer is down -1.9%, but the reality is not that shelf prices went down.

Indeed, the average shelf price of each brand was up in September, but it is not enough to overcome the strong brand mix change on non promoted unit sales :

• BRAND Z (which Shelf Price is generally higher than average) has decreased in the mix of non promoted units by -3.4pts
• BRAND W (which Shelf Price is generally lower than average) has increased in the mix of non promoted units by +3.0pts

This is a typical case of consumers trading down, potentially because of the 6.2% shelf price increase of brand Z (which can  be caused by price increases and/or a mix effect at product level within brand Z). Not only it is an issue for the business but it also creates a false impression that shelf price is overall down. 

Conclusion

Analysing the valorisation is key but it is time consuming, complex and data can be misleading if looked at too quickly.

Yet, insights people, brand managers, category managers, sales people, finance people often do (and should do) such analysis at least each month, and it comes on top of many other analysis they need to do.

 Using DataMa’s interactive waterfall is an efficient way to tackle these complexities.

Instead of spending 80% of their time running analysis and only 20% making decisions, our clients dedicate 80% of their time to drive business.

This is our objective at DataMa: to increase the usage of data to drive business decisions, everyday.

If you are interested to see a full demo, you can contact me at cyril@datama.io