Context is king for value proposition profitability

Some time ago, a colleauge warned against the danger of putting customer profitability at the core of a retail strategy: “Customers do not have a choice on how much margin they give to a retailer or brand,” he wrote. “Treating customers differently based on their ‘profitability’ is counter-productive to building loyalty and toward creating a healthy retail customer experience.”

A few years on, and that argument continues to ring true. A genuine Customer First culture will always triumph over a myopic focus on wringing value from a small subset of shoppers. Focusing only on those customers deemed to be high value is reductionist at best and can in fact be highly damaging to a brand’s reputation at worst.

All of that said, profitability isn’t a dirty word – and nor should it be. While measuring customer profitability in isolation may lure retailers into making poor decisions, evaluating the profitability of a customer value proposition can be hugely beneficial.

Nuance and depth

To quote my colleague once again, “in a respectful, Customer First approach to business growth, each value proposition delivers recognizable value to customers as well as recognizable margin to the retailer or brand”.

Value, argues David, flows both ways. And only by measuring the profitability of a proposition can we judge whether an appropriate exchange between retailer and customer has been achieved.

How best to judge that, then?  Tempting though it may be, consulting the raw sales data around a value proposition is unlikely to provide a sufficient level of insight. While “revenue generated less the cost of the proposition” might sound like a solid enough equation on the surface, in reality it fails to capture the nuance and depth inherent in a multichannel operation.

That’s because evaluating a proposition based purely on sales fails to account for the value it may deliver elsewhere. A dotcom channel that looks unprofitable may in fact pay for itself many times over in terms of brand loyalty. That low-margin item that barely breaks even may have connected products in a customer’s basket that make it much more valuable than you might think.

When it comes to proposition profitability, context is king. The only way to measure the true worth of a value proposition is to understand its place within the organisation as a whole. And the only way to achieve this is to combine different sources of data, customer profitability included.

Better data makes for smarter questions

The first step in understanding the true return on a value proposition means bringing together three sources of data.

These are:

  • Product profitability: the sum profitability of every item sold on a channel-by-channel basis.
  • Basket profitability: the sum profitability of all items in the same basket.
  • Customer profitability: the sum profitability of each transaction made by the same customer.

While the last of those can be problematic in isolation as discussed above, including customer profitability in a broader piece of data analysis can actually give retailers great insight on how their value propositions can be improved. That’s because when one group of customers is less profitable than another, it is typically because they are buying into value propositions that fall below “accepted” levels of profitability.

Take mass promotions, for instance. They might appear to have a negative impact on profit in small channels, where customers might visit just to buy that one item. But in bigger formats – where there are more opportunities to convert a singular purchase into a whole basket – the returns are much higher.

Does that mean that mass promotions don’t work for small channels? Or, worse still, that they don’t work for that retailer at all? Does it mean that small channel customers should be deprioritised over large format shoppers? Almost certainly not, but without all of those data sources combined it would be easy enough to jump to one of those conclusions.

Better, richer data ultimately means that retailers can ask smarter questions:

  • “Was this value proposition developed as effectively as it could have been?”
  • “Was the investment justified by the elasticity of the product, or the uplift delivered by the rest of the basket?”
  • “Do reward mechanics point to the most or least profitable areas of the business?”
  • “Could better results be achieved using different metrics (e.g. units sold, size of the basket) at lower cost?”

These are all smarter, more nuanced questions than “do mass promotions work for small channels?”, and they are only possible through a 360˚ approach to value.

Smarter questions, stronger strategy

Successfully answering any one of those questions above could help a retailer improve the efficacy and profitability of an existing value proposition, albeit quite tactically. Where the real benefit of a contextual approach to profitability comes into play, though, is in developing a strategy for future investment.

Optimising future spend around a value proposition follows a similar framework as outlined above. Rather than product, customer and basket profitability, however, the metrics used here focus on:

  • Engagement: which customers engaged with a value proposition, and to what extent?
  • Volumes: what is the impact in terms of units sold?
  • Overall profitability: what contribution did incremental sales make to the business?

By applying these metrics to a portfolio of value proposition activities, retailers can gain a much clearer understanding of the kind of customer behaviour that said activities provoke. So rather than being used to tweak an existing proposition’s profitability, these insights can instead be used to pursue specific goals – footfall vs. basket building, say, or volume vs. profit.

This level of understanding engenders a more strategic approach to future spend. By arming themselves with deep insight into not just the profitability of a proposition, but the kind of behaviour it provokes amongst different customer groups, retailers have a better chance than ever to align their future spend against both their own mission critical objectives and the demands of their customer base.

The latest insights from our experts around the world

customer first data science analytics & machine learning services
Ready to get started?

Speak to a member of our team for more information

Contact us