Traditional retailers are in dire need of price optimisation. The help they need is finally at hand.

Even the best performances are a distinctly mixed story below the surface. Tesco have been celebrated for strong Q4 sales, but the growth is well behind the rate of inflation. That Tesco has managed to hold its profit forecast is an outstanding success in this climate. Others will not be faring so well. 

The standard retail response – ‘grow your way out of it’ – is not an option for the foreseeable, and accepting a margin squeeze is not affordable for an industry with already paper thin margins. The only choices for retailers trying to address this challenge seem to be binary bad ones: hold price and hurt percentage margin, or increase price and lose volume, both resulting in net margin loss.

Price optimisation is a reality – for some.

The situation calls for better net revenue management, with price optimisation front and centre. It’s not a new idea – the relationship between price and demand, what affects that relationship, and how to model it are all well researched areas. It offers the prospect of small changes to pricing that add up to big gains in margin.

We see this playing out in the case of digitally native e-commerce retailers. Amazon uses analytics to optimise pricing for market to market conditions in real time. Uber combines willingness-to-pay with supply and demand analysis to optimise their pricing second by second. Any retailer with this capability should be well placed to tough out a difficult time.

But what of traditional brick and mortar retail? The picture is not so rosy, with pricing decisions often left to judgement within rules on minimum percentage margin and gap to competitor pricing. This results in at best reactive pricing, and at worst cedes control of price to the competitive dynamics of the market – resulting in a constant downward pressure that retailers can scarce afford.

Almost all traditional retailers have attempted some form of price optimisation, only to find it either didn’t work or never got used. It’s perhaps unsurprising that there’s a lack of faith that price optimisation is worth pursuing again. 

The trouble is, they can’t afford not to – getting price optimisation right can unlock 300 basis point EBITDA growth.

It would be easy to assume that these retailers are somehow behind the curve; unable or unwilling to keep up with technological advances. But the reality is that the incumbent price optimisation solutions on offer today – be they price elasticity studies, virtual store experiments, or black box AI – are not fit for purpose.

Why price optimisation in traditional retail has failed…so far

The first step is to be clear on what the shortcomings of attempts at price optimisation have been.

1) A poorly conceived methodology: Price optimisation has previously been treated as a vanilla regression problem i.e. training a model on historic price and sales data, and using it to predict the future. To understand why this is a problem, consider two facts of traditional retail. Firstly, prices in traditional retail don’t tend to change much – there is very little pricing signal in the data. Secondly, in portfolios of thousands of SKUs, complex interactions mean that a price change on one might affect the sales of others. Naive application of a regression algorithm will fail to account for both problems, and the result is poor, potentially dangerous, recommendations. 

2) The outputs aren’t useful: Approaches to brick and mortar price optimisation have ranged from little more than high-level insight to sku by sku recommended pricing. The former clearly doesn’t provide the granularity needed for line by line price optimisation. Even if accurate, the latter approach very often fails to explain its recommendations. This is unacceptable to the business users who – when faced with the potential risk of getting pricing decisions wrong – tend to work around or ignore such systems.

There is another way: Decision Intelligence.

Decision Intelligence brings together cutting-edge AI algorithms with seamless user interfaces that are focussed on enabling human experts to make the best decisions extremely rapidly.

How can this approach transform price optimisation? We firstly need to find the right AI for the challenge. This means a sophisticated ensemble of techniques that can carefully extract pricing signals, understand and utilise a network of product similarity, and reliably search for complex optimisation of multiple objectives. 

It then requires a deep understanding of the requirements of the pricing decision making process. Pricing is a constant decision process for retailers, it’s useful to conceptualise it as a Decision Loop.

Observe: ingest and decompose the drivers of your current sales performance

Understand: users must be able to see and trust how a model estimates future performance

Decide: find pricing that meets multiple commercial objectives. Flexibly examine scenarios.

Act: integration of decisions into parallel processes and systems.

Intelligence at every stage of the decision loop, for every iteration of the loop, creates compound improvement over time – offering retailers a new and enduring growth lever. 

Just a model – even a really good one – isn’t enough to enable price optimisation. But with Decision Intelligence, retailers will be able to cyclically manage their pricing in order to make pricing the growth lever it should be. 

True price optimisation is now within reach for traditional retail.  And it couldn’t come at a better time.

Joe Mills is a Pricing and Revenue Management specialist in our Retail & Consumer team. If you would like to learn more about our intelligent price optimisation capabilities – please get in touch.

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