Setting up a pricing tool: assessing R.O.I.

Choosing the right pricing management and optimization solution can prove to be a real headache. The market is fragmented, with solutions having widely varying features, performance and costs. To make a decision and convince internally of the relevance of a pricing tool, assessing a realistic Return On Investment is essential.

At Mercio, we use “back testing” with our customers to validate the value provided by our pricing platform.

Considering the total budget envelope to be released

The choice of a pricing tool represents a significant financial and human investment. Modern pricing solutions are in fact highly specialized systems embedding advanced technologies. In addition, pricing is a determining field which impacts the whole business performance as well as purchasing or marketing decisions. This explains why a successful transformation requires the commitment of many people within the company.

The TCO (total cost of ownership) of a dedicated pricing solution thus includes:

Annual or multi-annual license, adjusted according to the technical support offered, the slots of availability of the platform and the desired service levels commitments.
Implementation (“onboarding”): this type of complex tool involves a personalization and configuration phase, in order to connect to your data flows, but also to integrate business expertise and processes.
Training: one or more members of your teams must receive extensive training in order to use the technology to its full potential
Product evolution: beyond support and ad hoc updates, a platform dedicated to a function as central as pricing is by nature destined to evolve regularly and adapt to a changing environment. It is therefore wise to provision for future developments.

These costs will in principle be justified by the gains made from the tool, but it is essential to obtain proof of this quickly enough in your purchasing process so that the stakeholders in the decision – who might have more trouble envisioning the future benefits – may give their agreement.

Visualize the gains thanks to a simulation under market conditions

The ROI of transformative technologies can be difficult to calculate. Many benefits are almost intangible, for example: improved collaboration, easier access to reliable data, reduced time spent handling it, improved responsiveness and quality of decisions. By putting the matter differently, to focus on measuring the impact of the tool compared to the absence of change; a method stands out to prove the value of the solution: back testing.

Back testing is a common method in science as well as in finance, where it allows to assess the relevance of investment strategies. It consists of simulating the effects of a strategy or model using real data from the past, in order to assess the viability of that strategy. This method is also perfectly relevant for the pricing case. It requires an excellent understanding of the business.

By calculating the impact of finer price strategies and the increased optimization of margins allowed by a dedicated pricing platform on data from the past (taking into account available stock, seasonality, elasticity or customer sensitivity at the price etc) we can visualize the gains brought by the tool and assess its profitability.

An example of a test in DIY

Step 1: Start with a representative data set.

To quantify the possible gains with Mercio’s pricing solution for a DIY brand, we collected six months’ worth of receipts for the 15% best performing products and enriched them with competitors’ prices for the same period.

Step 2: Build performance indicators

Sales, margin, but also complex indicators such as the price index for each store compared to competitors and price drift (the difference between the prices recommended by the central purchasing office and the prices actually applied in-store).

Step 3: Use data science and AI to generate realistic scenarios

From the context of this retailer, 3 scenarios were simulated, under the supervision of the pricing manager, to measure the potential impact of the tool. In the first, the strategy is based on the concept of price-image: the tool uses AI to detect the level of product sensitivity, the purchasing behavior of customers, and apply price reductions or increases accordingly.

In the second scenario, we apply a geopricing strategy: by identifying the stores whose price index is too low compared to surrounding competitors, we correct the prices to a level closer to the average. This scenario has proven to be the most profitable.

The last scenario combines the first two: by making price increases only on the least sensitive products and in stores with a low price index, this scenario is the one that allows margin gains while minimizing as much as possible the risk of impact on sales volumes.

The results of the test varied, of course depending on the scenario adopted, but each one showed that the solution generated 2.6 million euros after only one year of use.

Evaluating the ROI of pricing software is complex and some impacts are particularly difficult to quantify, such as the ones on price image. Back testing makes it possible to effectively quantify in terms of margin and revenue the possible gains based on data from the past which realistically reproduces the environment of the company. The comparison of several scenarios makes it possible to ensure the solution stays viable no matter what. Once your pricing solution is implemented, the effective ROI will also depend on organizational considerations: transform the organization to get the most value from the precision and responsiveness gained.

Go further with Mercio:

Mercio’s retail price optimization platform will enable your teams to implement and industrialize differentiated pricing strategies to accurately drive traffic, margin and price image, across tens or hundreds of thousands of references.

Our experts and Business Solutions Engineers will assist you in evaluating our pricing software in relation to your brand’s needs and setting up an ROI study as presented in this article.