Reading time: 4 min

Business problem

Retail pricing is one of those fields that can greatly be supported by data analysis, with measurable, high returns on investment. In many cases it’s simply not possible to set different prices on product level – due to missing data or technical limitations. In such situations pricing may be differentiated by location or by time period.

As an example, there are many several days and weeks in the summer whose sales are influenced by transit customers when the shops can expect less price elasticity and react with appropriate price changes.

Our client is a multinational company in the CEE region with partial retail focus, owning and operating a store network consisting of 1000 shops. With regards to convenience goods sold, the selection and definition of re-priced products and the affected store locations was done by Hiflylabs.

Let’s get to the data!

The basis of our solution is that price-changes will affect not all products and not all stores, as a general price increase can negatively impact sales.

Where to re-price?

The target stores were selected by checking which locations experience a growing ratio of transit customers in the summer season which indirectly decreases price elasticity.

Shops located near touristic, waterfront and high-traffic areas were selected in our first simulations which was in line with our assumptions, however many unexpected shop locations ended up being included in the project.  

What to re-price?

The included products were ones that experienced higher-than-average sales rates in the summer season, such as energy drinks, water or ice cream.

What to check our results against?

Using last year’s sales numbers we’ve come up with correction metrics (customer number growth, adjustments for inflation, etc…) that were used to clean the data from effects independent from pricing. 

Results were evaluated in 80 locations. Compared to base sales the gains are the following:

But what is the ROI?

Without any data analysis, the logical step would’ve been to apply seasonal pricing only in high-traffic areas, where the achievable margin increase’s base would’ve been 10-20 million in HUF. Carrying out data analysis however allowed us to find stores where we could increase margins by 54% more than in only high-traffic locations. This is the result of 15 days of work.

An interesting fact is that stores that are less visited by transit customers experienced losses during our test, which further proves the necessity of data analysis and data-driven decision making in business environments. 

In the following seasons the same methodology can be applied with only 1 day of work, or a software can be created and integrated into existing pricing systems.

Any questions?

No matter how successful data projects may be, our experience is that business stakeholders treat them with caution most of the time. This case study shall serve as an example that even with relatively little invested time, great results can be achieved.

Author: Márton Biró – Data Scientist

Reading time: 3 min

This case study describes our results of a recently completed project. Our partner has shops in hundreds of locations, where impulse products give the major part of the revenue. One feature of these kind of products is that their consumption patterns are related to the location of the business, thus it is worth differentiating pricing on a location basis.

The system of location rules has developed over the years and decades, but experts estimate there is plenty of space for data analysis-assisted validation and optimization.

Our main goal was to set the optimal price level at certain locations and reach maximum margin growth.

We developed a customized pricing methodology: instead of the classic pricing model, we developed a product group + shop based model. This was used at the store level in aggregate.

Detailed purchase data and other external data (for example promotions, available information on store renovation periods) were also used for analysis.

In the following, we describe the steps of the analysis.

1. Filter out distortions!

We have seen a type of mineral water that brought in the sales volume thirty-five times by using a strong marketing activity month after month, then the yield declined to re-promotion status next month. One popular energy drink was offered at bargain price every two months, therefore, we have experienced tremendous fluctuation in quantity. Most of the shops were open during renovation, but most product groups were not offered for sale, and the other product for sale indicated upgrading reduces in traffic. We filtered out outlier values, which otherwise affected the model adversely, so we ended up keeping 60% of the stores and 20% of the products.

As a result, we could develop a reliable model, which is free from interfering effects, but is still based on a representative sample.

2. Assign a clear price to product-store pairs!

The business didn’t record the shelf price of the products to an easily accessible database. Many products were sold at several different prices, and lower prices were available with a loyalty card discount, multibuy discount or coupon. We needed shelf prices, because these influence customer decisions. Fortunately, these prices changed rarely within a day, so we chose the daily median price, on which 97% of the quantity was sold.

3. Adjust quantity with seasonal effects!

That we can compare the quantity sold at different prices at different times, we needed seasonal adjustment. Impulse products have strong seasonal (weather) effects, which affect their sales volume regardless of their selling price. Higher rate means higher traffic, lower rates means weaker traffic. Typical seasonality is the surge in the consumption of frozen products and drinks during the summer.

With the seasonality rate, we can normalize monthly sales to get a monthly sales volume.

4. Determine price elasticity of demand at store + product group level!

Price elasticity of demand of a product shows that a 1% price change causes a percentage change in demand. We approached this from past price-sold quantity data.  In this project we fitted a price elasticity curve to the price-quantity data points at the store + product group level.

The effects of the outliers were reduced with a few tricks, for example, we used robust regression and calculated a confidence indicator to filter out poorly performing models.

5. Raise the results to store level!

The store + product group models were aggregated to store level by weighted averaging. By using this method, we got an indicator for all stores showing the price elasticity of the customers visiting that store.

6. Set optimal prices!

We can determine an optimal price on shop level compared to the last known price of the given product, and by setting this, the product of unit margin and quantity is maximal, and the total revenue can be raised. Based on the results, the optimum of the total selection showed a higher total margin than the current one. As usual in retail, instead of the prices given by the model, we rounded them to psychological price points. We could not determine e.g. a price of 66,5 HUF, therefore, a rounding system changed the prices to the psychological price points (e.g. to 69 HUF) as a last step.

As a result, our model estimated an annual 3-3.5% rise in margin at our customer, which is approximately equal to the profit growth of opening ten new shops.

The methodology works, we successfully optimized the price for location differences. Besides, there are significant opportunities forward – for example product-level pricing and production system development. Industry trends show, that in the increasing competition, more and more retailers have the opportunity and need to make data-driven optimization decisions with improved data quality and availability.

Authors:
Marton Biro – Senior Data Scientist
Marton Zimmer – Managing Partner