How to increase profit by 50%, earning an additional EUR 18 470 per month?

OBJECTIVE

Optimizing pricing policy based on competitor pricing and availability data, as well as efficient calculation of established pricing rules. To start with, it is worth quoting Gartner’s division of the price management development stages:

  1. Prices set manually.
  2. Prices set manually based on Excel rules.
  3. Prices online determined based on competitor data, in other channels calculated using Excel rules.
  4. Pricing models based on rules in various sales channels, optimization of some prices.
  5. Advanced price management using algorithms, taking into account all sales channels. The last stage of maturity bringing measurable profits for business.

Many clients start at stages 1-3, of which the vast majority contact us while at stages 1-2.

 

TRUST

The client prepared an appropriate list of products in XML format, containing the following data: EAN product name, purchase price, VAT rate, sale price, inventory. Although the data had to be gathered from more than one record system, the client quickly managed to submit the file.

An important task by Dealavo at the beginning of cooperation was to build the client’s trust with the data they receive. The industry is very demanding when it comes to product search (numerous products and competitors) and matching (often unstructured names, mistakenly described products, e.g. EAN code of a single SKU for a multi-pack offer), which is why we decided to introduce two-step verification of matches not only by advanced algorithms, but also manually by the Quality Assurance team. Thanks to this, we have obtained a 99.5% match quality index that consists of two classes of False Positive (incorrectly matched product) and False Negative (offer not matched even though it is available on the competition website). Thanks to this, the customer could focus on creating pricing rules and analyzing their progress, instead of verifying data quality

 

CUSTOMER SUCCESS SUPPORT

An equally important factor to achieve the client’s goal was the support of our Customer Success team. Every customer at Dealavo receives a dedicated account manager whose work is particularly important in the first stages of cooperation. In the initial phase, our Customer Success spent with the client an average of 40 hours per month supporting them in creating pricing rules.

 

FIRST PRICING RULES

The first product groups were selected. After over 30 days, we saw an average daily increase in conversion by 0.46pp (up to 1.43%), compared to the customer data from before using the tool and knowing competitive prices, when the conversion was at an average level of 0.9%.

Conversion analysis – decrease around 13-15 days due to external factors

 

In the following chart, data on profit (here shown in PLN), which was generated on automated product categories, are added.

Due to fast responses to the competitors’ behavior, not only conversion but also profit increased by nearly 20%.

 

AI PROFIT OPTIMIZATION APPROACH

These are the first successes, but the limited resources of the client to create and then compare the effectiveness of pricing rules for the entire range resulted in greater interest in artificial intelligence. To run it we also needed:

– a large amount of data on competitor prices that we already had collected

– the client installing JavaScript to analyze the views and conversion on the online store.

Initially, we recommended semi-automatic price management on selected product groups – the client had to accept/reject/complete a change suggested by the algorithms to provide further data to AI.

In a very short time we could observe measurable benefits:

Day 0 – the start of the AI Optimization powered by historical data

 

Before launching the module (day 0), the average daily profit earned by the client was PLN 5,222, and the conversion 2.23%. After a month of running AI, the average daily profit is at PLN 7,968 and the conversion is 3.62%.

 

SUMMARY

The most important task for the near future is full automation of price changes for product groups supported by AI, so that their management is as maintenance-free as possible. Then, we will gradually add more product groups to achieve full automation for all products offered by the client.

Dealavo together with the client generated an average 60% (1.39 p.p) increase in daily conversion, which translated into a 50% increase in profit in categories managed by AI.

Within a month of launching Dealavo AI Profit Optimization, the client gained PLN 82,380 (EUR 18 470).

 

Talk to us and find out more!