Recommendations on the website and in the mobile app are a must have for any online store. But what if most of the business is offline, and you want to make it digital and personalized?
Ramenskiy Delikates, with the help of Retail Rocket Group and other services, solved this problem — implemented recommendations in offline cash registers, increased the average check by 80% and received other impressive results. Details are in the case.
About the partner
” Ramenskiy Delikates ” is a Russian store of sausage products, smoked meats and delicacies from meat and poultry. The company has been on the market for 50 years. The company has 130 brand stores and franchise outlets in Moscow, the Moscow region and Ryazan. Depending on the location and traffic of the store, they are visited on average by 1,000 to 13,000 people per month.
Ramensky Delikates was the first
To implement recommendations in retail stores and increased the average bill by 80%
The assortment of Ramensky Delikates includes more than 500 SKUs of its own production
Why did we decide to implement recommendations in offline cash registers and what difficulties did we encounter?
In 2022-2023, the main goal was to make offline stores more modern, digital and personalized, since 99% of sales occur offline, and the website and mobile app account for 1%.
The Ramenskoye Delikates stores are counter-type, with cashiers working behind them. And because of this, a problem arose – the cashier could not know the purchase history and preferences of all visitors. Most often, he would upsell products that were not interesting to the client, but those that were beneficial to him from a financial point of view – for example, to close a shift, get a bonus, etc.
To solve this problem and automate
The upselling process, Ramensky Delikates, as an experiment, installed 5 cash register monitors that displayed the receipt and recommendations.
The recommendations included only promotional items that did not take into account the user’s purchase history and interests. It was also difficult to calculate their effectiveness – promotional items were sold, but it was unclear whether this was due to the cash register monitors or the cashiers.
To make retail stores more personalized and understand the results this approach brings, Ramenskiy Delikates turned to Retail Rocket Group.
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Ramensky Delikates was the first to implement recommendations
In retail stores and increased the average bill by 80%
Example of recommendation design on checkout monitors
Second attempt to car ownership data implement recommendations in offline cash registers
To implement smart recommendations from Retail Rocket Group in offline cash registers, the company built a complex ecosystem that included an online store and technologies from 1C and Retail Rocket Group. It was a complex task, the implementation of which took a year.
We have implemented three recommendation algorithms:
1. If the buyer has logged in using a loyalty card, and we know their history and preferences, we show the products they have previously purchased. This algorithm was developed taking into account the behavioral characteristics of Ramensky Delikates customers, most of whom, as a rule, buy the same products.
Ramensky Delikates was the first to implement recommendations in retail stores and increased the average bill by 80%
2. If the buyer has not entered anything, has not logged in, and we know nothing about him, we show popular products that numbers lists most visitors to retail outlets prefer. For example, this could be beef, the beloved doctor’s sausage, vareniki, pelmeni. The algorithm was based on the hypothesis that introducing an unknown client to top items is more effective than showing promotional products.
Ramensky Delikates was the first to implement recommendations
In retail stores and increased the average bill by 80%
3. As the customer starts to scan products, we show related products. The recommendations are updated as the cart is filled, after each new product is added. This helps visitors see that the recommendations are “live”, responsive to their interests, and that they can be interacted with.
Ramensky Delikates was the first to implement recommendations in retail stores and increased the average bill by 80%
Results for three months of recommendations in offline cash registers
Over three months of the pilot version, the store managed to determine internal metrics by which they evaluate the result, collect statistics, and understand what to pay attention to in case of certain improvements. One of the important observations is the need to encourage “non-digital” customers to interact more with the checkout monitor.
50+ monitors installed in retail outlets
48,000 purchases made with recommended products
the average check for orders with recommended products increased by 80% compared to the usual average check for the network
11% of shoppers on average make a purchase with a recommended product
+53% to the number of SKUs in the receipt
When talking about the results of offline recommendations, we can’t help but pay attention to the methods of measuring them. We calculate attribution on the website and in the mobile app based on actual clicks on product cards/adding products to the cart from recommendations. We usually don’t attribute views in any way due to the strict attribution model.
Offline, a person does not always know that they can click on recommendations, or does not have such an opportunity. Therefore, when working with Ramensky Delikates, we check the IDs of the products specified in the recommendations and receipts, and if they match, we attribute orders and revenue to Retail Rocket Group.
Ramensky Delikates was the first
To implement recommendations in retail stores and increased the average bill by 80%
Mansur Taibov, Senior Account Manager Retail Rocket Group
Conclusions and recommendations
Almost any business can be personalized – not only online platforms, but also offline points
Break the project down into stages, and for each one, define the goal you are working towards. For example, improve customer service, understand how ready the audience is for innovation, etc. If you are not getting the desired results in a certain iteration, it may be worth reconsidering the goal of that stage.
Don’t be afraid to create your own metrics that will allow you to evaluate the success of a particular stage.
