Anticipate customer needs and deliver the best customer experience with unique recommendations

Personal recommendations from LoyaltyLab is a product for retail chains with already installed loyalty programs. Although these retail chains have already embarked on the path to personalization, there is still room for growth. The results do not meet the KPIs, and customers still consider such recommendations irrelevant and begin to outflow towards competitors.

You no longer have to rely on blind business rules. Trust the formation of recommendations and moment of communication to the state of the art algorithms of AI and predictive analytics. The only thing you have to do is to send a message.

Created impact

Revenue growth
Average check growth
Due to the turnover increase
Due to the relevant recommendations
Communication costs decrease
Due to precise targeting instead of mass communication
Decrease in share and depth of promo in turnover
Due to modeling the sensitivity to a discount
Why recommendations contribute to reaching goals?
For data analysis, we use self-developed state of the art algorithms of artificial intelligence and AutoML technologies. Thanks to this, we are able to form a clear understanding of the consumer habits and behavioral patterns of each customer.
This allows to predict the wishes of the client and make fantasticlly accurate recommendations.
What data is needed for algorithms?

The principles of the personal recommendation system

The primary source is the history of shopping in a store. Its analysis allows us at a deep level to understand the preferences of users and create a basis for the formation of recommendations.
Such a synergy of data allows to capture hidden patterns and use them to increase the effectiveness of communications.
Transactional history
We enrich transactional information with over 300 public sources. We take into account everything: from the availability of parking at the store and the weather to the color of product packaging.
3d-party data
Forecasting the time of the future visit
For each particular customer, we predict the period of their next visit. In some segments, prediction is possible with an accuracy to one hour.
This allows to conduct communication at the moment when a person only thought about a visit to the store.
Forecasting the composition of the future check

Predictive analytics as a performance driver

Due to our knowledge of consumer habits and patterns of customer behavior, we predict concrete items in the future checks.
This allows to generate recommendations that are relevant in the moment of purchasing.
Modeling of consumer activity
We identified triggers that precede a client's transition from one segment group to another, including the outflow group.
To manage this process, we have developed a set of methods for switching customers to the group of the most loyal consumers and a separate tool to prevent outflow.

Value-orientated solutions

Key results our clients achieved with personal recommendations

Outflow prediction
Time prediction
of future visit accuracy
For the large
supermarket chain
For the small chain
with narrow products range
For the sub-premium
droggery chain
Personal recommendations response rate

Get to know more about personalization system

We will get in touch with you to discuss potential strategies

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