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

Personal recommendations from LoyaltyLab is a product for retail chains with a loyalty program that have already embarked on the path of personalization. However, the results do not reach the KPIs, and customers still consider the recommendations irrelevant and begin to outflow towards competitors.

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

Created business value

Revenue growth
4%
5%
Average check growth
Due to the turnover increse
Due to the relevant reccomnddations
Communication costs decrease
35%
Due to precise targeting instead of mass communucation
Decrease in share and depth of promo in turnover
10%
Due to modeling the sensitivity to a discount
Why recommendations get right on target?
For data analysis, we use self-developed state of 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 fantasticly accurate recommendations.
What data is needed for learning algorithms?

The principles of the personal recommendation system

The primary source is the history of shopping in the 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> 300 public sources. We take into account everything: from the availability of parking at the store and the weather to the color of the product packaging.
3d-party data
Forecasting the time of the future visit
For each particular customer, we predict the period of his next visit. In some segments, prediction is possible with an accuracy of 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 the 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 customers and a separate tool to prevent outflow.

Value orientated solutions

Key results our clients achieved with personal recommendations

Outflow prediction
accuracy
92.0%
Time prediction
of future visit accuracy
79.8%
64.8%
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 contact you and discuss the results we can achieve together