SPAR Kaliningrad implements personal offer tools based on Manzana Predictive Analytics

2021: Implementation of personal offer tools based on Manzana Predictive Analytics

On April 13, 2021, the Manzana Group announced the implementation of personal offer tools based on the Manzana Predictive Analytics solution in the Spar Kaliningrad supermarket chain .

Future loyalty programs are, first of all, an opportunity to form personal offers for customers. The buyer expects not so much mass promotions and standard mailings from retailers as offers and customised content. Each customer is a person with special preferences and, taking into account the peculiarities of individual buying behavior, retail chains should provide customers with individual conditions for some goods or increase the bonus rate for certain purchases. This is a win-win strategy - both customers and retailers win, since personal offers stimulate both regular buyers and rare guests of retail chains.

SPAR Kaliningrad uses Manzana Loyalty in its work. At SPAR, Kaliningrad was well aware of the benefits of predictive analytics technology and, after the launch of the loyalty program, expected to accumulate enough transactional data. In 2020, following a pilot launch, Manzana Group was involved in the implementation of this task for the entire audience of the loyalty program in SPAR Kaliningrad.

The first results showed good results and quite high efficiency of personal offers. SPAR Kaliningrad recorded an increase in increased trade for the weekly wave of 2.5% over the period of its operation. The response of the test group in one wave amounted to more than 20%, which indicates the attractiveness for buyers of personal offers. The response efficiency of this approach compared to the existing one averaged 2.5% and reached a maximum value of 11%, and the average cost per buyer was 2.7-5% higher than that of the control group.

The solution of Manzana Predictive Analytics allowed SPAR Kaliningrad to carry out the tasks of promoting more marginal positions and recommending more expensive products. Personal offer tools contributed to the expansion of the basket of customers who did not buy previously offered goods.

In addition to receiving additional turnover due to the preparation of personal offers, the retailer's marketing received another significant advantage in its work - established regular marketing communications with customers. And the communications that buyers are waiting for and whom they are glad. This is a great example of how predictive analytics tools provide not only tangible material benefits to the retailer, but also help build relationships and contact with the buyer. Now retailer shoppers can really feel the care of themselves, the competent use of information, instead of general marketing messages, often perceived as spam. A few years ago - when such experiments appeared on the market and we ourselves began to use artificial intelligence in retail marketing - this opportunity was available only to the largest networks that could afford such experiments, "said Yuri Vronsky, Manzana Group.