Analytic outputs built above data of the fidelity system are intended for evaluation of the fidelity system effectiveness. They allow to monitor not only shopping behavior of fidelity customers and compare it with whole shopping behavior of all customers but they also make the fidelity system structure analysis possible in light of demographic indicators.
Key business indicators comparison between fidelity customers and anonymous customers enables trader to evaluate effectiveness and capacity of the fidelity system. Other analyses focused on shopping behavior help revealing hidden patterns in shopping behavior of customers and thus supporting fidelity of customers. Within fidelity system implementation, the trader obtains information not only about average cart composition but also about continual development of single groups of customers (shopping frequency, purchase amount trend…). Such data enable trader to submit personify offer of goods and related services for example through direct marketing.
Analysis of the fidelity system structure in term of age, gender, nationality and other composition then allows to find a space gap in the fidelity system as well as to monitor differences in shopping behaviors of single groups. Such differences can impact significantly for example a share of realized margin.
We use two basic analyses for the detailed structure analysis of fidelity system customers – segmentation and RFM analysis. Within segmentation, customers are divided into several groups by virtue of required criteria combination being used a number of them in praxis – at random average volume of purchase, average price of item, average items number in a cart, shopping frequency.
Customers’ segmentation is applicable to fidelity system insight – in term of whether single customers’ groups share on revenues or assortment purchased by given segment the most. Based the segmentation output, promotions can be targeted more precisely – to address concrete group of preferred customers. It increases an effectiveness of costs spend on the promotion propagation. On contrary, we can discover by combination of more criteria so called “promotion tourists” or “promotion hunters”, which mar us economic results.
RFM analysis divides customers pursuant to three criteria – time elapsed from the last purchase, shopping frequency and total or average value of purchase. Such analysis allows to guess customers suitable for focus on, for example customers who were shopping big purchases, but a long time have elapsed since their last visit.