People bound to each store Supplement

And also successfully r.Uc. The blocking rate of new friends. Line member management binding performance optimization figure 2｜after optimizing the member binding process. The binding rate increas. A clear overview of the store’s membership recruitment results case 2: overview of store recruitment results. Quick and time-saving resource allocation adjustments let’s take a look at the second case. In order to increase the number of people bound to line friends. The brand plann. To increase the marketing budget of each store and sprint to bind the number of people.

We can directly sort the number of people

When analyzing. bound to each store. From figure 3. We can quickly see which stores are good and which are bad. But it is impossible to determine which stores should be strengthen.. Therefore. A  Australia WhatsApp Number Data benchmark is ne.. To help judge. Common benchmarks such as overall average or kpi of store binding rate can be us. For comparison. Figure 3 | ranking of the number of By average judgment line member management operation performance benchmark points must be set according to the situation however. Stores will definitely have differences in foot traffic. So if the benchmark we set is bound to the number of people. It will be unfair to stores with less foot traffic.

The selection of indicators can also be adjust

Therefore.  To the binding rate to view the effectiveness. And then allocate the marketing budget to lagging Afghanistan WhatsApp Number List stores. The indicators are select. Bas. On the situation. When the store’s line friend recruitment and binding rate reaches a certain number. The member only when the pool is large enough can the marketing resources in membership management and membership systems be us. More effectively.