DATA SCIENCES

Data sciences and analytics are the key to successful rewards programs. Zinrelo has a separate Data Sciences practice that is solely focused on generating customized insights for our Clients and converting their rewards program into an actionable, revenue generating program. We believe that it is not enough to launch a rewards program. It is the use of data that will turn this retention engine into a solution that delivers meaningful revenues and ROI.
Here are some examples of how our data sciences team helps our Clients.

Customized Program Design using Analytics

When a Client is getting started, our data sciences team analyzes past purchase data to recommend a “perfect-fit” rewards program structure for the Client. The program structure may be completely different for each Client. No “one-size-fits-all” program. This methodology results in each Client starting with a precisely optimized rewards program that boosts revenues and delivers meaningful ROI.

Customized Program Design using Analytics

Machine Learning-based Customer Segmentation and Targeting

Zinrelo’s Data Sciences team uses machine learning algorithms to segment loyalty members into actionable clusters. It will also suggest loyalty program strategies to maximize revenues from these customer segments. Examples of loyalty campaigns are points top-up, free tier upgrade, bonus rewards and many other options. The image shows how for a Client, the machine learning algorithm identified three clusters, High Spenders, Deal Seekers and Experimenters. The cluster properties and the number of clusters are typically different for each Client.

Deep Data Analytics

Zinrelo Data Sciences team also engages in data sciences projects with Clients that are not directly related to loyalty rewards but use similar algorithms to provide insights.

Here are a few examples of reports that we have done.

Purchase Associations Report
Customers who bought X also bought Y
Purchase Trends Report
Seasonality analysis, Demand forecasting model
Returns Report
SKUs which are returned most often, popular days for returns

Deep Data Analytics

Zinrelo Data Sciences team also engages in data sciences projects with Clients that are not directly related to loyalty rewards but use similar algorithms to provide insights.

Here are a few examples of reports that we have done.

Purchase Associations Report
Customers who bought X also bought Y
Purchase Trends Report
Seasonality analysis, Demand forecasting model
Returns Report
SKUs which are returned most often, popular days for returns

Data Sciences Success Stories

FSAstore.com is both the largest online marketplace for guaranteed FSA-eligible products. FSAstore.com were able to create powerful marketing promotions supported by relevant deep data sciences to maximize eﬀectiveness. Zinrelo loyalty rewards platform helped FSA maximize their revenues by creating a successful rewards program that signiﬁcantly improves engagement with customers. They used the regression analysis to measure the customer lifetime value uplift. By comparing the predicted vs actual value of CTLV, FSAstore.com concluded that the loyalty rewards program
promotions resulted in a 22% uplift in CLTV.

Data Sciences Success Stories

Xtrema Cookware tried points launch promotion recommended by the Zinrelo Data Sciences team. They got an 80% uplift in customer retention
after launch of loyalty program.

Xtrema Cookware tried points launch promotion recommended by the Zinrelo Data Sciences team. They got an 80% uplift in customer retention after launch of loyalty program.