Dunn Solutions at B2B OnlineApr 29, 2019
B2B e-commerce can be complex, but with the right partner it doesn't have to be. Join Dunn Solutions at Booth #405 to learn how we help clients like yourself with their digital transformations.
The Dunn Solutions team are B2B e-commerce implementation experts that can work with you to drive revenue and automate your most complex e-commerce business processes like custom pricing, quote negotiation, personalized product recommendations, and automatic re-orders.
Tapas & TechMay 7, 2019
A lot of attention has been given to the topic of customer retention activities that organizations use to minimize the number of customer defections. Telecommunication companies, banks, insurance companies, and other subscription-based organizations have long used customer churn analysis as one of their key business metrics, knowing that the cost of retaining an existing customer is much lower than the cost of acquiring one.
But understanding and preventing customer churn merits special attention in the e-commerce and retail spaces as well. This is due to the increasing number of choices and the relatively low barriers to switching to a competing product.
Unfortunately, churn in retail and e-commerce is neither well defined nor easily understood, as customers disengage without a clear warning and can do so at any time. Although the exact time of churn cannot be clearly pinpointed, predictive analytics can help determine the probability that any given customer has entered this silent attrition phase and help us plan our retention strategy.
The aim of this workshop is to guide the audience through the definition of churn in the retail/e-commerce space, and give an example of how predictive analytics can help us determine who, among our loyal customers, is at risk of churning, and who is worth saving.
In this workshop session, we will:
-Discuss the different kinds of churn and the cost to any organization
-Understand the difficulty of defining churn, and why most retention efforts fall short of succeeding
-Give an example of how predictive analytics and machine learning should be applied to this kind of scenario