Part 3 | Customer Notification | Using Predictive Techniques to Decrease Consumer Returns

This series of posts talks about the problem of customer returns in the retail industry, using return rates as a proxy for customer satisfaction. We explore solving this problem by using a data driven marketing automation approach to increase customer satisfaction, via reducing return rates.

In 2016, US online retailers reported return rates of between 20% and 40%. Research by Accenture estimates that 68% of consumer electronics returns are labeled as NTF (no trouble found), and another 27% are due to buyer’s remorse which can occur for a variety of reasons similar to NTF, including subpar consumer experience and a lack of consumer education. Taken together these figure suggest that 95% of consumer electronics returns are for reasons other than product defects.

According a study by the National Retail Federation, 49% of retailers now offer free return shipping, and high rates of returns are putting an increased financial burden on retailers that are already operating on razor thin margins. Decreasing costs by increasing customer satisfaction and reduce rates of return, has become an imperative for retailers hoping to compete in an increasingly competitive online marketplace.

To create a data driven, automated method to doing this, there are two big ideas — the first is to ensure that the customer purchases the right product configuration upfront. The second is proactive outreach when you have reason to believe that the customer is likely to return. In either case, predictive data & messaging technology that understands customer behavior holds the key.

There are three aspects to this method:

1. Data Collection
2. Predictive Modeling
3. Consumer Messaging

Before reading further, make sure to catch up on Part 1: Data Collection and Part 2: Predictive Modeling.

Consumer Notification Campaign

Once the user was classified (3 days post registration) as an at-risk user, we sent them a series of real-time notifications at 7,14,21 and 30 day periods to get them to change behavior (and re-classify as not at-risk). Within the at-risk group, users were grouped into two groups, control and treated. 90% of users were treated with notifications and 10% of users are at-risk but not provided a real-time notification. What this allows us to do is measure lift, which is the difference between the % of users that were at-risk, notified and did not return the device vs. those who were at-risk, not notified and did not return the device. This allowed us to concretely measure the effect of a real-time notification campaign.

R+7, 14,21,30 day notification campaign strategy for an at-risk user cohort. ~35% of users who were targeted with messages offering support and education were responsive, and a majority within the ~35% did not initiate a return.

So what does this mean for Jon?

Using the techniques outlined in these posts Jon would have been targeted as an at risk user as a result of his initial pattern of interactions with the device in the first three days of owning his fitness band. Jon would have been contacted with targeted notifications offering additional support and education regarding the features of his new purchase in the critical first weeks of use. Our work indicates that as a result of this type of targeted messaging, Jon is likely to have converted to a satisfied customer.Doing this type of collection, modeling and messaging for a single variable (risk vs not-at-risk cohort) is conceivable to do manually, IF a company has access to an in house data scientist & marketer combo.In reality however, users are often in a multitude of different states with your products, and there are a requisite number of particular notifications that they will respond to. Figuring out these micro cohorts of current user behavior, and running a variety of campaigns simultaneously to get them to more valuable cohorts predictably, requires a software tool to work at scale from collecting data to predictive modeling to providing notifications. After years of trial and analysis working in this area, Intempt Technologies has built that tool.

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About Sid Chaudhary

Sid Chaudhary is the founder & CEO of Intempt (https://www.intempt.com). Intempt employs machine learning to chomp through large swaths of data, analyze your customer’s unique fingerprint, and build a profile that not only targets your consumer in real time, but predicts their future behavior. Intempt employs AI capabilities to immediately analyze the success of those offers on a consumer-by- consumer basis, and incorporates that data into your database of consumer profiles, empowering marketers to implement sophisticated, data-driven campaigns, that fully optimizes on a level that only multi-billion dollar technology companies have been able to achieve. All on an easy to use platform. Without writing a single line of code.

Sid lives and works in San Francisco.