Returns Management System hacking

Matt Clarke, @techpad

Sunday, 17 February 13

The problem...

• We promised customers we’d handle their return in 7-10 days.

• We suspected that sometimes returns didn’t get handled within this time, but our existing RMS couldn’t tell us.

• When returns handling was slow, call volume went up, so did email queries. This annoyed customer services and customers.

Sunday, 17 February 13

Step 1: Track emails

• Before we started on a major rebuild of the RMS, we added event tracking to the form used to categorise customer service emails from the site.

• We do a similar thing with complaints analysis (there’s a post on that on my blog).

Sunday, 17 February 13

Step 2: Monitor emails

• I built a dashboard to provide an overview of emails received.

• I used the API to report on increases in returns emails/proportion, which might indicate a failure to meet customer promise.

Sunday, 17 February 13

Step 3: Dig deeper

• I spent three months writing an MSc project to further investigate the problem, and proposed a solution to tackle it.

• The proposed solution used GA, among other things...

Sunday, 17 February 13

Step 4: Re-build the RMS

• We rebuilt the RMS to tackle the issues the business was facing, as well as those that impacted customers.

• We added a metrics system so we could record which returns were pending, due today, late etc, and help staff prioritise and hit KPIs.

Sunday, 17 February 13

Step 5: Plan event tracking

• I made a spreadsheet of events. There were lots...

• Why GA? Using GA would mean I could analyse and report on the data much more easily than I could if I had to write SQL queries to pull the data out of the RMS.

Sunday, 17 February 13

Step 6: Sent events in PHP-GA

• There were too many events to send using the client-side code, so I used PHP-GA, which allows you to bypass the token bucket algorithm.

• Primary keys in the events allow related events to be re-joined in the API.

Sunday, 17 February 13

Step 7: Set up reporting

• The default Google Analytics dashboards were too limited to be of use for this problem.

• So, we’re using Google Drive and the Google Analytics Core Reporting API “magic script” to create detailed reports.

Sunday, 17 February 13

We can now answer these questions on returns

• What percentage of returns are handled on-time?

• What is the average time to handle returns of different types - replacement, exchange, refund?

• How many returns are unscheduled arrivals?

• What are the return rates for different items, and why are they being returned?

• What proportion of faulty goods are non-faulty?

• How much working capital is tied up in returns?

Sunday, 17 February 13