The problem:-

Whilst implementing mostly bulk campaigns, the client wanted to understand more. They had a good view of success by campaign, analysed some shopping habits, and thought they knew how much the average customer spends online, but had the ambition to learn more.

With their current communications designed around old customer models we started to interrogate these insights. We set out to answer the following questions:

  • Can we validate our current models?
  • What does a typical customer look like?
  • How often do they purchase and how much do they spend?
  • How do we optimise our campaigns based on these insights?

The Current Model

The marketing team had implemented a couple of lifecycle automations based on old models that thought that just over over 40% of their customer base were repeat customers. However, they were very dependent on their bulk email sends.

They also had an idea of average order value and were confident these were true. 

They were not sure how behaviour had changed since COVID-19.

What Pure360 Analytics found

After analysis we found many insights that would disrupt the current models.

The data showed that the historical assumptions of buying behaviour were missing many opportunities for growth, or were incorrectly overinflated in some crucial life cycle stages.

Moreover, most of the client’s buyer life cycle stages did not have automations that optimised the customer experience and put too much pressure on bulk campaigns to perform.

After analysing their online transactional and email data here’s just a few of the insights we unlocked:-

  • 80% of their customer base had only ever bought once, leaving only 20% of repeat customers online 
  • The 80% (buys-once, then lapse) customers spent between £15 and £24, which was lower than expected
  • Of those that lapse for more than 100 days, there is a tiny chance of them ever purchasing again
  • A pre and post COVID-19 analysis identified that customer lifetime value had gone up, driven by an increase in purchase frequency of the “buy once, then lapse” segment to 1.6 times
  • A new set of actionable customer profiles created to allow for accurate lifecycle automations

What happened next?

The fashion retailer is now able to implement a series of automated emails across the true customer lifecycle, which will not only take the heavy lifting off bulk campaigns, but also deliver truly personalised messages to the customer. 

With easy to read dashboards, the retailer can now understand and action priority segments from champions, loyal to lapsing. 

Based on our new analysis and insights we are implementing the following campaigns, taking into consideration the 100 day engagement window:-

  • Champions programme – To encourage those who purchase frequently to tell their stories, leave reviews and refer their friends
  • Increase Basket Size – With upsell notifications on site through the checkout process as well as personalised nudges – “people who bought this also bought these” messages
  • Increase CLTV – For those we see as lapsing we are implementing additional triggers including A Welcome Nudge series, Abandoned baskets, Abandoned Browse, and a rewards/offers for continued purchases
  • Increased lead generation – To continue to drive new customers to the top of the funnel

Post launch they have been able to show the revenue uplift, proving ROI back to the business. Initial results show an increase in revenue 3 times BAU. Needless to say the client is extremely happy!

We can’t wait to continue working with the client to help them unlock continuous insights from their data, with an ever changing consumer landscape this has never been so important!