There are a number of marketing automation tools out there and they all share the same workflow:

• Manually segment customers into groups such as ‘new’ , ‘risk of churn’ , 'high value’ etc
• Link and schedule promotions for these groups
• Report, analyse and optimise
• Repeat

We believe that this approach has three flaws which affect three core areas:

1)       Defining the segments

The definitions used to segment customers is always somewhat crude and ad-hoc.

For example, a lot of systems define customers as churned if they have been inactive for a given amount of time, say 2 weeks. But such a one-size-fits-all definition doesn't allow for the fact that customers have very different patterns of engagement: while one customer might log in every day, another might be active once every 2-3 weeks.

For the former, 2 weeks of inactivity likely mean that they have long since churned. For the latter, 2 weeks of inactivity are completely normal.

By forcing everyone into one of a handful of groups we are missing these nuances.

2)     Analysing the results

Similarly, when analysing the results customer groups are analysed as a whole. Ideally this is done via campaign / control groups, but in any case this will only lead to results on the campaign level: has this offer led to an overall increase in revenue or profit?

This misses the fact a promotion might work really well for a group of customers while also performing poorly for a different set. This nuance is lost when looking only at the overall picture and a lot of valuable campaigns get discarded for this reason.

3)     Scaling the approach

The traditional approach requires a lot of manual analysis, monitoring and tweaking in order to be successful. Customer groups have to be redefined regularly. Promotions have to be analysed and reassessed. Campaigns need to be scheduled.

As the customer base grows, so does the number of customer groups and the number of promotions. For example, the ‘churn’ group could turn into multiple sub-segments based on behaviour or customer value.

This makes it harder and harder to stay on top of what does and does not work. In addition, the information about how this is set up usually sits with the few specialists who set up the customer segments and offers.

This means that it becomes harder and harder for new team members to be brought up to speed - but the approach relies on scaling the team in line with the customer base.

What we do differently

Ibex solves these problems by doing away with customer segments and applying artificial intelligence in order to automate and optimise the scheduling of offers.

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