Using AI And Predictive Analytics To Increase Customer Value By 34%

by Matthew Kelleher RedEye

I recently presented at the Technology for Marketing event, showcasing a series of case studies on how RedEye has worked with clients to use AI and Predictive Analytics to increase Customer Lifetime Value.

It’s such a huge topic that in I don’t know whether I was able to do it justice! I was really rushing the key elements, which were the actual results, and didn’t leave enough time for questions! So, I thought I would write a series of blogs expanding on the key points I wanted to get across!

Five Key Points

1. Predictive, AI and Machine Learning are not a fad, they are here to stay.

Things come and go, some faster than others. But the results AI and Predictive Analytics are able to produce for multi-channel marketing are incredible.

What RedEye achieve for our clients is enough evidence for me to believe that this is here to stay.

2. When trying to understand and predict a prospect or customer’s ‘next best action’ the most powerful data is recent data.

Recency is king, and today recency is behavioural or engagement data. Or to be a bit more explicit, how a prospect or customer is engaging with your brand.

By mapping patterns of behaviour at an individual customer level, they can give you clues to likely outcomes. Then marketing activities can be developed to influence the final outcome you want to see.

3. Data is absolutely key.

There is no firm agreement about the outlook for the Customer Data Platform tech space. Although I would argue that the requirement is definitely there. For too long organisations have ‘made do’ with limited or siloed databases that do not give a full view across online and offline data and therefore do not give a complete Single Customer View.

To ascertain the patterns in the data that I describe above, businesses need to be able to tie together customers not just by household and email address but by website activity and app engagement (to name but two). This also needs to happen across the various devices they might use.

CDPs and other capabilities now resolve this problem. And it is this data that opens up real opportunities for marketers using Predictive Analytics and AI.

4. Integration is crucial.

Integration is at the heart of marketing automation. AI is not going to function if the key elements (data, analytics and channels) are manually driven or not tightly integrated.

So many organisations are still reliant on customer databases dumping data into an ESP on a weekly basis. But this is not an architecture that is going to open up opportunities in AI and Predictive Analytics.

5. Using Customer Lifetime Value (CLV) as a business KPI.

I asked the audience for a show of hands of whether their organisation used Customer Lifetime Value as a business KPI. I would estimate that about 10 per cent of the audience said that they did. Assuming that brands were in the minority in the audience, that would fit with Econsultancy’s estimate that 42 per cent of organisations track the measurement.

But for me it is a perfect fit with AI and Predictive. CLV is measurable and resonates throughout an organisation, from platform users to Boards.

As marketers, given the knowledge of where a prospect or customer is likely to be on their individual journey with your brand, do you feel as though you could influence that prospect or customer towards a favourable outcome for your brand?! That is, of course, a rhetorical question… I hope…

If you didn’t get a chance to come and hear about the case study, I’ll be at Festival of Marketing discussing the same topic.

Keep an eye on my LinkedIn page for future blogs!