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AI in B2B Marketing - Thoughts from B2B Ignite

Pascale Smith
by Pascale Smith - June 23, 2017


As someone intensely interested in marketing technologies at the recent B2B Marketing Ignite summit, I was all over the stands offering new technologies in predictive marketing and intent marketing but something which really caught my eye was how AI (or artificial intelligence) can be used to do some really smart things.

We are all – whether we are aware of it, or not – given customer experiences based on AI in our day to day consumption of digital goods – whether it’s Shopping on Amazon, or what we’re watching on BBC iPlayer (iPlayer are going to enforce sign in before you can watch on demand, partly in order to serve us more relevant content).  We’re so used to it in some contexts, those seamless personalised experiences, that we take it for granted and hence underplay the significance.

The adoption of AI in the B2B world is much lower – but can be equally powerful.  According to a recent article, only 10% of B2B companies are using AI in their marketing.  Furthermore, that percentage is predicted to increase to 80% in the next 3 years.  That’s a huge level of growth and it makes sense to me; the purchase of any B2B product is based on need – not emotion.  This makes it somewhat predictable and therefore lends itself well to AI.

Take this example; imagine your entire database of customers – what they’ve been exposed to in terms of emails, content they’ve downloaded, what value they are as a customer and you apply modelling to that historical data, it gives you the ability to predict things about your customers’ likely future behaviour.  E.g. they are likely to be successfully upsold to, or, when exposed to this combination of content pieces, they are likely to convert into a customer, or, based on this pattern of behaviour they are at risk of churn.

You end up with a matrix of predicted value versus predicted churn which can inform both your marketing strategy and your sales team’s resource, making them far more efficient.  For example, you can segment your data into groups such as high predictability of churn and low value; this group can be served the right automated messages to turn them into a lower churn likelihood.  A high value customer with a high predicted churn should be given much more face to face attention.  Equally important is the segment of low predicted churn customers with low predicted value, there isn’t much point spending too much resource here and they can stay in the feel-good automated email campaigns.

Can you see how this is helpful?  It’s that ability to focus resource where it will deliver the most to your pipeline.

But it goes further. Oh, so much further. If you can predict the behaviour of your current customer base – you can link these insights into your acquisition marketing.  What message on display ads and at what point in the purchase cycle?  What level email marketing with what subjects?  What content should they be sent to?  When is the best point to pick up the phone?  You can much more effectively target new leads.

I love the idea and I can see how people believe that it will be an inevitable part of our future marketing strategies. I do feel that the 80% adoption in 3 years is a little optimistic, but I’m always happy to be surprised!

If you want to chat to us about this and how you could build it in to your current marketing activities, drop us a line.

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