4 Reasons to Implement Machine Learning within your Marketing Department

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ScanmarQED has been at the forefront of using Machine Learning techniques since 2007 so we consider ourselves “old hands” at this.

However, I often get asked why we don’t talk about it too much and so I thought I’d pen this article to explain why that was and why now might be the time for us to make a bit more out of it.

When we first started applying Machine Learning technology to solving marketing problems, it was a discipline that few outside of the operations research community had heard of. Cloud computing was in its infancy and big data was yet to become the ubiquitous term it is today. I remember going to some early meetings with prospective clients who wanted to know how we might help them analyse their marketing mix and finding that if I mentioned Machine Learning or Artificial Intelligence, eyes would quickly glaze over and people would lose interest (of course that could have just been me). It quickly became apparent that talking about technology and techniques wasn’t the best way to win friends and influence people! Instead we focused on the problem at hand and how we would help our clients solve the challenge. And that made sense at the time.

Since 2007, the world has of course moved on. There are now thousands of articles talking about ML and AI, countless movies looking at the hellish consequences of a dystopian future we all face when the machines take over (!) and a million shiny suited sales people walking into offices every day talking about their AI solutions as being the missing link in performance for their prospects (despite them rarely understanding the key concepts).

So why might now be the time for us to talk about this topic? Well as somewhat “old hands” in this area, we know a thing or two about some of the practical benefits and how to make them work for you. For example, in our strataQED solution, we can use our technologies to crunch through literally thousands of complex mix models per minute – testing combinations and permutations that a human simply can’t test – and checking that no stone is left unturned in the quest to build great insights. We also understand that machines on their own can be dumb – they need human insight to make smart decisions and results often require a human to interpret them before these insights are deployed. We know Machine Learning is valuable but it also has limitations. Our job is to maximise the usefulness whilst minimising the constraints. I think we’ve done a good job on this in the mix modelling and optimisation space.

So why start to talk about it now? Well obviously there’s now a greater understanding of what’s possible and what’s not from our clients. Many are starting to get familiar with some of the classic Machine Learning techniques, although I do of course still see many people asking what it’s really for. We also see that the use case for these techniques is increasing. The weight of data that marketers are now expected to analyse and process is increasing rapidly. Marketing is genuinely one of the few business areas where Machine Learning is being applied at scale across multiple problems. Other business departments are becoming interested and so the areas where Machine Learning will improve our marketing are increasing rapidly.

For me, the key benefits of Machine Learning that I’ve seen are as follows:

1) It speeds things up – a great analyst can only crunch the numbers at a certain speed whereas machines can do it all day without a coffee break

2) It considers the unexpected and can challenge our assumptions – with little real bias, the machine can take a different perspective on the data it’s supplied and this can provide a useful fresh perspective on a problem

3) It scales really quickly – want to get more granular insights into a problem? – the cost is no longer linear

4) It forces us to think – ML will quickly find inaccurate answers to problems if you feed it incorrect assumptions. By forcing us to test our assumptions, we see a more structured way of thinking about analytical problems emerging – i.e. it helps the humans do their job better

The use cases for Machine Learning are huge and we’ve got some exciting plans to inject more of this exciting technology into our solutions. Not every marketing problem needs a Machine Learning solution but for those that do, we reckon we’ve got a cracking answer!


About the author: John Dawson

John Dawson co-founded marketingQED with Partners to offer marketing analysis software to the marketing, media and consultancy communities. In 2016 marketingQED merged with Scanmar to become ScanmarQED. We specialise in helping organisations quickly extract insight from their data through simple to use tools which are accessible to all.