Predictive analytics has been around for a while, as has machine learning, but it’s only now with the profusion of cloud-based software in marketing that this form of data analysis has started to take off. 

AgilOne is a US company, launched 2012, now branching into the UK, that provides predictive analytics software. I spoke to CMO Dominique Levin to find out more about this technology.

Is it powerful enough to make one-to-one marketing a possibility and not a fallacy?

The landscape

Data is of course the foundation of marketing. Data cleansing might be a fairly dry subject, but it’s important.

“Send duplicate catalogues to a customer, get their gender wrong, or try to sell them lawnmowers when they live in an apartment”, Dominique pointed out, and you’ll soon compromise your marketing ROI.

With clean data, accurate segmentation of your customer base is possible. To achieve sophisticated segmentation of a large database is, however, resource heavy. You’ll need data warehousing, a data scientist (not exactly ten-a-penny at the moment) and plenty of time set aside.

Rules based segmentation can get longwinded as it entails digging deeply into traditional measures of RFM (recency, frequency, monetary value) as well as product mix and reverse-engineering marketing spend.

That might be something, though, that’s set to change with the coming of machine learning and predictive analytics.

Of course, machine learning doesn’t make the use of rules-based data science redundant, but the power of computing in the cloud means predictive analytics software often trumps what can be achieved in-house.

Dominique referred to this technology as a ‘democratizing power’ for marketers. But what exactly is machine learning and predictive analytics?

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What is machine learning?

Wikipedia: 

Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. 

A core objective of a learner is to generalize from its experience.[5][6] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set.

How is it used?

AgilOne’s solution has a number of different algorithms for analysing data:

  • A clustering algorithm
    • surfaces segments of an audience exhibiting certain behaviours. This modelling also helps to group company’s products and brands.
  • Propensity modelling
    • gauges a customer’s likelihood to buy (lead scoring), whether they have money left to spend with you, how likely they are to convert or unsubscribe.
  • A recommendation engine, like Amazon’s
    • surfaces other products or services a customer might be interested in.
  • Marketing spend analysis
    • determines the most successful acquisition and retention methods by looking at how existing customers were caught and kept. 

One of the main uses of these algorithms is to plug the results into an email service provider (or more broadly into a customer experience management package)

As with many SAAS solutions, the possibilities for tying predictive analytics into other solutions are manifold, whether in ecommerce, B2B CRM etc.

Third party data can also be included in analysis, from loyalty schemes to local weather. Ultimately, the power of the single customer view is only as powerful as the analysis one can then perform with it.

The data can also be used with social media, allowing a company to better target social audiences that perfectly match an ‘ideal’ customer base (for whatever product, offer or service is in question).

Future uses?

Dominique rightly pointed at that what excites marketers is tailoring a message to an audience.

The logical culmination of this is tailoring content to individual people in the market. In practice, this may never occur, but certainly fine segmentation is a goal.

Machine learning may eventually allow even for automated content creation and curation at an individual level. The goal is ensuring there’s nothing in your brand communications that isn’t compelling.

What about the uncanny valley?

At the moment, the software works with registered users and doesn’t use anonymous visitor data. There’s a lot of talk in personalisation about the ‘uncanny valley’ and how much is too much?

Many believe that behaviour-based personalisation (other than transactional) is a step too far, because many customers are already shopping with intent.

Especially if the user arrives through search, they already have an idea of what they want to achieve, and pushing them somewhere may interrupt this journey.

It’s also the case that the uncanny valley is felt more by customers who aren’t registered, because they may wonder just how the company has information about them that they feel they haven’t explicitly provided.

And what are the implications?

Well, one might infer that this software does so much of the legwork that it could turf some marketers out of a job (that’s the line I tried with Dominique).

But truth be told, if these solutions mean that marketing at a personal level becomes more sophisticated, then for now, the strategy, content creation and customer service involved will only increase.

Increasingly sophisticated marketing means its success and importance will increase, which is surely a great thing for all marketers.