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The dawn of the psychic marketer?

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29th May 2013
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Marketers will soon be using predictive technologies to know who will click, buy or lie. 

Just like Paul the octopus, who famously predicted the results of football matches during Euro 2008 and World Cup 2010, predictive technologies are set to make a psychic splash. With a success rate of 85%, Paul correctly predicted 11 out of 13 matches in 2010 as well eventual winners, Spain against the Netherlands in the final on 11 July. Not a bad for a cephalopod.

Paul’s psychic powers may have been down to divine intervention, but new predictive analytics technologies use data patterns for the basis of its accurate forecasts. A recent Gartner report said the business intelligence market is growing 9% per year and will exceed $80 billion by 2014, with about 50% from predictive analytics. However, this isn’t about the traditional business intelligence (and data mining) software, which was good at showing you where you had been - it’s all about new technology able to examine and interpret Big Data.

The value of Big Data - i.e. data collated from everywhere and every electronic interaction we may have - is from the quality of information you can extract from it. In other words, the devil is in the detail. Data is only ever valuable if you can actually do something with it. At the moment marketers tend to have a rough idea of how many customers they have, how much they spend in total, and the average spend per client. You know what the ‘average’ is and treat every customer based on that average. 

The holy grail has been to know customers individually as it stands to reason you can then treat them as individuals. More data equals more appropriate suggestions to entice someone to buy, return or recommend something to others. The best data gives you information that can be acted upon creating value.

Big Data is the fuel for predictive analytics

Here’s an example:

You have a High Street, which is crowded or busy at certain times to the point where customers walk away in frustration. An online store faces different problems; poor website design, cheaper goods or complicated payments steps may lead to abandoned shopping carts.

For offline, a suggestion may be to better distribute activity throughout the day, which will increase revenue. To do this we’d be expected to firstly watch the behaviour of the customers to build a picture.

You will find people doing different things. Some will shop at off-peak hours, others at peak times. There will be those that buy certain things at particular times. Others will vary the times they come to the store/online. A few will just give up – having not found what they were looking for.  

Now take this raw information and translate it into action. Perhaps some of the shoppers who come at busy times are simply not aware it will be busy. An information campaign may be useful. This could be as simple as posting signs in the store or including this information in a regular communication you have with customers.  You could also try tempting peak-shoppers to come during quieter periods; special discounts or the types of offers to spread purchases over a longer period.

The off-peak shoppers may be coerced into buying more. If you know what they’re buying, you might want to offer a coupon for something they haven’t tried or throw in a deal on a favourite item.

Online will follow the same principle minus the timing issues as it’s a 24/7 store. Web activity logs reveal details about shopping behaviour and why we abandon shopping carts. Look at them for tell-tale patterns.

You certainly won’t be able speak to every customer personally yet you want to get to know them individually. You also won’t be able follow and observe every individual but you can look at the behavioural data. Imagine examining one specific customer’s habits. The data tells you that their wife has a penchant for exotic lingerie and is a size 12.  You can offer that customer a special promotion before their wife’s birthday, which you know from another dataset. However, the data also tell us that this same customer bought some lingerie for a lady of a slighter frame in Paris a month ago. Has the psychic marketer, unwittingly, discovered something about that customer’s personal life...

Big Data is the fuel for predictive analytics and can tell you a lot of things. Expect it to help you target your marketing. The challenge has always been to increase response rates and propagate a single view of the customer, by integrating customer data from multiple web and social media interactions. You will then be able to create campaigns to narrower customer groups.

Online, we all want to know which advert a customer is most likely to click. If you knew that then consumers get to see an advert that is of interest.

And what about customer churn - every business wants to know which customers are about to leave and the reasons why.

Those are the scenarios where it’s going to be useful but handling all of this is no easy task. The amount of data generated creates a massive set of headaches with storage, maintenance and management issues. In 2013 some interesting answers lie in wait with predictive capabilities being offered to businesses at a low monthly cost.

Without an octopus in sight, the psychic marketer will be making some incredibly accurate predictions about your buying habits. Will the psychic marketer be able to guess the results of football matches or know of illicit affairs? I’ll leave that up to you.

John Fleming is VP of marketing at emarsys.

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