Predictive Analytics Part 1

In my last article I outlined my belief that what we call “web analytics” is becoming a more diverse and complex field. What we have traditionally considered to be web analytics has been the analysis of site behavioural data captured, processed and reported on by a proprietary system designed to do just that. But as the online channel evolves and becomes more complex , the tools to help us understand what’s happening must also evolve and become more complex. In some areas, such as in the case social media, this may mean the development of new tools. In other areas it may mean the application of old tools to this new channel.
One of the areas that we work in a great deal is in the use of data mining and predictive analytical techniques. I first got started in this area about 15 years ago when at ACNielsen using these types of methodologies to help clients to try and figure out which half of their advertising money they were wasting. I have a book on my bookshelf that was published 25 years ago on the use of model building techniques in marketing. So the techniques aren’t new but what is relatively new is the systematic use of these techniques in the online marketing space.

I think that there are some reasons for this. Historically our main concern has been on managing the vast volumes of data and wrestling out of the web analytics systems a few numbers that told us how well we were doing and that we could do something about. Also, in the past, the natural organic growth in the channel has meant that we have not been faced with the need to scramble for market share and to fully optimise our business processes. And to some extent, we have not been asking the right questions. This is now changing. We understand our few numbers and we want to know more. The online world is far more competitive and we are beginning to ask questions that go beyond the limits of our traditional analytical tool set. Questions like:

  • “How do I understand the effects different marketing channels have on generating sales?”
  • “What does the purchase lifecycle look like over multiple visits and how can I optimise it?”
  • “How should I be segmenting my audience or customers, to improve the effectiveness of my marketing activity?”

To answer these types of questions we are going to have to start to organise the data in different ways and we need to bring in some different tools. First of all we need to integrate our data so that we can see different aspects of the acquisition, conversion and retention processes in one place, Secondly we need to aggregate our data so that its focuses on the visitor or customer rather than the click or the visit. Thirdly we need to cut through the noise in the data using more sophisticated analytical techniques to get at the key insights. Let me give you an example of what I mean.

We all know that different types of people come to our websites for different reasons and to do different things. If I treat everyone the same, I am being sub-optimal in my decision making about how I allocate marketing funds and about how I manage the user experience. I need to segment my audience so that I can market to these different groups more effectively. However, I can’t do that on the basis on how they behave on the website alone, I need to also understand their demographics, their intentions, their aspirations and their opinions. So I need to integrate my hard core behavioural data with profiling and attitudinal data drawn from other data sources like surveys.

Next, I am interested in the behaviour of visitors over multiple visits rather than what they do in a single visit. So I need to aggregate the data so that I have a record of the behaviour of different visitors over a period of time. Also I probably need to summarise the data and create additional attributes which describe aspects of that behaviour over time such as number of visits made, number of conversions events, types of conversion events and so on.

Finally, I need to analyse the data to identify interesting and meaningful segments of visitors. In all likelihood I will probably have quite a large and noisy dataset where I won’t be able to see the forest for all the trees. Traditional querying and reporting techniques are unlikely to be an effective method of identifying the patterns, I need to use something that will find the patterns in the data for me. In this case I decide to use cluster analysis. The cluster analysis process looks for groups of visitors in the data, where the people within the groups have something in common but what they have in common is different from group to group. What I have to do then is interpret that data to understand what it is the visitor segments have been clustered on and decide whether these are meaningful and useful segments that I can do something with. This process may yield some surprising results and enable to think about the audience in a way that I had not previously thought of them before. I may find patterns and relationships in the data that I would never have found using traditional analysis techniques.

So using data mining and predictive analytical techniques will allow organisations to unlock more value from their data but it requires a different approach to managing your data, different tools and different skills. Next time I will look at another application of data mining and predictive analytics; to understand what are the important factors are that affect someone’s propensity to buy something during the purchase lifecycle.

Till then…

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