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The Analyst’s Toolbox: Explanatory forecasting techniques

In my last article I started to look at forecasting and investigated a set of methods called time-series techniques. Using these techniques you make your forecasts based purely on the patterns in the historical data of thing that you are trying to forecast. For example, you may looking to forecast  the number of visits to the site next week based purely on the volume of traffic over the past few weeks. These techniques don’t take into account any external  factors, they rely mainly on identifying underlying trends and seasonality to make the forecasts.

But what happens if you’re about to launch a major new TV advertising campaign next week? Won’t that possibly affect the amount of traffic to the site? You would certainly hope that it would but by how much?

This is where explanatory forecasting techniques come into play and have been used for many years in offline marketing analysis to understand the impact of marketing activity on important outcomes such as sales or brand awareness. These techniques build a model where the thing that you are interested in, such as visits, registrations, leads or sales, is explained quantitatively by external factors such as TV advertising, promotions, price and so on. This branch of techniques is often called Econometrics and one of the most popular methods is regression analysis.

A constant theme throughout these articles on more advanced analytical techniques is that the use of these methods is as much an art as it is a science. This is certainly true when it comes to techniques such as regression analysis. Regression analysis is widely accessible through programmes such as Excel and even in PowerPoint you can do some basic linear regression. An old colleague of mine used talk about these types of tools being like “putting guns in the hands of children”. Whilst I thought it was an arrogant way of making the point, the point he was making was quite valid. These algorithms can be quite dangerous if there isn’t the right kind of care and thought about how they are being used and what the results are saying.

The trouble with having these kinds of techniques available is that there is a tendency for them to be used in ways that are inappropriate or when the construction of the model or forecast hasn’t been fully thought through.

I got myself into trouble many years ago when I was cutting my teeth in the world of econometric modeling. We were doing some modeling work on price elasticity. We were trying to forecast the effect on sales of a proposed price increase on a client’s brand. The results of the model suggested a massive detrimental impact, bigger than anything that they had ever seen before. The client didn’t like the results (naturally) and said there must be something wrong with the model. I said that the model was technically correct according to all the diagnostic statistics but that I would go back and look at it again. At this point I noticed an event which I hadn’t picked up the first time round and which I hadn’t taken into account in the model. This event had made it look like that the brand was far more sensitive to price changes than it really was. When we factored this event into the model, the price sensitivity became something more appropriate and we were able to make a much more sensible forecast.

I took two key lessons of that experience:

  1. If the model looks wrong, it probably is wrong
  2. Modeling is like baking a cake

The first lesson was really the law of common sense. Whilst you are trying to look for insight through the use of these more advanced analytical techniques, the results should still make some sense at the end of the day.

The second lesson was that you need to make sure you have all the right ingredients in order to get the right result. If you are looking to forecast sales for example, you need to ensure that you are capturing as many of the likely impacts on sales as possible in your model. If you don’t then you will either inadvertently have the wrong effects coming through or there will be large errors associated with your forecasts.

Is there any use for these types of techniques in the world of online marketing? Well, much of online marketing analysis is based upon direct response or the tracking of individuals over time. This level of granularity is fantastic and allows us to get deep into the analysis of individual visitor behavior. I wonder though whether sometimes we can’t see the wood for all the trees. How do TV advertising and press ads influence online behavior. What can we infer about the synergistic effects of multi-channel marketing? These are all areas where I believe that modeling techniques will help us better understand the return on investment on marketing spend. As they in the offline world for a number of years.

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