Predicting Sales from Online Buzz - 2

For several years now, many organisations have been actively monitoring and analysing the online debate in order to gain further insight into consumers’ wants, needs and experiences.

Increasingly, organisations are now taking online analysis a step further by using online buzz to help predict sales, market share and other outcomes, and to detect changes in competitors’ MarCom activities.

The idea is compelling in its simplicity: by listening to what stakeholders are saying about different brands, a reliable forecast can be made as to whether or not customers may prefer one brand over another; and why.

Unfortunately, however, it is not as straightforward as it may sound. Simply counting up the change in brand mentions is not good enough and may often lead to disastrous results.

A crucial element in transforming the online buzz into reliable predictions is the ability to attribute to each online ‘voice’ the correct weight; often referred to as ‘influence’. This of course is very intuitive: It counts more when somebody who is a recognised authority or has a large following on a particular topic talks about a particular brand than if somebody with no following voices his or her opinion. If a particular car brand is mentioned in the driving section of The Times it counts for more than if a competing brand is mentioned on my blog or indeed in The Sun.

Some may now be thinking that surely more sales will only be the result if a brand is mentioned in a positive context or is unreservedly recommended. However, that is not necessarily the case.
All things being equal, it is normally better for a brand to be mentioned in a positive context than in a negative one. But we have to remember that every time a brand is mentioned in a negative context there are two opposing forces at work. The first force is negative. The reader may be slightly less likely to favour the brand because of the negative context. However, because the mention of the brand increases the reader’s familiarity with the brand and brings the brand to the forefront of the reader’s mind, a positive force is at work too.

Whilst the old saying that “any PR is good PR” is not entirely true, research shows that it is, in fact, almost true. Unless the talk about a brand is either very, very negative or unanimously negative, any brand mention is likely to have an overall positive impact. The occasional negative mention actually often contributes positively to increasing outcome.

In fact, when we at Onalytica test prediction models we can see that in most cases we get a better (and very good) prediction of outcome (e.g. sales) if we leave sentiment out of the model. (However, I must stress that in certain situations the model actually improves slightly by including sentiment.)

When we started out predicting sales and other business outcomes from the analysis of online buzz, we were concerned that the data we collected online were not representative of the total debate. However, our ability to satisfactorily predict sales of goods that cannot be purchased or delivered online, i.e. cars, movie visits, and prescription drugs, based solely on analysing the online debate, has largely satisfied these concerns.

In fact, when we collect the buzz online what we get is a very large and very representative sample of the overall buzz (off line and online); when it comes to the debate about the vast majority of interesting issues and brands there is no separate online or offline debate. When there is an increase in the online debate about a brand there is also an increase in the similar debate at the pub and in the work place.

The ability to predict business outcomes from online buzz has sparked new ways of working among several of our clients.

Some now set out targets known as “influence budgets” that are similar to traditional budgets where revenue is the target except that here the target is related to how much online ‘influence’ a brand earns; on its main brand, its individual products and services, and on key marketing messages.

Using “influence budgets”, organisations can now predict more precisely whether or not they are on track to meet their actual revenue or market share targets; and if they are not on target they are in a position to take action earlier.

The actual actions initiated are often more traditional. They most often involve adjusting their MarCom activities, including (but not limited to) the total spend.

An interesting side-effect is that the process of benchmarking brands against an influence budget also gives organisations early insights into changes in competitors’ MarCom spends and their effectiveness.

An overview of predicting business outcome from online buzz would not be complete without a few comments on how some of the key elements differ across markets.

When it comes to predicting outcome from online buzz there are two main factors that differ from market to market.

The first market-dependent factor is the lag from an observed change in the online buzz to the change in business outcome. This lag may range from a week (e.g. prescription drugs) over 30-60 days (e.g. cars) to over 6 months (e.g. white goods).

The second market-dependent factor is how accurately changes in business outcome can actually be predicted. In certain segments where the goods/services are difficult or expensive to sample before purchase (e.g. cars, travel, mobile phone services, gadgets, financial services, etc.) the models work extremely well. In areas such as cheap FMCGs where the goods can easily and inexpensively be sampled, the ability to predict changes in sales may work less well, but can still compete very favourably with traditional models.

In a market place where products and services increasingly are at par, earlier access to better and more precise information is likely to become even more important.

By transforming online buzz into actionable intelligence, managers can now act earlier and on safer grounds if they are not on track to meet their business targets.


(To see an example of the realtionship between sales and online buzz see this previous post.)