Category Archives: Media Evaluation

DRTV Response Rates

Published / by Simon Foster

We’re often asked to forecast or estimate campaign response rates, especially in DRTV. Here are some guidelines for those who want them:

Set 1 – DRTV Phone Response Rates (high to low range as a percentage of total impacts)

  • DRTV Type 1 – Hard Hitters – these DRTV hard hitters, with no nonsense creative, usually on a 60 second time length can achieve between 1% and 0.05%. But please note, exceeding 0.05% is a very rare achievement in DRTV. It’s usually delivered through a combination of an extremely powerful ad, very strong product, with a great offer transmitted on a low level but highly responsive audience. It is very difficult to exceed 0.05% at scale.
  • DRTV Type 2 – Lead Generators – these DRTV ads are usually seeking subscription trials, leads, quotes etc and run on time lengths between 30 seconds and 60 seconds.  Response rates tend to be around 0.05% and 0.005%.
  • DRTV Type 3 – Brand Response – these ‘BRTV’ soft sellers produce lower responses generally in the range of 0.005% to 0.0005%

Set 2 – DRTV Web Response Rates (high-low range as a percentage of total impacts)

  • DRTV Type 1 – Hard Hitters –  these are high response rate ads will generate 2-3 times their phone response equivalents so around  2% and 0.1%
  • DRTV Type 2 – Lead Generators – web response rates to these tend to generate around 0.5% and 0.05%.
  • DRTV Type 3 – Brand Response – these BRTV soft sellers produce lower responses generally in the range of 0.05% to 0.005%

Advertising Evaluation Techniques

Published / by Simon Foster

Techniques for tracking advertising are often discussed by both advertisers and agencies as they seek to identify and maximise the ROI effect of media budgets. Deciding on which techniques to use can raise a number of issues depending on the data and budgets available for advertising evaluation.  All are data dependent which means that if you are not collecting response or sales data you will need to. Costs for implementation can vary but should always be viewed in the context of the potential savings that can be made from subsequent optimsation. For example, if a regression model costs £25k, but can optimise a £2.5m budget to save £500k, then the £25k is money very well spent. Here is a short summary of advertising evaluation techniques.

You will  notice there is no mention of coverage or frequency here. That’s because coverage and frequency are useful media planning metrics but they are not direct measures of ROI – coverage and frequency are measures of audience delivery not sales response. Reach and frequency may be linked to sales response but in my opinion spend levels or GRP weights and diminishing returns in specific time intervals are more robust ways of understanding sales response to advertising.

Does social media drive sales?

Published / by Simon Foster

The question of sales generation is a growing problem for social media. Despite all the hype, it’s almost impossible to find any conclusive cross-category evidence that social media drives sales.  Yes, there are some isolated examples of success; Dell’s Twitter pages announces some great deals and I’m sure ASOS can whip up a bit of extra demand by tweeting Axl Rose’s US flag shorts, but the reality for most brands is that they are going to struggle to make social media deliver measurable sales.  This view might not be flavour of the month, but the four experiences of social media listed below certainly give the “no sales” view a high degree of credibility.

  1. In 2010, Pepsi undertook a massive social media initiative called The Refresh Project which was designed to give $20m to good causes. According to Bob Hoffman, the AdContrarian, it delivered over 80 million votes, almost 3.5 million Facebook likes and nearly 60,000 Twitter followers. But there was just one big problem; it didn’t drive sales – despite the funding coming from Pepsi marketing budgets. Pepsi’s sales fell in the year the project ran and the brand lost 5% market share worth about $350m. To make matters worse, if that were possible, Pepsi slipped to third in brand share behind Coke and Diet Coke.
  2. In both 2012 and 2013 IBM used data from around 800 e-commerce sites to track social media’s contribution to sales. In 2012 it arrived at a figure of 0.34%. In 2013 it didn’t publish the number, but hinted that it was even less.
  3. In September 2012, one of the world’s leading digital research companies, Forrester Research reported that “Social tactics are not meaningful sales drivers. While the hype around social networks as a driver of influence in ecommerce continues to capture the attention of online executives, the truth is that social continues to struggle and registers as a barely negligible source of sales…”
  4. In March 2013, Mark Ritson, formerly a professor at London Business School observed in Marketing Week that “….marketers are finally beginning to apply some measures to assess the ROI of their [social media] efforts. Once they do that they can do the one thing the social media mavens have counselled against: compare the value of social media with other options, apples to apples. And, in many cases, they are discovering the hullaballoo drummed up by the marketing media and various industry events is not quite all it was cracked up to be.”

I think most people in social media are well aware of this “no sales” problem. And because social media can’t deliver sales, they’ve invented a snow-storm of flaky measures designed to obscure harsh commercial realities. These measures include: ‘likes’, ‘fans’, ‘followers’, ‘shares’, ‘retweets’, ‘pins’, ‘follows’, ‘friends’, ‘influence’, ‘amplification’, ‘forwards’, ‘mentions’, ‘tags’ and ‘reactions’. In a commercial context these are nothing more than diversionary measures. They might enable some positive looking PowerPoint charts but they don’t deliver positive looking sales. These are ROI potatoes, when everyone else is comparing apples.

Amazingly, when social media campaigns fail to deliver sales, social media experts almost always suggest that it was the company management who got it wrong rather admitting to any shortcoming of social media itself. Whilst this claim blames marketers and management, it also spawns a convenient stay of execution for social media’s “gurus”; failure brings an opportunity to “learn lessons”, to “revise approaches” and to “develop new strategies”. In other words social media failure provides a new opportunity for marketers to waste even more money on social media activity.

Marketers badly need a serious reality check on social media. Social media environments aren’t much more than an online version of a public waiting room. People drop in, take a seat, look around and leave. They may leave a bit of rubbish. They may take a bit of rubbish with them. But that, I’m afraid, is pretty much the long and short of it for most brands. Don’t spend too much time in there, nothing will come of it.

If this sounds old-fashioned, I make no apologies. Advertising exists to drive sales.  To have advertising that doesn’t drive sales is like going to a dentist who doesn’t look at your teeth, or a barber who doesn’t cut your hair, or a mechanic who won’t fix your car. If what you’re doing can’t be directly or indirectly linked to generating sales, you’re wasting precious budget.

What is predictive modelling in marketing?

Published / by Simon Foster

Predictive modelling is a term with many applications in statistics but in database marketing it is a technique used to identify customers or prospects who, given their demographic characteristics or past purchase behaviour, are highly likely to purchase a given product. In this context, ‘predictive’ does not simply mean predicting the future; it means identifying the quantitative factors that can be used to predict buyer behaviour. Predictive modelling is a powerful data analysis technique that can be used to target email and direct mail activity, and to some degree behavioural targeting in online media.

Here’s an example: Let say you sell 10 products. It may be the case that all purchasers of product 8 are: 1) in a certain geodemographic group, 2) married with more than one child and 3) own more than one car. All these factors can be analysed and combined to predict the likelihood of any consumer in your database buying product 8. Usually this combined measure is referred to as a ’score’ i.e. a figure which represents the presence or combination of certain variables in the consumer record. Once you have developed your scoring model you can rank all customers by their score. When you’ve stripped out those who have already bought product 8, you are left with a set of high potential prospects.

Predictive modelling can also be undertaken based on transactional information about past purchases. Going back to the 10 products, it may be the case that 80% of people who buy product 7 have previously bought products 2, 5 and 6 and in that order. So we can say that people who have bought products 2, 5 and 6 (in that order) but who have not yet purchased product 7, are much more likely to buy product 7 than everyone in your database. Again a score is attached to these behaviours and that score can be used to rank your prospects in terms of untapped sales potential.

Of course as well as predicting purchase behaviour, these techniques can be used to predict risk. In credit assessment for example, it may be the case that those customers who have certain demographic characteristics combined with a certain type of past purchase behaviour are highly likely to default on a credit agreement. This is sometimes referred to as credit scoring. If you are rejected for credit at a bank or in a shop it will be because your data has been analysed and your credit risk score is deemed too high or low to meet the criteria of the lender.

These predictions can help you target your communications very efficiently and also help you control commercial risk in customer behaviour. What’s interesting about these techniques is that they help both the marketing department and the finance department. Marketing delivers customers who are both highly likely to convert to sales or high lifetime value whilst at the same time, producing customers who are less likely to cause problems for the finance department. Overall, this means that the resources of the business are being better utilised.