Category Archives: Forecasting

Advertising Response Rates by Channel

Published / by Simon Foster

Understanding response rates by media channel is a vital component of marketing and media planning. If you know the response rates, media costs and likely conversion rates of each channel you are using, you can forecast the ROI of your planned activity – before you spend any budget. This helps to de-risk your marketing activity and optimise how budgets are deployed to maximise ROI.

Unfortunately, many marketing and media channels are planned, negotiated, delivered and evaluated in silos. This means it can be difficult to get a set of comparative response rates which allow you to forecast how well any one channel may work for your business or brand. If you can’t compere them side by side it’s difficult to optimise budget distribution – particularly for customer acquisition activity.

Guide to response rates by media communication channels

With over twenty years’ experience of planning, managing and evaluating campaigns across practically all mainstream media channels, I thought it would be useful to share the metrics that I use as standard response metrics. These are given as percentage response rates of the audience seeing the ad.

Note: These are the response rates I would expect to see based on my experience. They should be used  as a guide and are not a guarantee. They are subject to the caveats listed below.

The caveats

  1. Response rates are driven by a number of factors including the product, offer, the creative treatment and the audience selection (media). Ideally, you should work to the highest possible standard in each of these four areas. Compromise on any of these factors will reduce response rates.
  2. Most channels have sub-sets of response rates depending on how the channel is being used. For example, TV ads can be “brand awareness” ads, “brand response” ads or “direct response ads”. Each of these have different levels of responsiveness. Brand awareness ads which are designed to change attitudes rather than short term behaviour will not deliver a high response rate.
  3. You must factor in the cost of media on a per audience basis. A favourite mistake of response rate observers is to look at response rates without factoring in channel costs. Here’s an example; the response rate from DRTV is about 100 times lower than the response rate from DM, but remember, DM costs around 100 times more per person than TV. In reality, both channels may produce a similar cost per response. That’s why it’s important to look at both factors when analysing and forecasting responses.
  4. Response rates aren’t everything; what generates revenue is sales so you need to factor in a conversion rate from response to sale.  As a general rule, personal channels like DM tend to convert at a higher rate than broadcast or online display. You can have a channel with a low response rate and high conversion rate performing as well in cost per sale terms as a channel with a high response rate and a low conversion rate.
  5. Marketing activity is subject to diminishing returns; response rates will fall as budgets increase.

2017 UK Marketing Predictions

Published / by Simon Foster

Always fun to gaze into the year ahead.  Here are my predictions for 2017:

  • Mobile web use will be an increasing problem for Google: Mobile is now the dominant web use platform. In 2010 over 95% of web activity was delivered via PC, now it’s half that. As of this October, mobile had the edge with 51.3% of web traffic according to Stacounter. Why? Because domestic web consumption has shifted to mobile and particularly to tablets. PCs still dominate at work, but workplace constraints mean most consumer web traffic is generated in the evenings, not daytime. In the evenings, the dominant web platform is mobile not PCs. As we move further into a mobile platformed, high-utility ‘appworld’ the need for traditional PC-based search will decline.
  • More large-scale ad fraud will be exposed: This is going to become a much bigger issue because advertisers are going to divert more resources into actually finding it.  Just as doctors screening for a specific illness find more cases, so advertisers will begin to understand the true scale of online ad fraud. The recent Methbot scandal has revealed the scale of fraud that is now possible; c. $5m per day via 6,000 fake domains
  • TV will remain strong: TV’s ability to deliver mass impact, reach huge swathes of the population and drive high volume, low cost brand search traffic make it a powerful and important communications channel for marketers. Couple this with the relatively high trust scores attached to TV advertising and the growth of dual screening and you can see why TV will remain an important part of the marketing landscape in 2017.
  • There will be a bid for ITV: This has been a long time coming. My prediction is that this will happen in 2017. It might be from Google. Hold / Buy.
  • Campaign goes online only: Campaign, the UK’s ad industry weekly, will drop its print format and go online.  Many of Campaign’s Haymarket stablemates have dropped their print version. Campaign has only been able to hold out because all ad people over 40 like to see their faces in print.
  • Brexit currency changes: As the value of the Pound has slipped against the Dollar, Euro and Chinese Yuan we will see increases in the cost of imports. With an estimated £21bn food trade gap, increases in imported food costs will present significant challenges to FMCG marketers. However, export areas like tourism and specialist manufacturing will benefit.
  • Continued decline of newspapers: Continued big problems for newspapers. Goodness me, how their fortunes have changed.  As the ad market has grown, newspapers have continued to lose share. It’s no surprise as our appetite for real-time news puts next day reading into the dark ages.
  • EUDPR: Marketers and agencies will start thinking much harder about the new European Data Production Regulations due to come into force in May 2018. Under the new EU regulations the use of non-permissioned data and other breaches will attract fines of up to 4% of global turnover.  Ouch. That’s enough to make every CEO in Silicon Valley sit up and take notice.
  • Digital backlash: Out of all this we can see the seeds of a digital backlash. It’s been a great ride since Google launched in 1997, but twenty years on, there are some very big issues in digital; huge and endemic multi-million – correction, billion – dollar ad fraud, the rise of politically damaging fake news and the fact that only 50% of digital ads are ever seen by living, breathing, humans.  All this is enough to push many a marketing director back to the drawing board. Expect to see some interesting changes in spend patterns in 2017.
  • Direct mail could benefit from a digital backlash.  Direct Mail. Thought it was dead and buried? Think again.  A digital backlash is the perfect breeding ground for the resurgence of reliable, effective, accountable and physical media. Which channel ticks all four boxes? Direct mail. Add to this the fact that most Millennials have never received direct mail and you can sense a real opportunity.

How is multivariate data analysis used in marketing?

Published / by Simon Foster

‘Multivariate’ means ‘many variables’ and in the context of marketing it usually means analysing multiple variables from customer records to get a deeper understanding of the customer base. This increased understanding of customer behaviour permits the development of customised offers, relevant creative messaging and more accurate media targeting – particularly with techniques like email and behavioural targeting. Very strong offer targeting will significantly increase your response and sales conversion rates.  Any company that has a database of more than around 5,000 records should be using multivariate data analysis to analyse customer data and improve marketing performance.

The most common forms of multivariate analysis in marketing are cluster analysis and hierarchical analysis. Cluster analysis uses statistical techniques to allocate customers into segments based on how similar, or dissimilar, they are to each other. So for example, if you had 10,000 customers and you were clustering by income and home ownership, you would be able to define groups of customers with similar levels of income and home ownership status, or those with high income and low home ownership status, or those with low income and high home ownership status. The number of clusters generated depends on how you set up your cluster analysis and of course, what patterns actually lie within your data. You can set up your analysis to produce either a large or small number of clusters, but most marketers can’t practically service more than about fifteen clusters.

Hierarchical analysis breaks customers down into sub-sets of the whole customer base. Results of hierarchical analysis are often shown as dendograms or tree diagrams. In a tree diagram, all customers belong to the ‘root’ and segments of the customer base are called ‘nodes’, nodes are connnected to the tree by ‘branches’.  So for example, all customers can be divided into males and females. Then the males and the females can be divided by age, and then by income and then by spend. You are then able to see what proportion of the whole base is composed of customers with certain characteristics.  Here are some examples of customer segments defined using hierarchical analysis:

  1. Spend more than £250 per year and are aged 18-34 and female and do not have children
  2. Spend more than £500 per year and are aged 25-44 and male and do not have children and earn between £20,000 and £30,000 and have a mortgage
  3. Spend more than £1000 per year and are aged 35-54 and have children and have a mortgage and live in the South East

Whichever technique you use, it is likely that you will see a small number of segments account for disproportionally large amounts of sales revenue or sales potential. When you have identified these segments you can leverage what you know to develop tailored offers, messages and targeting. Over and above this you can identify customers who have the characteristics of high performance segment membership, but are not spending at the rate they could be. You can use this information to target your marketing messages to the sales prospects with the highest untapped potential.

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.

Data planning and market research – mind the gap

Published / by Simon Foster

I once attended a research debrief to report the results of a survey into the communication effects of a direct mail campaign. The survey asked if the target group had received the direct mail piece and what they thought of it. The survey results were not good. According to the research, hardly any of the respondents could recall seeing the DM pack and even fewer claimed to have responded. There was disappointment; it was a big mailing and a strong offer, surely someone must have seen it and been motivated to respond. But all was not lost. In reality, away from the results of the survey, the campaign had in fact been very successful. I knew that the campaign was in the process of beating all its response, conversion and sign-up targets.  From a hard data point of view this campaign was on track to become one of the most successful DM campaigns ever run by the client.

So why was the recall in the research so low and the actual response so high? I can think of three explanations:

First, we were targeting a large group of the population. It was possible that even though the hard data results were good, we were drawing our DM response from portions of the population that simply hadn’t been included in the sample.   If we had a 25% response then that was a record-breaker from a DM planning point of view, but it still meant that the vast majority of the target – 75% – hadn’t responded. Those that had engaged with the mailing were far more likely to recall it than those who had not. So if our sample happened to comprise of 85% or 90% of those who did not responsd, then the recall results would be much lower than the response actually experienced.

The second explanation is more intriguing. Could it be that even though 1 in 4 of the target had responded, those that did respond had failed to make the connection between the what they’d actually done and what the research was asking them? In this scenario the sample is accurate and reaching our 1 in 4 respondents, but those who had responded forgot that they had done so when asked in research. Had they failed to connect the research question to the campaign and to their response behaviour?

The third explanation is that some of the respondents deliberately disconnected their actual behaviour from the answers they gave in the research. In other words, they did respond, but they didn’t want to say so.  They were using the research as a communication channel to share a point of view along the lines of ‘I’m not going to tell you exactly what I did. What I am going to tell you is that I didn’t like being perceived to be in your target audience, or perceived to be the sort of person who would buy the sort of product you were offering’.

Whatever the explanation, this taught me an important lesson; market research and behavioural data can say very different things. Asking people what they did, or think they did, can be very different to what they actually did. If market research tells you something, take it as an indicator not a fact. If it’s something big, do more digging around the research before you act on it.  But if hard data tells you something, whether it’s good or bad, whether you like it or not, you can be sure that it reflects changes in actual behaviour, the ultimate measure of marketing success or failure.