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.
- 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.
- 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.
- 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.
- 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.
- Marketing activity is subject to diminishing returns; response rates will fall as budgets increase.
What response rates can you expect from Direct Mail?
Warm Direct Mail – mailings to your active customer file: In our experience, warm direct mail, i.e. DM sent to your customer file should deliver a response rate of between 1% and 5%. The average figure is around 3.5%.
Cold Direct Mail – DM send to prospects via a “cold” list: Response rates here are lower as the consumers you are mailing are less familiar with you and your brand. Typically 0.5% to 1.5%.
The DMA in the UK cites a response rate of 4% and claims that overall 7% of recipients will take some kind of action as a result of receiving direct mail.
The DMA in the US has produced a lot of information in its 2015 Response Rate Report and cites response rates of 3.7% for a house list and 1% for a cold prospect list.
Your customer database is a potential fountain of opportunities to improve campaign targeting, creative messaging and return on marketing investment. Good database analysis can have a huge positive effect on your business. Your database can tell you who your customers are, where they live, what kind of people they are, what they buy, how they pay, what they might buy next and how you should advertise to them to maximise sales. Let’s look at each of these in turn.
At the most basic level your database should contain a name and address for each record. The name and address can give you valuable information. The postcode in the address opens up the potential for geodemographic analysis using tools like ACORN or MOSAIC. These tools work by grouping consumers into clusters of similar people based on the types of neighbourhoods they live in. The principle behind these systems is simple; birds of a feather flock together. The owners of these segmentation systems undertake research into the clusters they have developed. For example, Cluster 1 may contain people who are known to be affluent pre-retirement couples with children who have left home. Research may show that these people are three times more likely to drive a certain car, purchase certain electrical products or take holidays to certain destinations. So from just the address record you can build a much wider picture of the record in question.
But the full name and address have even more potential.They can be used to match your customer file with an external data file containing more information about the same person. This data can come from many sources, but more often it comes from lifestyle surveys. If a customer in your database has completed a lifestyle survey then you can buy supplementary information to significantly expand what you know about that person.Here’s an example. You may only know the name, address and age of a customer. But if that record can be matched with a respondent to a lifestyle survey then you can see the answers to tens or even hundreds of other purchase preference questions that person has shared. For example, you may be able to see what type of car they own, when it was bought, when they intend to replace it. They may even tell you what type of car they are considering next.
If you have transactional data then you are able to undertake an analysis of the types of products and services bought by the customer. From this data you would be able to say that a customer owns products X, Y and Z and you will probably know when they bought those products. You will be able to see how the often products are purchased and the preferred means of payment. If there is cyclical behaviour in the purchase pattern you may be able to predict when this customer is likely to purchase those products again.
With these high levels of customer understanding you are able to take a lot of the guesswork out of marketing. You can be much more focussed in terms of selling specific products to specific individuals. As a result you response, conversion and customer value rates are likely to improve significantly.