Category Archives: Media Planning

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.

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.

What is a TVR?

Published / by Simon Foster

This is a question that many marketers don’t want to ask, especially when they are halfway through the agency’s TV presentation. The trouble is, the agency team have been talking about TVRs for about 20 minutes, the coffee’s gone cold and you daren’t chip in to ask “exactly what is a TVR?”

Put very simply a TVR is a TV Rating point and it means a given percentage of a base population watching a TV programme where that base is defined as 1) a given target audience in 2) a given TV region or area.  What’s important here is that because we are talking percentages the bases from which those percentages are taken can change, and this can mean huge differences in the volumes of audience actually seeing an ad. Let’s look at some examples of the effect of different base criteria when establishing TVRs.

If a TV spot runs across the UK TV network and delivers 1 Adult Network TVR how many people will see that spot? The base criteria here are 1 TVR, meaning 1% of a) the UK TV Network and b) the adult demographic population base. If there are 49 million adults in the UK i.e. across the whole UK TV network, then 1 Network Adult TVR is 1% of 49 million. That’s 490,000 Adults.

But we could also have 1 Adult TVR in the London ITV region; these are very different base criteria.  If there are 9.5m adults in London then 1 Adult TVR in London would be 1% of 9.5m – that’s 95,000. So we can already see that 1 Adult Network TVR equates to more than 5 times the audience volume of 1 Adult London TVR. Remember 1 TVR against one set of base criteria is not the same as 1 TVR against another set of base crieria. In other words, not all TVRs are equal.

Then we can look at different audiences. The UK media industry breaks audience down from all Adults 16+ into a number of sub-groups refined by age and socio economic group so we might have ABC1 Adults or Men aged 25-44 or ABC1 Women or Women aged 25-54. Each of these sub-groups (sometimes called “demos”) has a different size of population base.

So, for example we might look at a programme that delivers 1 ABC1 Adult Network TVR. As there are 26.7m ABC1 Adults in the UK network area then 1 ABC1 Adult Network TVR equates to 267,000 ABC1 Adults.  If there are 5.8m ABC1 Adults in London, the 1 ABC1 Adult London TVR would equate to an audience of 58,000 ABC1 Adults.

We need to remember that when we measure a sub-group, we are only measuring audience in that sub-group. So, whilst a programme may deliver 58,000 ABC1 Adults, it could still deliver 100,000 Adults in total. 100,000 Adult viewers in London would mean the programme had an Adult London TVR of 100,000 / 9.5m – that’s 1.05 Adult London TVRs.

TVRs are important because they are used to populate models which estimate the coverage and frequency effects of an advertising campaign. As TVRs build so do coverage and frequency. More on that in later posts…

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 programmatic advertising?

Published / by Simon Foster

If you are an advertiser you may have heard the expressions “programmatic buying”, “real time bidding” and “ad exchanges”. You may be wondering what all this is and what it means for advertisers, if you are then read on…

“Programmatic” advertising, is effectively automated online media buying – often at large scale and at very high speed (faster than lightening in some cases). Advertisers use computers to participate in real-time automated auctions for digital ad space across a large number of publisher web sites.

Bidding is supported by big data analytics; predictive algorithms are used to target bids using information about web users such as location, platform, device, browser, and where available, other forms of behavioural data relating to specific but anonymous users. There is some big data science behind this, much of which has its origins in high frequency algorithmic trading in financial markets where the principles are very similar to automated online advertising. For example, information about a user is matched to a bidding rule in a minute fraction of a second – enabling a bid to be made and a relevant ad to be served by the time the page being visited by the prospect fully loads. This high speed automated decision making is not dissimilar to rule-based or algorithmic trading in financial markets.

So is programmatic advertising important? Yes; it’s important to the long-term health of digital display as a medium and it’s important to advertisers in terms of increased advertising efficiencies. Let’s look at each of these.

Firstly, automated buying is boosting the fortunes of digital display advertising by creating renewed interest in the medium. Online display has struggled to demonstrate efficiency in the face of PPC which is based on pay per click (PPC) trading. For many years display has been traded on a CPM basis, that’s simply the cost of reaching people in their thousands, with no accounting for click or sales performance. That’s why Google has commanded such as large share of digital budgets over the last decade. But programmatic buying allows advertisers to place data-driven bids to ensure campaigns deliver the most responsive target audience at the right rate. This will significantly boost the ROI delivered by digital display and make it much more competitive with PPC. This in turn should enable it to take larger share of digital advertising budgets.

For advertisers automated buying offers a real opportunity to increase ROI from digital display. This opportunity comes from three sources: ROI-based trading mechanics, better ROI based audience targeting and clear performance transparency. All this offers advertisers a chance to make digital display much more cost effective. Moreover, the increased efficiencies delivered by automated trading will make digital display more competitive against PPC. Long term, automated display buying could have the effect of diffusing spend out of PPC alone and across the two platforms – theoretically this reduction in demand could reduce bid prices in PPC.

Are there any down sides?

It remains to be seen whether automated buying – which by its nature can reduce ad revenue – will deliver consistent long-term growth to digital display. It’s also worth noting that not all media owners will sign up to ad exchanges; those who feel they can realise the value of a web site more holistically than the lowest CPC denominator may well be resistant to signing too much inventory over to automated trading platforms – leaving them to fight over the lowest value inventory.  There are also  issues around the quality of the traffic delivered through high volumes of remnant inventory – remnant inventory is by its nature ad space that can’t be sold by normal means because it’s not demanded by media buyers. Buying remnant inventory through ad exchanges can mean you are buying into some low quality sites which may not be right for your brand’s image.

Advertising Frequency and Diminishing Marginal Utility

Published / by Simon Foster

diminishing-marginal-utility

Economists have a concept called Diminishing Marginal Utility. This means that each additional time a consumer consumes something they get less satisfaction from consuming it. So, if I have one coffee, I find it very satisfying, two could be OK, but by the time I get to three I’m not getting much additional satisfaction, infact, I’m going off coffee pretty fast. And if I were to drink ten coffees I’d feel like I was being tortured.

Now let me apply this thinking to the world of TV advertising and in particular, sponsorship. In the UK, quality drama is a favourite for sponsorship. One of the reasons for this is that these programmes attract a high quality loyal audience who make an appointment to view. Certain drama strands can be sponsored heavily in a cross-programme deal covering different programmes in the same genre. Whilst this may appear to present great media value it can mean over-exposure for both brands and consumers. Seeing a break bumper a couple of times is fine, but seeing the same branded break bumper ten times in the same evening can seem like drinking that tenth cup of coffee.