Author Archives: Simon Foster

TV Media Planning Terms – calculating media reach and frequency using TVRs

Published / by Simon Foster

When media planners develop TV campaign plans they are often optimising the relationship between three sets of numbers:

  1. TVRs (or GRPs, or TRPs) – TVRs or TV rating points are a reflection a target audience using both demographics and geography. If there are 50m adults in the UK then 1 Adult National TVR is 1% of 50m – 500,000. If a national programme is watched by 2m adults it delivers 4 TVRs.
  2. Reach (or coverage) – Reach is the percentage of your target audience that is reached by a spot or a campaign.  TVRs build reach and frequency.  This is explained in more detail here.
  3. Average opportunities to see (OTS) – Average OTS is the number of times on average that a member of your target audience will see you ad.

There are some simple calculations to illustrate how these numbers work together.  There is one thing you will need to begin this process and that is a reach curve. A reach curve looks like this:

This curve describes the relationship between TVRs and reach. It  illustrates how your reach (sometimes called coverage) increases as you add more TVRs to your campaign.  You will see that with each additional 100 TVRs you add to your campaign, your reach increases more slowly. In practical terms this is because some of your target audience will see you ad more than once, even in the early stages of the campaign. This is called opportunities to see or OTS.

How do  we calculate OTS?

OTS is calculated by dividing your TVRs by your reach: At 300 TVRs we have about 70% reach. That means the average OTS is 300 / 70 = 4.2.

How many TVRs do you need?

To calculate this you need to multiply your target reach by your required OTS. So if you wanted 70% reach at 4 OTS your calculation would be 70 x 4 = 280 TVRs. If you wanted 70% reach at 5 OTS you’d need 350 TVRs. If you wanted 85% reach at 7 OTS you’d need almost 600 TVRs.

Can we calculate reach from TVRs and OTS?

Yes, but only to a point. Reach = TVRs /  OTS. So if you are planning 50 TVRs at 1.5 OTS it would seem that your reach would be 30%. However, reach is not easily predictable. This is for two reasons:

  1. First with you will see that whilst the growth in TVRs is linear, the growth in reach is non-linear i.e. it decreases as you add on every 100 TVRs. Between 0 and 100 TVRs we generate 50% reach. But when we add on the next 100 TVRs we only generate 65% reach at 200 TVRs.
  2. Different types of campaign on different stations, phased in different ways, with different use of daily schedules (dayparting) will increase reach in different ways. For example, a campaign that runs in weekday daytime between 9am and 5pm may struggle to get over 50% reach even at more than 300 TVRs. This is because you will not be reaching the audience that is working during the day.

Understanding brand awareness, consideration and preference

Published / by Simon Foster

Brand awareness is vitally important in the marketing process. As consumers need to be aware of a product and brand to purchase it, then the more consumers who are aware, the more purchases take place. This hierarchical approach, begins with awareness and moves through consideration and preference to purchase.

This hierarchical path is sometimes called the sales funnel. Funnel metrics allow marketers to measure and control brand awareness, consideration and preference. These metrics allow marketers to say things like “We have 75% awareness and of those 35% would consider purchasing from us and 75% of the consider group place our brand in their preferred set.” Most marketers spend most of their time working to raise brand awareness, consideration and preference. Let’s explore brand awareness, consideration and preference one by one:

Brand awareness

Awareness is the number of people or percentage of a group that are aware of a brand. Awareness is measured in two ways, either as prompted or unprompted (spontaneous) awareness. Prompted awareness is measured by asking people if they are aware of the mentioned brand. It could be the brand name itself, a logo or the brand as part of a list of other brands. Unprompted or spontaneous awareness questions do not mention a brand name but asks consumers to name brands they are aware of in a given category. Examples would be “could you tell name ten airlines that you are aware of” or “can you name five soft drinks brands”.

Brand consideration

Consumers do not purchase all brands they are aware of. They purchase some but may actively avoid others. So, consideration examines whether consumers would consider purchasing a brand they are aware of.  Those who would consider purchasing a brand are measured as a subset of those aware of a brand. Here we see the hierarchical nature of the funnel in action; it is not possible to consider a brand you are not aware of. Consideration questions might ask “of the ten airline brands you are aware of, which would you consider using?”

Brand preference

Consumers tend to have a “preferred set” of brands – these are the set of brands within a category that they prefer to use. Going back to our hierarchical model, the preferred set can only come from within the considered set. Getting into the preferred set is the Holy Grail for many marketers but it’s not easy. Finding a place in the preferred set requires a contribution from all elements of the marketing process. A place in the preferred set is the result of many factors that can drive brand preference; a strong product developed through strong NPD, a product made available through good distribution,  a product pitched at the right price and a product back by good service.  You can see why advertising in itself cannot guarantee brand preference, but advertising can communicate a brand’s attributes which in turn can help to secure preference. Brand preference is measured by asking consumers which brands they prefer to purchase and use within a particular category.

How are these metrics measured?

Brand awareness, consideration and preference are attitudinal and exist within consumers’ minds. The only way they can be measured is through surveys which ask consumers about their relationship with brands.  These surveys can be collected though online panels, face to face or via phone research.

Proxy measures can also be used to build understating but theses are not a substitute for brand awareness research. Digital proxies include search traffic metrics which can be correlated to awareness and consideration. The important point with digital metrics is that they are behavioural, not attitudinal; they tell you that something is happening, but don’t explain why.

How do TVRs build media reach and frequency?

Published / by Simon Foster

As we saw in the “what is a TVR” post a TVR is a percentage of a given target audience in a given geographic base.  But is a TVR any more than that? Well, yes it is. A TVR is an important factor in calculating how media activity builds reach and frequency. Reach is the percentage of your target audience seeing your ad at least once. Frequency is the number of times they see it.

How TVRs build campaign reach

Let’s assume you buy 100 TVRs in a given region. We know from our last post on TVRs that 100 TVRs is an amount of audience that is the equivalent of 100% of our target audience base.  But here’s the first important lesson in how TVRs build reach and frequency. 100 TVRs will not deliver 100% reach of that base.  In fact 100 TVRs will probably build around 50-60% reach depending on how those TVRs are distributed in the plan. So what is delivered by the TVRs that don’t deliver reach? Well, they deliver frequency.

How TVRs build campaign frequency

In the early stages of campaign, most people will see the ad only once. But some will see it twice and some may see it three times. Let’s say, for example, that 50% see it once, 20% see it twice and 15% see it three times 10% four times and 5% five times. These percentage total 100 and this is effectively how your 100 TVRs are distributed. This is called frequency distribution.

How to estimate frequency from TVRs and reach

There is a simple formula for estimating how TVRs deliver both reach and frequency.  Let’s continue to assume you have 100 TVRs. Frequency (sometimes called average opportunity to see or OTS) is calculated by dividing your campaign reach into your campaign TVRs. So, if you have 100 TVRs and your campaign delivers 50% reach then your average OTS is 100/50 = 2.

How many TVRs does my campaign need to be effective?

This depends upon whether or not you adopt the view that reach is more important than frequency.  Modern “recency” planning advocates (John Philip Jones, Erwin Ephron, Byron Sharp) argue that each point of reach will deliver more sales response than additional points of frequency (i.e. the percentage of people seeing the ad twice, three times etc). So they advocate building maximum reach on a weekly or a monthly level, but not building frequency. To achieve this objective media planners will seek between 100 and 150 TVRs per week and often plan the delivery of these TVRs in a week on, week off “drip” pattern. This type of campaign plan tends to suit campaigns that are designed to regularly remind consumers about a product they are already aware of.

More traditional media planning approaches (Krugman for example) suggest a minimum frequency of 5 OTS before a message begins to resonate with a prospect.  Our calculation tells us that if we want to achieve 80% reach at 5 OTS we will need 80×5 = 400 TVRs. Targeting an average of 7 OTS would require 560 TVRs. You can see why a launch campaign would typcially be around 600 TVRs.

More advanced forms of planning use statistical modelling to estimate the sales response curve to advertising. These models show how budget and TVRs drive sales response (could be retail or online sales) on a weekly basis and forecast when spend levels will hit diminishing returns. For more on this please see www.mus3.co.uk

Thanks to Ivan Clark for comments.

What is first, second and third-party data and how is it affected by GDPR?

Published / by Simon Foster

First party data is data that your organisation has collected and owns about your customers. It is information that has been gathered in the course of your direct relationship with your customers. They key here is that your organisation is the owner of this data; this is your database of your leads, your enquiries, your customers, your subscribers or your members. This data may combine demographic, transactional and media source information.  It can also be used to report business and marketing performance – transactions per hour, day, week or month. First party data is the source of lifetime value information such as revenue, purchase frequency and evolving customer value.  This type of transactional data can be analysed to predict next likely behaviours based on past purchase behaviour patterns.

Second party data is data you share with a known and named partner. For example, if you are a hotel group you might exchange data with an airline to improve your targeting models;  might add data (sometimes called appending or augmenting) from its airline partner to improve its targeting model. The appended airline data might reveal that a customer always travels business class by air but always books an economy room thus presenting an opportunity for cross-sell. The data added by this cross-party transfer improves the level of insight that can be generated about a given customer and presents commercial opportunities on both sides. This data sharing is enables by the consumer if they tick a data sharing box in a permission request.

Third party data is data that does not belong to you but can be bought or used by you to improve insight or targeting. This is usually sold by third party data suppliers such as Acxiom or Experian. This data has been sourced directly from the consumer and permissioned through an opt-in. Third party data is often used in “matching” projects where a first party database is matched to a third-party database (like the way second party data is used in the scenario above) which can add incremental information to that already held by the first party data owner. So, for example, if you are a retailer of clothing you might want to match your database to a database of clothing purchasing habits to target consumers with products which appear to be relevant to the third consumer base.

Impact of GDPR on first, second and third party data

The General Data Protection Regulations (GDPR) will affect all three types of data and all the companies who are storing and managing that data. Companies holding first party data will need to make sure that their data is properly stored, consented, encrypted and secured in order to meet the regulations. And whilst there is a strong onus on first party data holders to comply with GDPR they, as the data owners, are in a strong position to comply because they are in control of their own data, storage environments and protocols.

The real complications arise when we look at second and third-party data. As first party data is shared with second and third parties, the responsibilities of the first party data owner “stretch” as far as the data goes.  So, if a second or third party commits a breach involving your data, you may still be responsible, at least at joint level with the party you have shared to.

This means you will need to ensure that the way your data is used after being passed to a second or third party remains compliant all down the line. It may not suffice to have your partners sign an agreement saying they will manage the data in line with the requirements of GDPR. If they do not, you may still be liable.

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.

Examples of brilliant Direct Mail

Published / by Simon Foster

Been digging around for examples of outstanding direct mail. If you’re tired of running emails and banners ads, and you’re looking to make a real creative and intelligent impact, you might want to consider something like this, especially if you have a discrete high value segment and want to get seriously noticed.

yr-air-force-radio

The Australian Defence Force wanted to hire engineers. So George Patterson Y&R Melbourne developed this DIY radio kit to engage engineering students as potential recruits without instructions.  It won a D&AD Gold in 2014.

Here’s the story behind the campaign

 

 

Direct Mail Response Rates

Published / by Simon Foster

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.

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%

How to get the best from DRTV

Published / by Simon Foster

Many advertisers are returning to Direct Response Television (DRTV). Whilst the goal today is to maximise web response as opposed to phone response, many of the rules of traditional DRTV remain constant. Here’s a summary of how to get the best from Direct Response TV:

  1. Remember all DRTV begins with the offer. Whilst issues around DRTV performance are often seen as “creative” or “media” we need to remember that the proposition to consumers is key to DRTV success. If you are offering free Ferraris you will not need to think in terms of creative or media optimisation. The offer will work. Equally, if you are offering a poorly differentiated product or service, you will find it difficult to sell. Your problems will be exacerbated further if you are in a mature market packed with established offers.  So ask yourself the “so-what” question against every line of copy. If you wouldn’t buy it, no-one else will.
  2. Develop compelling DRTV creative. DRTV seeks behavioural change, and consumers need to be given good reasons to stop what they’re doing and do something else. You need to talk in terms of meaningful benefits. There are certain category rules that are helpful. If you’re selling a financial product don’t use jokes. For most people, talking about their hard-earned money is not a funny business. Concentrate on explaining why the product is different, what it offers that is new and why your target audience should find out more.
  3. Be careful with emotional sales messages. Most mainstream advertising seeks to build emotional connections between people and brands. For many brands this is the right approach, but if you want to sell off the screen, stick to promoting the benefits that make you different and giving good reasons to buy.
  4. Make sure the creative identifies your target audience. Everyone watches broadcast media. The trick to making DRTV work is to create a sense of identification between you and your target audience. Show people and situations that your target audience will identify with. Create the impression that your target audience belongs in the ad.
  5. Understand the economics of broadcast media. TV companies use every possible device to maximise the financial yield on the audience they are selling. Yield is the revenue generated by advertising over the cost of attracting that audience i.e. producing or buying the programming.  High quality peak programming is expensive to produce or buy. More people are at home available to view during peak viewing times (5pm to 11pm) so audiences are higher. So TV companies put their highest cost, highest quality programming into the times of day when most people are available to view.  Moreover, ad agencies want high reach, so there is high demand for high quality, high audience programming. All this makes peak airtime expensive both in terms of unit cost and capital cost. The premiums embedded in peak mean it sells at rates that rarely work for DRTV advertisers. But off-peak airtime is an entirely different matter….
  6. Unlock the benefits of off-peak airtime. Everybody watches off-peak TV; young, middle-age, old, affluent, less well off, single, married, students, working people and those who’re retired. People take holidays and have days off. But most importantly, the homemaker watches daytime TV and the homemaker is often the person who researches and makes financial plans.  Daytime can be ideal for reaching family decision makers. Daytime can be ideal for reaching students and it can be ideal for reaching affluent grey markets. Off-peak is the place to test your product in DRTV.
  7. Test. When you construct a test think about the questions you want to answer. You could test creative A versus creative B.  You could test phone versus SMS or web response. You could test a longer ad versus a shorter ad. And of course you need to test channels and stations. You can construct your test in time blocks – Station 1 with creative A in week 1, station 1 with creative B in week 2,  station 2 with creative A in week 3 and station 2 with creative B in week 4. This way you can analyse the incremental response in each week an look at its cost and conversion characteristics. When you have analysed your results you can “roll out” into the optimal mix of creative and media options.
  8. Measure and learn. DRTV is direct response marketing and the lifeblood of direct response is quantified planning and control. Using BARB data it is possible to match minute by minute audience data with your minute by minute click traffic. This allows advertisers to build a response database which matches TV audience with web response. Each spot can be defined by a number of planning variables that can be controlled: day of week, time of day, channel, proximity to previous spot, length of spot and creative execution. All these factors can be combined and used to optimise future DRTV media buys.

I’ll second that B2B emotion

Published / by Simon Foster

the-man-in-the-chair-mcgraw-hill-885x1024For some critics, B2B advertising is too rational and doesn’t contain enough emotion. If that’s the case in 2014, then something’s changed since McGraw Hill ran this, perhaps one of the most famous B2B ads in the 1960’s.  This ad is 100% B2B and yet it is stuffed with emotion.  It plays on fears of the unknown, making mistakes and it reinforces the need for brand trust. It makes a case for “business brands”.

So, what’s happened?  B2B advertising has been “over rationalised” as marketers and agencies have reinforced the idea that business buyers and purely rational buyers who base purchase decisons on nothing but facts and figures. We all know this isn’t true; business buyers are you and me (i.e. consumers) when we are at work. When we make a B2B purchase with someone else’s money, whether it’s a photocopier, a USB stick or a media deal with an online ad network, we need both rational and emotional reassurances that the decision we are making is a good one and will not be a let down to others.

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 reach and frequency of an advertising campaign. As TVRs build so do reach 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.

Would you give your agency this brief?

Published / by Simon Foster

 

 

 

 

 

 

 

 

 

 

 

A few aspects of this brief make it both remarkable and very interesting reading:

  1. Mick sends Andy “2 boxes of material which you can use, and the record”. In other words some pictures and the product.
  2. Mick adds “I leave it in your capable hands to do whatever you want”. Total trust. But then, not every agency copywriter or art director is Andy Warhol.
  3. Money is almost incidental.
  4. Mick warns Andy that he may be chased to deliver, but also advises him to ignore the chaser. That puts account management in their place.

I can only conclude that clients give their agencies such complex briefs because a) they don’t think their agencies really have to creative talent to deliver what’s required and b) they are convinced they are going to get something they don’t like. In other words, the brief becomes the client’s insurance policy document. Funny, that is what they often look like.

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 and Media Planning Books

Published / by Simon Foster

Here’s a selection of must-read selection of adverting media planning and buying books for those working in advertising and communications strategy.

If you click the link you can find the book on Amazon.

  1. The Communications Challenge: A practical guide to media neutral planning A practical guide to communications planning. Becoming quite collectable. Even I’m in it.
  2. Media Planning: A Practical Guide, Third Edition (NTC Business Books) This is a great place to start with good accessible coverage of all the basics in media planning and buying. Jim Surmanek.
  3. Advertising Media Planning, Seventh Edition Sissors and Baron offer a comprehensive run down on all aspects of the media process, a great practical book.
  4. Media Planning & Buying n the 21st Century: Integration of Traditional & Digital Media US handbook of media planning and buying covering both traditional and digital channels in a comprehensive way. Very good practical read. By Ron Geskey
  5. The Media Handbook (Routledge Communication Series)The Media Planning Handbook is another comprehensive practical guide to media planning. Again, a US title so leans to how the US media market works. By Helen Katz.
  6. The Advertised Mind: Groundbreaking Insights into How Our Brains Respond to Advertising by Du Plessis, Erik (2005) Hardcover Interesting look into how advertising works from another practitioner, Erik Du Plessis
  7. Advertising Effectiveness: Findings from Empirical Research This is a serious tome on understanding advertising effectiveness. Not a high profile book, but actually one of the best books on advertising effectiveness you can buy from Giep Franzen.
  8. Effective Advertising: Understanding When, How, and Why Advertising Works (Marketing for a New Century) A comprehensive “meta study” of research into advertising effectiveness from Gerard Tellis.
  9. How to Do Better Creative Work (Prentice Hall Business) I worked with Steve Harrison for a couple of years. A remarkably understated authority on developing creative work.
  10. 101 Contrarian Ideas About Advertising: The strange world of advertising in 101 delicious bite-size pieces Contrarian thinking from Bob Hoffman who revels in challenging the industry’s status quo and accepted wisdoms. And he’s often right. Practitioner.
  11. My Life in Advertising and Scientific Advertising (Advertising Age Classics Library) As far as I know this is the only pre-war (and I mean WW2) book on advertising that is still in print. That’s probably because it was written by a copywriter who was paid by how much he sold. And he was paid a lot.
  12. Sexy Little Numbers: How to Use the Data You Have to Increase Sales and Grow Your Business at Virtually No Cost A lot of business and marketing problems can be better understood and even solved with numbers. Not many people realise this. Maex points the way.
  13. Disruption: Overturning Conventions and Shaking Up the Marketplace (Adweek Magazine Series) TBWA used the disruption model for new business for about 20 years, as far as I know they still do. And so do many others. You still can’t attend an industry event without hearing the word “disruption”.  If you want to chat with some authority, you will need to read this book.
  14. Ogilvy on Advertising This is still a classic from David Ogilvy and his autobiography Confessions of an Advertising Man is also a great read.
  15. How Brands Grow: What Marketers Don’t Know Many brands suffer from problems that are defined at the category level, but not many marketers or agency staffers understand how categories behaviours actually work. They naturally prefer to look at consumers. Byron Sharp’s How Brands Grow shows that you must look at both.
  16. Predatory Thinking: A Masterclass in Out-Thinking the Competition There have been mixed reviews for this selection of Dave Trott blog posts, but as far as I know non of the reviewers have achieved anything like the reputation he enjoys.

Facebook ‘likes’ don’t increase brand preference or sales

Published / by Simon Foster

Here’s an iron for the fire: “Facebook ‘likes’ do not cause increased brand preference or increased sales so marketing campaigns designed to increase the number of ‘likes’ are unlikely to increase brand preference or sales.”

I was moved to develop and explore this hypothesis after reading an article on the real cost of brand building in social media by Mark Ritson who is a professor of marketing, formerly at London Business School. Ritson is renowned for injecting some good solid critical thinking into the often sloppy logic of marketing. Ritson argues that whilst there may be apparent relationships between brand preference, share or sales and Facebook ‘likes’, the relationship between these factors is unlikely to be causal.  Causality is important. It’s about understanding the the cause of relationships between variables in order to assess their significance; just because there is a relationship between two things, it doesn’t mean that one of them causes the other. To say with certainty that one factor drives the other, causality has to be proved.  Ritson argues that causality is being overlooked or even ignored in studies that set out to consider the value of Facebook likes in relation to brand performance.

There have been a number of studies which show that the most popular brands have the highest numbers of Facebook fans. but this shouldn’t come as a surprise to any marketer with more than a handful of brain cells.  Common sense tells us that the most popular brands are likely to have the most Facebook ‘likes’ because they have higher numbers of users and advocates.  But we need to remember that these  ‘likes’ are an expression of pre-existing brand preference and not a cause of it. Moreover, when studies try to assess the financial value of a Facebook ‘like’ they find that Facebook fans spend more on a product than Facebook users who are not fans.    One study found that Facebook likers of Starbucks coffee spent more in store than non-likers.  Well that shouldn’t come as a surprise either. Those consumers who prefer certain brands are likely to spend more money on those brands – after all isn’t that the whole purpose of consumer marketing and the process of building brands?

In both cases, there is a relationship between Facebook likes and brand performance but the relationship is caused by the strengths of the brand that almost certainly existed before the impact of Facebook. The Facebook like is not the cause of brand preference but simply a reflection of it.

If we use logic to extend these observations into prediction we can say that if likes do not cause brand preferences or increased sales, then strategies and campaigns that seek to increase the number of likes will not increase brand preference or sales. However, the predictive power of logic doesn’t stop there; brand owners developing social media strategies to grow likes risk creating “false-positive” brand advocates. These false-positives are consumers who have no genuine relationship with the brand or product but simply click the like because they are incentivised to do so. Corralling opportunistic consumers into Facebook fan pages may actually skew the brand’s Facebook page and community away from genuine fans. Worse still,  subsequent eCRM activity to develop these prospects may prove to be far less fruitful than initially anticipated.

Marketers, Ritson argues, would do well to remember the factors that really did build their brand preference.  These are likely to be product quality, availability, consumption experience and visual branding. They might also bear in mind the fact that research company TNS says that 61% of Britons do not want to engage with brands via social media and suggest that much of what is being build by brands in the social media space amounts to little more than “digital waste”. I wouldn’t go that far, but I would say that brands need to tread carefully when investing in these areas.

When we plan any social media activity at Teqtonic our objective is always to add new value to a brand in some way. That invariably involves strategies that take the consumer and the brand beyond the territory of the like. If you are going to have a meaningful social media strategy you need to think in CRM terms.  Some of your brand advocates may be gathering as a segment within certain social media environments. You need to be there to recognise and respond to their statement of loyalty and preference in a relevant way.  When you do meet up with them, make sure you give them something that reflects their commitment to you. And whatever you do, don’t mix these high value customers with competition chasers who’ll move as quickly to the next brand as they did to yours.

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.

Social Media Metrics Made Simple: Focus on Sales and Customers

Published / by Simon Foster

I am amazed that so many people spend so much time defining and discussing social media metrics. Why? Because the answers marketers (and shareholders) want are very, very simple. Marketers want only one thing from marketing budget investment. Marketers want sales – sales are key; almost everything else is a proxy for some point on the journey to the sale. Make no mistake, companies and marketers are working to deliver sales. Sales are the elixir of life for commerce. Sales drive economies of scale and increase profitability. Sales are the business. In fact, sales are business. Period.  And despite this,  the ever expanding list of social media metrics contains virtually no hard commercial measures. Here is a list of 30 popular social media metrics I am aware of as of today:

  1. Active network size
  2. Amplification rate
  3. Applause rate
  4. Bookmarks
  5. Channel views
  6. Comments
  7. Downloads / Installs
  8. Email subscribes
  9. Engagement
  10. Fans
  11. Favourites
  12. Feed subscribes (RSS)
  13. Followers
  14. Following
  15. Forwards
  16. “Influence”
  17. Klout score
  18. Likes
  19. Lists
  20. Mentions
  21. Reactions
  22. Re-Tweets
  23. Sentiment
  24. Shares
  25. Subscribes
  26. Tags
  27. Tweets
  28. Tweet Reach
  29. Tweet Velocity ( I like this one!)
  30. Wall posts

There is a big problem here. Most of these metrics have little or no commercial meaning. What for example is the value of a “Like”? A like is no more than a mouse click on a web page. It requires no effort and takes a fraction of a second to perform.  A like requires no trade in information between the user and the item being liked. Anyone can do it and it signifies virtually nothing. Even the popular ‘email address for download’ exchange has limited value; I have downloaded a number of papers from companies it’s unlikely I’ll ever do business with – even though I am sufficiently interested in the content being provided to exchange my email address for it.

It’s ironic that whilst social media commentators and practitioners are busy churning out metrics with no real commercial meaning, traditional media is moving away from proxy data like coverage and frequency and into measuring and proving commercial behavioural change (fancy talk for sales) resulting from media activity.  It seems to me that social media evaluation has slipped into reverse gear and no none has noticed.  If social media is to advance its cause it needs to show either a direct or indirect link to more commercial measures like sales and customers. Is that possible? Well yes it is and it’s relatively straightforward.

All communication and media channels including digital media feed into sales funnels. Digital media traffic is the most measurable of these and can be tracked and measured in great detail from clicks to basket values.  This means it is possible to measure the commercial value of traffic generated by social media. If your Facebook page is generating traffic you can identify it in your inbound traffic logs. And if you can track the traffic through to sales baskets you can measure the sales generated by Facebook. And then you can start looking at your social media ROI numbers. If your Facebook page is referring 1,000 sales a month with a profit of £10 per sale, and costing only £1000 per month to manage and maintain, it’s making a valuable contribution to your business. If other hand it is producing 100 sales per month with £10 profit per sale and costs £10,000 per month to manage and maintain, then you are throwing money away.

The truth is that many social media variables only exist because of a strong supply side data push. Social media metrics are easy to produce; be they likes, friends, tweets, connections or channel subscribers they’re just descriptive data. At worst these metrics are a distraction for marketers. At best they are a rough proxy that needs to be calibrated with more meaningful commercial data. What marketers and business leaders want is sales, share, customers, customer value and profit. If social media sticks with likes, friends and subscribers sooner or later it will have to show what they mean.