If you’re a media planner allocating budgets it’s impossible to do this properly without an appreciation and understanding of cost per thousand by media channel.

Calculating this metric is not difficult – you simply divide the cost of an ad by the number of audience it will deliver.

Let’s work through some examples:

• A radio station tells you an ad will costs £500 and be heard by 50,000 listeners. Good value or not?  Let’s divide £500 by 50 (that’s 50 thousands). Answer £10. The range you’d expect to pay for radio is between £1 and £5 so £10 is very expensive.
• A newspaper rep tells you a half page ad will cost £10,000 and run on print run of 1 million. Let’s do the calculation: £10,000 divided by 1,000 (thousands) is £10. In press you could expect to pay between £5 and £15 for a half page so a £10 CPM isn’t bad.
• A DM data owner tells you it will cost £20,000 to buy a list of 10,000 names. The CPM is £2,000. That’s extremely expensive.  Even the very highest quality premium DM lists rarely cost more than £500 CPM.

Accounting for the size of format of your ad

Some ad are bigger than others, in radio and TV some are longer than others. In digital display some are static images, some are animated and some are video. The size and format of an ad can have an impact on the CPM.  Let’s look at a press example:

A newspaper sells 500,000 copies a day. A half page ad cost £5,000 and a quarter page ad costs £3,000. The CPM on the half page is £10 whilst the CPM on the quarter page is £6. By using a quarter page you are reducing your CPM from £10 to £6. Across a large campaign that’s a big cost saving.

How CPM affects reach

Assuming you have a fixed budget of £200,000, you will reach 20m people with a half page campaign (£200,000/10*1000), but you’ll be able to reach more than 33m with a quarter page campaign (£200,000/6*1000).

The post How to calculate cost per thousand (CPM) in paid media first appeared on Marketing IQ.

What is the definition of a TV audience impact?

An impacts is:

1. One person
2. Seeing one ad
3. At one time

Example:

If 5 million people watch an ad in the centre break of a TV programme on Thursday evening at 7.30 pm that is 5m impacts.

If the exactly same 5m people watch another ad at 8pm, that is another 5m impacts.

Across the two ads, 10m impacts have been delivered for the client – but remember, it doesn’t mean 10m new people saw the break. In this example, 5m people saw the ad twice.

Are there different types of impact?

Yes, impacts can be counted by different demographic groups. So an ad might deliver:

• 5m Adult impacts
• 2m ABC1 Adult impacts (i.e. 2m of the 5m Adults were ABC1 Adults)
• 1m ABC1 55+ Adult impacts (1m of the 5m Adults were ABC1 Adults who were aged 55+)

Why are impacts important?

TV is traded on cost per thousand audience rates. So if you have a budget of £500k and you are buying CPT (Adults) is £5, then the number of impacts you will be able to buy (using a 30 second ad) will be

(£500,000 / 5) x 1000 = 100 million impacts

We multiply by 1,000 because your TV buying rate is a cost per thousand impacts.

You can learn how impacts are measured as TVRs here

The post What is a TV Audience Impact? first appeared on Marketing IQ.

Background

Although TV attribution is growing in popularity, it is not new. Direct response advertisers have been using simple ‘spot matching’ routines for around 20 years. These spot matching routines typically used a 5-10 minute window or ‘response curve’ which followed a TV spot transmission to match the spike of phone traffic that followed a spot transmission back to that spot. As phone response was usually received on one unique number used for each TV campaign it was a relatively simple task to match that single file of time stamped response data to a spot transmission schedule.

Using attribution to understand how TV drives web traffic

Today, as more and more brands invest in TV advertising to drive web traffic, the focus is on using attribution models to explain how TV spots drive web traffic. However, this is a much more complex area than analysing phone response.

The main challenge is that all brands receive web traffic from a wide variety of sources, 24 hours a day, seven days a week and when paid media advertising is either running or not running. Just a quick look at a Google Analytics report will show you how many sources drive your web traffic:

• Organic search
• Paid search
• Direct visits
• Referrals
• Affiliates
• Display campaigns
• Paid media campaigns
• Revisits

Which traffic do we analyse?

Let’s look at what TV viewers do when they see an ad. They are likely to do one of three things:

1. Enter the brand address directly into their browsers or
2. Click on a paid search (PPC) link or
3. Click on the top organic link.

Reflecting these behaviours, most brands look at:

1. New user traffic through direct browser entries
2. New user traffic through paid search
3. New user traffic through organic search

You will notice that there is a focus on new users. Clearly, new users are of more  interest to brands targeting new customers.

Identifying the baseline web traffic

Identifying the base is complex. This is because a campaign can have a number of baselines depending on the time sample you are looking at. Each hour of the day may have a given level of “natural” traffic. Each day of the week may also have a given level of traffic (this is often the case) and weeks and months may have repeating patterns. Over and above this the brand may have a long-term upward trend in web traffic where each week increases slightly on the previous week. All this means that applying the same baseline to all your analyses will make your results flawed.

The answer to this problem is to use a model which incorporates different baselines based on different times of day, days of week etc.

How does the TV spot matching algorithm work?

Algorithms are mathematical equations that allow a number of variables to be considered simultaneously. So, for TV attribution we need an algorithm that considers the following:

1. The seasonal base
2. The trend
3. The weekly base
4. The day of week base
5. The hour of day base
6. The time of the spot transmission
7. The volume of audience delivered in the spot transmission
8. The time the response is received
9. The way the response distributes over the time period following the spot transmission (the curve)

With this algorithm in place it is possible to calculate a probability that a new web visit that occurred within say 7 minutes of spot transmission was caused by that spot transmission. This process is then repeated across all the spots in the campaign until a probability for all new traffic response to be driven by the TV activity has been calculated.

What type of reporting is available through TV attribution?

Because we have the attributes of the spot (TV station, date, day of week, time of day, type of break, type of creative etc) we can report all these metrics on an aggregated basis. So, for example, we can say Fridays are the most responsive on a % response rate basis, or the most efficient on a £ CPA basis. We can also say which channels and time of day or most responsive.  With this insight we are able to optimise the TV buy to focus budget into the station, days of week and times of day that will deliver the highest ROI.

You can read more about optimising DRTV campaigns here

The post What is TV attribution modelling? first appeared on Marketing IQ.

1. TVRs (or GRPs, or TRPs) – TVRs or TV rating points are a way of sizing target audiences across both demographics and geography. 1 TVR is 1% of a target audience universe as defined by both demographic and geography. So, 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. TVRs are also a planning currency that allow us to calculate the reach and frequency of media campaigns.
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 your 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 your 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.

For information on our TV Planning and Buying training course please follow this link

The post TV Media Planning Terms – calculating media reach and frequency using TVRs first appeared on Marketing IQ.

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 typically 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 these pages

Thanks to Ivan Clark for comments.

The post How do TVRs build media reach and frequency? first appeared on Marketing IQ.

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 very rare in DRTV. It’s usually delivered through a combination of an extremely powerful ad, a very strong product, 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%

Find our more information about our DRTV Media Planning, Buying and Evaluation course please follow this link

The post DRTV Response Rates first appeared on Marketing IQ.

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…

For information on our TV Planning and Buying training course please follow this link

The post What is a TVR? first appeared on Marketing IQ.

“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.

The post What is programmatic advertising? first appeared on Marketing IQ.