TV attribution modelling is an analytical process used to assign web or phone response to TV spots. When this analysis has been undertaken it is possible to aggregate all the spots and matched response into a database and report which TV channels, days of week, times of day and creative edits are most responsive or cost-effective. This reporting allows TV buys to be optimised to maximise short-term TV advertising ROI.
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:
- Enter the brand address directly into their browsers or
- Click on a paid search (PPC) link or
- Click on the top organic link.
Reflecting these behaviours, most brands look at:
- New user traffic through direct browser entries
- New user traffic through paid search
- 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:
- The seasonal base
- The trend
- The weekly base
- The day of week base
- The hour of day base
- The time of the spot transmission
- The volume of audience delivered in the spot transmission
- The time the response is received
- 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