Traffic Sources , Attribution , Budget Allocation
What to do when your brand client tells you they want to cut their TV budget
1st December 2022
A recent ISBA report found that 39% of marketers plan to reduce their TV budget in 2023.
In the vast majority of cases, this decision is not based on data but is rather based on lack of measurement data for TV advertising. Marketers who are under strong pressure to cut budget, cannot prove the effectiveness of their TV budget. In contrast, their digital counterparts use data to calculate the ROI of their campaigns and therefore are under less pressure to cut their budgets.
The problem is that in many cases the decision to cut the TV budget is just wrong and can cause the brand to create a sub optimal media plan that will actually harm them both in the short term and in the long term.
The following post is meant for media agencies that want to make sure their brands take the right decision and maximise their media performance as well as brand managers who need to feel confident that they are allocating their budget in the right place.
Agencies need to ask brands 4 main questions in order to qualify whether this is the right decision:
Question 1: On what data is the decision based on?
More often than not you will find that the decision is based on lack of TV data that prevents the brand from building a case to maintain or increase the TV budget.
Question 2: What was the uplift of you received from your previous TV campaigns?
This is a critical thing to understand before taking any decision regarding the TV budgets – without knowing the uplift from TV, it is impossible to calculate the ROI of the campaign and assess whether it is worthwhile cutting budgets.
Some online brands use “response windows” to measure the effectiveness of TV campaigns – they look for spikes in the website traffic and attribute them to the last ad that appeared on TV. These probabilistic models can catch only 1% of the traffic that should be attribute to TV and therefore give a wrong view of the effectiveness of the TV campaign. Furthermore, these models cannot really isolate the uplift the brand got from TV.
The only way to measure TV uplift is by using Single Source data and creating a natural, retrospective A/B testing by comparing the purchase rate of the exposed and non-exposed groups before, during and after the campaign.
The above graph shows the increase in sales of Coca-Cola that was driven by their August 2022 TV campaign– for each 10M TV impacts, the purchase rate increased by 3.3%. By using this data, Coca-Cola can now calculate the ROI of their campaign and understand whether it is worthwhile cutting, maintaining or increasing this budget.
Question 3: What were your site’s traffic sources?
Before deciding which budget to cut, online brands need to understand what actually drove traffic to their site. This is especially tricky because 99% of brands rely on tools such as Google analytics that are based on ‘last-click’ models. These models give 100% attribution to the last online click and are completely blind to the effect of TV advertising.
In other words, these tools show a skewed version of reality and can lead brands to take wrong decisions.
Single Source data solves this problem by looking at the entire user journey and attributing actions to each touchpoint in the journey including TV.
The above graph shows the traffic sources for people who visited the Airbnb website – this looks at the entire journey and shows a very different picture than the traditional last click models – in this case, TV is responsible for almost 45% of Airbnb’s traffic – cutting the TV budget may have a strong negative effect on website visits.
Question 4: What was the effect of TV on online ad clicks?
One of the things we hear from our clients over and over again is how they reduced or stopped TV advertising and started to see their online CPAs soar. The reason for this is that online ad clicks are strongly influenced by TV advertising – Single Source data shows that in the majority of cases, people who click on an online ad were exposed to an average of 50% more ads in the week prior to clicking compared to the general population.
This means that the TV creates the interest and online closes the loop and brings the user to the website.
The above graph shows that people who clicked on a Compare the Market Google ad were exposed to 31% more TV ads in the week before clicking on the ad compared to the entire population (4.07 vs. 3.1 ads).
While in some cases it makes sense for brands to cut their TV budget, any decision needs to be based on hard data and on lack of measurement data.
Only Single-Source data that sees the entire journey to purchase can give brands data on which they can confidently base their decisions.