How do big companies assess whether their TV ads were useful or not?
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Family owns a huge construction business..
Target a specific station at certain times. The TV stations (Fios,spectrum) are able to track and group together age groups based off simple account info. so if we want to increase leads for decks, we already know from experience that most people looking to spend 15-30k on a deck are usually above like 45-50 and don’t plan on moving soon.
We know it succeeds based off increased sales and quality sales leads (closing ratios, average sale etc) after we changed and targeted a specific service or product towards a certain demographic. Of course there will be a few strays, which is fine, but for the most part companies know exactly who they are advertising to
Sometimes the business will have a customer survey that asks how the customer became aware of the product, in order to try to determine which marketing campaigns are working.
Some ads will have specific promo codes offering a discount. The real purpose of these promo codes is to pinpoint which channel and show the customer was watching or listening to at the time.
Other than that, they will just look at sales data. The data doesn’t specify which marketing campaign worked, but they can try to line up the graphs with the schedule of ad slots. For example, they can look at the February sales graph and see if there’s a noticeable increase in sales in the week after their ad played during the Super Bowl.
Of course, TV ads rarely work, because the vast majority of people treat them as bathroom and snack breaks.
This is what the Nielsen rating (and other similar types of data gathering) is for. It’s a survey of ‘typical’ US households, that determines what people are watching at any given time.
TV stations then use that data (not just how many are watching, but also their demographics) to sell ad spots. So ad spots can be priced based on the amount of viewership, and can also be used to target specific kinds of people.
So part of the answer to the question is that companies can actually choose how many eyeballs are exposed to their ad, based on the prediction for that show and time slot. And they get the ‘real’ results later.
And the second half is that they compare it to their sales/revenue figures. If they ran an ad at a specific date, and it corresponds to a big spike in their sales (accounting for delay), they’ll know that the ad was effective.
I work in the industry and can tell you it depends on how advanced the marketing team is. The less sophisticated way is running “tests” where you place ads in one market and not another and see whether it made a difference in sales. The problem with that is there are so many other things that could impact sales other than ads – maybe your competitors ran a sale in one market and not another. Maybe you also had digital ads running. Maybe your product is weather dependent and was nicer in one market and not the other. Maybe your sales people are better at their jobs in one market.
That’s where more advanced techniques like multi-touch attribution or mixed media modeling comes in – basically using statistics to estimate how much each possible explanation (ads, competitors, weather, sales staff, inflation, etc…) likely impacted your sales.
(Edited to include an attempt at an actual ELI5 answer since people seem genuinely interested in the topic:)
Let’s say you have a lemonade stand that you run in your front yard every Saturday in the Summer. Everyday you write in your diary about your day with all the details about the lemonade stand including how many customers you had, what the weather was like, if there was a BBQ or block party on your street and whether that annoying girl Suzy a few blocks down was also running her lemonade stand (she uses powdered lemonade mix not fresh lemons btw). After one summer I looked back at all my notes and learned that on a typical summer afternoon I can expect 20 customers. When it rains I only get around 5 customers. When Suzy is also running her stand I get around 15 customers. When there’s a BBQ I get around 30 customers! Knowing this information I could start to estimate how many customers I can expect on any given day. Now I had the idea to start putting out posters at the front of my neighborhood that say I have FRESH squeezed lemonade. Ever since then I average about 30 customers on a typical summer afternoon, that’s 10 more than before! When it rains I get about 10 customers now and when Suzy is out I get about 26 customers. So I can estimate the posters (advertising) is helping make up for the competition and the rain by about 5-6 customers and gives me around 10 more customers on most days.
As someone who works in sales/marketing, I can tell you that most marketing campaigns (where we display the client’s content across webpages) involves “placing a pixel” on landing sites like their homepage, a product page, etc. We’ll then track how many targets were reached with their ads, how many clicked on the ad (impressions) and how many went to 3 or more pages on the client’s site (engagements).
It’s something which can be proven to have an effect on the small scale, and on the larger scale it becomes much harder to prove but can be assumed to be having SOME effect.
A big example was an isolated experiment by the tobacco companies in the US. They picked a few towns and ran a series of experiments. In a normal town that they all spend a fair amount of money on advertising in, they all had roughly equal shares of the market. Each company was assigned one town that ONLY they would get to advertise in, the others would stay out. And what they saw was that after a few months about 80-90% of the market was JUST buying the advertised brand even though the other brands were available, just not advertised. Then they doubled up with each experimental town shared by two advertisers. After a few months, they both had an almost perfectly even split of the same 80-90% of the market.
And the biggest experiment of all was a set of towns where all the companies entirely removed all advertising. Their products were still on the shelves, but no billboards, tv/radio ads, no posters, etc. And the result? The same as when they were all spending loads of money on advertising, a near even split of the town’s market for tobacco products.
So the result in these small scale tests was that the tobacco companies realized they had a bit of a game of “Prisoner’s Dilemma” on their hands. If everybody advertised (and spent money) then everyone got an equal share. If NOBODY advertised (and saved money) then everyone got an equal share. But if ONE company advertised when nobody else did, that company won the lions share of the market.
The result?
The tobacco companies immediately stopped lobbying against laws meant to restrict tobacco advertisements. If it was legally enforced that NOBODY could advertise, they didn’t have to worry about anyone breaking ranks for individual profit.
One thing many of these answers is missing: data science. Companies will build media mix models which essentially take in media spend as one set of many sets of variables that are used to predict sales quite accurately. Based on the coefficient for TV advertising spend in that model, you can establish a return on advertising spend.
Or better yet, run a test with a holdout region.
Correlate GRPs or essentially reported viewership of ads with sales uplifted above a normalized baseline (ie. What would sales be if all else were equal, accounting for changing weather or price) the more accurately you can link a sale to someone seeing (or not seeing an ad) the better.
Edit: But as someone else mentioned, most companies seem to think that the artistic perception or resonance of their ads is enough. It isn’t.
I am a data scientist and work in a market research company, and we exactly deal with this shit. We collect data ( approximation) on how many people in the locality did watch the ad when it was aired and how many people ended up purchasing it. It’s usually geographical computation. You take a region and you calculate since, calculating over ,let’s say a locality would have high chances of it being null.
Nielsen ratings + sub second telemetry on subscription streaming data.
They still exist after having adapted to digital streaming and subscription platforms. Now they have second grain details of what is being watched which they sell to digital platforms as a data service.
The subscription platforms have even finer grain details and can use the Nielsen data to correlate switching streams to the point in time of ads. So they can model whether people watch the ads.
Source: Telemetry Data Engineer.
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