- Marketers like Airbnb and eBay are increasingly using scientific tests to run different ads across different segments of audiences and then using the results to shift budgets accordingly.
- Such tests help to measure incremental returns on a particular advertising campaign or even an entire media channel using test and control groups.
- Facebook too is pushing the concept of measuring advertising based on its ability to drive business outcomes as an industry-wide initiative along with multiple third-party researchers.
Airbnb has always relied on data to drive its marketing. But testing how well different ads perform with different audiences and on different platforms – and then ideally shifting budgets to the ads or tactics that were performing the best – wasn’t part of the marketing mix until two years ago.
Today, this kind of scientific ad tracking, which seeks to measure incremental returns on a particular advertising campaign or even an entire media channel using test and control groups, is standard practice at the short-term home-rental brand.
“It’s important to understand with as much precision as possible how many extra dollars in booking value we bring to the business with each extra dollar we spend on a platform,” Airbnb’s director of data science, Alok Gupta, told Business Insider. “It has become an increasing priority because we have a finite marketing budget and want to maximise ROI.”
By comparing different results from ad campaigns delivered to distinct groups of consumers, Airbnb – like many big brands – has been able to calculate lifts in ad performance and justify marketing spend on specific campaigns or channels.
This kind of testing is particularly potent on Facebook, given that it has rich sets of data (like age and location) on over 2 billion users worldwide and is able to easily deliver different messages to different users at a given moment.
Indeed, over the past several years Facebook has been vocal about pushing the ad industry to focus on how ad spending through techniques like incrementality drives tangible business outcomes (like, say, sales).
Naturally, the tech giant believes it has a key advantage on this front over competing traditional and digital media: that it has more data on its users than anybody else and thus is more capable than other media vehicles at proving it can drive real business results for brands like Airbnb.
It is touting incrementality as an industry-wide initiative and has also set up several partnerships through its Lift API. Partners such as Oracle Data Cloud and Nielsen Catalina, for example, help measure offline sales from Facebook ads, and vendors like Visual IQ and MarketShare help measure multitouch attribution.
The social-networking giant claims that advertisers are catching on too and that it saw a 40% increase in the number of lift tests advertisers ran on Facebook in 2017 compared with 2016.
For example, last year Airbnb wanted to target a bunch of ads highlighting family-friendly destinations to people on Facebook. Using the platform’s tools, the brand identified its target audience and divided them into two sets people – the first set was shown the ads, while the second wasn’t.
The company declined to share specifics but said it found that the group that was shown the ads converted significantly more and booked more rentals. Armed with that knowledge, it was able to double down on its ad investment.
With efforts like these, Airbnb has been able to estimate the marginal efficiency of its channels relative to one another and shift budgets accordingly. The brand has doubled its efficiency on Facebook versus this time last year, according to Gupta.
In fact, effective measurement has been so instrumental in helping Airbnb make business decisions that its marketing data-science team has grown tenfold in the past 18 months, Gupta said. The team is a hybrid mix of data scientists, marketers, creatives, and engineers.
“We advertise across so many channels and campaigns,” Gupta said. “There is a lot of data we need to ingest from third parties, and it helps to have a dedicated internal team to match that with the internal data we already have.”
Measuring incrementality also helps fill in the gaps that crop up when brands focus simply on which ads drove immediate action, which is often based on direct signals from consumers, like the final click that led to a purchase.
This focus on ad budget “attribution” – i.e., which parts of an advertiser’s budget led to a consumer making a purchase – tends to overlook other interactions with a brand, such as emails or banner and search ads, said Tony Flanery-Rye, the senior director of global growth analytics at eBay.
“Did the presence of an unopened email make you recall the brand?” he asked. “What about appearing in a search result but not clicking? Or a display ad floating at the bottom on your favourite mobile game you play between meetings?”
Being more scientific about how different ad tactics perform can help “you identify the best scheme to match your specific customer behaviours,” he said.
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