Getting OMNI-channel insights is extremely important for retailers. It is key to measure online sales ánd offline sales, to determine the success of your online marketing strategy. In some cases, offline sales can outweigh online sales up to 4 times. However, many retailers have a blind spot when it comes to measuring offline sales because of measurement difficulties.
This article will show a unique experiment involving a fake dummy website, to get a better understanding of Store Visit measurement.
Google Store Visits are awesome, yet adoption is low
When it comes to Paid Search, Google Store Visits can shed a light on O2O (online to offline). Google Store Visits measures the amount of people that go to a store after clicking on an ad. Awesome right? Well, at iProspect we noticed advertisers have doubts about the incrementality of the measured Store Visits: i.e. can the Store Visits that are measured really be attributed to the Paid Search efforts? Would people not have gone to the store regardless of clicking on that ad? These are fair doubts.
To uncover the truth, iProspect developed a test together with Action (large retailer in Europe) to determine the incremental value of the measured Store Visits. The results of this test have had a huge impact on determining the profitability of Paid Search for Action. In this article, we will go into the details of this test, and why this was a game changer for Action.
> Have a look at the Case Video:
How does Google measure Store Visits?
Google Measures Store Visits in the following way:
Google tracks users who are logged on into their Google account (Android, Chrome, Youtube etc.);
With GPS, the user location is tracked;
Via Google My Business, Google knows the GPS coordinates of all offline stores;
When a user clicks on an ad, and then walks into a store, this is tracked as a Store Visit;
This data is then extrapolated for the entire population;
Google validates and advances the measurement by WiFi triangulation, Google Surveys (“did you recently go to store X?”) and by physically mapping stores.
Because the data has to be extrapolated, Google needs a large sample size before it will show results. This means that certain thresholds have to be met.
O2O measurement for Action
Action is a large retailer in the Netherlands, Belgium, France and Germany. O2O (online to offline) is particularly important, because Action has no webshop. So the main goal is to drive Store Sales. Also, Action is a discounter with low order values and with a high density of stores. This leads to millions of people visiting the Action stores annually.
The problem of using Google Store Visits data
Paid Search for Action showed high Store Visit rates. But how optimistic should we be? Millions of people visit the Action store annually already. So a part of the people who are measured visiting an Action store in Google, were going to visit that store regardless of clicking an ad.
The question is: how much should we discredit the measured Google Store Visits? By 25%? 50%? 75%? This has a huge impact on performance and budget decisions.
Below you can find a number of scenarios for a made up retailer. Each scenario has a different incrementality percentage, which could be realistic to occur:
Now imagine your CPA target is €20:
* In Scenario 1 (25% incrementality of Store Visits), this would mean that you are burning a lot of money (actual CPA is €40); between €2.000.000 to €3.000.000.
* In Scenario 3 (75% incrementality of Store Visits), this would mean that you are missing value. You are one third behind your target, and you are missing volumes against competitors. You could profitably be spending between €2.000.000 to €3.000.000 more.
In order to hit the sweet spot in Paid Search advertising, the difference in budget between scenario 1 and 3 is up to €6.000.000! All three scenarios are likely to occur. What matters is that you know what applies to your business.
To find out what the incrementality rate is of Action, we thought of the following experiment.
The design of the experiment
For this experiment we started with the following research question:
To what extent can measured Google Store Visits be credited to Paid Search?
So the goal was to find out what the incrementality is of the Store Visits that are measured. To test this, we wanted to compare the Google Store Visit rate of two groups of users:
Users exposed to an Action ad (control)
Users not exposed to an Action ad (experiment)
By comparing the Store Visit rates of both groups, we could then calculate the incrementality of Google Store Visits for Paid Search.
Different website, same store visits?
To create a non-exposed group, a group of users would have to be tracked, who were not shown an Action ad.
To do this, we created a dummy website: a website completely different from Action.com. This website had a URL and style that was implying that it was a discount retailer, while we made zero connections to Action. We advertised on exactly the same non-branded keywords for the exposed and the non-exposed group, to make sure the conditions were exactly similar.
We ran the experiment in an Action AdWords account. This way, we could measure the Google Store Visits for Action for the exposed group and the non-exposed group.
Below you can find a visual representation of the test:
The experiment result for Action was quite spectacular; the incrementality of Google Store Visits is more than 2/3rds! This means that over 66% of the Google Store Visits that are measured for Action (non-branded) could completely be attributed to the Paid Search efforts.
Before this test, we did not know by how much we should discredit the Google Store Visits. Now we know exactly by how much. For Action, this had the following effects:
It convinced stakeholders in the organization who (justly) had doubts about the incrementality of Google Store Visits;
We can make much better decisions about the profitability of Paid Search, and to some extent about Online Marketing as a whole;
We can make better use of Google Store Visits to optimize and steer our Paid Search campaigns.
When you know your incrementality rate, you can calculate the value of each Store Visit. We did this in the following way:
Based on this information, you can calculate the value of each measured Google Store Visit. You can then assign this value to each Google Store Visit.
Does your organization already have an OMNI-channel strategy?
If you know the value of each store visit, you can combine the Store Visit Value with your Webshop Sales Value. This way you can optimize towards a total Online + Offline revenue. Now you can start an OMNI-channel strategy.
If you start steering on Online + Offline revenue, it will have awesome effects but also big implications on all your Paid Search efforts. Expect the following things to happen:
Your budget will be spent differently over your devices (because mobile often has a higher Store Visit rate);
Your budget will be spent differently over your keywords (because some keywords have a much higher Store Visit rate);
You will be able to expand your keyword scope (for example local keywords);
Your ad-texts will be a combination of strong brick and mortar USP’s, and your Webshop strengths;
Your targeting will more closely correspond with opening hours, because weekends and business hours could grant higher Store Visit rates;
Your location and radius targeting will be more sophisticated; bidding higher in a closer proximity of the stores;
You will be testing more advanced features (e.g. GEO ad customizers).
Google Store Visits can be a very useful feature for your business. However, without incrementality insights, it might be hard to get a grasp on the real added value. For Action, this experiment helped to get closer to the truth, get buy-in from the organization and work towards an OMNI-channel strategy.