Google Ads store visit conversions are the first nicely packaged tracking solution in the paid search space to answer questions about online to offline behavior, attribution, and ultimately, return-on-investment. Even in certain industries where in-store purchases are still common, like appliances or automotive, online research is a key influencing factor to these in-store purchases. For example, 58% of appliance purchasers research online before buying in-store.
Image Source: Value Walk
For click-and-mortar businesses with an online-to-offline commerce strategy, the store visit metric can reveal elusive business and consumer insights. While we already understand general reasons why a customer might be compelled to shop in-store (tactile needs, fit concerns, shipping costs, etc.), up until now, we have known very little about what type of digital advertising ignites an urge to leave the digital realm and enter the store.
Before I dive into several lessons we’ve learned from Google Ads store visit data, I’ll first give a brief background on Google Ads’ previous and current methods to track online to offline data.
Simply put, a store visit is a conversion metric that measures the users who have clicked on an ad and visited a store. Google calculates this metric by using the following methodology:
Google only reports data when it reaches a strict confidence level, and they only record aggregated anonymized data. So, while some of this data is derived from estimates and projections, store visit conversions do provide a degree of measurement that is beyond directional.
As a click-and-mortar business (or the party responsible for its advertising), you might find yourself wondering if and how your digital marketing efforts influence your in-store activity. With this new store visit conversion data, we have helped many of our click-and-mortar clients better understand how their online marketing efforts have successfully driven in-store purchases. Below are four common problems we have recently solved with our clients by applying this data from Google AdWords.
This type of information can tell you how far to cast your net in all of your marketing endeavors. It can also show you customer loyalty trends by neighborhood or city, which can inform your influencer strategy, your messaging, and even your inventory decisions. For one of our multi-location furniture clients, we found a surprising concentration of their customers travel 1-5 miles to visit their stores, and significantly fewer people drive less than a mile or greater than 5 miles.
This was a new data insight for us. We track online-to-offline indicators on our client’s site like visits to a store location page, but the 1-5 mile group had never stood out like this with any of our traditional metrics. In fact, the 5-10 mile group looked just as strong. How do we explain the difference? Our theory is that people who live within 5 miles of the store generally don’t need to reference a store location page for directions: they already know how to get there.
The only way to complicate online to offline attribution more is to add multi-device consumer activity to the mix! Having this data is especially valuable to businesses that rely heavily on e-commerce data to inform their device strategy. For a client of ours who sells shoes online and in stores, we found that while users who engaged with their site from a computer made the majority of online purchases, most of their store visits came from users who first engaged from a mobile device. Without the complete picture of in-store and online data, mobile would get discredited.
Ten years ago, you wouldn’t dream of buying a mattress without conducting the obligatory routine of going down the row of pillow-tops and memory foams at your local store and lying down on each well-worn pad to determine your ideal fit. Now, we are in a position as marketers where we know people will buy major furniture items online, but we need to gauge just how likely they are to do so. For one of our furniture clients, we lined up their products by conversion rate to determine how likely it was for someone to come to visit a store after clicking on a product-specific ad. We found that recliners have the highest conversion rate at 6.5%, and chairs and beds had the lowest conversion rates at 5.1% and 4.9%. From this analysis, we recommended to our client that he reorganize his showroom to feature recliners more prominently and consider testing online-only deals for beds and chairs.
This can be a fairly easy analysis if you just consider your e-commerce or offline data alone, but it doesn’t tell the entire story. You break down your store visits by store location and some new data points will jump out.
For example, one of our Bay Area clients hosts their bi-annual warehouse sale, an event in which you can only partake in person, at one storefront. Not surprisingly, this store had a conversion rate that was three times higher than any other store. This not only validated to us that store visits are tracking accurately, but it also gave us fodder to put heavy emphasis on this store’s radius for our Google Adwords campaigns in the times leading up to the warehouse sales.
Each of our click-and-mortar clients has unique business problems, even though they share the fundamental one of trying to demystify online to offline attribution. For this reason, we cater the questions we ask about their store visit trends to their needs. What sort of online-to-offline attribution questions does your business have?
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