With over 90% of retail sales still happening offline, we can say with confidence that most marketing efforts lead a shopper to a physical store. Today, these marketing efforts are more diverse than ever. As a media publisher, how do you prove that you can bring consumers to your client’s doorstep and therefore are worth investing into?
Location data generated by mobile devices has given us an opportunity to connect the digital and physical worlds. You can use it to measure if your digital marketing campaigns actually bring people to physical stores. This process is commonly referred to as online-to-offline attribution. You can find more information about online-to-offline attribution here.
After an extensive analysis of our 2017 location data, we would like to share our findings on the store visits initiated by coupon and flyer apps. In this study, we measured if a user visited the promoted store after looking at an in-app flyer. We hope this report would help you benchmark your digital marketing performance and assist your internal estimations.
1. Store Visit Rates by Industry
Store visit rate is the average visiting rate of stores (e.g. each weekday/weekend on average X% of the population visited a store). The store visit rate of a user not exposed to a coupon is the orange bar. The store visit rate of user exposed to a coupon is the blue bar.
Our study confirmed that exposing users to an in-app flyer increases the visit rate to stores. That’s a trend you can notice across all the industries.
2. Store Visit Uplift by Industry
Store visit uplift measures the effect of your in-app marketing in bringing users to a store. To calculate it, we look at two groups – one exposed to a coupon and one non-exposed (control group). If more users from the exposed group went to the store compared to the non-exposed, then there is a positive uplift.
1. There is a positive trend. Users are 4 to 7 times more likely to go to a store after seeing an in-app flyer.
2. Store visit uplift varies. This reflects on the appeal of a flyer.
3. Dwell Times by Industry
Dwell time is the amount of time a shopper stayed in a store. It helps filter out passersby from actual visitors. This is especially important if your store is in a dense, busy urban area where you need to be more accurate with separating these two groups.
Dwell time reflects on the engagement of consumers in the store. As our results show, the engagement levels differ, which correlates with the purchase probability of consumers.
4. What Do You Do with These Insights?
If you’re not yet measuring the store visits generated by your app marketing:
- Definitely get started with measuring store visits. It will increase your chances of repeat and new business. It will certainly set you apart from competition and prove to your clients that your digital marketing works and really brings consumers to their physical stores.
If you’re already measuring store visits generated by your app marketing:
- Benchmark your performance. Lower conversions could mean room for improvement on client’s side. One flyer may work much better than other. A/B test flyers, provide feedback to your client and help them optimize their efforts for better results.
- Use these insights for internal ROI estimations. Store visit data could be easily combined with your client’s data about average customer spend. This can help you calculate how much extra sales you bring to your clients.
- Use dwell time to see how engaged consumers are at your clients’ stores. How long users spend in the store indicates their purchase probability. The longer they stay, the more likely they are to purchase more. Compare your results and provide this feedback to your clients so that they could improve their in-store experience and increase dwell times.
The study was based on mobile users in the Netherlands, covering around 2% of the population. Our study involved an analysis of over 2M location data points to insure accurate outcomes. All store visits were subject to a dwell time qualification. We have filtered noise by excluding all the dwell times that were either too long or short (likely indicating either passersby or employees). All user location data was anonymised and aggregated to ensure utmost privacy and compliance with GDPR.
Want to get started with store visit attribution?
Get in touch with us and we’d be happy to answer all your questions and help you set it all up.