In-Store Analytics Isn’t Just for Retailers…
4 Ways Brands Can Use It
Today omni-shoppers account for 40% of retail purchases (comScore).
But, this isn’t news to you. We all understand the value of omnichannel. As marketers, the struggle has been unlocking the omni-shopper’s path to purchase—connecting hard-to-track behavior in-store with the “info-richness” of online analytics.
Typically, in-store analytics has focused largely on offering retailers/stores rich insights, but what about the brands in these stores? How can they get actionable metrics as well with in-store analytics?
In my last article we discussed the pros and cons of available in-store analytics solutions. Here, I’ll explain what metrics are available with in-store analytics and how brands like you can apply these metrics for marketing.
First, let’s talk available metrics.
Here is a sample of key metrics you can now get with in-store analytics…
- Visits: Total # of shoppers that walk past the particular brand at a given hour/day/week/month.
- Looks Vs. Stops: Counts & conversion statistics of shoppers walking by that actually look at the brand/products or the brand’s display versus shoppers walking by that look and then stop.
- Dwell Time: Amount of time shoppers spend with the brand compared across store locations.
- Stopping Power: Compares shopper looks, stops and dwells around in-store investments such as POP displays and promotions.
- Engagement: Time shoppers spend with a display as well as what content they respond to and interact with.
- Conversion Power: How displays / in-store investments are performing relative to POS data or other displays in other store locations.
- New Vs. Returning: New versus returning shopper counts and habits.
- Demographic Counts: Shopper age and gender counts.
Great… more data. Now what?
Here are 4 ways you can apply these metrics for marketing…
- Maximize In-Store Investments / Display Performance
- Create More Targeted Marketing Campaigns
- Measure Your Customer Experience
- Solve the Omnichannel Puzzle
By comparing sales data to dwell times and conversion statistics around shopper visits to looks to stops, you can identify which displays or other in-store investments are most effective, with possible comparisons from store-to-store and region-to-region or even by gender and age. This information can direct spending to the best in-store marketing mix with the highest ROI for your brand.
With a better understanding of who your shoppers are—new vs. returning, gender, age, shopping habits—across store locations/regions, you can analyze the behavior and needs that drive individual shoppers. As a result, you’ll be able to move away from mass-marketing spray-and-pray in-store tactics to more targeted marketing campaigns that significantly contribute to driving loyalty and sell-through, just like you do online.
Comparisons such as dwell metrics against sales data can be a good indicator of overall customer experience measurement and help compare performance across stores or regions. And, by constantly monitoring in-store performance, benchmarking allows quick detection of positive and negative deviations from standard performance, allowing for better planning and investments to ensure more predictable and stable sales numbers.
We all know that over 90% of retail sales take place inside brick-and-mortar. With an understanding of shopper traffic and behavior in-store—you can complete your picture of your shoppers’ path to purchase and gain invaluable insights around how to better influence these shoppers. While platforms vary, this information can also be easily segmented to give you insights specific to a certain region or location, and further identify trends by date/time/season or even demographic information such as age or gender. Additionally, by comparing traffic benchmarks against traffic during the run of specific out-of-store campaigns, brands can also gain some perspective on how multi-channel efforts affect or drive in-store performance.
Are you ready to start exploring how in-store analytics could help your company?
If so, then I recommend checking out our whitepaper: Retail Analytics: Understanding the Options
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