7 Tips For Retailers Considering In-Store Analytics
Only 5 percent of marketers say they have mastered predicting and adapting to the shopper’s journey to derive maximum value (CMO Council).
For the remaining majority, one key roadblock has been understanding and monetizing shopper behavior inside brick-and-mortar.
In-store analytics is closing this gap. But, is it right for your store? What are the red flags to watch out for? And, how can you ensure you are getting the insights you need?
Here are our top 7 insider tips for retailers considering in-store analytics…
- Find out if the solution will capture every shopper that walks by.
- Determine if you’ll be able to get the answers you need.
Wait. Isn’t that a given? Why wouldn’t it capture every shopper?
Well, here’s the thing. There are now a handful of ways to capture in-store analytics, but not all of them can capture 100% of shoppers. Based on what you are trying to achieve, that could be a problem or it might be just fine.
The three most widely used in-store analytics methods are WiFi, Bluetooth and video. Of the three only video can actually capture every shopper. Why is that?
While video relies only on the quality of its camera, both WiFi and Bluetooth tend to revolve around the tricky business of getting the shopper to do something. For example, requiring that the shopper carries a smartphone, opts-in to the connection or downloads an app on their phone. Keep in mind that only 64% of U.S. adults actually own a smartphone (Pew Research Center). So, cumulatively these kinds of dependencies can really put a big dent in your sample and create strong bias.
The next step in narrowing your choices will be determining what you want to know. With that in hand, it should be pretty easy to not only pinpoint a method, but a provider as well. Here are some ideas of things you can get:
- Is there a consumer browsing one of departments/categories or interacting with my merchandising displays?
- Is this a male or female consumer? What age?
- What is the consumer doing/looking at?
- How long are consumers lingering? Are they really engaged or just passing by?
- How many consumers total passed by this department/category/display?
- How is this store’s departments/categories/merchandising performing compared to another?
- What are our conversion rates by dwells? By engagement?
Here we’re mainly concerned with payback. No sense deploying something that will cost you more rather than earn you more. Think about it this way: If you want to do a store footpath analysis to learn how shoppers navigate your stores and optimize your footprint, how could this understanding and change impact your sales? How many additional units would you need to sell to get a return? Perhaps, you might need to only focus on certain areas/zones. What if you only focused on high-margin, low-traffic and high-traffic but low conversion zones, what kind of impact could that understanding have?
Here’s another example, if you want to do category/end-cap/merchandising display-level analytics to understand how these zones are performing and determine how to optimize them, how could this understanding and change impact your sales? How many additional units would you need to sell to get a return?
Typically, analytics metrics will be collected and delivered through an online dashboard. The best of these dashboards are cloud-based and therefore always available via a browser. The insights gathered should be available on the dashboard no later than 24 hours later. Additionally, you’ll also want to make sure this dashboard will enable you to slice and dice the metrics easily to get deeper, actionable insights in just a few clicks. Key segmentation filters you should look for here include:
- store location/region
- category/department/merchandising display
We all know data security and privacy are a hot topic right now. So tracking shoppers via their personal smartphones will require very clear opt-in and opt-out options. Ideally, you’ll want to stick with a solution that anonymizes your data, i.e. you’ll understand who they are via age, gender and shopping behavior. However, you won’t store their name and personal data. Something else to keep in mind, companies like Apple, AVG Labs and Blackphone are all already working on solutions to protect shoppers’ anonymity and reduce mobile tracking accuracy, so WiFi analytics could soon become obsolete. There are other reasons that you’ll want to start accumulating personalized information, but that’s not about accumulating analytics in-store, that’s more about developing marketing campaigns specific to that person. We’ll touch more on that in another article.
So what do I mean here? If you are deploying a new analytics solution, it’s not always cost-effective or viable to deploy a structure to mount it your analytics in as well. So, for example, if you are looking for category-level analytics, ask if the solution can be “retrofitted” to structures that are currently in the field or do you have to wait until you are refreshing those areas.
According to an InReality study, 75% of shoppers stated that they would be more likely to buy in-store if given targeted offers/promotions delivered in the moment.
A Google study, also found something interesting: one in three shoppers will purchase from a company or brand other than the one they planned to because of information received in the moment.
It’s clear that real-time, targeted messages/offers and experiences are the next step for stores. And, it makes sense… we’re already doing these things online. Based on the solution you’ve chosen, keep in mind that it is possible to use the analytics to deliver targeted content to in-store shoppers in real-time, via digital screens in-store. Triggers could be based around shopper behavior (for example, what they’re looking at) or shopper demographics, including age/gender. Ask the provider if this will be an option for you in the future when you are ready to take that next step.
If in-store analytics is something you are interested in, I highly recommend checking out our whitepaper:
Retail Analytics: Understanding the Options
Learn everything you need to know about in-store analytics to make the right choice for your store.
Image Copyright: iStock /peterhowell