The data you collect massively increases in value when you cross 100 percent of your store’s coverage. Without foot traffic data at each location, you simply can’t make informed decisions about your business’ future.
On our latest webinar, 3 things every retailer needs but 90% of them don’t have, Joanna Rutter, Content Marketing Manager, and Woody Schneider, Director of Sales, explore the dangers of sampling, or making fleet-wide decisions using only a subset of store data.
Here are some of the highlights, and be sure to catch the full webinar below.
Customer case study: Does this apply to you?
Multiple customers come to us with the same story: After massive growth, expansions into new property, they experience a massive shortfall in revenue but don’t know why. Said customer spends a tremendous amount of money on an analytics system, hires professional services, yet fires some of their best managers by accident and choose to close the wrong stores. The problem still isn’t solved.
Postmortem: What went wrong? The high expense and complex process of installing cameras got them stuck with partial deployments, generating data that was actually unusable, and ROI negative.
Is your data accurate enough for business decisions?
Precise data sets are key to remove inaccuracy. Ranked in order of their significance, the three main sources of inaccuracy in people counting data sets are:
- Full coverage. The dangers of sampling: Is your data set usable to your team? If you’re making business decisions that affect your entire fleet of stores, then you need your data set to be representative of your entire fleet of stores. The only true actionable data set is one that represents 100% of your stores.
- Actual accuracy of a sensor. If you only have sensors in 20% of your stores then your accuracy is also 20% on that tech’s best day. Our sensors deliver uniformity, using machine learning to teach itself what is a uniform data set. This helps reduce inconsistency in data.
- Sorting of traffic. This is only valuable once the first two are solved. Some types of traffic to sort out are: Employee exclusion, factoring out the delivery person, shopping carts, pets, and inanimate traffic such as a security guard. However, trying to sort your traffic can do more damage to your data set than you think. For example, if an employee that you’re trying to exclude walks out of the store at the same time a customer walks in, you are damaging your dataset by trying to sort out traffic.
The 3 essential metrics (which 90% of retailers don’t have)
Why is foot traffic data valuable in the first place?
As soon as the customer walks through the door, your marketing funnel ends and the journey of your sales funnel begins. It’s the handoff point between your marketing, your product and your team. Misleading data from an incomplete deployment and a lack of full visibility can mean the success of that handoff is measured unsuccessfully. Here are the 3 essential metrics every retailer needs, yet 90% of them don’t have:
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Traffic. The number one metric that a lot of retailers don’t have is foot traffic. That is, how many people showed up at each of your locations and when?
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Conversion. What is the value of that traffic?
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Marketing attribution. What did you pay to get that person in the door?
Watch the full webinar here for the full details of why the only truly actionable data set is 100% coverage of your stores.