Collecting customer data is great in terms of growing your CRM and directly engaging your total addressable market.
However, more importantly, is aggregating this customer data and overlaying it on top of other data sets to give you insights.
Google Analytics and other online analytics tools provide you with an easy way to gain insights into how your customers behave online. For example, you can see bounce rates, dwell time and your most popular pages.
If I said to you, you’ve got the most fantastic website in the world but you had no insights into how people interact with it, what would you think?
This is basically what many companies are experiencing with their offline environments.
They have no idea how people are interacting with their physical spaces.
How do I collect offline insights?
One of the most obvious ways is through WiFi analytics.
Offering free guest WiFi allows you to start collecting vast amounts of demographic data whilst also offering a great in-store experience for your customers.
Using a Purple customer – a multi-national retailer – as an example, let me give you an idea of the types insights you can capture with WiFi analytics.
Time of day
From the below graph we can see that Customer A’s peak time of day is between 4 and 5pm. When we looked into this in more detail and overlaid this data with their other store locations, we were able to come to the conclusion that this was caused by many of their stores being located near busy commuter locations.
New vs repeat visitors
The below graph shows how many of Customer A’s store visitors are new as opposed to repeat customers. From this, Customer A were able to identify that on average 75% of their customers were new; allowing them to take action and launch a marketing campaign aimed at turning those one-time visitors into repeat customers.
The below chart shows the frequency of customers who have visited one a Customer A’s stores more than once. From this, Customer A were able to identify that the majority of their customers have visited between 2 and 5 times. Being able to action these insights has allowed them to concentrate on moving customers from the 2-5 band into 6-10 band, therefore increasing spend per head.
The below graph shows the average length of time between customer visits. When analyzing the data, it showed that 78% of customers do return within a month showing high levels of customer loyalty.
Weekday by age
The graph below shows the average age of customers broken down by day of the week. It shows that under 18’s are more likely to visit on weekends, whereas earlier on in the week is more popular among 45-64 year olds.
Customer A has been able to use these insights to ensure that marketing communications are highly personalized and targeted, hitting the right audience at the right time.
This reporting has proved vital for informing Customer A’s offline strategy.
As you can see, there’s a wealth of insights available offline helping you to mirror what you’re doing online with your physical spaces.