Inside Footfall:

By counting the number of people inside a store at specific dates, times, and areas, you can optimise the number of staff on the floor, fitting rooms and tills, improving customer experience as well as cost efficiency.

The data is available in real-time so you can make quick changes to the store. And using historic data, you can accurately predict future footfall, helping you manage occupancy levels and store design and layout.

Video analytics also helps determine the ‘role’ of each store. For instance, a store in Covent Garden will attract different types of shoppers than a store in a Heathrow Airport Terminal. However, both have a role in consumers’ buying journey and footfall data can reveal that role.

A store with high footfall and time spent but low conversion could mean shoppers use it as a showroom to interact with the brand and products while making purchases online; similarly, a store with high footfall and low time spent may indicate the store is not doing well or perhaps shoppers are only coming in to use click & collect services.

Outside Footfall:

Outside footfall reveals the exposure your store has. Comparing inside footfall with outside footfall at specific days and times can help you understand the ratio of passer-byers that go into your store and make purchases.

If you delve deeper and compare different stores and locations, you will be able to assess which areas and types of locations are better suited to your business and provide the highest exposure. Perhaps you target tourists so tourist hotspots and airports are your ideal locations.

With outside footfall data, you can also quantify the effectiveness of window displays in attracting shoppers. Over time, you will know which types of window displays are effective at which time and day of the year, helping you maximise their impact.

Time Spent:

For every 1% increase in time spent in-store there’s a 1.3% increase in sale. This is an important KPI as it has a direct impact on consumer spendings

Data on time spent will help you predict peak times and optimise staff in fitting rooms, shop floor, and tills accordingly.

You can also spot trends on specific days such as weekends, where shoppers have more time to browse stores. Or perhaps your store is located in a busy office area such as London’s Canary Wharf in which case weekday lunchtimes and between 5-6pm are when your customers spend the most time in-store.

Comparing your existing stores will also shed light on the popularity of each of your stores and the role they have in the purchasing journey.

You can layer time spent and zone analysis to work out which shelves and products shoppers interact with the most, which will help with product placement as well as future predictions for R&D.

Heatmaps based on footfall:

Heat maps are a type of data visualisation which uses colours to represent high and low trafficked areas. By identifying high trafficked areas, you can optimise the exposure of certain products such as new collections, holiday specials, and sale items.

Knowing how consumers navigate the store, you can plan in-store promotional activities and accurately predict the impressions. You can also place staff in the right areas which will allow shoppers to get help quick and efficiently.

Heatmaps based on time spent:

These offer the same insights to heat maps based on footfall and more. Higher time spent suggests higher interaction with products so you can identify profitable and unprofitable areas and products quickly.

If you discover that one of your shelves is struggling to attract shoppers, you might decide to do A/B testing with differently priced products or make it stand out more. You can track the success of the changes implemented by checking heatmaps over the next few days or weeks and even compare it with other shelves in the store. Ultimately, it’s an effective tool for optimising merchandising and store layout.

Path analysis:

The most used paths in the store or departments are displayed in a simple image. This can help you quickly understand shopper navigation and optimise product placements and marketing.

If your store layout permits, you can identify the percentage of shoppers who pass through the tills, therefore paying for a product, at different days and times. This will reveal the best days and times for conversion.

Dwell Analysis:

With this tool, you can select an island or shelf and measure the number of shoppers dwelling on them and the average time they spend engaging with the product(s) displayed.

This tool will help you understand shoppers’ likes and dislikes as you can take the dwell analysis of a product and correlate it with actual units sold. It’s important to connect all the data available as low engagement in store does not necessarily mean low conversion. Perhaps, shoppers do their research online and purchase in-store or vice versa.

You can also use shoppers’ likes and dislikes to create new products, price them correctly, and predict sales.

Queue Monitoring:

This tool reveals the number of shoppers queuing, and how long they’re queuing for, on specific days and times.

Monitoring queues is important when it comes to reducing cart abandonment. Oftentimes shoppers leave stores without making a purchase because the queuing time is too long but optimising till staff using this tool will reduce time spent queuing and thus cart abandonment.

Special Area Analysis:

This will provide you in depth analysis of specific areas. We can consider the area with a new product line as an example. How many shoppers visit the area? Are they engaging with the products? Do you need to change the pricing, promote better, or is the product simply disliked by the target market?

You can also use this tool to monitor staff rooms or stock rooms to see the average time spent in there by staff in comparison to the opening hours.

Demographics:

This tool allows you to filter the reports by age and gender range, helping you understand the profile of customers that visit the store or departments.

Of course, customer profiling is important at all stages of selling, from R&D to marketing, which renders this a very important tool.

For example, your target demographics may be women between the ages of 18 and 30 but the days leading up to Christmas you may see a surge in male shoppers between the ages of 18 and 40 and assume they are purchasing presents for a female. You can even compare the data with different stores and across different years to see how effective your holiday sales are. This may prompt you to create more or less holiday bundles and change marketing. As previously mentioned, demographic analysis helps improve all stages of retail sales.