What Analytics Do Offline Retailers Want To See?

Retail Analytics

As good as online insight

For many years, when it came to customer analytics, the online world had it all and the offline retailers had gut instinct and experience with little hard data to back it. But times are changing and an increasing amount of data is now available in legitimate ways to offline retailers. So what kind of analytics do they want to see and what benefits can it have for them?

Why retailers need customer analytics

For some traditional retailers, the first question isn’t so much about what metrics they can see or what data they can access but why they need customer analytics in the first place. And it is true, businesses have been successful without it but as the online world has proven, the more data you have, the better.

Added to this is the changing nature of the customer themselves. As technology becomes increasingly prominent in our lives, we come to expect it is integrated with most everything we do. Because shopping can be both a necessity and a relaxing hobby, people want different things from different shops. But one this is universal – they want the best customer service and data is often the way to offer this.

The increasing use of smartphones, the development of smart tech such as the Internet of Things concepts and even the growing use of virtual reality are all areas that customer expect shops to make use of. And to get the best from the tech, you need the data to decide what to do and how to do it.

Retail Offline Analytics

Staffing levels

If one of the most basic things that a customer expects from a store is good customer service, key to this is having the right number of staff in place to provide this service. Before the advances in retail analytics, stores would do rotas on one of several ways – how they had always done it, following some pattern created by management or head offices or simply as they thought they would need it.

However, using data to monitor customer numbers, patterns and being able to see in bare facts when a store has the most people in it can dramatically change this approach. Making use of customer analytics software, businesses can compile trend data and see exactly what days of the weeks and even hours of the day are the busiest. That way, staffing levels can be tailored around the data.

The result is more staff when there are more customers, providing a higher level of customer service. It means there are always people available when the customer needs them. It also reduces the inactive staff situation, where there are more staff members that customers. Not only is this a bad use of resources but can make customers feel uncomfortable or that the store is unpopular for some reason as there are so many staff lingering.

Performance metrics

Another reason that this information can be useful is to motivate staff. Many people working in retailing want to be successful, to offer good customer service and stand out from their colleagues for promotions, awards and even financial benefits. However, due to a lack of data, there can often be a feeling that such rewards can be randomly selected or even suffer due to favouritism.

When a business replaces gut instinct with hard data, there can be no arguments from staff. This can be used as a motivational factor, rewards those who statistically are doing the best job and helping to spot areas for training in others.

Daily management of the store

With a top quality retail analytics software package, retailers can have real time data about the store that allows them to make instant decisions. Performance can be monitored during the day and changes made where needed – staff reallocated to different tasks or even stand-by task brought into the store if numbers take an unexpected upturn.

The data provided also allows multi-site companies to gain the most detailed picture of all of their stores at once to learn what is working in one and might need to be applied to another. Software will allow the viewing of data in real time but also across different time periods such as week, month, season or even by the year.

Understanding what customers want

Using offline data analytics is a little like peering into the customer’s mind – their behaviour helps stores know what they want and what they don’t want. Using smartphone connecting Wi-Fi systems, it is possible to see where in a store a customer goes and, just as importantly, where they don’t go. What aisles do they spend the most time in and which do they ignore?

While this data isn’t personalised and therefore isn’t intrusive, it can show patterns that are helpful in many ways. For example, if 75% of customers go down the first two aisles but only 50% go down the third aisle in a store, then it is best to locate a new promotion in one of those first two aisles. New ranges can be monitored to see what levels of interest they are gaining and relocated within the store to see if this has an impact.

The use of smartphone apps that offer loyalty schemes and other marketing techniques also help provide more data about customers that can be used to offer them what they want. Already, customers are used to receiving discount vouchers or coupons for products they use or might have used in the past. With the advanced data available, it might work for stores to ping offers to them as they are in store, in the relevant section to catch their attention.

Conclusion

Offline retailers want to see a range of data that can have clear positive impacts on their stores. From the numbers of customers who enter and don’t purchase to the busiest days of the month, all of this information can help them make the most of their business and can allow even the most successful retailer to maximise their profits and improve their customer service.

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