How In-Store Analytics Can Improve Retail Experience

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With the phenomenal growth of the internet, many people thought that the high street retail store was on borrowed time.  A string of big name stores going into administration and ceasing to trade seemed to back the idea.  But moving through the recession and out into better times, it is clear that the customer still values the high street store.  It is also clear that stores need to offer the best possible retail experience to wow customers and keep them coming back – and in-store analytics is one way to help do this.

What is in-store analytics?

In-store analytics is a system that uses technology to gain insight into customer numbers, movement patterns and a number of other data areas for physical stores in similar ways to the analytics offered for online ones.  The system uses existing technology such as CCTV cameras and Wi-Fi hotspots to build a picture of what a customer does while in the store, without collecting personal information or infringing data protection rules.

The ability to see what customers do and don’t do while in a shop allows the business to better understand what works and what doesn’t.  Stores can learn about how many people look at the shop window and then enter or who walk away.  They can learn where in the shop people visit and where they don’t.  They can even gain insight into the reaction of customers to different elements within the shop such as new products, promotions or store layouts.

But how does this improve the retail experience for the customer?

Better customer service

One of the biggest topics of discussion for high street stores is customer service in retail.  Around one-fifth of shoppers still say they prefer to visit a shop to make many of their purchases, including food and big ticket items.  The main reason for this preference is that people buy from people – and that means top quality customer service creates loyal customers.

The key to the best customer service levels is having the right staff in place at the right times.  Too few and customers don’t get the help they came for.  Too many and the store look disorganised and lingering staff can actually make people feel uncomfortable.  Getting the balance is where in-store analytics is key.

The staff planning element of the software allows management to create the right rosters based on hard data.  Traditionally, weekends are busier than weekdays for many shops but what part of the day warrants the most staff?  Is there a weekday or part of a weekday that specifically sees more shoppers that would mean a higher level of staff is needed?  By analysing data, the software can help make suggestions to answer these questions and offer customers the best balance of staff to help with their queries.

Staff also feel more involved with the whole process from rostering to understanding daily sales figures and trends.  By getting everyone involved, businesses see happier staff that are more productive and can get behind management decision – because they can understand the reason for them.  No more instructions from behind closed doors but instead data-driven information that people can understand.

Using traffic patterns to make decisions

When businesses look at the retail experience, they often consider where is the best place to location different items.  They might guess that someone buying product A might also be interested in product B and therefore locate the two near each other.  However, with the use of in-store analytics, the decision can be made based on data.  By locating product B in aisle 4 for a week then in aisle 7, which drew the most attention as well as sales?

From a customer point of view, the result of this attention to detail is a store that is well laid out and meets their needs.  Of course, everyone has unique shopping patterns but by opting for the most commonly seen routes or patterns, a store can offer a better retail experience.  They can also ensure that promotions and new products are placed where they are most likely to be seen, benefiting both the business and the customer.

Better use of loyalty schemes

Many companies have created loyalty schemes over the years with varying results.  Often, they weren’t the success they could have been because there was a lack of hard data behind them.  Transactional data was used to make decisions but this couldn’t factor in behavioural data.  With the development of in-store analytics, loyalty schemes and other promotions can be created with multiple data streams to offer the best deal for customers and the business.

By offering the best deals to customers combined with the best loyalty schemes and top customer service, businesses can maximise the profits from each person.  They can see who spends the most in the store if they use a loyalty card and how many items they purchase.  They can see how much of their basket is in a promotion or has been discounted.  And all of this can be paired with information such as if they were greeted at the door and what parts of the store the customer visited.

Once the data is gathered, personalised offers can be made even as the customer is walking around the store.  One example from the US was a store that sent a customer a restaurant voucher after they had been in the shop for 45 minutes – it was approaching lunchtime and they guessed the shopper might be a bit tired and hungry.  That person received an exclusive text coupon and went to the restaurant for something to eat – maximising profits and making a great impression on the customer.

Conclusion

The use of in-store analytics is changing the retail store in several ways.  But it is also allowing businesses to offer a far better standard of retail experience for customers than ever before.  In the way that the online world has become personalised, so too is the high street store, ensuring it has a strong place in the shopping world for years to come.

 

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