How retailers know
as much about their customers offline,
as they do online

What retailers struggle with is how to leverage the data that can lead to better, more effective campaigns and personalized shopping experiences.

When a customer does their research online, we know things about them like their age and gender, where they live, and what other things they are shopping for. We know how many times they visit the site, which pages they linger on, and even how they were directed to the site.

Ironically, once they are in-store, we know virtually nothing about their shopping journey.

Imagine the same customer who spent thirty minutes on the website researching bicycle helmets comes into the store, their mobile device interacts with beacons, and the store learns where they spend their time while browsing. Proximity beacons track them throughout the store and will show what they peruse, where their attention is drawn, and can even show which items they consider during their trip. They find that the customer also spent time looking at gloves and cycling shoes, and after checkout they communicate that during this trip, only the helmet was purchased.

The retailer can now connect the online interaction with the in-store visit and understand things like:

• Did the shopper get distracted by other offers?
• Did they go right to the product they researched, or did they do further investigation?
• What in-store tools influence the buying journey?
• Do similar groups of people share buying habits?
• Which in-store marketing efforts are most effective?
• How effective was the store team’s interaction with the customer?
• What is the customer’s average spend per visit?
• How effective is the store’s layout?
• What other retailers and products does the shopper research while on site?

Research shows that personalized email messages vastly improve customer engagement and conversion. With the same shopper in mind, consider that following their visit, the customer receives bounce back offers for cycling accessories like the gloves and shoes they visited on their last trip. The data cycle can continue, by watching which campaigns are most effective in prompting returns to the store.

The store app is key to making the connection between online and in-store buying personas. It allows for market segments that recognize personal data to connect what is in a shopper’s online basket with what they do in store. A 50-year old woman who buys hair color online might receive a digital offer for color-care shampoo when she nears the health and beauty department in-store, while a 35-year old man who shopped for camping gear online could receive an offer for grilling tools. Personalized messages and offers increase brand loyalty, and repeat customers spend more with businesses that understand them.

Retailers see data such as:

• Footfall traffic
• Heat maps
• Merchandising effectiveness
• Customer loyalty
• Customer segmentation
• Effectiveness of store schedule and resources
• Increased revenue