For brick and mortar retailers, this is no easy time. The boom in online retail has left the sales of physical stores at a standstill. The market space which is becoming increasingly digital is compelling retailers to turn towards advanced analytics techniques to ensure that their business survives the competition. Moreover, juggling between offering customers the right products at the right prices along with an exceptional customer experience can seem to be a daunting task for retailers. Adopting in store analytics could be the answer for companies in the retail industry to overcome this hurdle.
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What is In Store Analytics?
In-store analytics in retail provides meaningful insights from customer behavioral data. Retailers leverage several techniques such as the use of smart carts with location beacons, pin-sized cameras installed near shelves, or the store’s Wi-Fi network to identify the number of shoppers entering the store, their movement inside the store, and the key areas/aisles that they visited. This can even be used to extract basic demographic data, such as gender and age group. In store analytics can connect the dots between consumer, retail store, and buying decisions based on the data collected.
Why is In Store Analytics Important?
Competing with online stores is no cakewalk for brick and mortar retailers. Ergo, it is imperative for them to collect as much data possible about their customers and their buying behavior. In store analytics in retail gives businesses clear-cut solutions to the following conundrums:
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Pricing is one of the trickiest tasks for retailers. Most retail companies invest in resources that churn out tons of in-depth data on sales and pricing. Out of this, only a few succeed in turning this data into actionable insights. Retailers often end up making knee-jerk reactions that could have adverse effects on their business. For instance, in the case of an increase in production cost, they tend to raise the prices in direct proportion to the manufacturing overheads without understanding its impact on sales volumes. In store analytics helps retailers perform a price sensitivity analysis, which gives better visibility about when and by how much should prices go up or down. This can generate additional revenue for the retail company and safeguard them from price hikes by the competitors.
Understand Consumer Behavior
Customer behavior is highly dynamic and often hard to track. In store analytics provides retailers with a better understanding of the customer behavior. A form of in store analytics in retail called the mood analytics uses CCTV camera images to monitor the facial expressions of a person. This can help retailers send targeted content and offer them in store depending on how a customer is feeling. For instance, if the mood analytics reveals that the customer is confused about making a purchase from a particular category, then the retailer can promptly send an offer message to the customer’s mobile that is valid if the purchase is made within a particular time frame, say one hour.
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Visual merchandising is key for retail companies to attract customers’ attention. But how would they decipher what would work for their business and what wouldn’t? In-store analytics can offer real-time data on the effect of visual merchandising on the customers in the store. This can help retailers to experiment with different visual merchandising techniques and eliminate the ones that do not bear fruit.