In-Store Analytics Solution
No matter what the naysayers say.
Brick and mortar stores have only grown in numbers and sophistication despite the popularity of ecommerce.
And they are rising to the digital challenge by embracing it in more ways than one.
The world of brick and mortar shopping is transforming itself into one where long payment counter queues, the inability to conduct thorough product research and gather visitor insights may be a thing of the past. Innovative digital technologies are helping forward-looking retailers solve for convenience, speed, information, and even personalized interactions.
Consider the case of Hema, Alibaba’s supermarket store in China - where app and QR codes help shoppers reserve dining tables, order food, and robots bring food from the kitchen to the diner’s table. Or Nike’s Speed Shop, where shoppers can try shoes, pre-selected online, and avoid queues using a mobile check-out.
But how does a store justify and optimize the investment in these technologies? Which digital adoptions are boosting sales, and which ones are merely attracting curious onlookers?
Enter in-store analytics!
It helps separate the helpful from the hype and makes it easy for store owners to assess the impact of in-store innovations on shopping behaviors.
What is In-Store Analytics?
In-store analytics is essentially a part of retail analytics that involves using data and tools to understand customer behaviour within the store and gauge the overall experience that the retail store provides. Examples include people counting, emotional and sentiment analysis, product placement and store layout, footfall analysis, the result of cross-channel marketing on sales, the influence of staff in decision making, and the overall predictability of buying behavior based on factors like shopper demographics, time of the day and so on.
The ultimate purpose of in-store analytics is to inform the retailer and about what’s working and what’s not. These analytics become the ‘all-seeing-eyes’ of the decision-makers and help them optimize the shopping experiences for consistently high conversions.
How Retail Analytics Can Help Optimize Shopping Experience for Higher Sales
Better Staffing Decisions
Which time of the day brings in the most traffic to the store, which season, how do marketing and promotions affect shopper walk-ins, or even, what parts of the store layout require the maximum staffing of in-store personnel? Here, in-store analytics help by capturing historical data and extrapolating the same for the future.
Ability to Shadow Your Shoppers Across Channels
Who entered when, how much time they spent in different sections, which items did they add to cart versus which they did not, which purchase decisions they took in the store versus which they took online - such wealth of information can be gathered and analyzed, thanks to retail analytics which shadows your shoppers across channels.
Measure the Impact of Digital Transformation
Any technology is only as good as the ROI it achieves. Whether it is the enablement of mobile checkouts or digitization of the shelf, you need to justify the investment by examining the actual increase in sales. Retail analytics helps examine how shoppers interact with various digital enhancements and if they buy more because of the same.
How to Choose an In-Store Analytics Solution
So, you have decided to make your store assessments smarter with an analytics solution, but wondering how to go about selecting one? There are six key factors to keep in mind when choosing your ultimate in-store analytics solution. Let’s take a look at each of them.
Upwards of 95%
An unsure shopper may pick up multiple products and cart them around the shop before returning them to the display, but does that mean your merchandising has been successful in getting his/her attention? A naughty child may take her mother’s cart, wheel it all over the shop, and even add scores of items to the cart, but does that mean the mother was on a buying spree? In-store analytics, like any other technology, will have its limitations as shoppers may not always behave in straitjacketed and rational ways that we assume from them. All said and done, in-store analytics technology is only as good as the accuracy it can guarantee. We have seen that anything below 95% may not be worth your money and time.
Data is currency. Knowing how many pairs of feet walked into your store is one thing. And knowing who the person was is completely another. Your in-store analytics solution must help you identify your visitor like an old friend. It should tell you their gender, their age. It should give you a peek into their world, their style choices, their likes, and dislikes, and must help you personalize your promotions to them outside the store – in emails, Facebook feeds and in-app advertising. Besides helping you personalize and enhance in-store engagement, these additional data points can help you adopt upcoming trends like visual loyalty. In short, you should be able to know and understand your in-store shoppers equally well, if not better than your online shoppers.
Demographics and style preferences
In the store, on the mobile and on the web
Accenture has found in its research that “60% of Gen Z shoppers prefer to purchase in stores,” and “46% will check in store to get more information before making an online purchase.” As evident, Gen Z shoppers do not see the retail store as distinctly separate from its eCommerce counterpart. They are merely using both for a cohesive shopping experience. Retailers will need to ensure this line of thinking extends to data and insights. Your in-store analytics solution must offer a 360-degree view of your shoppers. It should, for example, tell you if those who left empty-handed from the store purchased something from you later via eCommerce and vice versa.
B2C sales are fast by their very nature. They are also seasonal and vulnerable to various market fluctuations. Say you are in the year’s most important sales season, and your in-store insights are updated only hourly; it is a handicap. Knowing every instant how your shoppers are behaving can help you take proactive steps in time before the peak sales hours fly past. Thus, real-time data presented to you in the form of simple but meaningful visuals can help you prepare your shop and your staff much better.
Updating in real or near real-time
Based on opt-ins and permissions
Since an in-store analytics solution works in a busy atmosphere, one with lots of the hustle and bustle, it needs to withstand and facilitate the way shoppers and staff behave in the shop. Data gathering should not irk shoppers or embarrass the staff. New ways of shopping or checking out must not become bad surprises for new walk-ins, and overall, the users must be empowered rather than impeded by the presence of technology.