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In-store analytics: Build on the understanding of your customers, to create an engaging in-store experience
and boost your ROI
This goal can be achieved through proven online strategies: create online exposure, offer new in-store services via the e-commerce website, or get products delivered at the shop. The web user is closely tracked and each of his actions is analyzed. Where he clicked and what triggered the click, how much time he spent on the page and how he got to the website… All this gathered data enables us to better understand shoppers, and their expectations, and optimize the user experience.
Taking control of the data to implement an innovative shopper experience is a key factor for the optimization of the online purchasing journey. Applying this to the physical stores is part of the ultra-personalized path used by shoppers.
Ultrasounds, Bluetooth, Wifi, video or NFC… there are numerous existing technologies delivering information with higher added value.
This way, stores can:
• Precisely define their attraction rate with the analysis of pedestrian traffic, versus the number of
shoppers entering the store at a given time
• Determine the number of customers, and differentiate the one-time shoppers from returning
shoppers, to measure the number of new customers (from a low to very precise segmentation)
• Or even to visualize the buying journey, and identify warm zones from cold zones.
Using these indicators, and the purchases made, brands and retailers can calculate their performance for
each point of sale, and thus, determine the network’s performance per store.
Pile up smart data to develop new services
The new perspectives offered
Before the implementation of any measuring and analyzing device, it is important to identify the sources of information that will be most suited to the strategy planned, and/or the company’s vision, so the implemented technologies perfectly fit the needs. Once the data has been gathered, centralized, analyzed and dispatched to the right person, the final step is to fully exploit the data, to identify new services/uses.
Structuring and analyzing in-store data will allow brands and retailers to leverage their optimization opportunities, offering a real tool for decision-making support.
Brands and retailers have the possibility to implement new services, going beyond the simple statistic interaction with customers, and to adapt their commercial offer or optimize their resource management by relying on the trends revealed by the data analysis.
Here are some examples:
• When the shopper has been geolocated, push him targeted ads (coupons, loyalty points) at the right time, to match his expectations and his profile.
• According to the flow identified in specific zones of the store (warm zones/cold zones), try different layouts and showcasing of products to optimize sales.
• The decrease of in-store conversion rate (ratio of buyers to visitors), coupled with the assessment of an excessive waiting time at checkout reflects the need to draw upon additional resources at checkout.
The above list is non-exhaustive, the goal for brands and retailers being to reinvent and enrich the in-store shopper experience, and optimize store management, to generate business.
New perspectives are discovered via the implementation of the devices, and the amount of gathered data.
By allowing computers to analyze data via powerful algorithms, the distribution market will undergo a fundamental transformation. Machine Learning is a road map to the predictive analysis and optimization of real time POS management.
Some networks are already able to associate the measurement of in-store data and external data. This way, by relying on the sales history data, inventory management, in-store flows, identified customer profiles, and weather indicators, brands and retailers can design contextualized marketing campaigns, resulting in high customization (i.e. as soon as temperature exceeds 80 degrees).
Moreover, predictive analysis will allow forecasting of market demand, tailored inventory management, and the showcasing of a very specific in-store offer (i.e. promo for iced tea).
According to a recent survey of 1700 distribution decision-makers, in 2021, 75% of POS will be able to identify their in-store shoppers, where they are, and will know how to implement an on-demand shopper experience*.
*study by Research Now and Qualtrics for Zebra Technologies.
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