Omnichannel
Getting more out of in-store
Florent Hédiard, global marketing director at Navori Labs, explains why marketing analytics technology for brick and mortar sites is the key to building and maintaining a successful omnichannel strategy.
February 23, 2021 by Florent Hediard — Marketing D, navori
Retailers have long worked to close the gap between the customer's interaction with their brand online, and the physical, in-store experience. Prior to the pandemic, e-commerce attracted consumers for its convenience; now, shopping online lets people avoid crowded spaces. This means that at the brick-and-mortar level, retailers must work differently, and complete the physical shopper purchasing cycle to enhance the customer experience and streamline their operations.
Powered by artificial intelligence, in-depth marketing analytics are a powerful tool that greatly assist businesses in developing continuous, meaningful metrics that enable them to assess how well they're doing –– and what they need to improve in their physical spaces.
Much like web analytics, in-depth marketing analytics designed for brick-and-mortar locations give retailers a detailed picture of performance down to the individual store level. It measures demographics (including age and gender); foot traffic; how long customers remain on the premises; how long it takes for them to receive service; how long they wait in line; and whether they're a visitor or a staff member. Armed with this information, store managers can examine not only how many employees are required on site for a given period, but also how effective sales staff are –– and make better staffing and training decisions accordingly.
The software also assesses the attractiveness of new items and product themes in the aisles. Perhaps of greater business value is the ability to assess the efficacy of merchandising layouts, and paths to purchase. This is achieved through the analysis of visitor focal points, including the items or signage displays that do––or don't––catch the customer's attention span.
Marketing analytics add further value through their ability to determine a potential opportunity to see for specific displays based on how well they fall into a visitor's line of vision. Along the same lines, they delivers metrics on the strength of a retailer's in-store advertising campaigns.
And, thanks to its context-aware automation functionality, in-store marketing analytics software can be configured to automatically update the content distributed to digital signage displays in real time. That distribution is based on the data it gathers about who is in the store at a given time of day, or time of year. For example, the distribution of current weather conditions can be coordinated with wintertime shoppers at the onset of a blizzard. Network operators can program their digital signage network to display ads for winter apparel in anticipation of these events with the right model, based on the audience's gender.
How it works
Through the application of AI, in-depth marketing analytics are produced through the interpretation of in-store video feeds. The software captures a customer's journey as they move through the store across multiple cameras and entrance points.
To determine length of stay and dwell time, it identifies store occupants on an individual basis, and distinguishes between visitors and staff members. Importantly, this solution respects current privacy standards: customer analytics data is 100 percent anonymous. No images are recorded or archived, and no personally identifiable information is collected, recorded or sold. The software conducts facial detection (to determine, for example, age and gender), but not facial recognition, and visitor attributes are collected and aggregated as metadata.
Metrics and reports are available on a daily, weekly, and monthly basis, providing granular data that enables a detailed analysis of visitors' foot-traffic and behaviors. Graphs are used to present the information for easy period-to-period comparison, which enable gap measurements as key indicators of your progress.
All of these features contribute to the development and continuous improvement of a retailer's omnichannel strategy. When combined with a purpose-built API, companies can extend this even further by feeding their business intelligence and data science systems into the software to produce forecasts, models, and algorithms for cross-analysis.
Retail use cases
Marketing analytics software for brick-and-mortar retailers and OOH professionals benefit a number of stakeholders. Here are some examples of how:
Store managers can measure visitor traffic over time to identify specific trends, and then compare these results with sales numbers to arrive at a retail conversion rate . By identifying a store's "hot" and "cold" zones, managers can examine how long customers spend in each area and adjust foot traffic paths in low-performing areas.
In addition, in-depth marketing analytics calculate how long customers have to wait before they are served by an employee, as well as how long they wait in line at the checkout. This gives managers the information they require to assess staff volumes, performance, and whether or not additional training is required. Visitor frequency data lets managers adapt to their customers' habits, enabling them to better plan product rotation, promotions, and advertising campaigns. Visitor classifications (such as age and gender) provide the necessary data to tweak product range, positioning and other retail merchandising parameters.
Retail chain managers have an objective means of evaluating and comparing each store location for foot traffic, visitor classification, and staff performance. They may test digital signage advertising campaigns in select locations prior to rolling them out across the chain. The software also lets them assign configurable access to store managers and marketing teams.
Shopping mall managers can measure entries, exits, and time spent on site (this data is also calculated on a per-zone basis). Visitor attributes data (age and gender) helps these professionals adjust tenant selection and product offerings. As visitors move throughout the shopping center, mall managers have the metrics to make well-informed decisions on how to improve signage, food court locations, seating areas, and parking lot access. This information also provides a valuable marketing tool when shared with potential and existing tenants.
Digital Out Of Home managers are able now to assess the real impact of advertising campaigns and optimize their screen placement with continuous audience metrics.
Quick-service restaurant managers can calculate customer wait times to optimize staffing levels, and ensure quick order turnaround. The software lets them count and classify customers, as well as the size and sum of their orders. Integrated digital display menus adapt to each customer's profile and wait time, and highlight complementary items to promote impulse buys/unplanned purchases.
Marketing/merchandising managers receive reliable, continuous, and comparable data to measure the effectiveness of specific campaigns, promotions, product themes, product positioning, digital signage display layout, and shelving replenishment. Based on this data, they may develop standard operating procedures to streamline sales management and contribute to corporate strategy and direction at local, national, and global levels.
A true omnichannel solution
When placed at the center of a retailer's brick-and-mortar operational infrastructure, in-depth marketing analytics software can streamline the overall omnichannel strategy. On the back end, it enables organizations to optimize processes such as human resource and queue management, and merchandising.
At the customer-facing level, its exhaustive data-gathering capabilities allow for a more tailored approach to messaging and service, greatly enhancing the in-store experience –– and boosting competitive advantage as a result.
Florent Hédiard is global marketing director at Navori Labs
About Florent Hediard
Subscribe
Get the latest news and resources from Retail Customer Experience.
