Boosting Retail Success: AI-Driven Strategies for Accelerating Revenues, Enhancing Engagement, and Minimizing Loss
Published: August 21, 2024 |
Updated: February 23, 2026
Reading Time: 5.8 minutes
As CEO at StrataVision, I have the pleasure of speaking with executives across various retail sectors about the challenges they face and the outcomes they are working towards in what can only be described as an uncertain retail environment.
While the specific in-store initiatives and desired outcomes may be unique to each organization, several reoccurring themes have emerged – each of which can be addressed using affordable, cutting-edge AI-enabled solutions.
The key topics that consistently top retailers’ list of opportunities include:
- How can I drive more in-store revenue (or its corollary: improve profitability)?
- How can I keep my customers more engaged and, therefore, more motivated to purchase now and in the future?
- What is the optimal level of service I should be providing based upon my brand promise?
- How do I combat retail crime to reduce the opportunity for theft without creating friction in my in-store customer service?
All these questions beg for insights that require greater visibility into what is happening inside a retail store. It’s interesting to me that a brand’s e-commerce group often has access to a larger, more detailed number of data points for analyzing online business, while their brick-and-mortar counterparts are limited to a critical few.
Examples of Online and In-Store Metrics
ONLINE
- Online Traffic
- Impressions
- Reach
- Engagement
- Average Order Value
- Online Conversion Rate
- Returning Customer Rate
- Cart Abandonment Rate
- Dwell Times
- Path to Purchase
- Cost per Acquisition
- Campaign A/B Testing
- Customer Retention Rate
- Net Promoter Score (NPS)
IN-STORE
- In-Store Traffic
- Sales per Square Foot
- Average Transaction Size
- In-Store Conversion Rate
- Sales per Shopper
- Sales per Associate
- Cross Selling/Upselling
- NPS
- Merchandise Return Rate
- Inventory Turn
- Out of Stock Rates
- Shrink
Given StrataVision’s advances in AI capabilities, combined with our in-house expertise in retail operations, loss prevention, and technology, we have been able to apply online analytics to in-store business acumen that can help executives answer the questions I have summarized, below.
Some examples:
Driving Revenue (and Profitability) Down to the Department Level
Today, the traditional store assessment is only taken to a total store view of the business. While merchants report on sales down to the SKU level, there has been no direct correlation between shopper activity to product assortments and placement. This activity analysis can only be found in e-commerce but not for the stores – until now. Leveraging StrataVision, retailers can observe shopper movement and interaction with both merchandise and associates, enabling more informed business decisions to improve both revenue and gross margin performance in ways unimaginable only a few seasons ago.
For example, rather than simply analyzing unit sales results to determine product category winners and losers, retailers can now match in-store shopper activity within pre-determined zones to eventual transactions. This provides insights into overall in-store traffic, department-specific (or even display) traffic, and how long (dwell time) shoppers spend in specific areas, assessing engagement. Retailers can also determine how frequently shoppers are interacting with targeted merchandise. Just as importantly, depending on a retailer’s selling model, engagement between shoppers and store associates can be monitored and play a critical role in understanding successful product unit sales.
These new insights match up well to an e-commerce analytics funnel:
- Overall in-store traffic represents awareness.
- Department level traffic goes one level further to interest.
- Department level (or display level) dwell time, along with product/associate. interaction data, measures engagement and desire to purchase.
- Finally, the transactions captured at the point-of-sale represent shopper commitment.
Traditionally, retailers’ visibility into this store-based funnel has been limited to awareness and commitment. Missing the interest and engagement means merchants planning a store are blind to shopper behavior when forecasting inventory turn, risking lost revenue and profit. Performing A-B product testing without these insights may reveal ‘what happened’ but not ‘why it happened’.
Keeping Shoppers Engaged to Motivate Purchasing Desire
A-B testing does not need to be product focused; it can also be shopper focused. Creating department or display-level shopper analytics provides feedback on shopper engagement (with merchandise, associates, or both) helping retailers determine which displays attracting shoppers, where there may be service gaps, and most interestingly, category-level conversion.
Using data revealing how shoppers move throughout a store and how long they spend in an area can significantly influence how merchandise is displayed for optimum visibility. Whether it’s a series of endcaps or an innovative approach to the entire selling floor, our video analytics can guide studies to determine the best shopper paths for product engagement, whether for impulse purchases or longer interactions. In full compliance with GDPR and other personal information regulations, StrataVision’s retail intelligence can differentiate shopper engagement by gender and age range, providing deeper insight into shopping patterns that can enhance customer satisfaction and inventory turns.
Service Levels to Improve Shopper Engagement
A department’s labor schedule represents the intent to provide a certain level of service, and associates’ timestamps represent the execution of that schedule. But how does a retailer know if the shopper actually received the desired service level, and that the service resulted in a purchase? For example, let us look at a busy shoe department. Revenue might be meeting the plan, but is business being maximized? Using department-based AI generated analytics, StrataVision can determine that Shoes has a 25% conversion rate, while the overall store conversion is 40%. Should we be satisfied with this disparity? By pairing additional analytics, StrataVision helps the retailer understand the real opportunity and make necessary adjustments in labor allocation.
Developing a Pro-Active Strategy to Reduce Theft
While this article has primarily been focused on shopping behaviors that drive revenue, another way to improve profitability (and customer service) is by reducing theft within the store. Leveraging some of the same data discussed here, StrataVision can detect in-store behaviors that lead to a stronger deterrence program. We examined shopper/associate engagement as a selling tool but reviewing these service gaps can also highlight theft deterrence gaps that are not as easily exposed through schedules and timestamps.
Risks can also be flagged for shoppers congregating outside an entrance or being detected in unauthorized zones within the store. Alerts can be created to provide store personnel with real-time feedback on shopper activity and associated risks. There are many other use cases for loss prevention, including deploying our self-checkout solution: CheckoutIQ.
The four examples above merely scratch the surface of AI’s potential to improve a retailer’s visibility into all aspects of their store operations. Armed with this data at both the individual store and enterprise levels, new strategies can be deployed that are highly focused on improving both revenue and profitability, like how retailers manage e-commerce.
If you are interested in learning more about what we do, I’d love to hear from you! Click here for more insight into how StrataVision’s Retail Intelligence can transform your physical space into a rich source of valuable insights.
Bjoern Petersen is the CEO and board member of StrataVision, a company dedicated to unlocking the transformative power of computer vision and AI in the physical world.
