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Spice Store

Leveraging Crowd Video Analytics to Increase your Average Spend per Visitor

6 min read

Published 2nd Nov 2023

“Access to data is a hugely powerful retail tool. It puts the retailer in control because it gives the retailer more information and much more insight into what’s really driving customer behaviour.”


Businesses are constantly seeking innovation to enhance their profitability and customer experience. The adoption of Artificial Intelligence (AI) has been huge in recent years, and it’s now more accessible than ever before.

Brick and mortar stores can now tap into their own data, helping them make better decisions with analytics that traditionally were only available to e-commerce businesses. Analytics such as understanding the journey your visitors take, what they interact with, and any purchases made during that visit.

Where competition is brutal and margins are slim, Crowd Video Analytics can be a game-changer when it comes to increasing the average spend per visitor. In this blog, we'll explore how leveraging crowd video analytics can help businesses achieve this and thrive in a highly competitive market.

  1. Venue Layout

  2. Up-sell Opportunities

  3. Calculating Estimated Loss

  4. Queue Management

Summary: Challenges Physical Stores Face Today

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Venue Layout part 1

Venue Layout: The Art of Store Design

Within the highly competitive world of retail, the layout of a store can significantly impact the average spend by a visitor. Video analytics software acts as an observant eye, continuously detecting objects and their movements. By tracking these trajectories, dwell times, product and marketing interactions, it provides retailers with valuable insights into how visitors navigate and interact with their store. We’ve listed some areas where AI can impact venue design to increase the average spend per visitor.

  • Identifying High-Traffic Areas: Understanding high value areas and why customers naturally gravitate to these areas.

  • Strategic Placement of Up-sell Items: Armed with data on customer traffic patterns and preferences, businesses can strategically position up-sell items in the most prominent and frequented locations encouraging impulse buys. Because of this, video analytics not only optimises store layouts but also creates a synergy between customer behaviour and up-sell opportunities.

  • Your brand: You want your store to reflect your brand and compliment the type of customers you want to attract. Think of the contrast between discount and luxury stores, discount stores being cluttered, and items might be seen on top of each other to encourage impulse buys, while a luxury brand may have less on offer but provide an ultra-curated shopping experience.

  • Longer Shopping Sprees: Encourage more productive, longer shopping experiences for your visitors.

  • The Journey:  What kind of journey are you taking your visitors on? Are there any displays/assets that will help stimulate spending? Is your POS terminal in the most ideal position? Kmart shifted their checkouts in the middle of their stores starting 2017, frustrating shoppers at the time, however their reasoning was to improve the overall customer experience.

“Self-serve and central checkout registers make shopping more convenient, ensuring store entrances are free of queues and clutter, and allowing customers to enter and exit with ease.

We've noticed the layout is more open now and more spacious without having the registers up at the front part, which can get congested during busy times of the year like Christmas.”


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Fig 1.1 Example of customer journey mapping.

Fig 1.2  Example of top 3 paths taken by visitors.


Venue Layout part 2

In a retail landscape where every inch of space is valuable, video analytics empowers businesses to make informed decisions about store layouts, resulting in increased sales, greater customer satisfaction, and a notable uptick in the average spend per visitor.

Up sell opportunities

Up-sell Opportunities: Unlocking Additional Sales with AI

AI, specifically Video Crowd Analytics, transforms the retail environment into a dynamic, data-driven space that caters to customer needs in real-time. Here are some examples of how AI plays a pivotal role in boosting revenue:

  • Integration with POS Systems: AI seamlessly integrates with Point of Sale (POS) systems, enabling real-time transaction analysis. By analysing customer purchase history, preferences, and current transactions, AI can suggest complementary items during the checkout process, or even use the POS terminal as a retention mechanic by offering discounts to repeat purchases.

  • Mapping In-Store Journeys with Promotions: AI can map a customer's in-store journey by tracking their location and behaviour. As customers move through the store, AI can trigger personalised promotions and recommendations. If a customer lingers in the clothing section, AI could direct a team member to chat about a sale or a special offer on related accessories, increasing the likelihood of an up-sell.

  • Personalised Marketing Campaigns: AI analyses customer data, such as purchase history and behaviour, to create highly targeted marketing campaigns. By tailoring promotions to individual preferences, businesses can encourage customers to explore new products, make additional purchases, and increase their average spend.

Video Crowd Analytics is an ally for retailers seeking to increase sales and create up-sell opportunities. By leveraging AI's capabilities in integration, mapping in-store journeys, optimising layouts, and personalising marketing efforts, businesses can enhance customer experiences and drive revenue growth. As the retail landscape becomes increasingly competitive, embracing AI is a strategic move to stay ahead in the game.

Calculating Estimated Loss: Measuring Under/overstaffing

Calculating Estimated Loss: Measuring Understaffing and Overstaffing

Calculating estimated loss due to understaffing or overstaffing is a critical aspect of optimising operations and enhancing profitability. However, this can only be done accurately by leveraging real-time occupancy metrics.

Real-time occupancy metrics offer a live view of customer flow within a store. This data is invaluable in assessing staffing needs, as it allows businesses to predict peak shopping hours, identify areas of high customer traffic, and efficiently allocate staff resources.

  • Understaffing, when not addressed promptly, can result in longer queues, frustrated customers, and potential revenue loss due to abandoned purchases. On the other hand,

  • Overstaffing can lead to increased operational costs without delivering a corresponding boost in revenue.

In a study conducted of 41 stores from a large retail chain, they found aligning staffing levels with changing traffic patterns can result in a 6.15% savings in lost sales and a 5.74% improvement in profitability.


Real-time occupancy metrics enable retailers to strike the right balance, ensuring that customers experience shorter waiting times, enhanced satisfaction, and a higher likelihood of making additional purchases. By accurately predicting customer behaviour and staff requirements,  businesses can mitigate potential revenue loss, improve customer experiences, and ultimately drive up the average spend per visitor.

In a competitive retail landscape where every opportunity for revenue matters, the ability to make data-driven staffing decisions based on real-time occupancy metrics can be a game-changer for businesses aiming to thrive and stay ahead in the market.


Queue Management

Queue Management

A crucial component of visitor experience is queue management, and it’s where Artificial Intelligence (AI) is making a significant impact. AI is transforming the way retail stores handle queues, ensuring smoother, more efficient, and enjoyable shopping experiences for customers.


  • Predictive Queue Management: Predicting peak shopping hours and identifying when queues are likely to form. By analysing historical data, current foot traffic, and external factors like holidays or special promotions, AI can enable retailers to allocate staff efficiently and open additional checkouts in advance. This proactive approach minimises waiting times and prevents customers from experiencing long queues, enhancing overall satisfaction.

  • Real-time Queue Monitoring: Retailers can use this information to direct staff to high-traffic areas and expedite the checkout process. Customers are less likely to abandon their purchases if they see that steps are being taken to reduce their wait.

  • Smart Queue Allocation: AI optimises the allocation of checkout lanes and staff resources. By matching customers with the most suitable lanes, AI ensures quicker checkouts, reducing frustration and increasing the likelihood of additional purchases.

  • Perceptions: What if the opportunity to make a sale doesn’t even present itself? Queues and their respective wait times can be deceiving due to the individual purchases and needs of each transaction. Take a theatre for example, outside of ticket sales, maximising merch, food, and beverage sales are paramount to their success and profitability. Who’s jumping out of their seat during intermission to grab a drink and a snack? The answer is not enough people as the perception is that queues will be long, and they might miss part of the show. AI can help communicate wait times to help stimulate purchases.

AI is revolutionising queue management in retail through predictive analytics, real-time monitoring, and intelligent resource allocation. This not only minimises wait times but also significantly enhances customer satisfaction and loyalty. By optimising the checkout process and minimising wait times, AI creates more opportunities for customers to explore and make additional purchases, driving up the average spend. The strategic incorporation of AI in queue management empowers retailers to not only provide a seamless service, but also boost their bottom line by capitalising on each customer's visit.

In Conclusion

As retail stores evolve to meet the shifting expectations of consumers, AI becomes essential for ensuring not only efficient and enjoyable shopping experiences but also for increasing the average spend per visitor.

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About - Nola

Nola is a zero-installation video analytics software that enables entertainment venues to utilise their existing infrastructure to gain anonymised insights on real time crowd flow, revenue leakage and visitor experience.
Contact Nola:


  1. “Access to data is a hugely powerful retail tool. It puts the retailer in control because it gives the retailer more information and much more insight into what’s really driving customer behaviour.” Martin Newman, founder, Practicology




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