oday, businesses thrive on collecting and deriving actionable insights from real-time data. Video analysis software generates alerts and can enable analysis of data to identify trends and patterns. By leveraging tools like AWS DeepLens and with Kafka corporations can organize and analyze data to make decisions. Video analytics has many applications ranging from monitoring vehicle, security alerts to customer behaviour. Within this blog we will focus on Building Customer Journey Through Video Analytics.
Retailers focus lies on offering a stellar customer experience on online and offline channels. While it is easy to track and analyse digital visitors to build a smooth customer journey, retailers in the real world have hitherto struggled to access such data.
Video analytics promises to be the way to go in offering a personalized customer experience by understanding their behaviour. Based on artificial intelligence, in-store video analytics empower retailers to enhance the shopping experience and increase sales. Much like digital analytics available to online retailers, video analytics gives offline retailers critical customer insight that is important for increasing customer satisfaction and building a seamless journey.
Video analytics software monitors video streams in real-time using sensors, cameras, image compression techniques etc. Video analytics is particularly useful for retailers to identify customer behaviour in the store, identifying trends and patterns. This enables retailers to leverage data for making smarter business decisions. Let’s look at some key areas where in-store video analytics can be deployed to optimize the customer experience.
The store layout is a key factor to a successful shopping experience. By using video footage to understand how customers navigate through the store and how long they spend in different locations, retailers can optimize product placement to encourage sales. Without proper video footage, it is difficult to maximize floor space, identify underutilized and high-traffic spaces, and zero in on the optimal store layout. By testing layout, retailers can understand shopper behaviour from the data gleaned from video analytics.
Business intelligence derived through video content can also be used to formulate effective promotions and display designs that encourage stronger engagement. Retailers can even identify ways to attract customers with cross-merchandising ideas. Video analysis is helpful in uncovering relationships between dwell time near displays and purchases. Using this data, retailers could implement new conversion prompts to impact change, training staff members to engage shoppers.
Through video feeds retailers have intelligence on how customers prefer to navigate the store to avoid overcrowding and bottlenecks. Video analytics can be leveraged to understand in-store traffic patterns to prevent friction in the path to purchase. Retailers can detect traffic early on and act to optimize the in-store shopping experience. This is also important for delivering a personalized customer experience by identifying demographic information.
Video analytics differentiates the staff from the visitors which can help retailers derive the staff to consumer ratio and reallocate staff to minimize the service time. Because staffing is one of the most significant costs to retailers, managing staff levels throughout the day is central to overall profitability. When there is a shortage of staff, the customer experience is severely affected. Video analytics can help determine the occupancy rates through the day, helping retailers plan staffing levels.
Facial expression analysis can change the entire customer shopping experience. Using close-range cameras on product shelves, retailers can perform emotion analysis to some extent, analysing customer satisfaction with the products. A digital signage with a built-in camera can understand feedback and can also help stock the goods when there is a positive response through eye-ball tracking.
Once the customer is engaged through their shopping journey in the store, an efficient checkout is equally important. Video content can help eliminate conversion hurdles by preventing long lines in the store. When a crowd starts to form, video analytics can trigger real-time alerts such that retailers can respond to increased traffic and deploy additional cashiers or EPOS stations. In the long term, retailers can leverage this data to determine if self-checkout solutions are necessary to maintain a positive customer experience.
With analytics, video footage’s utility is extending beyond security and loss prevention in stores- identifying suspicious behaviour and repeat offenders feature recognition. By leveraging customer data over time, retailers can spot trends and use them improve the customer experience. It is thus no surprise that retailers are looking towards video analytics for business intelligence and improving operations in the store.