How Video Analytics Can Help Enhance Customer Experiences
Retailers, restaurants, banks, health care providers, schools and other types of service providers can potentially benefit from real-time, data-driven video analytics. The collected customer behavior metrics can be used to:
- Enhance the customer experience
- Map the customer journey
- Spot behavior trends
- Reorganize store layouts
- Respond to real-time in-store indicators
- Efficiently reduce queue length
- Improve security
- Maximize labor hours
- Improve product and staff placement
There are patterns in every business. Identifying and responding to those patterns can lead to increased revenue and client satisfaction through a better buyer journey.
Video analytics can also provide immediate feedback to improve the implementation of data-derived processes. For example, systems can be established so a store’s staff are notified to take action when certain conditions are met. The results of those actions, such as increased purchases or better loss prevention, can then be tracked to measure effectiveness of those processes. Video analytics can help close the loop – linking strategy, action and results.
Video analytics can also play a role in A/B testing and measuring the effectiveness of marketing campaigns or alterations to customer service strategies, a strategy that online retailers have enjoyed for a long time.
Actionable Data Gathering
The value of data gathered by video analytics and the lessons that can be derived from that data is highly dependent on industry and geographic differentiators.
Maybe the average customer at your retail store will wait in line if there are three people queued up at the register but will leave if there are five or more customers ahead of them. This phenomenon may happen more reliably during rush hour. Once enough data has been gathered to establish that behavior pattern, your store can adjust staffing or the number of open tills at that specific time.
Staffing decisions likely shouldn’t be made based solely on a single day or week’s worth of data. Persistent, long-term data gathering is the best way to improve the reliability of predictable customer behavior.
Maximizing labor hours is especially important in industries with tight margins and high rates of local competition. A business may be able to solve the queue-length problem without altering their staffing by simply implementing push notifications.
Your store’s data analytics may identify a reliable correlation between one-on-one customer service and increased sales, in which case you have an incentive to maximize the number of employees on the floor consulting with clients.
A push notification can be sent out to these sales representatives when a set number of people queue up at the checkout stand. Once the notification is received one of the floor representatives can go open an additional register.
This is just one of a potentially limitless number of ways push notifications could be used in conjunction with video analytics to enhance the customer experience and improve security.
Data gathered through video analytics can identify behavior and location patterns associated with theft losses. For example, there may be a direct correlation between a customer’s time spent in a specific aisle or at a specific product and the likelihood of theft. Video systems can alert store security if a customer is exhibiting those specific behavior markers.
Audio and video technologies can even be implemented in those high-risk areas to prevent that behavior from occurring.
If a potential thief is loitering in one of these high-risk areas, an audio message can be automatically played informing them that a customer service representative is on the way to assist them. That kind of innocuous message can either indicate to a legitimate customer that help is on the way or let a potential shoplifter know that they’re being watched, and they need to rethink their choices.
Measuring the Effectiveness of Operational Strategies and Campaigns
Internet advertising and online shopping has provided retailers, service providers, educational institutions, health care providers and all types of financial institutions with reams of data on customer demographics and spending habits.
Countless valuable correlations, many of which may not be obvious or intuitive, can potentially be identified by comparing this shopping and customer demographic data with in-store video analytics. Data derived from many varying sources can be collated to further increase its usefulness, identify behavior trends and sharpen demographic grouping.
Video analytics is likely one of the most effective tools for gathering this type of data in stores. These systems can also be coupled with other technologies (Wi-Fi, mobile apps, passive networks) to flesh out marketing data sets.
Data may show customers of a department store that purchase a specific type of jeans are more likely to purchase a specific brand of shirt or shoes. That data can then be used to better organize product placement or develop specials, deals and other incentives to increase sales or individualized marketing.
The possibilities are limited only by the creativity of business owners, marketers, decision makers and data analysts.
The Only Way to Determine Your Business’s ROI from Video Analytics Is to Implement a System
It’s safe to say that every business or organization that implements video analytics systems discover some customer behavior trends that are actionable.
You may make entirely unexpected discoveries, like shoppers make more purchases when a specific artist or genre of music is playing on your store’s sound system.
How those discoveries are leveraged to improve sales and competitiveness is entirely up to each business.
The value derived from an investment in video analytics is based on your implementation of lessons gleaned from data management. It can help retain customer loyalty, reduce losses from theft, increase revenue through better product placement and sale strategies and improve decision-making based on data-based customer service lessons.
A footnote on personal information
The solution is designed to capture what is considered non-personally identifiable information. It collects pixel-level environmental information on selected objects, such as a car driving into a gas station or a person walking into a building.
Our AI platform aggregates and analyzes that environmental information against environmental information collected in previous encounters of similar class objects to recognize patterns. It then generates confidence interval data as to whether an object is recognized as the same class of object as in one or more previous encounters.
The confidence interval data cannot be used to distinguish, identify or trace an individual’s identity (no biometric records, etc.), and is only used to recognize, track, and help understand behaviors of classes objects.