Retailers need to move beyond “considering” artificial intelligence (AI) in their enterprise to integrating it as an essential business tool.
Chain Store Age recently spoke with Tom Stanek, president of cloud-based AI and machine learning (ML) solutions provider RXA, about how retailers can optimize workflows such as labor management and customer engagement with AI.
What technologies can retailers invest in to incorporate artificial intelligence (AI) and machine learning (ML) into daily operations?
“All retailers are poised to leverage AI and ML in their day-to-day operations. The question is no longer around should we use these technologies, but rather how we can leverage them to curate solutions that will have the most impact on our business.
The largest variable expense for a retail business is often labor cost. Scheduling employees based on human judgment alone leads to shifts that are consistently overstaffed or understaffed, due to the inability to accurately predict demand. This causes either excess labor costs and high employee downtime, or lost revenue when customers have poor experiences as employees struggle to keep up with demand. When applied AI is employed to add structure to scheduling, demand is more accurately forecasted, and schedules are optimized to match demand.
“In addition, understanding consumers’ brand and product perception and sentiment is an important factor for retail’s success. AI-based social listening tools and understanding the voice of customer directly affect the bottom line. Social listening tools automatically monitor the Internet and parse through billions of reviews, blog postings, and forum comments to provide companies with insights into what customers are saying about their products and services.
Voice of customer tools add additional value by integrating this with other forms on consumer data, such as call center and survey data. These products then employ AI and ML techniques to transform the data into actionable insights.
Can you provide more details on workforce optimization technologies for retailers, and how AI can help?
Workforce optimization tools work in three steps to affect retailer’s bottom line: build reliable demand prediction models, generate a robust staffing mix with location-specific role-level recommendations, and deploy intelligent team scheduling integrated with existing processes.
Predictive models are built to forecast daily demand and are powered by all relevant internal and external data. For retailers, this means aggregating data on past demand, staffing levels, and sales, contact centers, weather, area-specific economic factors, and promotions.
With demand forecasted, labor optimization tools then create an optimized schedule to fill that demand, and it can account for a ‘new normal.’ The schedule is programmed to automatically harmonize with capacity and provides a clear picture of both the number and skill mix of employees needed. Ensuring retailers have the right people on at the right time provides each customer with the experience they deserve, and the experience they will pay for.
Finally, scheduling must be sent to management to implement. By combining human discretion with the structure of AI-based models, these types of tools minimize uncertainty and eradicate the understaffing or overstaffing phenomenon, while ensuring customer satisfaction reaches an all-time high.
How specifically can social listening solutions help retailers understand consumer brand perception and sentiment?
Social listening solutions are used by retailers of from all verticals to understand consumer perception and sentiment. They allow businesses to gain insight into what consumers are saying about their brand, products, and services. By automatically scouring the web and identifying posts on forums, social media, and blogs that are pertinent to your brand, social listening identifies overall sentiment as well as individual experiences.
All retailers can use these tools to identify what may be hurting their brand and track measures put in place to mitigate negative news. While social listening is an important step in understanding consumer perception and sentiment, companies must also collect, analyze and act upon all forms of data, including surveys, call center metrics, and mystery shopping.
Once this data is gathered, it must be analyzed and visualized to become actionable. AI is often used to score the positive or negative sentiment of each comment, post, or review at the sentence level. Using the score, retailers can focus in on those comments that are truly detracting from products or services in the marketplace or identify what is resonating with customers and take advantage of those trends through marketing or outreach.