In their first guest exclusive with us, Acuvate Software shares the many different ways in which predictive analytics, combined with AI, is changing our industry. Acuvate provides AI and predictive analytics solutions for consumer goods and retail businesses. Their mission is to produce intelligence applications that simplify processes.
A retailer can predict the number of footfalls for a given period, but cannot predict where these footfalls are likely to pause in the shop, stop for exploration and for purchase. But, for those retailers who have incorporated predictive analytics and Artificial Intelligence, knowing these gray areas is possible.
Predictive Analytics in Retail
Predictive analytics is the structured science of collecting data, analyzing it and applying statistical data modeling, empowering retailers with predictive insights. And predictive analytics gains intelligence only when powered by artificial intelligence. AI-powered analytics are able to achieve the creation of insights – just like a human – and at an exponentially faster pace.
The application of predictive analytics in the retail industry is helping retailers make better pricing decisions, personalize messages across campaigns, improve inventory and supply chain efficiency, make product recommendations, detect fraud ahead of time and reduce risk.
Better pricing decisions is one strong benefit of predictive analytics. The guesswork determined by intangible factors such as likeability and brand image are replaced by data-driven insights. Predictive analytics draws insights for retailers, that helps them reduce and increase price during different seasons to create an optimal revenue effect.
Personalized messaging has been one of the most tedious tasks for marketers. While manual means based on wild assumptions was the only known way some time ago, automation slowly replaced it. Now, with automation as the means and predictive analytics providing data for validation, marketers are able to target product catalogs, offers and new releases for a specific target audience. Retailers are equipped with preemptive communication templates, content, and recommendations for consumers using insights rendered by predictive analytics.
The ‘Promotions Advisor’ is another role that predictive analytics takes up, since it reads in between data points to make sense of the lifestyle and habits of consumers. These behavioral traits reflect interesting purchase trends of the consumer, which otherwise would be almost impossible to know without a direct interaction with the customer. While some promotions are woven around seasons, a majority of them which achieve revenues are personalized promotions exclusively for the individual consumer.
Product recommendations is another interesting area that has been empowered by predictive analytics. With subtle behavioral trends from the past and recorded shopping experience, retailers can help consumers with better product recommendations and experience. A study by MarketingSherpa revealed that 11.5% of the revenue generated in the shopping sessions was attributable to purchases from the product recommendations. Chatbots, enabled by machine learning and natural language process (NLP), help in understanding and predicting consumer behavior. Consumer behaviour has, until now, been one of the esoteric areas of study for most marketers. Predictive analytics is able to decipher and predict consumer responses to different positioning, pricing, and features.
Inventory and supply chain efficiency is an obvious outcome of analytics. Retailers are able to predict demand for every product, consumer type and season of sale. This helps them from being overburdened by excess inventory and to avoid the hassle of complexities involved with supply chain management. Similarly, they can forecast the slug in demand for products, allowing them to maintain modest inventory. The beauty lies in the fact that it is not linear. Multiple demand levels can be predicted at the same time for all product lines and categories. Predictive analytics help in forecasting OTIF losses and minimize MSL non-compliance.
Detection of fraud is a major advantage for e-tailers, provided by predictive analytics. Data can reveal phony buyers and suppliers that will help retailers to refrain from associating with them. Though not completely eliminated, predictive analytics can minimize fraud to a great extent.
In-store sales is an often overlooked advantage of predictive analytics. A Google survey shows that 97% of consumers are using their phones while shopping for appliances to research for further information. Retailers can promote in-store sales by optimizing over the existing footfalls. Data analytics combined with visual perception, heat mapping, and cognitive computing informs the retailer about where, when and how a consumer is likely to pick a product. This can help retailers in promoting relevant products, without any historical data of the shopper.
Customer service is a popular area that has adopted predictive analytics. It helps the agent across the phone, chat or email by indicating the problem of the griever ahead of time. This reduces time spent on each support ticket and enhances the customer service experience for the shopper, associated with the brand.
While predictive analytics has given the power of predicting the future in the hands of retailers and marketers, there is another exciting technology that is shaping the face of the retail industry.
AI Breakthroughs in Retail and their Implication to Business
With gesture recognition, virtual mirrors, video analytics, robots, cognitive computing and AI-enabled chatbots, retailers have started to learn more about their customers. “AI will provide business users with access to powerful insights before they are available to them, through the use of cognitive interfaces in complex systems, advanced analytics, and machine learning technology,” said a Forrester resource.
The fashion and apparel industries have been able to personalize a wardrobe experience for shoppers with the use of virtual mirrors. A simulated image of the shopper using the products is projected for the consumer to have an experience in real-time. Some are also interactive by providing suggestions and product recommendations. Brands like Van Heusen and Rebecca Minkoff already have these mirrors in-store.
AI-enabled chatbots are the fastest adopted AI solution by a majority of retailers for some of the obvious reasons — 24×7 access, improved knowledge base, speedy interactions and real time. For instance, many e-commerce players use Chatfuel available via Facebook Messenger which can be programmed with commerce features and other specific features, such as checking reservations, media players, and event notifications. Chatfuel uses machine learning to deliver these capabilities. While this is the B2C use case of chatbots in retail, they also help in bringing analytics and data to the messenger app of the employees. By integrating chatbots (like Acuvate’s SIA chatbot) into the BI systems, employees can get quick access to analytics.
Gesture recognition and control gathers data such as length of engagement, products viewed and product popularity – along with its ability to recognize shoppers, store their purchase history and make product suggestions. Also, for data scientists, data gathered through this would reveal a whole new set of insights which numerical data may not be able to cohere.
Apart from e-tailers, offline retailers also use AI to create personalized product catalogs, customer support and provide exclusive product information that aids a sale. Studies show that 86% of consumers indicate personalization plays a significant role in their purchasing decisions. Famous businesses like H&M and eBay have explored these areas with chatbots to see an increase in customer engagement.
AI-enabled chatbots are being adopted quickly, as some of the features empower human agents — 24×7 access, improved knowledge base, speedy interactions and real time.
The retail industry is going see the walls disappear between the seller and buyer with predictive analytics and AI. One of the most promising manifestations of both predictive analytics and AI combined are chatbots. Chatbots have been a catalyst to some growth factors in the retail industry like customer satisfaction and experience, improved inventory and supply chain efficiency and personalization. Some more advantages a retailer could enjoy with predictive analytics and AI are an omnichannel presence, increased revenues, real-time notifications, proactive feedback, order tracking and more.
The retail industry thrives on three factors — better customer experience, greater choice and buyer advantage.
Even in a negative economy, customer experience is a high priority for consumers, with 60% often or always paying more for a better experience – according to the Harris Interactive Customer Experience Impact Report. This puts the responsibility on the retail ecosystem to create a customer-friendly environment. Another prediction by the Walker study states that customer experience will surpass price and product differentiations on the list of purchase determinants in the retail industry. This tells us that personalized customer experience would be the top-most priority of retailers, driving them to invest more time and resources in understanding predictive analytics and AI. Acuvate provides AI and predictive analytics solutions for consumer goods and retail businesses.