Using artificial intelligence for taking your digital commerce and customer experience to the next level
Many of us have read about the predictions regarding the proliferation and applications of artificial intelligence (AI) and related emergent technologies. IDC predicts that “40% of digital transformation initiatives will be supported by cognitive/AI capabilities, providing timely critical insights for new operating and monetization models”. Gartner, in turn, has stated that “by 2020, customers will manage 85% of their relationship with the enterprise without interacting with a human”.
A number of companies are already using AI to gain better insights into their customers, improve their conversion rates, and offer a unique, differentiated customer experience. It is easy to see the applications and benefits of AI only via chatbots, more intelligent search, improved product and content recommendations, and resulting improvements in customer service and sales.
Needless to say — these are important areas from the business and customer engagement and expectations standpoint. For example, a growing portion of customers nowadays expect to communicate with service providers and vendors via chat. In addition, AI-powered search and recommendation tools have helped a number of merchants do a better job of making far more relevant, personalized product and content recommendations for their customers and increase customer engagement and sales. Of course, the customer does not know and does not even need to know that AI plays a role here — they simply see the recommendations provided for them get more intelligent and relevant. This “invisible” use of AI and related emergent technologies as a set of optimization tools for existing service processes and services is an area where we have already seen a lot of progress, and where the best practices have clearly started to emerge.
However, AI and other related emergent technologies provide for much more business potential. They can be used in more transformational ways to drive even greater operational efficiencies, improved customer engagement, and fundamentally more sales. Transformational in this context can mean that many aspects of the business model change in some cases vs. incremental that more typically goes beyond modest productivity enhancements. The following examples shed some light on a couple of more transformative ways of putting AI into practice.
Assortment management and intelligence
Digital commerce operations — and retail at large — require managing the assortment of products, i.e., which products to sell, add, and discontinue. Similar to inventory management, assortment management and planning require forecasting capabilities. Merchants need to monitor and discover market trends, hidden signals, and changes in demand to understand the competitive viability of their products.
Assortment management must be augmented with assortment intelligence. This refers to enabling visibility and insights into competitors’ assortments of products and product mix, such as segmentation by product and brand and the percentage of potential overlap. This intelligence then provides merchants with the ability to make specific assortment and planning decisions and track the business impact of those decisions in more real time.
Although a live human being can analyze the historical business performance of products and categories, truly accurate forecasting requires a sophisticated algorithmic model. It must be able to assess the relationships across products, influence of various events, and impact of competitors’ actions and pricing.
Inventory planning and forecasting
Managing inventory availability in the omni-channel business model is oftentimes one of the biggest headaches for merchants. The key challenge with inventory forecasting in a fast-moving market is that demand, customer preferences, and the competitive landscape tend to change frequently. Needless to say, the historical perspective provided by traditional BI technology does not help here. Instead, more accurate user interest and demand forecasting and predictive analytics capabilities are required.
AI and machine learning (ML) help with forecasting order velocity. They can identify key factors that affect order velocity and monitor their impact to accurately model velocity, inventory, and other logistics requirements. The beauty of these systems is that they learn and get more intelligent over time, enabling merchants to accurately predict their inventory needs and when to order more — or actually let their application systems do this automatically. Merchants and supply chain and logistics experts will eventually need to recognize cognitive learning in generating an autonomic, self-sustaining forecasting and order process. Yes, we already see some of the leading merchants do this as key part of their data-driven culture and operational model.
When applying AI, ML, and other relevant emergent technologies to take your digital commerce and customer experience to the next level, it is important to keep two things in mind. First, you have a prime opportunity to improve both your core processes and customer-facing service processes — so, not only the latter ones. Second, you can truly make your digital commerce (and digital business) more intelligent and more meaningful for your customers and reach operational excellence.