Faraz Mohamed, Director, Head Of Innovation, NISUM
Artificial intelligence (AI) and machine learning are disrupting business as usual across industries. This is true from supply chain to customer experience and perhaps nowhere more so than in ecommerce and retail.
Following the definition, AI is the process of machines carrying out smart tasks, and machine learning is an application of AI in which machines use data to learn for themselves. And today’s leading retailers are reaping the benefits of applying machine learning and predictive algorithms; Amazon has reported that 35 percent of the company's sales come from recommendations made by machine learning algorithms and Target has reported 15-30 percent revenue growth with machine learning predictive models.
How will AI and machine learning continue to play a role in today’s changing ecommerce landscape?
The ecommerce industry is worth around $2 trillion, thanks to rapid growth over the past two decades spurred by the adoption of social and mobile platforms. As ecommerce platforms continue to grow, strategic customer segmentation becomes increasingly critical to understand a customer’s age, gender, demographic and more, so that brand messages can be customized to the individual.
Enter AI and machine learning will be the key for today’s retailers in enhancing customer segmentation using factors like metadata, semantic analysis, collaborative filtering and predictive recommendations to increase conversions and grow their platforms.
Bots can understand consumer needs to facilitate price negotiation around a specific product or the entire cart
Also referred to as “conversational commerce”, AI-based negotiation platforms use intelligent pricing engines that allow customers to name their own prices. Bots can understand consumer needs to facilitate price negotiation around a specific product or the entire cart. As a result, instead of marking down inventory or creating promotions, ecommerce platforms can focus on generating more visitors, and leave the conversion to AI.
Personalized Product Offerings
Understanding a buyer’s past and present purchasing behavior is no longer enough. AI and machine learning can be used to up level a retailer’s understanding of their target customer by using data to predict what they will do next, and optimizing offerings accordingly. This comes into play for seasonal products, like sunscreen or snow tires, but can also be applied to shoppers’ changing demographics, for example, consumers won’t be purchasing baby diapers forever. Machine learning looks at the entirety of a customer’s buying behavior, so that certain aberrations, such as a luxury wedding gift purchase, doesn’t send up the signal that the customer should now be targeted with higher pricing tiers when that marketing isn’t likely be effective.
Smarter Supply Chain
A smart supply chain is key to delivering exceptional customer experiences. This is especially the case for ecommerce platforms; recent survey data tells us that a third of online shoppers will contact customer service if an item arrives just one day late and two-thirds of shoppers will share this experience with family and friends even after contacting the retailers.
While traditional forecasting was heavily based on historical trends, machine learning allows online retailers to smartly predict which distribution centers can most efficiently fulfill orders according to a shopper’s desired shipping method. This allows e-tailers to more smartly manage inventory according to which locations receive the highest demand.
Ecommerce has reached an inflection point. Customers now have more options than ever when shopping online. For retailers to win, they must leverage AI to meet customers where they are in terms of device, price and product offering.