Love it or loathe it, Black Friday has become an annual ritual that has consumers fighting (at times literally) for the hottest deals, and retailers planning months in advance to maximize profits on the biggest spending day of the year.
While Black Friday, Cyber Monday and all its derivations (Black Month, Cyber Week and more), provide the perfect sales opportunity; countless retailers have fallen short of its full potential due to a lack of preparation. From supply chain and inventory management, to accurately predicting shopping patterns and handling an influx of customer queries, AI has proven invaluable in ensuring both traditional retail and ecommerce businesses can cash in on the shopping frenzy. Here’s how.
At a time when consumers are bombarded with highly promotional messaging and aggressive calls to action from every retailer, relevancy is key in order to stand out. Hotel chain Best Western demonstrated AI’s potential to boost advertising effectiveness when they used IBM Watson to develop highly targeted, personalized and interactive ads with results that allegedly “crushed the industry average”. Gleaning insights from user behavior to develop creative targeted to its typical customer persona, the ads invited people to start a conversation about their travel plans, to which they received AI-driven recommendations. Using speech recognition or natural language processing, this type of model could be developed and applied to a range of industries, with conversations around anything from Holiday gift shopping, new year health and fitness goals.
Though still somewhat in its infancy, the potential for AI to create more insightful and personalized campaigns will become key to making the most of Black Friday.
While getting the word out to new buyers is an inevitable goal in any Black Friday campaign, targeting your existing base has even greater potential, since brand recognition and share of wallet are already established. AI-driven customer analytics can be used to review transaction history and purchasing patterns, then generating customized offers more likely to pique interest. These can then be served up to customers through email campaigns, targeted display ads or other direct marketing channels.
Once existing customers enter a store, whether online or on land, they should be presented with a further personalized experience in order to optimize conversion. The Macy’s On Call assistant is a great example of how AI can be used to shape shopping journeys in brick-and-mortar stores, while ecommerce businesses have even more possibilities to tailor content to the user for a more seamless journey through to checkout.
Chatbots for increased conversion
Retailers are increasingly adopting machine learning techniques to improve the overall shopping experience and convert more shoppers by helping them choose the right product with confidence. Chatbots are a powerful tool to help answer questions about all kinds of product specifications, from tech to fashion, and have led to a 20% increase in conversion when available.
The Levi’s Virtual Stylist used natural language processing to help guide customer’s decision-making based on years of in-store sales experience, posing specific questions around leg fit, stretch and waist height to help them choose the perfect jeans.
Demand forecasting & supply chain
Attracting buyers and converting browsers is all well and good; but it’s wasted effort if you can’t fulfil the order. Apart from losing a sale at the time, businesses can also miss out on potential future sales as frustrated customers are put off coming back. While managing stock inventory based on insights, pricing and distribution isn’t exactly new in retail, AI’s capabilities mean it can be done at a much greater scale with huge volumes of data. By anticipating trends, machine learning and AI tools can enable retailers to more effectively capitalize on a sudden spike in demand. According to a Capgemini survey, 80% of the $340 billion annual savings predicted for AI in retail by 2022 will come from more efficient supply chain management.
Remorse or rejoice? AI for returns and reviews
Impulse buying is never more inevitable than during Black Friday and Cyber Monday season. So while raking in the profits, even the most successful retailers need to be set up to process a certain number of ensuing returns – once again, AI can enable a more effective process to ensure a smooth experience. On a higher note, those millions of consumers who hang on to their purchases can offer huge value to retailers, not only in revenue but also in providing valuable insights through product ratings and detailed reviews.
Using NLP techniques, sentiment analysis enables businesses to learn more about their products by interpreting the large volume of written reviews likely to flood in following a peak in sales. The learnings can be used to enhance product descriptions to set clearer expectations or emphasize the most popular features called out by customers.
Black Friday offers unparalleled potential for retailers, from the more obvious wins of increased sales and customer acquisition, to gathering invaluable data for future growth. However, the sudden spike of activity can lead to missed opportunities if businesses are unprepared. AI provides the tools for retailers to make the most of the busiest shopping season of the year.