The “stores versus e-commerce” debate is largely over as retailers realize they need to adopt smarter, customer-centric strategies that can be deployed across all channels, with a phygital mindset, by leveraging new digital capabilities and, especially, artificial intelligence (AI).
Only by making their business smarter with AI can retailers get closer to their customers, both in stores, where physical distancing measures have raised barriers between brands and consumers, and online, where competition is just one click away.
Customers are more demanding than ever and expect brands to understand better and address their wants and needs. But with the current COVID-19 pandemic / ongoing pandemic, consumer behavior patterns have become less predictable.
Take, for example, a customer who previously ate out regularly in a restaurant chain but has now stopped visiting or only orders take-out; that doesn’t mean the customer is less loyal, simply that the customer’s priorities have changed while the pandemic persists.
In the current climate of uncertainty, many consumers attach great importance to safety and reassurance, so brands that offer supportive advice and impartial recommendations will be perceived to be trustworthy and attract greater brand affinity.
We all are familiar with Amazon’s “customers that bought this item also bought”/ “customers also bought” feature. Recommendation algorithms are a relatively simple application of AI and one that works well. They help undecisive consumers make the right buying choice, and they enable retailers, who can use the technology right across the customer journey from pre-sales to post-sales, to strengthen the customer bond and generate incremental sales.
Another increasingly popular application with great potential in retail is conversational AI. The use of contact centers to deliver customer service is well established, but, many consumer-facing organizations were swamped with increased customer calls during the pandemic.
In some cases, centers had to close or function at reduced capacity because of staff illness. Using a conversational AI platform such as everis eva, brands now have the opportunity to improve and scale their customer service using virtual agents. Moreover, organizations may better identify the human-machine collaboration model that balances which interactions should be personally addressed and which others may be better managed through virtual assistants.
In post-sales support, for example, most sales interactions are done manually, but AI-powered virtual agents can answer simple queries and resolve problems faster, with less need for human interaction, reducing the cost of serving customers and increasing satisfaction.
One of the biggest areas where AI can help retailers is “joining the dots”: helping make sense of the mass of structured and unstructured data they have on customers’ interactions, purchases and preferences. The problem is compounded in the omnichannel era, as today’s shopping journeys are more fragmented and involve different touchpoints and platforms.
For instance, a consumer may find out about a new dress on social media, visit a retailer’s mobile site, cross-check the price on Amazon, try on the dress in a store, purchase the item in a different color through the store’s “endless aisle” online inventory, and pick it up in the store later or have it delivered to their home.
AI allows retailers to better understand and predict customer behavior across all channels and touchpoints. Also, operational data hubs will play an important role in building this 360-degree customer view.
In a nutshell, retailers succeed with their personalization strategies, which are rated second only to e-commerce as priorities for retailers this year, according to the 2020 Digital Trends: Retail in Focus survey by Adobe.
Basic personalization techniques, such as addressing a customer by name in an email, have lost their ability to impress – and can even backfire. Returning to the restaurant example used earlier, sending the once-loyal customer a coupon offering “10% off your next eat-in meal” is unlikely to reassure a customer concerned about Covid-19 and may be seen as insensitive.
To build an accurate, up-to-date profile of what a consumer likes or might like, the retailer of tomorrow needs to adopt what everis calls “knowledge-based hyper-personalization.”
This uses AI to take personalization to the next level by combining demographic data, social media behavior, and purchase history with real-time data to deliver relevant, real time or close to real time content and information on products or services to the consumer.
It is more important than ever for a retailer to make fast decisions based on real-time data, as relying too much on historical data is like continually looking in the rear-view mirror while driving a car.
Just as AI is set to revolutionize the future of car transportation, we can look forward to AI technologies having a similar transformational effect in the retail sector.