The dream of any retailer, let's face it, is to be able to read the mind of its customer and consumer, in order to predict their demands and needs. Almost like having a crystal ball, which would allow him to assertively choose the assortment, prices, reduce stock to a minimum, minimize theft and expiration of perishable products, customize the experience for each shopper, and consequently leverage your business. Any retailer and shopkeeper would dream of something like this, am I right?
Now, the dream of any store and shopper customer is, let's face it, to be able to buy or consume exactly what they want, where they want, whenever they want. Fundamentally, all they want is infinite options, and control of the entire purchase journey, from being able to choose exactly which channel to make the purchase through, having better in-store experiences, to avoiding long lines at checkout and receiving personalized promotions in real time. To sum up, to have a less transactional and more pleasurable shopping experience
So far so good. But what if I told you that all this is increasingly possible thanks to the power of Artificial Intelligence? How can Artificial Intelligence already generate customer value, reduce costs for the retailer, integrate physical and digital sales channels in a multitude of different formats, and bring "Smart Stores" to life? That's exactly what we're going to talk about today, namely about how AI can help us better achieve customer centricity, and not only in retail!
Brinks is a 163-year-old business well-known for its fleet of armored trucks. The company also licenses its brand to a lesser-known, independently operated sister company, Brinks Home. The Dallas-based smart-home-technology business has struggled to gain brand recognition commensurate with the Brinks name. It competes against better-known systems from ADT, Google Nest, and Ring, and although it has earned stellar reviews from industry analysts and customers, its market share is only 2%. But its systems have generated a wealth of product usage information; its call centers have accumulated voluminous historical customer-level transaction data; and its field reps have been gathering competitive data since it began operations, in 1994.
Brinks wanted to find a way to use all this information to accelerate growth and optimize every customer touchpoint across all channels, especially in its messaging, personalization, and delivery of the user experience. In the fall of 2020, working with OfferFit, an AI start-up, the company tested thousands of combinations of messages and offers, varying the creative content, channel, and delivery times. It reorganized its structure around customer acquisition, service, and renewal and began using AI to optimize service-call scheduling, help cross-sell recommendations from call center reps, and conduct customer outreach for wireless system upgrades. In less than two years Brinks increased A/B testing from two or three tests a day to roughly 50,000 (with the capability to add more as needed). This process has dramatically reduced the need to wait for test results and has allowed Brinks to personalize every customer touchpoint. During the first half of 2021 its average direct-to-consumer (DTC) package size increased from $489 to $968. DTC revenue per user increased from an average of $42.24 to $45.95 during the same period. Overall revenue increased 9.5% compared with the same period in 2020.
Impressive isn't it? Let's start off by looking at the growth of AI adoption, which is impressive: the pace of investment is only increasing: The 2021 Stanford AI Index reports that the total global investment in AI has increased by 40% in 2020 relative to 2019, for a total of $67.9 billion. That's impressive indeed. And this is because AI proves to be beneficial to any customer experience through some levers, 3 of which we are going to focus on now:
- Personalization
- Less friction
- Generation of insights based on data
Starting off with personalization, companies across all industries are putting personalization at the center of their enterprise strategies. Recently, Kroger CEO, Rodney McMullen called seamlessness and personalization two of the key competitive “moats” in which Kroger is investing. Likewise, companies in home improvement (such as Home Depot), banking (JPMorgan Chase), the restaurant industry (Starbucks), and apparel (Nike) have publicly announced that personalized and seamless omnichannel experiences are at the core of their corporate strategy. We are now at the point where competitive advantage will derive from the ability to capture, analyze, and utilize personalized customer data at scale and from the use of AI to understand, shape, customize, and optimize the customer journey.
Although you might dismiss personalization as something that does not impact business, the truth is that yes, the impact is very big! Here's an example: an Accenture research shows that 91% of consumers say they are more likely to shop with brands that provide offers and recommendations that are relevant to them, while another from Adobe said that 66% of consumers say that encountering non-personalized content would prevent them from making a purchase. Adweek has shown that personalization can reduce customer acquisition costs by up to 50%, and can increase marketing spend efficiency by up to 30%.
Artificial Intelligence can leverage many variables that reveal how, when and what customers buy. Variables can include factors such as contextual data, behavior, demographics, expressed interests, customer reward programs, seasonal data (such as Christmas and summer trends), and even information about the local weather, while machine learning can identify patterns from all that and use them to make predictions about customer needs.
And finally, the ever-increasing power of GPU-based computing allows us to build increasingly complex (and often more accurate) models for a wide variety of tasks. Take this case: Hilton hotels, for example, use a robot concierge named Connie to make guest experiences as personal and enjoyable as possible. This two-foot-tall robot is in the lobby ready to greet guests and answer questions in select units. By knowing which guests are entering the hotel, these robots can learn over time to greet and provide more specific services based on the user's personal preferences.
In other words, in general we saw that personalization is essencial for the business as it is for the customer, and that today with the combination of Big Data, Artificial Intelligence and Machine Learning, it is increasingly assertive as it gets scaled. Why? Because the more we interact, the more the machine learns.
Now, the second factor is that A.I. reduces friction. What do I mean by that? Compare having to solve a problem with your internet provider, and on the one hand you have to do it on over the phone, where you wait in line for half an hour (a lot of friction), or via Whatsapp through an A.I.-powered chatbot (less friction): definitely the second scenario is much better for the customer! It is true that chatbots are not perfect either, but also it’s the same point as above: the more they interact, the better they become, through machine learning.
So how is it possible that conversational A.I. reduces friction in user experience? Thanks to a subfield of AI, namely Natural Language Processing, or NLP: customers can ask precisely what they want using their natural language, instead of interrupting a lot the customer’s experience, trying to fetch information or make a purchase creates friction. By minimizing the number of steps required to finish a task, you take away that frustration from customers when they go through a series of steps while purchasing a product. This issue can be resolved by opting for Conversational AI, and we can decrease the number of steps involved in the process, thus reducing friction. Currently, Amazon offers a ‘Buy now with 1-click’ solution to its users to decrease the friction while purchasing their products. It is a game-changer for the company and in the eCommerce industry. Zero waiting time!
And last but not least, A.I. can help much at obtaining a good Customer experience because it helps companies and leaders make better sense of customer data, and therefore provide better solutions and innovations to them. Think about the Netflix algorithm: it actually summarizes all the points above: personalization, low friction, making sense of data: being powered by A.I., it is able to predict much better what you might be interested in watching, than yourself.
And this is how A.I. can be better used to achieve Customer centricity.
