The top 7 AI solutions for retail you shouldn’t miss
AI is driving customer experience
For a few years now, artificial intelligence has influenced and enhanced many parts of our everyday life. The most prominent examples include what happens when you search for anything via Google, how Facebook filters content on your news feed and the fact that Spotify creates weekly playlists with music you like. If you are looking for a job, chances are LinkedIn might actually have some pretty accurate suggestions for you. And when you come home, Alexa or Google Home is there to manage music, tv and your grocery shopping list for you.
All of these applications of artificial intelligence and deep learning algorithms have one major success factor in common: They personalize a customer’s experience to an extent that he/she feels valued and understood, which leads to increasing loyalty towards the system regardless of which type of system it is. Ranging from a networking platform or a music streaming service to a smart home assistant – all the aforementioned systems use big data to get to know their customers and make everyday life more convenient.
Why does offline shopping remain mostly AI-free?
Even so, there are still industries and markets, which, though they are equally dependent on the experience and loyalty of individual customers, have yet to employ such personalization. They have largely continued to do business without much AI despite having all the prerequisites for a successful AI implementation: huge amounts of data and recurring, sometimes dull processes. One of these industries is the offline retail sector.
Take shopping for groceries in an offline retail store: this comes with an anonymity which isn’t necessary anymore. Currently, you are not being remembered as a customer and neither are your preferences. Nothing about shopping for groceries – the way you interact with the retailer and vice versa –is personal.
Moreover, the loyalty programs that are in place in grocery retail don’t make use of the opportunities at hand. They may try to target customers based on sociodemographics or previous shopping behavior, but they are far from delivering a truly personalized experience. Perhaps grocers just haven’t known how to make sense of the tremendous amounts of data that is accessible to them. But with Amazon becoming a major (offline) grocer, many retailers are now realizing just how important it is to find a way to use this data to establish a meaningful 1-to-1 relationship with their customers.
In their Technology Vision 2017 for Consumer Goods Report Accenture refers to the fact that “Seventy-eight percent of industry executives agree that AI will revolutionize the way we gain information from and interact with customers.” Two other powerful sources are pointing in the same direction – which is why embracing AI is inevitable for the retail industry: in his 2016 Letter to Shareholders, Jeff Bezos, CEO and Founder of Amazon, talks about why it is so important for Amazon to keep the agile vitality of a Day 1 mentality, with a Day 2 mentality being a sure path to stagnation, followed by irrelevance and decline. One essential or key factor in maintaining a Day 1 mentality is “the eager adoption of external trends”. Bezos rightfully says “the outside world can push you into Day 2 if you won’t or can’t embrace powerful trends quickly. If you fight them, you’re probably fighting the future. Embrace them and you have a tailwind.” This statement could not be truer for the brick-and-mortar retail industry and artificial intelligence.
One other piece of evidence that now is the time to adopt personalization technologies is the fact that algorithmic retailing, as well as deep learning and machine learning, are on the rise (or at their peak) of technology trends in retail, according to Gartner’s Hype Cycle for Retail Technologies 2017. Algorithmic retailing is here defined as “the application of big data through advanced analytics across an increasingly complex and detailed retail structure to deliver an efficient and flexible, yet unified, customer experience” which goes hand in hand with Bezos’s advice to embrace external trends and stay extremely customer focussed.
Proven AI solutions for (offline) retail to drive customer experience
So here are some of the top AI solutions for the retail industry that have already proven their functionality and usability. These solutions (in alphabetical order) could help retailers get to know their customers’ behavior and therefore immensely enhance the customer experience and efficiency:
Aislelabs (based in Toronto, Canada, founded in 2013) has created a wifi location marketing platform for brick-and-mortar retailers, airports, museums and restaurants, which bears highly relevant insights about their customers’ demographics but also their behavior in the store and across stores of the same retailer via live dashboards. With the insights gathered via guest wifi, retailers are also able to run advertising campaigns and surveys to specific target groups.
Dôr (San Francisco, USA, 2015) is a foot traffic analytics platform which enables retailers to gain a clearer general overview of their in-store traffic and understand what attracts traffic to their stores in order to improve staffing and marketing decisions and increase revenue.
Fellow Robots (CA, USA, 2011) has developed a robot that uses AI, computer vision and speech recognition to help customers find products and thus creates personalized in-store navigation, and which also notifies staff when products are out of stock or misplaced.
Focal Systems (CA, USA, 2015) surf on the wave of customer engagement and understanding in a slightly different manner. They claim to enable store managers to gain differentiated insight into time, shopping trip path and trip length of a customer. Their system works within a tablet (including a camera facing the shopping cart) which can be attached to the handlebar of regular shopping carts and can handle out-of-stock detection, in-store location detection for location-based advertising and product search by the customer.
Phizzard (Berlin, Germany, 2014) has created smart changing rooms and an omnichannel solution including smart handhelds for in-store personnel, and an endless aisle solution that connects online and offline to reduce storage costs while still offering customers a broader selection of products. They are mainly active in the fashion, footwear and sports retail sectors and are a great example of how digitalization in retail can go hand in hand with improving customer experience and satisfaction.
Sensape (Leipzig, Germany, 2015) focuses on the evolution of regular digital signage (as it is used widely today) towards a smart marketing tool for customer engagement – going from plain digital screens to an informative and entertaining digital promoter. Their product brand ambassador uses artificial intelligence to determine age, gender and emotion of a customer in real time, and decides on specific targeting based on this information to draw customers in and create stronger customer relationships.
So1 (Berlin, Germany, 2012) uses the power of artificial intelligence and neural networks to enhance price promotions in the grocery retail industry. So1’s targeting engine analyzes basket data to understand customer behavior, product preferences, price sensitivity, and purchase intentions on an individual level and thus programmatically creates, ranks and distributes personalized and price-optimized promotions for individual users. This is automatically aligned with strategic goals of a retailer such as increasing loyalty, revenues and profits.
“The need to manage the business through algorithms is undeniable”
Huge disruptions are happening in retail – especially in the grocery retail market. These started getting a lot noisier when Amazon bought Whole Foods. And with the generally fast pace of innovation in the e-commerce sector, brick-and-mortar retailers cannot afford to stay with traditional same-for-everybody communication and promotions strategies. And they most certainly cannot continue to neglect the insights they can derive from the data available to them – which could be put to work with AI to enhance their engagement with customers.
As Gartner’s Hype Cycle for Retail Technologies 2017 sums it up: “The need to manage the business through algorithms is undeniable”.
At the moment, it still seems that retailers’ decisions on customer interaction are still being taken blindly. Trying to measure the success of customer loyalty programs and marketing activities should not start at the checkout, but by looking at the bigger picture of customer behavior and decision making. It should begin with the question of whether a customer will enter the store at all, and the reasons behind this decision in each and every single (recurring) case. Then in-store behavior can be tracked and analyzed – thankfully today the advanced technology to do this on an individual level is available.
Accenture advises “unlocking big data”: “To really know your customer, you need a 720-degree view of that customer, where the “second lap” is all about what you do on a daily basis with the insights generated. Only then can you make more effective decisions around how products, prices, and promotions influence an individual’s buying behavior.”
Artificial intelligence gives retailers a new way to bring back a more personalized shopping experience for their customers. The need to act quickly is clear – brick-and-mortar retailers will continue to fight an uphill battle unless they adopt some of the game-changing tech that online retailers have built into their business models.