Nine innovative use cases of AI in retail to show you where the opportunities lie.
Nikole Wintermeier | Jun 19, 2019
Nine innovative use cases of AI in retail to show you where the opportunities lie.
You’re running a retail store in an age of digital disruption and competing with innovative future technologies like Artificial Intelligence.
You are pitted against behemoth retail stores like Amazon who maximizes on customer intelligence.
The 4th industrial revolution (or so they are calling it) is everywhere you look:
Artificial intelligence, cryptocurrency, the internet of things (IoT), the Black Mirror shoppers carry in their pockets wherever they go….
Your customers are growing digitally literate and more and more emancipated. They require immediacy, trust, and gratification from omnichannel shopping experiences.
‘The customer is always right’ has now shifted to the customer is both right and empowered.
So what’s the good news?
The digitized economy of experiences is growing at an exponential rate:
Moreover, 72% of retail stores reported that intelligent technology will be critical to their organizations’ market differentiation. And it's predicted that AI will add 16 trillion dollars to worldwide GDP by 2030.
So, how do you gain a competitive advantage in this digital retail future?
Answer: By leveraging AI in an innovative way.
This article will chart how the innovative use cases for AI in retail, in both the offline world and online. Then, exclusive for you, we give you five recommendations for implementing an AI culture in your business.
Firstly, how is AI functioning in physical stores (or: brick and mortar)?
Frankly, its function is prolific.
Here are two extreme (but very cool) examples to understand the physical market that you’re dealing with.
When Amazon moved to brick-and-mortar, they brought with them AI to drastically change the way we shop.
They recently launched ‘Go,’ a supermarket without check-outs, cashiers, or lines.
The ‘just walk out technology’ uses AI to observe what product shoppers choose before - nonchalantly and not feeling criminal at all - they just walk out of the store.
Amazon actually accounts for 44% of all US eCommerce sales. Because of their obessive customer-centric strategy, they've found a way to reduce the psychological stress of shopping in shopping in-stores.
In the wake of the pandemic, reduced foot traffic and seamless in-store experiences will become more relevant.
During this eBay-Saatchi experiment, machines were used to analyze the subconscious of shoppers and determine what products they (or their neurons) responded to best.
The products with higher brain activity were then added to their virtual shopping carts, and voila!
The first subconscious shopping experience.
"Shopping has always been intensely personal [...] It's an expression of what makes you, you." (Julia Hutton-Potts, Director of Communications, eBay)
You don't have to go as far as eBay, of course. This was merely an experiment. But on a basic level, understanding the subconscious purchase behavior of your shoppers will go a long way.
IKEA’s augmented reality app ‘Place’ allows customers to visualize how IKEA furniture fits into their household spaces.
IKEA boasts over 817 million customers per year, and is one of the "most loved companies worlwide".
While this technology is great for personalization, you don't have to blow budget on new shiny toys.
What IKEA demonstrates is how to make shopping more efficient, bridging the gap between the digital and physical worlds. AR could also be virtual try-ons or changing rooms.
It's about bringing the same in-store experience to your webshop. Take a look at how ASICS has emulated their in-store shopping assistant online, for example.
But there are way more examples of AI in eCommerce.
Ever watched House of Cards?
If you did, you probably liked it.
That’s because Netflix used AI-powered pattern learning to analyze vast amounts of customer data to predict what show people would enjoy.
They found a trend that people who liked Kevin Spacey were also watching political dramas.
Armed with this data, people were hired to write what would become the award-winning show that is House of Cards.
The same kind of concept where Big Data gets a creative spin is being leveraged in eCommerce. AI is not only being used to automate processes, but to generate valuable information about your webshop visitors.
In eCommerce, AI science provides:
These are just part in parcel of a host of other use cases for AI in eCommerce. eCommerce directors are leveraging AI science to facilitate customer-centric strategies across the entire business structure.Let's take a look at some particularly innovative AI use cases online.
Pinterest recently introduced an updated chrome extension which allows users to search for any product online through an image.
This means that if you stumble across a picture of a man wearing a cowboy hat and denims and you think, “cool jeans!”, you can now hover over the jeans and Pinterest’s visual search technology will let you browse similar products by the same webshop.
The same applies for the cowboy hat, if that’s your thing.
[Although visual search is not always accurate because it relies on the resolution and clarity of the product photo... I mean, Professor McGonagall is wearing a witch’s hat. But we’ll get to her later].
eBay’s Shopbot is a chatbot that engages with customers by giving recommendations and advice.
This simplifies online purchase decisions especially for mobile phone users, fostering them that one-on-one time with AI (shoppers can communicate through text, voice, and pictures).Ubisend reports that 40% of consumers use chatbots to look for offers and deals.
Remember how many different shopper states there are?
Segmentation starts by collecting data, and automating the profiling process. This is where AI comes in to facilitate customer intelligence.
We've already seen this pivot in other industries. As brands start to recognize the untapped opportunity of customer intelligence, they will gain a competitive advantage.
Take Mitingo, for example, an AI-powered platform that synthesizes billions of customer data points.
Their mission is: to place the ‘power of AI in your hands’. Their goal? To implement AI across the whole business funnel for their partners.
Mitingo’s predictive lead scoring and data enrichment application enabled Red Hat to increase customer acquisition and conversion rates.
They do this by centralizing their data, and finding ways to make it actionable. For Red Hat, they leveraged were able to profile customers and therein offer personalized experiences tailored to those segments.
Similar to Mintigo is Beabloo, who call their AI project Minerva - making me nostalgic for my Harry Potter childhood and Professor Mcgonagall.
Except their use of AI and machine learning is not magic.
For Fira Barcelona they implemented a CMS for unified campaign management. They also implemented an audience analytics dashboard which delivered relevant metrics for campaign planning.
They have also piloted a virtual assistant for brick and mortar - this one called Halo - and set up digital interactive kiosks in NBA stores in China for improved shopping experiences,
“integrating the advantages of online shopping into the offline world” [Beabloo: FYI, the blending of physical and digital is a marketing trend called “Phygital”
Qloo taps into "cultural artificial intelligence". They predict consumer tastes and trends across different industries.
For eCommerce, they can leverage consumer insights with data-backed assumptions.
By combining audience exploration, cultural personalization, and location/geographic analysis, they not only innovate the use case of AI means in retail, but across multiple disciplines and service verticals.
This way, we’re able to highlight products on the PLP and inform shoppers of what they want to know on the PDP.
“When explaining Crobox we say derived from behavioral psychology, optimized by AI. Our machine learning algorithms learn from the behavioral data of shoppers on a specific client’s platform.” (Janelle de Weerd, Marketing Manager at Crobox [read the full interview here] )
In short, AI can work to optimize your entire conversion funnel. But it would be nothing without personalizing the shopping experience at every touchpoint of the funnel.
As we’ve seen, psychology and the subconscious are becoming increasingly important for customer-centric approaches in retail, both online and offline.
So while AI is a great first step, psychology can provide those deeper insights that can be leveraged to create more meaningful relationships with your shoppers.
Now you may be thinking: Easier said than done. Which is why I've compiled some recommendations for leveraging AI. Armed with these tools and the use cases above, you'll be on your way to innovation in retail.
AI will enhance your digital platforms and existing analytics, allowing you to structure and integrate your data to deliver personalized customer experiences.
We therefore recommend:
1. Scaling up eCommerce, business technology & development roles.
2. Providing tech skills-trainings for all departments.
3. Investing in an enterprise-grade analytics solution.
4. Making sure a company wide AI-ambition is echoed across all departments.
What could be more important?
Harness the power of AI to analyze Big Data and use this to improve customer experience across all touchpoints.
This 360 customer engagement should be a mandate from C-level down, ensuring that machine learning for customer intelligence is implemented across all segments rather than operating in a silo.
A mature analytics solution (and not just, for example, aggregated data) that identifies high-value segments can drive campaign strategies, channel mix, ad budgets, etc.
The future of retail will depend on these continuous learning tools and, in order to adapt, we strongly recommend using AI to enrich your data analytics model.
Therefore, allocate enough budget for this to get ahead of the curve.
Empower the data experts within your company!
“Leveraging customer data to deliver hyper-relevant experiences takes a new level of marketing intelligence”[Salesforce]
The more digestible and unified these insights are, the better the 360 customer engagement across all channels.
We therefore recommend equipping all departments with the tools, data, and insights to make smarter, more customer-centric business decisions.
Having this universal decision making engine will increase conversion rates, customer loyalty and retention, reduce churn, driving revenue and growth.
Your customers have high expectations for every touchpoint they interact with. Technology can help make shopping easier, relevant, and more enjoyable.
You can now create authentic shopping experiences - maybe not to the extent of eBay, but still to an extent where you can empathize with shoppers using the data synthesized by AI and machine learning.
The Bottom line?
With all these use cases under your belt, you'll soon be able to participate in your own innovative future.