It’s easy to lose ground in e-commerce. In a crowded market where customers can buy from a competitor as simply as following a link, retailers have to work hard. Every search term, product description and user pathway matters.
Not surprisingly, smart players have come to depend on the abundance of data consumers make available at every step of the business process. They deploy predictive analytics, machine learning and other Artificial Intelligence techniques to redefining the rules of the game, helping some stay ahead of the competition, and improving the customer experience overall.
Here are just a few examples of how.
Enhancing product discovery with image classification
Some of the most interesting attempts at augmenting the shopping experience have involved classifying, understanding and interpreting images. With , you can see the beginnings of how AI fits in a retail context, supporting the customer experience by providing alternative yet intuitive ways of searching for products.
For example, a user may be looking for something that they can’t specifically name. When we think about the importance of craft and style to brands like Pinterest and Etsy, the commercial strength of being able to say “find me something that looks a bit like this” is obvious.
is a visual search engine which uses AI to connect consumers with information and brands. Once a user has snapped a picture, the CamFind app uses mobile visual search technology to identify the object and provide information about it.
You might spot a nice pair of shoes – actually on someone rather than in a shop window. The application will identify the brand, present product information, and link to online marketplaces that have the shoes in stock. Camfind can also extract other types of data from pictures; for example movie posters, which will immediately generate results for local showtimes, movie trailers, and links to ticket sites.
The key element here (beyond the heavy processing power required to interpret pictures in the first place) is that picture classification is being used to shorten the sales cycle and leverage motivation: put simply, the customer was interested enough to take a picture, so let’s remove the barriers which might prevent that enthusiasm from turning into a sale.
Merging marketing and sales for better a CRM system
Predictive analytics is creating entirely new opportunities for marketing and sales, creating and identifying leads, and increasing conversion rates across the board.
is a predictive platform whose newest application, the , uses AI to directly provide actionable sales intelligence to Customer Relationship Management (CRM) systems. Where CRM has traditionally been a data repository – useful, but requiring interpretation; Mintigo can identify who will buy from your company, what they’ll buy, why they need it and how you should meaningfully engage with them to maximise your chances of a sale and the size of sale.
Mintigo’s application combines predictive insights, purchase intent, and sales guides into an interface that is easily and directly accessible from modern CRM systems (such as Salesforce, Oracle Sales Cloud, SAP, and Microsoft Dynamics.) Beyond individual leads, it can also work to categorise audiences, and then build strategies which will support marketing campaigns.
Some of the most successful marketing businesses of the 2010s have optimised marketing effectiveness, to the detriment of consumer experience. Nobody, for example, has ever been glad to have adverts which chase them round the web or across their devices. Conversely, the most successful marketing businesses of the next five years will use AI to collect, collate and interpret multiple data sources to optimise marketing effectiveness whilst improving the consumer experience.
Re-engaging customers with smart technology
Of course, AI will also lead to new product categories in their own right, driving e-commerce sales to new heights. The Internet of Things (IoT) uses AI to connect consumers with e-commerce in new ways; driven by technology, but which will be valued for a very human experience.
has collaborated with FreshDirect and ShopRite to create a smart refrigerator that couples AI technology with three cameras that can keep track of food levels and order groceries when levels get low. The integrated smart tablet and accompanying software can recommend recipes based on ingredients to-hand and even organise your family’s eating schedule.
And as our devices become more connected (and remember, software can be updated in ways a fridge can’t), we can expect everything from diet plans to diaries to influence our food and eating habits in real-time. Want to cut the carbs? Ask the fridge. Staying out late tonight? Don’t call Mom, just tell the fridge and food won’t go to waste! Whilst that sounds like pure convenience, brands will also capitalise on the seamlessness and simplicity which consumers crave – is the rudimentary poster child for the way processes in e-commerce will be automated.
Offering a more personalised customer experience
Every business needs to identify the right person at the right time in order to make a sale, and technology is assisting e-commerce by providing customer analysis with seamless speed. Through machine learning systems and cognitive computing, AI drives conversions by personalizing a consumer’s online experience.
Virtual assistants and chatbots are two examples of AI personalizing the customer experience. For example, is using IBM’s Watson to help customers find the perfect jacket for their next adventure.
When you visit The North Face website, Watson pops up to ask: “Where and when will you be using this jacket?” and as the customer speaks or types a response, Watson follows up with a few more clarifying questions. After compiling the responses, Watson scans the available online inventory and recommends a selection of jackets, based on relevance rather than traditional catalogue order.
is another example of a company using AI for e-commerce applications. It builds branded chatbots that businesses can use to interact with customers by using natural language processing (NLP) to offer 27/7 customer support, track user behavior, and achieve brand continuity. Wizeline claims to increase engagement rates up to five times more than other channels and increase retention rates up to six times.
As these AI technologies continue to develop, we can expect two general trends. On the one hand e-commerce will become ever more prevalent and automated: commercial entities will know more about us, and use that data to wrap us in environments which encourage us to buy, present us with relevant offers, and reduce the barriers to purchase. Equally, thee-commerce and traditional retail experiences will both become more natural, engaging and personalised, with bespoke products, services and processes that transform and improve the customer experience overall.
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