Artificial intelligence is all around us: in self-driving cars and drones, virtual assistants and speech recognition tools. It’s how Google answers our searches, Spotify plays tending tracks, and Amazon recommends top deals. Marketing specialists predict that online retail stores would simply become extinct in some 5-10 years if they neglect AI-driven technologies today. Let’s see how AI can empower your eCommerce.
7 min read
Reshaped CX with Data Analytics
These days, to increase sales and have a competitive advantage over rivals, companies should not just meet customer expectations but exceed them - especially, with these demands getting higher each year. The level of customer experience (CX) personalization should go beyond usual loyalty programs or 10% discount on a second purchase.
A few years ago, Netflix became the talk of the web when it understood what its viewers wanted even before they realized they do. The media-services provider applied big data analysis to explore audience choices - which trailers and episodes they watched and for how long - and, based on this data, suggest content that the users might like. The company even went further to create, license and market new content that would be enjoyed. A recent Netflix release of Black Mirror: Bandersnatch is surely an innovation in interactive content and, at the same time, the company’s secret marketing weapon. Interactive TV rises Netflix’s capabilities of collecting unique user data to an incredible new level.
Now think of Netflix as a data company, not movie theater, of Ebay - as a data company, not an auction, of Amazon - as a data company, not online store. And the list goes on...
This way, marketing models turned upside down. Companies started to loath old-school salesperson approach of flogging products to customers. Instead of trying to satisfy customers’ factual needs, global brands predict and recommend products - even before customers know they need them.
Rather than simply knowing which purchases a customer did, brands now try to understand why they did these. What are their needs in specific products or preferred features? Do they have any special needs, like allergies or disabilities? Collecting, aggregating and analyzing customer data become crucial for a brand’s existence. So data analytics, AI and machine learning, and predictive modeling empower companies to build new business models and re-shape customer outreach.
Artificial intelligence (AI) refers to the ability of a software to imitate cognitive processes of human beings.
Machine learning (ML), as a branch of AI, makes software learn from data, find patterns, and generate business insights based on these.
Customer Genome Analytics
Every move in the World Wide Web is spied on, including how user interacts with the web (mobile/web, device type), responds to emails, which social networks uses, which products searches for and buys online. RankBrain, Google’s machine learning-driven software, monitors user engagement 24/7/365 to cherry-pick the best search results for every query in Google search bar. Each user move gives clues about a person’s preferences, passions, purchasing habits and potential needs. These attributes across all interactions create the so-called customer genome (the term used by Accenture.com), or a living profile of the most unique aspects of each individual.
If they care to, companies capture all these digital attributes of their existing and potential clients, aggregate and further analyze them to build a picture of typical target buyer personas. Their preferences, in turn, help brands deliver an orchestrated personalized customer experience: search results, recommendation engines, custom-tailored marketing email campaigns, new products, and whatnot - and eventually achieve expected ROI and increase sales.
Analytics and AI
It is impossible to keep up with such large volumes of data manually. So why not create an algorithm for this? Both the diversity of these preferences and the amount of data for analysis call for the use of artificial intelligence, in particular - for machine learning. However, many companies still fear AI in their eCommerce platforms.
E-marketers lack vision and strategy to integrate it
Managers do not know how to “interpret” big data for machine learning
Companies simply do not have the required skills and tech people
Yet, embracing AI today is crucial for any business innovations. It can offer a 30-50% increase in profits, and its impact on online commerce is predicted to reach nearly $40 billion by 2025. More so, customers already expect online stores to have AI-enabled technologies. Per 2017 studies, 44% of consumers use some type of virtual assistant; 22% do so daily. Eighty-six percent are satisfied with the experiences their virtual assistants provide.
You should definitely consider powering your business with AI to remain commercially viable.
How AI Helps Online Retail
Machines complete some tasks better than humans. So why not let AI take routine and analytics workload off your staff and let them focus more on “humane” tasks: creativity, empathy, intuition.
Here are a few areas AI can tackle easily:
- Improve customer service. AI-powered Chatbots, which could answer up to 80% of routine customer queries, are used to automate and speed up customer support, save on service costs, and allow humans to complete more complex and creative tasks. Similarly, Voice Assistants make services accessible even for people with disabilities and increase customer satisfaction rate.
- Predict future sales trends and help introduce new products and services that would satisfy this demand. Based on current sales rates, AI could as well predict future revenue and sales volumes.
- Optimize search and recommend products. Based on user’s purchasing history and feedback/reviews, AI-driven algorithms personalize the products assortment on an eCommerce platform and suggest fitting products - even before customers realize these are needed.
- Manage back-office tasks and customer information in a CRM system. These tiresome mechanical yet error-prone tasks could also be entrusted to AI.
- Track inventory, cataloguing, product content management (PCM). Another scope of routine mechanic tasks that AI could free you up from.
If you do not know where to start with AI in your online store, consider adding a predictive recommendation engine. We implemented a couple AI-driven wizards for a recent eCommerce projects, and they proved a huge success.
Logicify AI-Driven Recommendation Widget
Product recommendation algorithms are great in guiding users through the ever-increasing purchasing options. Moreso, 65% of consumers are more likely to buy from a retailer if they are recognized, remembered and receive relevant recommendation.
For our recent eCommerce project, we developed a few AI-driven recommendation widgets. Step-by-step, they prompt users to answer a few sequential questions about their product preferences and individual peculiarities (pregnancy, allergies). Depending on the given answers, the widgets recommend specific product(s) as a best fit.
The widgets do not simply suggest an old-school kit of supplementary products (aka “Frequently Purchased Together” items) but generate an intelligent hyper-personalized recommendation based on unique characteristics of a customer.
To predict the best product choice, we analyzed purchase history, collected and aggregated thousands of user reviews and web analytics applying machine learning. This big data was further subject to regression analysis and Bayes classifiers - to determine patterns and make assumptions - and transformed into predictive models. The more reviews analyzed - the more accurate the prediction. The higher customer mark for a product - the better perception among users with similar characteristics. Machine learning also allowed us uncover patterns in user behavior and choices that had remained hidden previously.
The widgets proved a great success and made users stay on the page for longer. Bounce rate reduced, while the conversion rate skyrocketed after the widgets were added to the website.
Brands used to focus on customer search as a key determinant for demand. With the emergence of machine learning powered by AI, companies now “shape” the demand by recommending products/services to their customers. Now, it is not only about what customers search for, but what they are advised to buy.
Using AI in your eCommerce app is no longer a nice-to-have; it’s a must. You could go with a chatbot, voice assistant, predictive engine - or all at a time. It helps you improve CX and grow sales. At the same time, using AI for PCM, inventory tracking, CRM, and back office will help you offload staff and save operational costs.
Before you make initial steps implementing AI, identify where automation and intelligence have the highest potential in your business workflow. It is crucial to first have a clear vision of how AI and ML should be used to power your eCommerce.
Once you have it, evaluate your current methods for data collection and, if necessary, adjust them before you start applying AI. Even the smartest of AI tools are useless with poor data.
Afterwards, collect both real-time and historical data on your customers and teach your software to analyze it in order to offer powerful personalized and contextual data to meet (and exceed!) your customers’ expectations. Achieve new levels of insight to keep consumers engaged.
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