Machine learning is creating a foothold in the business world, especially when it comes to innovative digital experience, and Web Content Management (WCM) players are diving headfirst into machine learning in the aim of supporting a smart experience across industries. As Forrester’s recent industry overview said, “The web CMS market is changing because more organizations recognize the necessity of contextual digital experiences. Every vendor in this landscape is tracking toward this goal.”¹ As contextual experiences increasingly become brand differentiators, the ability of machine learning to provide these experiences at scale is massively advantageous.
The broad statement that machine learning and other AI technologies are going to infiltrate all corners of our lives, while likely true, paints an often dystopian picture that can be a bit overwhelming. But I’ll let you in on a secret: machine learning isn’t magic, it is simply an analysis of data in the same way you would manually. Computers are just able to do it many, many times faster.
Let’s say that data is like paper – it comes in different shapes and sizes, you can manipulate it in many ways to serve a multitude of purposes – write on it, wrap up a present, soar a plan around a room. Machine learning is the equivalent of bundling large amounts of paper, folding it, and making a stronger cardboard. Could you make cardboard manually? Probably. Can a machine make it faster? Definitely.
Just as cardboard has a wide array of uses (packaging products, holding coffee, supporting innovative technologies, even anchoring a business) so does machine learning. Every industry – and every individual business – can mold it to support their core initiatives.
What will this look like? Here’s how 3 industries could go forward with machine learning to create a more contextual digital experience.
Retail: Engagement beyond the basket
Helping customers find exactly what they’re looking for, and making the experience more enjoyable, can be accomplished at scale through machine learning.
A personal shopper for everyone
Every customer’s journey is unique. Even those searching in the same categories – DIY kitchen supplies, holiday decorations, running shoes – will have different preferences and be at different stages of the buying process. For example, a customer who repeatedly purchases on every second Friday? I think we have a good idea of when payday it. You can provide them with more lifestyle focused content the first week and send a nice discount code the Thursday before payday. This type of personal experience can be cultivated automatically, and at scale, through machine learning.
Knowing exactly what they want
Natural language processing (NLP) can take a simple site search bar and create a highly efficient marketing machine. One person’s “gray sneakers” is someone else’s “grey trainers”, and NLP continuously learns to identify and connect intent. These high-powered analytics provide insight on more than just trending search terms, they can also identify what overarching categories and topics are likely to be the next big thing – and show you which of your current catalogue can nicely fit into these trends.
Contextual Experience
For retailers ready to go beyond product pages, contextual content can be automatically intertwined with products via metadata. For example, two visitors view the same copper lamp – visitor A then searches for “light wood coffee table” while visitor B jumps to “steel counter stools”. Your content engine will see the overlapping themes in these choices, and provide visitor A with a “Beginners guide to Scandinavian design”, and offer visitor B “10 coolest industrial apartments”. (If I just caused design enthusiasts to cringe, apologies. The machine learning engine will have a far better understanding of design themes than I ever will.) So how will this technology match content to products? Machine learning can automatically read your existing content, add metadata, and match it to product metadata, all while constantly learning through a feed of internal data and through crawling external domains.
Digital Brick-and-Mortar
When today’s customers visit a physical store, their brick-and-mortar experience is disjointed from their digital one. Machine learning can make the in-person and digital experiences cohesive. Through previous purchases and viewing history you can know their preferences, based on location understand that they are in the store, scan the current available inventory, and suggest to them items that are in stock, in their style, size, and typical price range. A digital display can facilitate checkout at point-of-purchase, feeding purchase information back into their digital profile to help deepen future personalization.
FinServ: A personal advisor, automated.
The financial service industry is charging forward in digital transformation initiatives and machine learning will catalyze this transformation by helping customers get more clarity, more transparency, and increased access to services and tools, at scale.
Automatic Advising
Creating a financial plan can be daunting, as a variety of personal factors go into every financial decision, and no two paths are exactly the same from start to finish. However, by analysing the financial choices (and successes) of all clients, machine learning can better understand where each individual currently is in his financial journey and automatically predict where they can financially succeed down the road.
Providing the Right Information
Chances are that a person searching for “bond growth over 18 years” and another person looking up “college fund for new baby” can be helped by the same educational content. With natural language processing, your digital experience engine will continuously learn search intent to provide the best information to your users, and can notify you when a commonly searched-for category needs further content.
Understanding Visitor Needs
Through analysing click behavior across all visitors, machine learning can help map out an information path (after viewing student loan information do many people head to budgeting tips?) and provide the most relevant and helpful content along the financial path. On a deeper level of personalization, financial history and current standing can be analyses to pinpoint exactly where someone is in her financial journey and automatically provide the information to prepare her for the next step.
Manufacturing: End-to-End efficiency
Data and analytic insights has always been a core aspect of manufacturing and with machine learning the information can be processed infinitely more efficiently to allow businesses to scale production and streamline supply chain management.
Intelligent Supply Chain
Machine learning will take the mass amount of data collected along the supply chain and use it to provide insight to drive a highly intelligent creation engine. Analysing information across inventory availability, raw materials, assembly line equipment, transportation, and even weather conditions will allow manufacturers to map out, and automate, an end-to-end journey with unprecedented efficiency. Low inventory at a customer can be flagged automatically, and trigger the optimal supply chain timeline – delivering new inventory with no gap in supply.
Personal Performance Metrics (and Forecasts)
The multifaceted data analysis through machine learning will not only drive efficiency, it can also keep stakeholders informed and in control of their supply lines. Automated performance reports can offer real time insights on the end-to-end supply chain and provide further time- and cost- saving suggestions. For prospective clients, metrics from current clients of similar industry, size, and location can be aggregated to create a highly accurate speculative report.
Product Innovation and Refinement
Purchase data not only informs you of the models and features your customers are prioritizing, but also identifies what features just aren’t performing. If focusing on 500 options with the most in demand features will keep customers just as satisfied as offering 5,000 variations, then processes can become far more streamlined and efficient – at a much lower operating cost.
A World of Convenience
At the end of the day, customers reward the companies that anticipate and satisfy their needs. Leveraging machine learning gives businesses the power to identify, and personally cater to, customer needs at unprecedented scale.
Look back on your day, how many times have many times have you used cardboard – a cup of coffee, a delivered package, a toilet paper roll – without thinking twice about it? This is how machine learning and AI will infiltrate our lives. Not through robot butlers (yet), but by using the data of our choices to make every day a bit more convenient, across every industry.
The Forrester Wave: Web CMS Q1, 2017. Mark Grannan. January 24, 2017.
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