Editorial
Machine learning is steadily making its way into the business world, especially in the area of digital experience. The CMS industry is jumping headfirst into this technology with the aim of providing smart content to drive contextual experiences.
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.” And as contextual experiences become a differentiating factor, machine learning supplies the answer to how businesses can provide these differentiating experiences at scale.
Machine Learning: Like Us, Only Way Faster
Reports that machine learning and other AI technologies will soon infiltrate every aspect of our lives, while likely true, can be a little 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 manually would, but many, many times faster.
Let’s say data is like paper — you can fold it and form it in many different ways to serve many purposes — write on it, wrap a gift with it, make a plane and soar it across the room. Machine learning is the equivalent of taking a large amount of paper, folding it and strengthening it, and making cardboard. Could you make cardboard by hand? Probably. Can a machine do it faster? Definitely.
Just as cardboard has a myriad of uses (shipping products, holding coffee, supporting the latest technologies, even anchoring a business), so will machine learning. Each industry, and every business, will mold it to support a variety of initiatives.
What will this look like? Here’s how three industries could incorporate machine learning into their digital processes, practices and operations:
How 3 Industries Might Use Machine Learning
Retail: Engagement Beyond the Basket
Machine learning in retail will be all about helping customers find the items they are looking for faster, but also about making that experience more enjoyable.
Personal shopping
Every customer’s journey is different. Someone looking for wedding items, DIY repairs or Halloween costumes will have different preferences and different stages of the buying journey.
For example, a customer who tends to purchase on the last Friday of the month? I think we have a good idea of when payday is. Provide them with more entertaining and educational content in the beginning of the month and a nice discount code the Thursday before payday.
Machine learning can cultivate this personal experience automatically, quickly and at scale.
Knowing exactly what they want
Natural language processing (NLP) will help create an incredibly efficient marketing machine from a simple site search bar. One person’s “Cable knit jumper” is someone else's “chunky sweater,” and NLP continuously learns to identify and connect intent.
These super-charged analytics give insight to more than just trending search terms, but can also tell you what overall topics and categories are likely to be the next big thing — and which of your current catalogue already fits nicely into these trends.
Automatic Cross-Sell
For retailers ready to go beyond product pages, metadata will make it easy to deliver contextual content.
For instance, two visitors check out the same copper lamp: visitor A goes on to search for “steel counter stools” and visitor B jumps to “light wood coffee table.” Your content engine will identify overlapping themes in these choices, and provide visitor A with “10 coolest industrial apartments,” and give visitor a B a “10-step guide to Scandinavian design.” (If I just made any design enthusiasts cringe, apologies. The machine learning engine will have a far better understanding of design elements than I ever will.)
How will this technology find these products on your site? Machine learning can automatically add metadata to enrich your existing content, constantly learning through a feed of internal data and crawling external domains.
Digital Brick-and-Mortar
Machine learning can make a customer's visit to a physical store location more personal and enjoyable. Thanks to previous purchases, you can already know their style preferences, scan the current available inventory and provide them with suggestions of in stock items in their style, size and typical price range.
A digital display can facilitate checkout at point-of-purchase, helping you to further understand the customer’s preferences for future personalization.
Learning Opportunities
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FinServ: A Personal Advisor, Automated
Machine learning will fuel the FinServ industry’s ongoing digital transformation, helping customers get more clarity, more transparency and better access to tools and services, at scale.
Automatic Advising
Embarking on financial planning can be daunting, as a number of factors go into a person’s financial decisions. By taking into account the financial choices (and successes) of all clients, machine learning can understand at scale where a client is currently in his financial journey and advise on the optimal next steps.
Providing the Right Information
People searching for “how to plan for a new baby” and those asking “how much do bonds grow over 18 years” will likely find the same piece of educational content helpful. With natural language processing, your digital experience engine will continuously learn search intent to automatically provide the best information to your users, and notify you when a commonly searched-for category needs further content.
Understanding Visitor Needs
Providing the most helpful content along the financial path can be done on a high level by analyzing click behavior across all visitors to determine an information path to help map out content (e.g., after reading about student loans do many jump to budgeting tips?).
On a personal level, analyzing financial history and current standings can pinpoint where a person is in her financial journey and automatically provide information to prepare her for the next step.
Manufacturing: End-to-End Efficiency
Manufacturing has always relied on data and analytics insights. Machine learning can process this information more efficiently to allow businesses to scale production and streamline supply chain management.
Inventory Optimization
Machine learning can take the massive amount of data collected down the supply chain and provide insights to drive a highly intelligent creation engine. Combining information on the availability of inventory, raw materials, assembly line equipment, transportation services and even weather conditions can allow manufacturers to plan and automate the end-to-end journey with ultimate efficiency.
When a customer runs low on a crucial item, this can be flagged automatically, trigger the optimal supply chain timeline, and a delivery can arrive with no gap in supply.
Personal Performance Metrics (and Forecasts)
Not only does the multifaceted data analysis through machine learning drive efficiency, it can also keep both customers and employees informed and in control of their supply lines through automated performance reports. These reports can give real time insight on the end-to-end supply chain and also provide further cost- and time- saving suggestions. For prospective clients, metrics from clients of similar industry, size, and location can be aggregated to create an incredibly accurate speculative report.
Product Innovation and Refinement
Analyzing purchase data will not only tell you what models and features customers are prioritizing, but will also tell you what features aren’t performing. If focusing on 500 options with the features people want will keep customers just as satisfied as offering 5,000 variations, you can streamline processes and gain efficiencies at a much lower operating cost.
A World of Convenience
Customers reward companies that anticipate their needs. Leveraging machine learning gives businesses the power to identify — and satisfy — customer needs at unprecedented scale.
Look back on your day so far, how many times have you used cardboard — a delivered package, a cup of coffee, toilet paper roll — without thinking twice about it? This is how machine learning and AI will infiltrate our lives. Not through robot servants (at least not yet), but through using the data of our choices to make every day a bit more convenient, in every industry.
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