[{"@context":"https:\/\/schema.org\/","@type":"BlogPosting","@id":"https:\/\/www.the-future-of-commerce.com\/2017\/08\/01\/machine-learning-predictive-analytics-b2b\/#BlogPosting","mainEntityOfPage":"https:\/\/www.the-future-of-commerce.com\/2017\/08\/01\/machine-learning-predictive-analytics-b2b\/","headline":"Machine learning and predictive analytics in B2B","name":"Machine learning and predictive analytics in B2B","description":"Companies that fail to react to changes in consumer expectations have closed doors or are struggling. Machine learning and predictive analytics can help keep them open.","datePublished":"2017-08-01","dateModified":"2024-04-18","author":{"@type":"Person","@id":"https:\/\/www.the-future-of-commerce.com\/contributor\/kevin-carlson\/#Person","name":"Kevin Carlson","url":"https:\/\/www.the-future-of-commerce.com\/contributor\/kevin-carlson\/","identifier":55,"image":{"@type":"ImageObject","@id":"https:\/\/secure.gravatar.com\/avatar\/adfd0eb8645b56bf7c63f06a6c2c595bc7311eff0f1719b8081fa78654dbb9b1?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/adfd0eb8645b56bf7c63f06a6c2c595bc7311eff0f1719b8081fa78654dbb9b1?s=96&d=mm&r=g","height":96,"width":96}},"publisher":{"@type":"Organization","name":"The Future of Commerce","logo":{"@type":"ImageObject","@id":"https:\/\/www.the-future-of-commerce.com\/wp-content\/uploads\/2023\/01\/logo-foc-schema-app-1.png","url":"https:\/\/www.the-future-of-commerce.com\/wp-content\/uploads\/2023\/01\/logo-foc-schema-app-1.png","width":172,"height":60}},"image":{"@type":"ImageObject","@id":"https:\/\/www.the-future-of-commerce.com\/wp-content\/uploads\/2017\/08\/thumbnail-3dbf6aef79950ac16d817c927fcea99d.jpeg","url":"https:\/\/www.the-future-of-commerce.com\/wp-content\/uploads\/2017\/08\/thumbnail-3dbf6aef79950ac16d817c927fcea99d.jpeg","height":375,"width":1200},"url":"https:\/\/www.the-future-of-commerce.com\/2017\/08\/01\/machine-learning-predictive-analytics-b2b\/","about":[{"@type":"Thing","@id":"https:\/\/www.the-future-of-commerce.com\/commerce\/b2b\/","name":"B2B Commerce","sameAs":["https:\/\/en.wikipedia.org\/wiki\/B2B_e-commerce"]},{"@type":"Thing","@id":"https:\/\/www.the-future-of-commerce.com\/commerce\/","name":"Commerce","sameAs":["https:\/\/en.wikipedia.org\/wiki\/Commerce","http:\/\/www.wikidata.org\/entity\/Q26643"]}],"wordCount":1364,"keywords":["B2B","Big Data","Customer Engagement","Data","E-commerce","Machine Learning"],"articleBody":"A little over three years ago, I wrote a series of posts about the pressures online retail was placing on traditional brick-and-mortar stores. From behemoths of the past, like Borders, to those at risk today such as Sears, companies that failed to react to broad changes in consumer expectations have closed their doors or are struggling to keep them open.Let\u2019s be fair, though. Physical retail isn\u2019t doomed. It\u2019s a piece of the puzzle in the omnichannel world. It\u2019s just not the centerpiece anymore. Companies that are succeeding are paying close attention to customer data, learning or predicting what their customer\u2019s actions will be and driving customer experience to an entirely new level. A level that is the new baseline for how to compete and succeed in retail.And if you\u2019re in the B2B space, I hope you\u2019re paying attention to the growing role of machine learning and predictive analytics.Over the past 20 years, I\u2019ve helped many companies implement both B2C and B2B sites. The challenges are different in some ways, but the expectations of those interacting with a site have been converging for years.\u00a0 And as companies like Amazon continue to expand in the B2B world, you can be sure that the demand for a \u201csmarter\u201d experience will grow.Most companies in the B2B space are using web analytics to track the basics; page views, visitors, bounce rate, and similar metrics.\u00a0 Today, that is the low bar.The true value that can be extracted from the way in which customers interact with a site isn\u2019t lurking in an obscure Google Analytics report. It\u2019s buried in the wealth of data that can be collected from customer interactions. For companies both small and large, the world of data science can be intimidating and knowing where to start can leave one\u2019s head spinning.But ignore this at your own peril.B2B market: 3 things to knowOK, so a full discussion of data science is beyond what\u2019s possible in a blog, but here are some key points that all in the B2B space should know:Being able to respond quickly and accurately to changes in customer behavior is critical.\u00a0 The days of taking a week or longer to peruse reports and figure out what your customers want are long gone.Predictive analytics and machine learning are here to stay and companies that employ these techniques will outmaneuver those who don\u2019t.Your commerce platform may or may not be able to gather all the data you need. It\u2019s a piece of the puzzle and there are other sources to consider.So how can B2B companies use predictive analytics and machine learning? Here are a few use cases that I\u2019ve seen.Machine learning and predictive analytics: Customer classificationUnderstanding behavioral tendencies of customers and grouping them with similar customers can be an effective way to focus marketing and merchandising efforts.\u00a0 One classification that most have likely seen is the \u201cVIP Customer\u201d but too often, B2B companies simply decide, using a single metric, what constitutes a VIP.One implementation I\u2019ve seen simply classifies any customer that purchased over $100 as a VIP and placed a little badge on the customer\u2019s online profile. That\u2019s not really classification; it\u2019s closer to gamification which gives the customer some sort of sense of achievement and hopefully boosts their loyalty.Real classification is based on a set of data features measured across all recent customer activity. In other words, whether someone is a real VIP customer or not, is going to change when compared to the behavior of the entire customer base.Knowing customer classification and being able to use a classification model to predict a group to which a new customer is likely to belong, can help greatly in determining which promotions to show to a customer while they\u2019re on the site or via email.\u00a0 In a recent machine learning implementation we did for a B2B firm, we grouped customers into several segments by using data from a recent time period:VIP Shopper: The highest tier of customers based on value of conversions and number of conversions.Engaged Shopper: A large number of visits, an active cart, numerous product views, and at least one conversion.Window Shopper: Several visits, numerous product viewsThis information was not shared with the customer \u2013 it\u2019s not a public distinction, rather it\u2019s an internal indication of how to interact with a customer. Having this information allows marketers to display targeted promotions on site during a visit. For example, if a customer falls into the \u201cEngaged Shopper\u201d classification and has an active cart that is above the AOV, a coupon for free shipping or a discount could be displayed to move this shopper toward conversion.It\u2019s also important to note that models aren\u2019t static. They must be recalculated frequently. In the recent implementation, the models would recalculate every few hours to ensure that they were as current and as accurate as possible.Prescriptive product and content placementThere are many reasons buying behavior may change. Seasonality is one such reason. Other reasons that may drive short term behavioral changes are weather, news, shortages, and manufacturer promotions. Using models to detect conditions that are \u201coutside of expectations\u201d and adjust site merchandising to respond to time sensitive anomalies are becoming more commonplace.This technique can be used to alter search results, home page item placement, category item placement, and guide users to product information when a search for a competitive product is conducted.Does a given product perform better when visible at the top of the home page? Some do, some don\u2019t. Being able to detect optimal placement for products given a recent history of activity such as conversions and cart additions can be accomplished through machine learning. When introducing a new product, similar models can be used to predict a best-placement for optimal performance based on product attributes.Improving personalization with machine learningPersonalization on most B2C sites has a long way to go. And on B2B sites, it\u2019s still in its infancy. While it\u2019s possible to group shoppers into a cohort and show them similar items, machine learning makes it\u2019s possible to make these cohorts smaller, approaching a more unique experience for your B2B customer. This can be especially impactful when there are multiple B2B buyers from a single customer.To begin moving toward a more unique experience, machine learning models can be developed starting at a high-level, then progressing to more granular levels that deliver unique insights into a customer.For example, beginning with geolocation as a factor, a B2B seller of industrial HVAC equipment should feature different products for customers in Minnesota vs. those in Florida. The buying seasons are not only different, but events in Florida such as an impending hurricane may influence buying behavior in the short term. A properly designed model can help spot these changes and alert the B2B marketer to changes that may require a change in site merchandising for a geographical region.To add on, a company could develop models that can predict the optimal sort order for search results for a customer, the ideal products and categories to feature for them, along with suggested promotions based on their recent behavior.Looking forwardIt\u2019s a fascinating time to be working in commerce and with data in particular. The convergence of low-cost cloud-based computing and the abundance of data available from a wealth of sources (not the least of which is a company\u2019s B2B site) and provide a lot of actionable intelligence with an investment orders of magnitude lower than a decade ago.That not only puts machine learning and predictive analytics within reach, it positions the technologies to become a core part of how you relate to your customers and how you operate your business.Those that ignore the call today, may likely be tomorrow\u2019s Borders.  Regain control of your business.From manufacturers to high tech,innovation drives B2B success.And it starts HERE."},{"@context":"https:\/\/schema.org\/","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"2017","item":"https:\/\/www.the-future-of-commerce.com\/2017\/#breadcrumbitem"},{"@type":"ListItem","position":2,"name":"08","item":"https:\/\/www.the-future-of-commerce.com\/2017\/\/08\/#breadcrumbitem"},{"@type":"ListItem","position":3,"name":"01","item":"https:\/\/www.the-future-of-commerce.com\/2017\/\/08\/\/01\/#breadcrumbitem"},{"@type":"ListItem","position":4,"name":"Machine learning and predictive analytics in B2B","item":"https:\/\/www.the-future-of-commerce.com\/2017\/08\/01\/machine-learning-predictive-analytics-b2b\/#breadcrumbitem"}]}]