With Big Data providing an unprecedented amount of customer information to businesses, the need to leverage it for business advantage is driving powerful forces in the market. This is where Machine Learning comes into play.
According to Forrester Research, insights-driven businesses will experience 27% annual revenue growth from 2015 to 2020, reaching $1.2 trillion in total revenue, and Machine Learning technologies are expected to become a $100 billion market by 2025.
By learning from previous customer interactions, Machine Learning helps companies improve the customer journey to conversion by providing the right content in the right place at the right time.
So compelling is this opportunity, that, according to IDC, 64% of marketing executives surveyed said that “optimized message targeting” and “real-time personalized advertising insertions” will deliver significant value by 2020. In addition, a joint study by MIT and Google found that 50% of businesses plan to use Machine Learning for greater customer insights, and 48% expect it to yield a competitive advantage.
Clearly, Machine Learning can open the door to deeper customer insights and more valuable customer interactions. Here’s what you need to know about Machine Learning to strengthen customer loyalty and retention in your business.
What is Machine Learning?
Machine Learning is an application of AI that uses statistics to help computers “learn” with data (that is, incrementally improve performance on a task the more it’s performed), without being explicitly programmed to do so. As inexpensive computing power and increased network bandwidth and storage capabilities became prevalent towards the end of the last century, it became feasible to “train” computers to learn with minimal human guidance.
Machine Learning is very good at working through large data sets and multiple combinations of variables to find predictive relationships. Smart, customer-focused businesses can harness these insights to retain customers and grow their business through loyalty programs.
Learning More About the Customer
To optimize customer relationships, businesses must collect and analyze customer data. Machine Learning, with the help of Big Data technologies, assembles a vast amount of historical customer data into focused analytics that inform customer touch points and the customer journey. This past history can help predict customer behavior and record actual behavior to improve the next prediction.
In the context of customer retention, Machine Learning algorithms can help detect customer churn risk. Forbes recently noted three main causes of customer churn:
- Customer Interaction Feedback Is Neglected, Resulting in Unmet Customer Expectations. Too often, businesses rely on low-response customer surveys instead of high-value call center agent notes.
- Inability to Uncover Root Causes of Dissatisfaction and Cancellations. Surveys and speech recognition tools yield insufficient insight into customer motives.
- Inability to Detect Churn Risk Early in the Customer Lifecycle. Lost customers usually happen at the end of a long chain of events, but most businesses have limited insight into early stages.
When combined with multiple sources of customer data, Machine Learning can help businesses assess customer motivation and identify early shortcomings in their customer experience. Armed with these insights, customer-savvy businesses can get ahead of potential churn candidates in time to do something about it.
Improving the Customer Experience
With customer data and analytics in place, companies should work to optimize all aspects of the customer experience. Machine Learning offers compelling ways to improve customer service, automate customer interactions, and handle complaints, all of which can help Machine Learning-enabled businesses retain customers.
Improving Customer Service
Everyone is pressed for time. Routing customers to the wrong resources for the help they need, or otherwise taking a more-than-reasonable amount of time to resolve their problems eventually increases churn risk.
Natural Language Processing (NLP), a subset of Machine Learning that enables computers to understand written and spoken human language, can make customer interactions more useful by enabling customers to describe their problem in their own words. Machine Learning algorithms can then predict why the customer is contacting customer support, and translate that content into an actionable message for contact center agents.
Automating Customer Interactions
In some use cases, contact center agents aren’t needed. Early in the customer journey, chatbots, which are messaging applications that use NLP to interact with users, can facilitate less complex conversations and enable users to find solutions to their problems without direct human intervention.
One survey reported that 44% of consumers actually prefer chatbots for customer support, highlighting the opportunity to save money and increase customer satisfaction.
More broadly, Machine Learning enables greater customer self-service. User interfaces powered by NLP are more free-flowing and natural for end users than traditional automated support solutions. This flexibility can enable users to more easily explore different topics as the need arises, and the ease of interaction can break down barriers to interacting with a company and increase the number of engaged customers. More customers engaged for a longer period of time with a business will have an obvious, positive impact on customer retention.
Of course, there are some types of customer engagement that could have a negative impact on customer retention, like customer complaint calls. A complaining customer is already unhappy, and a perceived subpar experience in handling the complaint will only make things worse. But with Machine Learning, business can turn these situations into positive, relationship-building opportunities.
By predicting which customers will complain, and when, businesses can get ahead of the complaint experience with proactive outreach, preemptive solutions, and enticing offers. The resulting positive customer experience could have an obvious impact on retention, and even turn the customer into a promoter.
The customer retention impact of a good customer experience is somewhat obvious: If a customer consistently has a good experience with a company, they’re less likely to buy from a competitor.
The relationship between customer experience and customer loyalty is more nuanced. A compelling customer experience sets the stage for a successful go-to-market strategy by building a platform for repeat, meaningful customer interactions, reinforcing brand awareness, and, most importantly, collecting data that can fuel targeted loyalty programs.
Optimizing the Marketing Mix
Machine Learning can help companies take a compelling customer experience to market by refining their product offerings, pricing, sales channels, and promotions. Combining a personalized customer journey with a customized marketing mix helps drive customer loyalty and retention.
Matching people to products is an emerging Machine Learning application often seen in product recommendations on ecommerce sites (e.g, “Frequently bought together” and “Customers who bought this item also bought”). Using customer purchase history and other data, automated agents can make very specific and surprisingly accurate product recommendations based on even broad inquiries. As Machine Learning matures in recognizing customer needs when recommending products, it can also assist with designing new products.
Ecommerce sites also use Machine Learning to direct pricing recommendations. Travel and vacation websites, for example, use dynamic pricing to account for changes in supply and demand though the offerings themselves. And as Machine Learning influences product development, there will also be opportunity for it to refine pricing models.
There’s also great opportunity for Machine Learning to personalize where and how a customer learns about a product. According to a recent IDC survey:
“Personalization, or ‘a segment of one,’ has for long been the holy grail for
marketers. Personalization has the power to increase the relevancy and potency
of marketing communications, customer sentiment, and advocacy toward the
brand, propensity for conversion, and brand loyalty. These attributes maximize the
brand’s opportunity for customer retention and life-time customer value (LTCV) to
assure enterprise revenues and cash flow.”
In other words, because of Machine Learning’s ability to customize buy pages, place targeted banner ads, and recommend products, it’s poised to significantly impact how businesses reach and educate their customers. The same study noted that over 75% of businesses personalize marketing communications to at least a moderate extent using Machine Learning, and 63% expect that Machine Learning will improve their brand awareness.
Personalized marketing channels set the stage for targeted promotions and loyalty programs that are more enticing and valuable. Instead of sending all customers a large number of generic offers, Machine Learning-enabled marketers can now use known customer preferences to send targeted offers. This can improve marketing ROI by resulting in a higher conversion rate from a smaller set of offers.
Machine Learning can can similarly personalize the customer journey to purchase. Savvy businesses can apply Machine Learning to send personalized messages to buyers in the ‘evaluation’ and ‘purchase’ stages of the buyer’s journey, giving these interested potential customers the last push they need to increase loyalty and ultimately click the ‘Buy’ button.
Machine Learning offers businesses a tremendous opportunity to retain customers and improve customer loyalty, in addition to winning new customers. By making sense of the mountains of customer data many businesses have, Machine Learning provides unprecedented insight into potential customer problems, and opens the door to resolving those problems before they result in lost customers.
In addition, Machine Learning can greatly enhance the customer experience and increase customer interactions, solidifying buying habits that improve retention and set the stage for increased customer loyalty. And finally, Machine Learning can help businesses craft a personalized go-to-market strategy that both surprises and delights customers, turning them into advocates.
Try Import.io to see how you can use Machine Learning to retain customers and build loyalty.