Rise of Machine Learning
Arthur Samuel, who coined the term machine learning in 1959, said that “rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves”.
In recent years, the rapid advancements in technology & infrastructure in Data science has made machine-learning one of the leading vehicles in development of intelligent applications & systems. Machine learning now provides a way for programming language based applications to scale in a manner that was not possible previously because all programming languages are essentially a set of predefined rules.
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Machine learning is now manifesting its power in a myriad of applications such as: Digital (web & mobile applications), healthcare, surveillance & robotics, etc. Industries such as retail & finance are at the forefront of leveraging machine learning, given they generate/possess vast amounts of customer data, which is the lifeblood of machine-learning. This allows these industries to connect with their customers in a much more scalable way by serving intelligent & personalized experiences.
How Machine Learning Impacts Customer Experience
Below are a few important applications of machine learning to improve customer experiences:
Predictive analytics allows brands to understand intent of their customers. It allows brands to predict future trends & imminent customer behaviour in order to fine tune marketing strategies. An insight that was previously unavailable.
2. Personalize Customer Experiences
Forbes recently said that most of the personalization available today is essentially items you’ve searched for that follow you around. With machine learning, companies can actually personalize the customer experience & serve the right content or product at the right time in their journey. This aspect will play an even greater role in maintaining customer loyalty & acquiring new customers.
“By providing better search results, Netflix estimates that it is avoiding cancelled subscriptions that would reduce its revenue by $1B annually.”
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3. Tag Your Content
Companies are able to save costs by tagging their content & other data using machine learning. Machine learning can understand your content automatically by either reading the text, image recognition & video processing capabilities. Once you automatically understand the content, then it becomes easy for companies to serve the right content to internal and external stakeholders such as sales teams – to help them engage better with prospects & customers.
4. Just In Time Engagement
Forrester research found that 77% of consumers in the United States suggested that valuing their time is the most important aspect of the brands interaction with them. Machine learning allows you to understand the user/customer intent, and serve personalized actions at the precise time your customer expects. This creates a distinct experience (for your audience) that values customer’s time and thereby increasing loyalty & engagement.
5. Customer Service
Natural Language Processing (NLP) is a subset of machine learning that enables systems to understand language. Today digital assistants that augment customer service help companies in not only giving better service but also saving significant human resources costs. Currently the Financial services industry is at forefront of using these applications.
Take an example of a bank’s customer who has repeatedly waited until the last moment to make a minimum payments. Applications can learn about this behaviour and anticipate that this customer might be facing a cash flow situation. Intelligent applications can then send this person personalized loan offers!!
Customer Experiences Powered by Machine Learning – An Option No More
No Marketer can afford to ignore the importance & power of machine learning in their marketing strategy. Today, every company is engaging with their customers through multiple digital & offline channels. This is generating unimaginable quantities of data, and growing rapidly – the digital ecosystem is expected to grow from a mere 130 exabytes in 2005 to 40,000 by 2020.
Frank Palermo, executive vice president of global digital solutions at VirtusaPolaris, “Responsive retail has peaked – the next step for the industry is predictive commerce”. Brands have no choice but to include machine learning in their marketing strategies in order to serve personalized customer experiences that keep customers loyalty & engagement. This is no longer an early mover field. Studies show that about half of consumers in US & Europe will interact with applications and services based on machine learning by 2021.
In future more and more companies will have fully cognitive websites such as Amazon. The Brand marketing & Brand identity will lose some of its power, when coldly rational machines are making decisions. Therefore brands will have to move away from a “traditional” marketing approach and they will have to rearrange their marketing investments to invest more in technologies that support their brand’s identity.
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