Offering customers a delightful experience is an important priority for companies that sell services and products. In today’s scenario, it is essential that businesses maintain a strong relationship with their customers for them to thrive successfully in the expanding and competitive market. Companies are now facing numerous challenges in delivering a flawless experience.
In the past few years, businesses have shifted to technologies such as machine learning (ML) to get better insights for improving customer experience (CX). They are also tapping into customer purchase behaviour and interests. Integrating ML and customer behaviour is what businesses are now looking for giving a hassle-free CX.
Impact Of Big Data
The boom of big data and analytics has largely helped certain areas of a business. For example, when it comes to pricing a product or a service, the business entity has to take care of factors such as customers’ needs which are vital to the product’s growth. This is just for one product. Imagine there may hundreds of product items to cater to. Tailoring to customer needs which involves hundreds of products requires digging up more customer purchase information and market trends. This is where Big data has made a positive impact, and analytics has helped in getting insights through big data’s results.
However, big data and analytics are not sufficient on a deeper level. It might not tap unique needs and desires corresponding to the latest trends. This is where the power of ML and the potential of customer psychology can take on a much higher level of CX.
ML For CX
ML has made stellar progress in just a few years. Powerful algorithms, which were once an ideation, have taken form and are slowly handling complex tech problems quickly and efficiently. Automation has progressed a lot thanks to ML. With the benefits ML could offer, it is suitable for improving CX significantly.
There are tons of information generated by businesses at a very quick pace every day. A system with a solid ML loaded in it can slice and dice data quickly. This is really helpful for larger organisations since they deal with humongous amounts of business data every day. For example, online clickstreams generate lots of data even for a few clicks by a user. A company which relies on online advertisements for revenue, can analyse users’ website activity to see how it fares in that section.
If captured and predicted accurately, ML can bring in scores of insightful value that can redeem CX as well as bring in revenue to the company. One instance of this could be responding to thousands of customer interactions perfectly through ML. This not only gives a positive appeal to customers, but also keeps them hooked to the offering provided by the company.
Another instance would be ML providing custom recommendations based on user activity. The catch here is the ML should entice the user to explore more on the offering rather than aiding users rejecting the recommendations.
Realising A Complete Behaviour
Along with understanding customer data through ML, customer behaviour also plays a vital role in bringing an enriching CX. This behaviour entails all the psychological aspects of buying interests in a customer. The generated data could act as a reference to the customers’ interest patterns for a product or a service.
For example, on digital devices, users sometimes leave a trail of data when shopping or browsing items online. Companies make use of this data to understand the customers even better. As Tuval Chomut of Clicktale says, “In the digital sphere, we have the ability to understand the psychology and behaviour of visitors, use advanced research, and then turn their behavioural signals into insights. Optimisation cycles are getting shorter and shorter and once you take behavioural insights and put them into the feedback loop, ultimately this will be a way for brands to achieve higher levels of personalisation”.
He goes on to say that behavioural data fused into supervised and unsupervised ML will eventually improve the system’s ability to get actionable insights and act on decisions based on these insights independent of humans. This holds true when the product portfolio expands in a company and it needs quicker insights from the latest offerings without compromising CX.
Behavioural insights combined with ML to take care of loads of customer-generated data, is attractive for any company to pursue. However, it should know where to draw the line between autonomous technology and human knowledge. In the end, businesses cannot afford to rely completely on technology without human interference in CX. Both are key to deliver a mesmerising experience to folks!
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