Ever since technology started blooming in the world, we’ve seen disruptions almost everywhere. Be it healthcare, education, food or business, you can easily spot technology manifest in various forms in different sectors.
This process has accelerated even more with automation and machine learning taking over the world. Even though all we’ve learnt from sci-fi movies is the breakthrough of technology that leads to doomsday, we’re far from there.
We’re still living in the age where a large number of applications of technologies like machine learning go unseen. One such area is marketing.
For those who are utilizing it, know that machine learning can be the difference between driving results and losing customers. Research indicates that 74 percent of the organizations believe that their goals can be better achieved with investment in machine learning. With data becoming more and more accessible, marketers are realizing the potential of machine learning for bridging the gap between brands and their customers.
Customer profiles now go beyond names and demographics. Marketers in today’s generation have access to customer personas. They have all the information right from device preferences of the customers to social posts, browsing and content history, interests among other things.
However, having all this data makes it much more difficult for the marketer to build campaigns, form strategies, create user segments etc. Mastering this data for the task of engaging and retaining customers becomes even more complicated. But, with machine learning, marketers can finally build meaningful connections with their audience based on valuable data.
In fact, data can be the difference between running front and getting thrown in the cut-throat market competition. Thanks to machine learning, marketers have ways to utilize it and get ahead in the race.
Let’s take a look at exciting ways how machine learning development is narrowing bridges between customer and brands
Forming better customer segments
Marketers know the pain behind manually keep a track of all customer data points. Not only is the task incommodious, but also leaves a significant chunk of customer data underutilized.
The inclusion of multiple parameters makes it difficult for the marketer to decide which ones to pick and in what quantities to segment customers. Not to forget that incorrect customer segmentation is one of the top reasons why customer churn rates increase.
However, machine learning backed customer segmentation can analyze your user base and find correlations between different parameters. This practice takes your personalization to an altogether different level. In other words, the better your segments are, the more valued is your relationship with your customer.
Predicting customer’s behaviour
Predictive analysis is one of the best applications of machine learning. It has helped brands understand a holistic view of their customer and devise campaigns for their next course of action.
Considering the market competition today, a business’s performance relies on its ability to predict the demands of the customers at every stage of their journey. With machine learning to the rescue, brands are already acing it.
Companies like Amazon have been predicting behaviours for years now. Notice its product recommendation engine that takes people‘s past behaviours into consideration for determining their future needs. This practice is also responsible for driving 55 pecent of its sales.
Polishing selling strategies
Making a move in your cross and upselling strategies can be difficult for customers. It is like walking on a tightrope without being disturbing the balance.
To accomplish it flawlessly, marketers can need to mine into data. They need to find out the products have been brought together, the segment of customers who prefer a certain kind of product, the products people love to splurge on and more. Understanding all this enhances the customer experience.
Machine learning, on the other hand, can act as a catalyst to this cumbersome task. It assists in data-based product recommendations and helps in reaching out to the customer at the right time.
Identifying the right engagement channel
We’re now in a world where multiple paths exist to reach a particular brand. For a business, there are customers scattered on different platforms. Furthermore, the brands who reach out to their customers with the apt content on the right platform, make all the difference.
Identifying the engagement channel is fundamental to any brand. This can help identify customers who are receptive to a particular channel and campaign. Using machine learning solutions, brands can answer these questions and deliver their campaigns at the right time to their customers.
When the brand Homestay started using machine learning, it was able to identify the customers who see their ads. This led to a 46 percent increase in their gross revenue since they started spending less on the wrong people.
Becoming a leader
To answer a question like what separates Amazon from a medium enterprise, one needs to take a look at their leadership. Becoming a leader isn’t something that one can accomplish in a day. It requires taking the right decisions at the right time. When it comes to marketing, a leader is one who can make data-backed decisions.
The right marketing activities, when linked with business goals, lead to an upward growth trend. Machine learning in such a scenario can help exercise better command on your customers with intelligent solutions. Be it chatbots for customer service or curated timelines for customers, ML is aiding one become a leader in the market.
Conclusion
Brands all across the world are using machine learning to devise their marketing strategies. Gone are the days when marketers built campaigns based on intuitions. Today, as the world moves forward customer’s purchase habits are changing more than ever. Unless brands have ML solutions to help make suitable decisions, they will continue to limit their potential and not even know what they’re missing out.
