So, the other day when I was surfing through a famous e-commerce site, I saw a super cool pair of earplugs but unfortunately skipped on ordering it. After a few days when I revisited that site, I noticed the same pair of earplugs followed throughout my surfing journey. By chance, I see an ad pop-up on my screen that showed the price drop on that product and tempted me to purchase those pair of earplugs! So how does the system know what customer exactly wants? This is the result of machine learning!
Machine learning is an interesting technology that will attract you to know more about it. As we move more into the technological world, we often hear of certain terms that are trending in the market and are considered many helpful technologies in improvising the overall marketing system of today’s time. “Machine Learning” is one of them and we all have almost heard of this and we roughly even know what it does!
To get into more details and see how Machine Learning helps in improvising marketing and enhancing customer experience, let us first understand what it is and how it works?
Machine Learning is a study of algorithms and statistical models that a computer uses to perform certain specific tasks without using explicit instructions. These algorithms build a particular mathematical model based on sample data, which is known as “training data”, which helps to make predictions or decisions without the use of any explicit program.
The application of machine learning is widespread, ranging from agriculture to anatomy to computer networks to optimization to search engines to marketing and much more. One of the areas where machine learning is tipping is User Experience.
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1. Why ML for customer experience?
As we already know, Machine Learning creates training data that helps users predict and make decisions related to specific tasks. Implementing ML in UX can help all brands understand the needs of their customers in an appropriate and better way. ML algorithm works precisely towards data rather than a human. This ultimately results in better decision making and provides relevant user experience and information that increase marketing and sales of the brand. In other words, it helps brands to acquire customer’s interests with ease and efficiently interact with them by providing experience based on data gathered by the ML algorithms.
2. Enhancing Customer Experience with ML
Predictive Purchases
Have you ever noticed while surfing on the e-commerce sites, you get relevant suggestions to the product that you require? That is because of ML. Amazon is one of the greatest examples that recommends you the relevant goodies to purchase based on your search!
Another example is the music app Spotify, that analyzes your music search data and suggests the playlist that might interest you! Predictive Purchases are of crucial importance as they make the search process more optimized and easier for the customers. Also, it helps the brand to understand the requirements of their customers and their interests!
Segment Discovery
Another field where Machine Learning tools can prove to be useful is segment discovery. Using Machine Learning for segmentation, allows you to access segments that are readily not visible to humans when they go through the data. It helps the brand understand who their customer is and what they want.
It allows the brands to focus on the minute topics that can help them improvise the marketing strategies and reach out to their customers in a more appropriate way. For example, a brand has high desktop users, but do they know how much ratio browses through Safari and how much using Chrome? This is where segmentation helps in and gives precise data to offer the right products and services to customers as per their interests, demographics, device usage, etc.
Predictive Analysis
The theoretical conventional analytics take in much manpower and are not that accurate. To reach the conclusions and the results using the conventional analysis, you have to go through all the data for the validation. And this process needs to be repeated again and again.
But with the implementation of ML, process analysis can be done much faster and easier compared to the conventional one. The augmented analysis provides the users with insights, and it gives an accurate study of the data of customers and website visitors. Some of the examples are Adobe Analytics and Google Analytics.
Google Analytics is one of the widely used tools that is used to provide us with insights based on the data collected. It gives us various features and filters, to find out the results accordingly and funnel in the data. And to get this insight, you just need to wait for a few moments and its done!
Concinnity of Customer Journey
Concinnity of the customer journey is also the most important field where Machine Learning tools can suggest with the next action to be taken, whether it be reaching out to the customers via emails or SMS or push notifications or even a phone call! Sometimes, a system takes the action as well without intervening in human interaction.
One of the examples is Adobe Sensei’s next-generation Journey AI service that recommends users about the next action that should be taken. The additional examples are various third-party tools that track your social media insights and suggests business pages about the peak time to promote their pages and reach the maximum crowd!
Conclusion
As we head towards digital everything, consumers are exposed to smarter systems with cognitive capabilities, that is possible through the implementation of Machine Learning. Without ML, it difficult for companies and brands to understand the needs of customers and reach the full potential of digital transformation. Machine Learning optimizes user interactions and helps develop a unique and personalized experience that every customer expects. Machine Learning helps ease things for the brands as well as for the customers too. It enhances the online experience of the customers!
