Written by: Mrudul

ML Techniques to Improve Customer Engagement

Struggling to improve your customer engagement? May be ML can help you here. In this blog we have tried to highlight how ML can help you to improve customer engagement

Companies since ages have been trying to engage customers with their new marketing strategies. Here is one of the reasons they are failing.

Lack of Segmentation & Personalization

They try various marketing gimmicks, inbound-outbound strategies, copywriting techniques, and still wonder what the next big step to gain more and retain the existing ones is.

But in the real world, it accounts for only a better understanding of your users.

And it’s not easy as it sounds.

The customer understanding cycle involves data management techniques and proper researching to recognize the best SEO terms, keywords that are trending, or anything close to it.

But do we even make efforts to follow the automated tech replacing the manual aims to research and replace?

Just the thought of the application of machine learning techniques scares businesses enough even to give them a try.

The data availability is extreme. Customers exist, their activities tracked, and their data created after extraction. A process just as we can very well derive from the raw juice extraction theme. Now, if you assume for once with such an immense market presence by the customers, it is hard to segregate their accounts and activities, then that is true.

Enters Machine Learning!

Machine Learning Techniques are here to make a difference in ways where the customer is the hero and users form the most significant human pyramid. Let’s see what you need to engage customers!

Their online footprints need a lot of hand-garnering and nit-picking to deliver what they want. This evolution has promised a lot of ‘to the point’ results and tired of endless scrolling that is what companies should do.

CATER TO THE EXACT NEEDS.

Advanced ML techniques adapt a regressive approach to creating a platform where it is all about experience and engagement. In such a case, ML excels at pattern recognition, and AI concentrates more on creating recommendation engines.

We inevitably take a stand on customer acquisition with such regression techniques by applying the Hybrid recommendation system.

What is the Hybrid Recommendation System?

With a Hybrid recommendation system, you can generate and provide suggestions by combining two or more recommendation strategies.

After applying such techniques, all you need is more consistency to maintain those data fragments and form actual data points. Problems like cold-start and data sparsity still exist, which are solvable by providing optimized, automated solutions. With Machine Learning As A Solution (MLaaS), the market is soon going to make almost $7.6 Billion by the year 2023.

But how do you engage customers then?

Engaging Customers

With numerous tools and techniques in place, you can decide on a pretty strong engagement strategy that puts your customer on a priority. Let’s have a look at some of them:

  1. Engaging them with Chatbots/IOT/Virtual Assistant

According to the Forbes recent article, chatbots are soon going to change the market paradigms.

People have started using social media messaging apps as their one source of truth for everything. Not just chats, but for document sharing, information sharing, and much more. Now, when so much data is in stake, where 77% of the social media users are contemplating data on private channels than public ones, it is vital to take care of everything.

No wonder, chatbots are the new talk of the town. Chatbots combined is everything that works both for customers as well as brands for engagement.

Getting what you desire with a single-click means so much. Building a chatbot might be an initial investment but still doesn’t count big in the market scenarios because the use-cases have elaborated big time!

Facebook Messenger Chatbots: Hassle-free with no extra app load, just integrate chatbots with Facebook messenger and get your business needs in place. It has been made simpler and more useful than ever to create an experience that people would like and would want more of it.

People refrain themselves now to download various apps to find a solution to their problems. All they need is one app that solves everything majorly.

Now, if Facebook is a leading channel that handles almost all customer needs, then try creating a messenger integration to make them follow everything else.

With tools like Manychat and DialogFlow, it has increasingly become more comfortable to build an integrated chatbot. Create your flows using

  • Personalization
  • Online shopping has been in the market for quite some time now. Even then, the number of the population adhering to it is not equivalent to the percentage it should be. What is lacking?

    Indeed a lot of parameters lack, but the biggest of them all is ‘Lacking a sense of ownership.’ Apart from a percentage of the youth population that likes cheap discounts and offers, the other generations still restrain from online shopping. They are used to the kind of shopping where they get what they want by talking to a shopping agent or keeper. They value relations more than the products.

    Providing value to a million customers is what AI is making possible. AI is all set to mark the beginning of personalization, where customers know what they want, how they want, and their preferences. AI engine creates a recommendation engine that reads individual customer data and activities using ML techniques and provides relevant suggestions.

    If Peter’s father had been looking for a screwdriver, the next time he opens up the site, he gets to see a lot more options from the toolbox. Intrigued, he buys many more such tools and your marketing strategy with the new tech in-house works perfectly

  • Real-Time Insights
  • Finding real-time insights as the AI engine is doing its job, is possible. It is essential as well as your sales directly depend on the CX engagement that you provide by tracking APIs. The maximum user conversion happens with techniques that even follow the APIs to know the user behavior.

  • The Real Data
  • Now, no matter what tracking the essential user data is the key to a customer’s heart and your revenue shell. According to a study by Forrester Research, only 12% of the customer data captured. And when you think, 12% is nothing close to the critical customer data that you might be losing. Your real metrics might be just in the 88% that you are missing!

    So, integrating tools is just not enough, Training those tools appropriately to engage and know the customers is of the utmost success.

  • Visualization is the key
  • Not everyone that you hire in your company and employee who looks at the data gets familiar at an instance. People in a significant context have not even touched the roots of the AI tech to read and understand data lest integrating it. People are far from technically grasping each aspect of AI or ML tech. The percentage of data scientists in a country like India is itself is around 12. And letting non-technical people understand the grievances of fragmented and unstructured data is not right on many levels.

    So, for not-so-tech-savvy or simplicity, it is essential to visualize the data better to let the metrics speak and not the complex algorithmic data. The better the information presented, the better is the strategy to plan out the engagement.

  • Recommendations
  • Every time you open an eCommerce site, you see the homepage opens up with a selected block of products that you might like. How did they know?

    They tracked

    • Your previous shopping experience,
    • What other customers searched or bought
    • What is trending on the site
    • What are your liked products
    • Your browsing history

    And so on.

    Based on these activities, a customer’s recommendation panel set up, which increases the growth of sales manifold times. Your products slaughter their way to a customer’s cart through their purchase history. Recommendations panel displays not only similar products but even the variants of related products with great brands and low prices.

  • Create a conjecture of their needs and values
  • We cannot expect sites are flooring multiple brands and products to worry about their clients or customers. But when it is no more a manual procedure, we should all leverage the AI tech to make sure customer feedback captured. 48% of the time, customers refrain from buying from brands that have given them a bad experience in the past. So, with ML algorithms helping out brands to absorb the feedback properly and function in a similar way is extremely important.

    When you see so many innovations that ML tech is bringing up with it, you ask yourself, is ‘My site worth all this?’, ‘Is it providing enough?’ and if the answer is No or Not Sure. Then there is some serious thinking to do, especially when major research statistics specify that 87% of the businesses are only going to compete in the market with AI technology in place for them.

Are you up for it?