The benefits of machine learning systems in customer satisfaction

AI machine learning systemThe gap between machine learning systems and customer satisfaction is decreasing daily. Businesses are beginning to invent new ways of using AI to improve customer satisfaction and loyalty.

Why is this important?

Two reasons:

  1. Improved customer data.
  2. Actionable insights gathered from customer data.

Practical insights retrieved from your AI system can help you understand your customers better.

So, nailing this could be the tiny push that tilts the odds of success in your business’s favor.

Now, how can you capitalize on the growing influence of AI’s machine learning algorithm?

And how can you unleash the power of AI-systems to improve your customer’s experience and increase your bottom line?

These are the questions we will answer in this post.

Advantages of Machine Learning System in Customer Satisfaction

According to a study by R. Hallowell of the International Journal of Service Industry Management. A boost in your customer’s satisfaction increases their loyalty to your brand. Which in turn influences the profitability of your business.

To unleash the power and advantage of artificial intelligence I suggest you choose a simple, yet recurring problem as a possible use-case for your AI-pilot project.

And one of the simplest ways is to relieve your sales and marketing staff off labor-intensive, mechanical tasks.

In addition to that, you can also automate processes which do not require subject matter expertise.

AI can increase the efficiency of your content recommendations

And this is made possible through the use of tailored personalized recommender systems.

Steps to a Successful AI Implementation:

  1. Select a business problem that AI-algorithms can solve easily.
    For example, how to recommend personalized products to your customers.
  2. Select clear and consistent datasets. This is an important step. You need to know who your users are, and what products or service they recurrently use.
  3. Focus on quality. Data is the lifeblood of any AI machine learning system.

Use Artificial Intelligence to Analyze a Customer’s Experience

A customer’s experience with your brand ranges from a website visit. In-app purchase. Unresponsive customer support. It goes on.

But without a proper data gathering process and data-analysis of your customer’s journey across touch different points within and outside your sales funnel, how do you intend to give them a personalized on-site experience that they’d value?

You can solve this by employing the use of customer-centric machine learning systems.

The system uses the available data on your user’s web and on-site journey. Data from social media channels. And data from emails, to predict behavior and deliver relevant and targeted recommendations. Which meets the utility requirements of each customer.

And one of the quickest methods of achieving this is by using AI-powered Recommender Systems.

What is a Recommendation System?

Recommendation systems, are one of the most advanced and mature subsets of Artificial Intelligence.

Recommender systems, as they are alternately called, use AI’s Machine Learning system. Which analyzes historic and live-data on consumer action and behavior, to recommend products and services which fit a customer’s profile.

For example, a customer intends to look up a product on your site, she types a search term in your online store; your recommender system delivers the search results and displays related products associated with her initial search query.

The idea here is to narrow down her option to a few highly specific options.

So how do you do this? It’s simple really. You need to gather the most data on your customers. Ranging from clicks, search history and data from other websites. The recommender engine analyzes and sorts through each datum for concurrent use.

A splendid example is Amazon’s recommendation system, which gives you personalized suggestions based on your interests, search history, and activity on their website.

In my next blog post, I’d post about types of recommender systems and how you can easily set one up for yourself.

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