it261: 9 Ways Machine Learning Will Affect Customer Interactions

Friday, 31 March 2017

9 Ways Machine Learning Will Affect Customer Interactions

How will machine learning affect your day-to-day interactions with your customers?

The Young Entrepreneur Council (YEC) is an invite-only organization comprised of the world’s most promising young entrepreneurs. In partnership with Citi, YEC recently launched BusinessCollective, a free virtual mentorship program that helps millions of entrepreneurs start and grow businesses.

1. Providing Answers at All Hours

Machine learning allows us to provide higher quality customer service at a lower cost. In the past, we had to direct customer questions to a static FAQ during off-business hours. Now we can use bots to help provide “live” answers to customers 24/7. We route questions we cannot address easily with machine learning to our human customer service team for the best experience.

– Adelyn Zhou, TOPBOTS

2. Anticipating Needs

This will enable huge amounts of data to be examined rapidly, with the aim to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business info. Ultimately, this could lead to providing better financial services for our clients by anticipating their needs and wants, as well as identifying and mitigating problems before they arise.

– Luigi Wewege, Vivier Group

3. Better Targeted Marketing
Machine learning will provide computers with the ability to learn more about a target audience and change the conversation when exposed to new data. This will help marketing efforts by offering highly customized information based on what the computer has learned about prospects and customers, and their desires, needs, fears and wants.

– Nicole Munoz, Start Ranking Now

4. Shifting Staff to Answer Complex Questions

By automating part of the customer service process, like using a help desk to cover easy questions, there’s more time to proactively contact customers and ask them if they need anything. It’s also a way to free up time to handle more complex customer problems and put in face time that could save relationships.

– Murray Newlands, Sighted

5. Applying Results of Research

Machine learning will take a lot of the grunt work out of auditing and research so that we can get to answers and high-level thought much more quickly. Without having to dive into the data ourselves, we’ll be free to use our brains for higher-capacity learning and apply whatever the results teach us.

– Erik Huberman, Hawke Media

6. Tailoring Campaigns via Clustering Techniques

One of the hottest research areas in machine learning is clustering customer data. This kind of approach automatically groups related customers according to the way they interact with your business. Clustering techniques extract information to tailor campaigns and promotions to each target audience with fewer costs involved.

– Humberto Farias, Concepta

7. Giving Specific Answers to Clients

Machine learning will allow us to process bulk quantities of data from different sources, and will allow us to make decisions more quickly that will directly impact how we communicate and interact with our customers day to day. For example, instead of giving subjective answers and estimates, we’ll be able to say things with greater certainty and predict performance with better accuracy.

– Duran Inci, Optimum7

8. Generating User-Specific Search Results

For e-commerce businesses, search is one of the most important technologies affecting how customers will interact with you. As machine learning becomes more sophisticated, advances will be made to display results that are more relevant to the individual user. Giving the customer the power to find what they are looking for will reduce the need for direct customer interactions.

– Diego Orjuela, Cables & Sensors

9. Discovering Need, Then Providing Better Products

The age-old problem for businesses is figuring out exactly what customers want and then either delivering it to them or building great products. Data allow businesses to find out exactly what customers are using, and what they want so that they can build those products.