Machine learning has inﬁltrated our daily lives in a myriad of ways. Often, these machine learning predictions happen in the background and we rarely notice them. But what about at your place of employment? Has machine learning made you better at your job? Has it helped you better understand your customers? Has a newly installed algorithm allowed you to deliver a better product or service? If it hasn’t, it definitely should.
This article will cover a high level overview of machine learning and three steps you can take to make the most of machine learning in your organization.
Machine Learning Defined
First, let’s define both artificial intelligence and machine learning as those terms are often side by side.
Artiﬁcial intelligence is simply the concept of computers performing tasks which are characteristic of human intelligence, such as interacting with the environment (autonomous driving, virtual agents/chatbots), perception (computer vision, language translation), or problem solving.
Machine learning is a subset of artiﬁcial intelligence concerned with getting computers to act without explicit programming.
In practice, machine learning is the most common method for developing and implementing artiﬁcial intelligence and it can dramatically enhance your customer experience by:
- Identifying what sways your customer’s experience, such as the factors which contribute the most to delightful or frustrating encounters.
- Learning more impactful customer communication at a much faster rate than you are using today to continually improve your messaging.
- Artificial intelligence and machine learning both learn over time. And by “learn”, we mean the resulting predictions become more accurate over time. Both analyze information they are given, make predictions, evaluate the accuracy of those predictions, and then adjust future predictions to improve their accuracy.
- Empowering comment trending at low cost so you can easily visualize the inﬂuences on customer experience over time.
- Anticipating which customer relationships are most at risk and would beneﬁt the most from an additional intervention.
3 Steps to Making the Most of Machine Learning in Your Organization
When matched to the right problem, up-to-date machine learning methods will unleash tremendous value in your organization. Even if you have advanced analytics in place to ensure great customer experience, you can likely double your improvement gains with modern machine learning methods. But while evidence for sizable improvements is mounting, your window of opportunity to catapult ahead of the competition is shrinking. These three steps can help you make the most of machine learning within your organization.
Step #1: Honestly assess your organization’s ability to act on algorithmic conclusions.
It’s easy to say you are data driven. It’s a totally different thing to make a decision that goes against your gut, stemming from an algorithm you don’t really understand, based on data you might not completely trust. No wonder so many decision makers are passive consumers of insights who only select the ones that are easily “digestible” and align with their preconceived thinking. With time, you can start the cultural change, if you successfully honestly assess your organization’s ability to act on algorithmic conclusions.
Step #2: Find one or two tactical business rules used across your customer base and replace them.
You’re going to supplant these rules with algorithms. For example, do you send the same survey on the same channel to every visitor the morning after checkout or do you place all active customers without a given product or service in the same upsell campaign with the same cadence and the same call to action? Those are the types of business rules you need to attack. Since you are not changing the strategy, but instead improving tactical decisions, you will have fewer people to convince of your idea. And because these are decisions made across the customer base, it will be easier to hold out a small control group to have bulletproof measurement of your dramatic improvements. The improvements will be dramatic because your customers are diverse, but the business rule is simple. With a few of these successes you can find one or two tactical business rules used across your customer base and replace them.
Step #3: Inﬂuence strategy based on robust customer data.
With several measured wins under your belt, your driver analysis will have a lot more authority. You’ll be conﬁdent that using machine learning pays off and that you can replicate your success on a new opportunity. Whether your victories lead to praise from senior executives or a promotion your ideas will be taken more seriously by your peers and by leadership. You will be a champion in your organization’s journey towards being insight led and customer obsessed.
Jason McNellis is the Vice President of Analytics and Data Science at Centriam, a customer experience management software built for businesses of all sizes to better manage customer relationships. With Centriam, you can launch company-wide CX programs powered by customer analytics in days versus months.
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