Data Science: Discover Customer Insights for Running a Smarter Business

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Episode 63 of Landscape Digital Show reveals how data science can help discover and interpret customer insights for running a smarter business.

This episode examines the role data plays in the success of your business, and how to acquire, analyze, and use it to gain valuable customer insights

I’ll introduce terminology associated with data science that that may be new to you. This will help you to intelligently research and study this important topic further. And I’ll give you an example that will show how to identify and analyze data points to run a smarter business.

It turns out that I’ve just returned from a conference where I had the opportunity to get a first-hand take on this subject from Ginni Rometty, the CEO of IBM

This event was a celebration of IBM’s top performers and significant others from around the world. If you were wondering, one of those top performers was indeed my wife Leslie.

While I imagine Ms. Rometty’s presentation on data and analytics was primarily intended for IBM employees, I was especially interested in what she had to say because it provides insights on how you and I can expect to be using data in our businesses.

Let’s take her high-level review and bring it down to earth.

Data Science is Storytelling

When you think of data, do you imagine cubicles of geeks poring over numbers and equations? The truth is data science is hot now and its practitioners are in-demand storytellers.

One thing to keep in mind is that numbers are never the point, but a means to an end.

As a business owner, you want to use data as a lens to filter and focus and make better decisions, presumably to better serve customers and generate profits.

Ginni Rometty says the purpose of acquiring data is building a resource of insights. And the most valuable insights are the stories data tell through the interpretation of the daily activities of regular people, your customers.

That’s what data scientists want to know, what people are seeing hearing, thinking and doing. That contextual data tells a story about what people want, and that is valuable information for any business.

To get ready for the data science revolution you can start by mapping out what customers are doing as they experience your business.

Empathy mapping is an invaluable tool for understanding what customers are seeing, thinking, hearing, feeling, and doing at every key interaction with the business, such as discovering it online, visiting it, and ideally make a purchase.

Check out this article I wrote for Speaker Magazine for more on how to use empathy mapping. It gives you a visual of an empathy map and helps to identify those interactions and use them for refining your process for serving customers.

Artificial Intelligence is Software at Work

Artificial intelligence may sound like it belongs in the playground of organizations with teams of scientists and engineers, like IBM, for example. In reality, you experience artificial intelligence every day.

You’ve used natural intelligence your entire career, that is, asking good questions, listening, and evaluating the results to make better decisions. Artificial intelligence, often referred to as AI, is simply using automation to emulate natural intelligence and gain more accurate insights more quickly.

The most common approach to artificial intelligence is machine learning that identifies human actions and responds to them. As the name implies, machine learning uses machines, better known as computers, to automatically respond to repetitive questions, and to get better doing it over time.

And some of the best-known examples of machine learning are artificial intelligence chatbots, with Alexa and Siri being two of the most recognizable examples.

AI is readily available to businesses large and small. In fact, some elementary schools are teaching children how to create their own bots. What’s a bot? Just a software program, but it takes a human being with natural intelligence to create one.

You can actually create your own AI bot to respond to your Facebook page visitors with Messenger. Here’s a free mini-course you can sign up for that will help you create a Facebook messenger bot.

The approach to using data science is not complicated:

1. Set goals
2. Collect and analyze data
3. Use data to make better decisions

IBM’s CEO used the term ‘man and machine,’ which is a phrase that for decades has come to stand for industrial progress. That’s the idea behind AI, machine learning, and bots. The intent is not to replace man, but to augment man for making him or her better, smarter, and faster.

Most important is you need to supervise your business data and train it to accomplish specific business goals.

Train Data to Be Smarter

Every industry has typical activities or functions that if improved will significantly affect the bottom line. In labor-intensive service industries like landscaping, this is production labor because that’s usually the most significant variable cost.

A logical goal is to reduce labor costs while maintaining or possibly increasing the current level of production. To accomplish that, the business has to track labor to better understand and manage it, and to determine the data points that will provide insights that tell a story.

Here’s an example that may give you some ideas. IBM used its Watson artificial intelligence technology to help Dave Haase compete more effectively in the Race Across America, which is a cycling event spanning over 3,000 miles from Oceanside, California to Annapolis, Maryland.

Competitors must navigate terrain, weather, and manage their schedule of sleep and nutrition, typically staying on the bike for an average of 30 hours and alternating with 2 hours off for rest for a period of 8 days. You can imagine there are a lot of variables to process.

Thanks to wearable technology devices that measured cycling cadence, skin temperature, heart rate, weather, etc., Watson was able to give Dave a big edge over competitors that relied mostly on intuition. While the Race Across America is athletic competition at its highest level, you can draw inferences about the importance of collecting and analyzing data to make better decisions in your business.

What are the important data points in your process? It may help to go back to the suggestions of the empathy map, and what people (your labor force, sales teams, customers, and so on) are seeing, thinking, feeling, and doing. Those human activities are sources of data that can affect change.

Your business needs to start building its future on a foundation of human data that in most circumstances does not yet exist. With or without AI, you can gradually acquire that data and periodically analyze it to make progressively better decisions over time.

We’ll talk more this in future episodes. For now, have the intention of becoming a data-driven because that alone will give your business an edge.

Show Notes

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