Building Customer-Centric Supply Chains: Q&A With Kirk Borne

Consumer demands and preferences have always driven how, when, and what products businesses deliver. But for much of modern history, “giving the people what they want” has been more reactive than predictive.

Today, the data and technology exist to drive proactive, customer-centric decision-making and personalization, ultimately allowing companies to enhance enterprise supply chain operations to reduce friction, anticipate customer needs, and create amazing customer experiences.

Q: How did data science become your passion?

But how do you cultivate and activate data to create a customer-centric supply chain?

To find out, we ask Kirk Borne, principal data scientist for Booz Allen Hamilton, to share the top data insights necessary to digitize supply chains and put customers at the center of each step along the journey. Continue reading to see what Kirk had to say.

Q: Today’s customer demands faster supply chains that enable instant gratification. How can data help organizations meet this demand and accelerate their supply chains?

A: I went to graduate school for astrophysics. So, I have always had a strong interest in scientific discoveries and the data behind science. In fact, data was a large part of my astrophysics research. When I left graduate school, I spent 20 years at NASA working on astronomy data systems for space missions like the Hubble Telescope. I was always working with data.

As the years went by, the volume of the data being collected was getting so large, I started asking myself, what we can do with so much data? That’s when I discovered machine learning and data mining, which we now call data science. It’s been a 22-year-long journey from when I first started thinking about a Big Data paradigm. And the data has changed. It’s not just analysis anymore. It’s really doing deeper discovery from data. So, I’ve always wanted to do science. I’ve always wanted to do discovery. And I’ve always worked with data. It just seemed like a natural segue.

Q: What pieces of customer data are most vital when creating a more customer-centric supply chain?

A: If you can see what customer interests are and where customer demand is headed for particular products, you can anticipate what will be needed and when. You can also do this by building predictive data models based upon seasonal terms or even context-based demand.

For example, let’s say that around the time of the Super Bowl every year, there’s a big demand for sporting apparel – fans want to represent their team before the big game. With predictive data models in place, you can anticipate increased (or decreased) demand before it happens. Companies and their suppliers are then enabled to move products closer to the place where the customer is going to be. There are some companies that even have pre-placement, where they place products in locations where they expect customers to be requesting them. This is what allows for next-day delivery – even Sunday delivery in some cases – from an online store. They’re able to provide instant gratification because they’ve anticipated the what, when, and where of their customers through data and predictive data models.

A: I would say interest, influence, and intent as they help you map the past, present, and future of customer interest.

Interest shows data that’s reflective of what a customer has purchased in the past. It’s descriptive data that’s backward-looking – it’s telling you what’s already happened to give you an idea of what the customer’s interests are.

Q: How does technology like artificial intelligence, machine learning, or the Internet of Things enable businesses to move “at the speed of data”?

Influence is also important for gauging interest. By tracking who your customers follow and are influenced by, you can get a sense of the products they’re looking for. For instance, if you have a customer that follows Kim Kardashian and she promotes a skincare product on her Instagram, there’s a strong likelihood that your customer will consider buying that product. Interest becomes spiked. Of course, that’s not going to be true in a lot of cases, but there are cases where that does happen. And it’s valuable information to track.

Finally, there’s intent. Intent is much more predictive. It tries to give you an idea of what a customer might do. For example, let’s say you’re an airline. Using a mix of transactional, search, social, and web data, you can discover a customer’s intent to take a summer vacation or go on a business trip. Those signals could be visiting your digital content on the best summer vacation destinations. Or, maybe they’ve searched for trip packages recently. Combined, this intent data shows you what your customer’s needs might be down the line – in this case, a vacation.

A: Great question.

A few years ago, people would say that you needed analytics to move at the speed of your business. And I thought it was almost like a sales pitch. “Oh, we know that your business moves fast. So, we’re going to give you stuff that moves at the speed of your business.” But the reality is: Data is what’s moving incredibly fast.

We have so much data coming in now, it’s a flood. You’re flooded with data on customers, products, competitors, worldwide events, social and economic changes. You don’t need data to move at the speed of your business – data is already outpacing you. Instead, you need business to move at the speed of your data.

Q: How does having a digital, end-to-end supply chain help enterprises deliver superior customer experiences?

One of the contributors to this influx of data is actually the Internet of Things (IoT). The technology that can actually help you manage the flood is really artificial intelligence (AI) and machine learning. Machine learning is a set of tools and algorithms that help you find the patterns, trends, correlations, and new segments in the data. AI is the actual implementation of the algorithm that takes the action to deliver an offer or other content that’s highly relevant to a particular customer and their interests.

I always like to say that AI and machine learning are like doing triage on your data. In disaster response situations, triage is used to set priorities. Who are the people that need immediate medical attention? Who are the people who can wait? In the context of data systems and the overwhelming amount of data coming in, AI and machine learning allow you to prioritize that information and take action. What are the pieces of information you really need to pay attention to? What’s the most urgent trend that needs your attention? What is the data you don’t need to pay attention to because it’s just noise or it’s just more of the same?

Q: Tell us about a memorable experience you had with a brand as a customer. What made the experience so special?

A: A superior customer experience is something that is frictionless, delightful, or even surprising. If an enterprise is able to deliver products and services smoothly and without friction, they’re already there. Ordering is intuitive and convenient. Delivery meets expectations. Returns are easy. Customers want a shopping experience where you just click the button and they have it. It’s coming. It’s already on the way. And a digital, end-to-end supply chain enables that experience.

It’s also worth pointing out that painful experiences turn good products into bad ones. You might love a product, but if it’s painful to get, you probably won’t order it very often or may even look at a competitor. Alternatively, you could have a product that doesn’t work. It could be defective or of a lower quality, but you had such a pleasant experience in returning the product that you wouldn’t consider purchasing from any other company. And the supply chain is essential in reducing headaches, improving quality, and accelerating delivery.

A: I used to be an avid runner some 20 years ago. And I would go through running shoes every couple of months because I was putting a lot of miles on my shoes – probably 50 miles a week. To plan ahead, I would order up to four pairs of running shoes at a time from this one particular online store. And they never charged customers for shipping, no matter how many boxes of shoes they ordered or where in the world they were located.

Q: What else is important for enterprise leaders need to know about data?

I shared this online with friends and on an online runner’s forum with people from all over the world, and suddenly all of these people were ordering shoes from this same company. As a customer, I felt excellent about this policy where they absolutely never charged you for shipping. But they also had really great prices on the latest running shoes. And it was the same experience for anyone, regardless of location or complexity, ordering from their company.

Looking back, it was a great branding strategy. Everyone knew the store for their policy and their policy became a huge part of their brand and identity. And I think that’s why the experience was so memorable, over 20 years later.

A: Look for the signals in the data. If you have return customers, you’re clearly doing something right. So just pay attention to the data. It’s telling you what’s going on in your world and how you can capitalize on it.

How to solve the experience economy equation

What are these strategies and technologies? How can you solve the experience economy equation? We asked 25 futurists, technologists, and experts for their recommendations. See what they had to say.

It’s a customer’s world – and they’ve set the bar high. Exceptional experiences are the standard that brands are racing to meet. But to meet and exceed customer expectations, it’s becoming clear that enterprises require deeper insight into their own organizations.

Enterprises need to tie their operational (O) and experience (X) data together for a more holistic, complete view of their customer experience and how to improve it. And they need the right strategy and the right technology to do it.