Today, data is undoubtedly a leading driver of business success. How well a company is able to leverage insights from the ever-increasing wealth and complexity of available data is, increasingly, a differentiator between market leaders and also-rans.
The potential for data to allow us to better understand and adapt to the changing behaviors of customers is one of its most powerful features. On a societal level, it’s been used to tackle the spread and consequences of the Covid-19 pandemic by allowing us to understand the activities and movement of people. Many of the techniques used for this were first developed by businesses – in finance, companies have become adept at preventing fraud by detecting outlying behavior that can signify something fishy is afoot. In marketing, it’s used to generate the “360-degree customer view” that helps to identify those who are ready to make buying decisions and put products and services firmly in their line of sight. Whether it’s about projecting the spread of pandemics or predicting what people want to buy, data improves our understanding of the world around us, and that includes the wonderfully varied assortment of people that live in it!
Data makes it easier to meet customer needs (and catch tuna)
The basic principle is the more you understand about your customers, the more accurately you can predict what they want. You can also deliver it in the way that is most convenient to them. A great analogy I like to use here is tuna fishing. Originally this was a very hit-and-miss occupation – fleets of boats would go out, with little more information to work with than what spots they'd had success with in the past.
As we developed technologically – first by learning how to navigate via the stars, then by learning how to predict the weather by meteorology, all the way up to the invention of GPS and sonar - our ability to find and catch fish improved. Today, we can monitor the movement of fish using satellites and sensors in the sea. If you’re fishing for tuna commercially, you have to keep on the cutting-edge of this technology arms race. If you don’t, you’ll simply be out-fished by competitors who do and quickly go out of business.
Fishing for customers involves the same principles. If you aren’t continually upgrading your ability to use data to pinpoint their location and understand their behavior, you’ll be overtaken by others who are.
What data do you need to understand your customers?
Transactional and point-of-sales data is an obvious starting point for a lot of businesses. It tells us what people are buying how much they are paying for it and allows us to understand broad consumer trends as well as local preferences. This helps us design products and services that we can be confident people will spend money on. It also lets us optimize prices – pricing our products at a level at which we can be confident they will sell.
Also hugely valuable is customer demographic data. Once we understand what people are buying, we can understand who is buying what. As always, these different types of data are much more useful in combination than either of them are in isolation. Working with personal data brings some risks and responsibilities, of course, and it’s essential to understand the principles of data governance – how to comply with all the necessary regulations and ensure you are deserving of the high level of trust your customers are putting in you if they are letting you have their personal data!
Another source of data that shouldn’t be overlooked is attitudinal data. This is data gathered through market research, at a basic level, or, in more mature data strategies, through methods such as social media sentiment analysis. Advanced analytics applications that involve artificial intelligence (AI) – usually machine learning – can create automated reports telling us who is using our products and what they are saying about them by monitoring chat over networks like Twitter and Instagram.
Transactional and point-of-sales data is internal data (generated and unique to your business), as can customer data be if you are collecting it through schemes such as loyalty programs or behavior tracking. You can also buy in customer data, though, and other external bought-in datasets can be incredibly valuable when it comes to generating further insights.
This can include economic data and GDP growth to determine where the spending power is, meteorological data to understand how the changing seasons or weather patterns impact buying habits, and local and world events. One enterprising installer and manufacturer of windows is said to have pioneered the practice of tapping into publicly available data on crimes such as vandalism to work out where to station its rapid-response window repair services most efficiently.
It also includes the type of data we use when we take advantage of services like Google Trends to learn what people are looking for online and more recent developments such as Facebook's Custom Audience service. This lets users upload what they know about their own customers, then use algorithms to put their adverts in front of other customers with a similar profile.
Putting all of these datasets together – which requires a fairly advanced analytics infrastructure – lets you work all sorts of magic. In a famous example from early on in the analytics revolution, retailer Target demonstrated that it was able to predict when customers were pregnant before they even started shopping for baby products. More recently, Amazon has talked about developing anticipatory shipping. At the moment, this lets it ensure products are in the distribution centers closest to where they will be wanted, but in the future, it plans to be able to send items to customers before they even buy them.
Real-time insights and micro-moments
As a business grows more experienced with data and analytics, and its capabilities mature, it can start to move towards advanced use-cases involving real-time data, with the aim of capturing what are called “micro-moments” – split-second buying opportunities that exist for mere moments but can be hugely profitable if identified and acted on at scale. Retail giant Walmart gathers petabytes of data on customer buying habits, but only the most recent and up-to-date transactions are factored into its prediction algorithms. This is because it understands the speed at which customer behavior changes and only data gathered very recently can be useful for telling us what's happening right now and in the near future.
A good example of a micro-moment is someone stepping off a plane following a flight. They might want to find a hotel room, take a taxi or just sit down for a meal. In the past, businesses might have hoped they would see an advert for their services in the arrivals lounge. Today, marketers can take the opportunity to identify this life moment and hit them with a personalized text message, phone notification, or a pop-up that will appear on their Facebook feed as they check in to let their friends and family know that they've arrived safely.
The data technology available today gives businesses unprecedented abilities when it comes to understanding their customers. By combining transactional, demographic, and attitudinal data from internal and external sources, we can predict what customers want more accurately and make sure we're in the right place at the right time to provide them.