Data is undoubtedly one of the main drivers of business success today. A company’s ability to leverage insights from the ever-increasing wealth and complexity of available data is increasingly a differentiator between market leaders and others.
The potential of data to allow us to better understand and adapt to changing customer behaviors is one of its most powerful features. At the societal level, it has been used to combat the spread and consequences of Covid-19 pandemic allowing us to understand the activities and movements of people. Many of the techniques used for this purpose were first developed by companies – in the financial field, companies have become adept at preventing fraud by detecting aberrant behavior which may mean something fishy is afoot. In marketing, it is used to generate the “360-degree customer view” which helps identify those who are ready to make purchasing decisions and put products and services firmly in their sights. From projecting the spread of pandemics to predicting what people want to buy, data enhances our understanding of the world around us, and that includes the wonderfully diverse assortment of people who live there!
Data makes it easier to meet customer needs (and tuna fishing)
The basic principle is that the more you understand your customers, the more accurately you can predict what they want. You can also deliver it in the way that suits them best. A great analogy I like to use here is tuna fishing. Originally it was a very random occupation – fleets of boats would come out, with little more information to work on than the spots they had had success with in the past.
As we have evolved technologically – first learning to navigate through the stars, then learning to predict the weather through meteorology, to the invention of GPS and sonar – our ability to find and catching fish has improved. Today we can monitor the movement of fish using satellites and sensors at sea. If you fish for commercial tuna, you need to stay ahead of this technological arms race. If you don’t, you will simply be overtaken by competitors who do, and quickly go broke.
Fishing for customers is based on the same principles. If you don’t continually improve your ability to use data to pinpoint their location and understand their behavior, you will be overtaken by others who are.
What data do you need to understand your customers?
Point-of-sale and transactional data is an obvious starting point for many businesses. It tells us what people are buying, how much they are paying for it, and allows us to understand major consumer trends and local preferences. This helps us design products and services that we can trust people will spend money on. It also allows us to optimize pricing – pricing our products at a level that we can be sure will sell.
Customer demographics are also extremely valuable. 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 alone. Working with personal data involves risks and responsibilities, of course, and it is essential to understand the principles of data governance – how to comply with all the necessary regulations and ensure that you deserve the high level of trust that your customers place in you if they leave you their personal data!
Another source of data that should not be overlooked is that of attitudes. This is data collected through market research, at a grassroots level, or, in more mature data strategies, through methods such as social media sentiment analysis. Advanced analytical 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 on networks like Twitter and Instagram.
Point-of-sale and transactional data is internal data (generated and unique to your business), as is customer data if you collect it through programs such as loyalty programs or behavior tracking. However, you can also purchase customer data, and other purchased external datasets can be extremely valuable when it comes to generating additional insights.
This can include economic data and GDP growth to determine where purchasing power lies, weather data to understand how changing seasons or weather patterns affect shopping habits, and local and global events. An enterprising window installer and manufacturer is said to have pioneered the practice of mining publicly available data on crimes such as vandalism to determine where to park his fast-response window repair services most effectively.
This also includes the type of data we use when taking advantage of services like Google Trends to find out what people are searching for online and more recent developments like Facebook’s Custom Audience service. This allows users to upload what they know about their own customers and then use algorithms to put their ads in front of other customers with a similar profile.
Gathering all these datasets – which requires quite advanced knowledge analytics framework – allows you to work all kinds of magic. In a famous example from the start of 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 talked about developing advance shipping. At the moment, this allows him to ensure that products are in the distribution centers closest to where they will be needed, but in the future, he plans to be able to send the items to customers even before they don’t buy them.
Real-time previews and micro-moments
As a business gains experience with data and analytics and its capabilities mature, it can begin to evolve into advanced use cases involving real-time data, with the goal of capturing what so-called “micro-moments” – buying in a fraction of a second. opportunities that only exist for a few moments but can be extremely profitable if identified and exploited on a large scale. Retail giant Walmart collects petabytes of data on customer buying habits, but only the most recent and up-to-date transactions are considered in its prediction algorithms. This is because it understands the rate at which customer behavior is changing and only very recently collected data can be useful in telling us what is happening right now and in the near future.
A good example of a micro-moment is someone getting off an airplane after a flight. They may want to find a hotel room, take a cab, or just sit down for a meal. In the past, companies might have expected to see an advertisement for their services in the arrivals hall. Today, marketers can seize the opportunity to identify that moment in life and hit them with a personalized text message, phone notification, or pop-up that will appear on their Facebook feed when they log in to make let their friends and family know that they’ve arrived safe and sound.
The data technology available today provides businesses with unprecedented capabilities when it comes to understanding their customers. By combining transactional, demographic and behavioral data from internal and external sources, we can more accurately predict what customers want and ensure we are in the right place at the right time to deliver it to them.
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