Knowing what your customers want may be an obvious piece of intelligence to gain on your path to success, but it’s only the beginning. The real key is to know what they are going to want next year, before they even realize it themselves. Many businesses try to get a sense of the future largely based on instinct, and sometimes – as in the case of Steve Jobs when he moved Apple into the mobile phone market before mobile phones were cool – they get it right. Most of the time, they don’t.
Creating a lasting customer experience is highly dependent on knowing the future, and anticipating customers’ needs and desires. How to gain that knowledge can be seen as a spectrum that ranges from insight on one side, to foresight on the other. “Insight is a fundamental knowledge about how the world works,” says Nick Weber, Principal at 4i, a foresight consultancy. “It’s the antecedent to foresight.”
“You can have all the insight in the world, but if you’re not leveraging it to look at what’s coming in the future, then it’s fundamentally less valuable,” said Weber. Foresight – knowing what customers will want and need in the future – starts with insight and intuition, but is more dependent on big data and predictive analytics that provides more guidance and specifics. It’s intuition, but with more knowledge. Intuition without big data and analytics is like white water rafting without a map of the river. Knowing the future needs of your customers requires split-second decisions, creativity, and subjective judgment to get to where you want to go, but having a map helps you avoid the pitfalls and dangers that lie ahead.
Moving from what you think to what you know
Business decisions are often based on what you think you know. It’s that gut instinct that is so often touted as the most prized characteristic of a company leader. But instances where a product captured the market because a dynamic personality followed their instincts (such as the iPhone) are few and far between, and the market is much more cluttered with a path of failed products such as the Apple Newton, the DeLorean automobile, and New Coke, which all seemed like great ideas at the time, but all failed to capture the market.
Moving along the spectrum to foresight, Weber says that the path “is about formalizing and quantifying what many of us think we already know to be true, but we don’t really.” Intuition is more valuable when it can be quantified and formally mapped out – and doing so will tell you more about how those dynamics about which you have an intuition, will affect your product and your category. Effective foresight is really less about instinct, and more about creating knowledge that didn’t exist before.
Even among larger companies, new product development and market research is based on creativity and personal insights from managers who are close enough to the product development cycle to get a good feeling for what will resonate with customers. As such, much of that knowledge is informal, undocumented, and exists largely inside the heads of a handful of people.
Starting a foresight strategy
Engaging in a foresight strategy may involve hundreds of thousands of dollars in specialized software, data warehousing equipment, and analysts. On the other hand, small companies and startups have been able to gain foresight with little more than a spreadsheet and publicly available data.
“It’s very accessible to small and midsize companies,” said Weber, who says that for smaller firms, publicly available information, often collected by government agencies and made available to the general public, provides a wealth of data. “You can download data and start to understand where are the pockets of opportunity and pockets of growth.” While Weber uses sophisticated modeling in his own practice, simple analysis can still be used by beginners to identify where the future is, and how to prioritize investments.
Weber cites an example of a client that used foresight analytics to successfully launch a new product into an emerging area. The existing product line had seen a strong lifecycle, but was starting to slow down. Rather than adding more line extensions to the product, the company wanted to launch something new, and did so into a growing market that had not yet peaked. “They were able to hit that at the right time, and were very successful with their product, and met an emerging consumer need. It was a need that all of us were aware of, but one that was set to peak in the next couple years. They capitalized on that.”
Ask questions first, shoot later
A common, but perhaps less useful approach to big data is “shoot first, ask questions later,” or simply going out and gathering as much customer data as possible, and then once the data has been amassed, attempt to find something useful in it. “Before you start talking about big data, talk about what questions you want to answer,” advises Weber. “Start with the business question, then work back into the analytical question, then think about the analytics, then go get the data.”
The data first strategy leads to the incidence of massive amounts of dark data, or data that is isolated, orphaned, or underutilized. The expensive solution to underutilized data is to standardize it – a difficult process when millions of data points are coming in from thousands of sources and in a wide variety of structured and unstructured formats. A more cost-efficient approach to deriving customer insights may be to create a “data lake,” rather than a much more sophisticated and expensive data warehouse, where customer data is collected into a single location for later use, then blending and visualization tools can be applied as needed.
Even simpler is to simply make a list of collections. Weber says, “We used to go collect a lot of that data ourselves, and then database it. What we found was that in many cases, it was much more efficient to have a repository of all the links and sources, so that when we needed something, it was easy to go out and grab it. Just make sure that people are aware of it, and that there’s a single place, or a library, for people to go out and find the data they need.”
Understanding the future when you have no past
Basic analytics has always relied on past performance and trends to predict future results, and this sort of forecasting is just basic accounting – analyze previous results and draw a line that continues that performance into the new year. Wall Street thrives on that sort of simplistic CAGR forecast, and lenders love it because it appears to offer a fact-based look at what is to come. Foresight analysis however, goes a step further.
In the startup world, there is no past on which to base future performance. This sets up a fundamental conflict between the startup innovator who believes in her idea, and the VCs, who want assurance that the idea will bear fruit. Even in well-established companies moving into new markets, product managers struggle to come up with the perfect new addition to a product line that has lost its luster. There is only one way to solve this dilemma, and that is to impose a scientific, data-driven discipline over the insights and instincts that managers have always relied on in the past.