Data Does Not Equal Intelligence: Predictive Analytics in the Enterprise
Modern enterprises run on data. However, much to some executives’ surprise, data itself is incapable of solving business problems.
Modern enterprises run on data. However, much to some executives’ surprise, data itself is incapable of solving business problems. All the bits of information your teams collect are worthless when it comes to improving supply chain processes, fine-tuning marketing campaigns, closing deals or meeting any number of other business challenges, if you don’t understand how to create intelligence from it. To make the most of the influx of data coming into your company, you need to know the difference between data, intelligence and insight, as well as how to apply all three to predictive analytics.
Here’s a break down of where data, intelligence and insight differ, and how they build upon each other:
- Data – Data is an essential foundation for the more valuable assets of intelligence and insights. It includes all the firmographic and demographic information you might have about your target market, as well as raw facts about the individuals and companies with which you do business (or want to do business).
- Intelligence – When you gather all of your separate data points and look at them in relation to each other, you get a clearer picture of challenges and opportunities. That’s intelligence. Intelligence shows, for example, that the visitor who just came to your website works for a company your sales team has been targeting, and that company recently expanded into multiple cities.
- Insight – With data and intelligence, you can draw insights that lead to better decisions. You’ll see the patterns behind individual or market behavior, and you can adjust your own strategies and tactics in response. With the intelligence you gleaned about that website visitor above, for example, you might decide your target is at a prime moment to buy and would be receptive to a sweetened sales deal.
Data, intelligence and insight are all crucial for brands that need to optimize budgets and target the right prospects. Together, the three elements lay a strong foundation for predictive analytics.
Putting insight and intelligence to work through predictive analytics
You’ve probably learned to harness big data to some extent, like most of the B2B enterprises that start with massive stores of information about their prospects. If you know prospects’ company size, locations, recent purchases and usage patterns, you can build a basic sales and marketing strategy. You can segment your targets based on broad parameters, such as company size and vertical industry. You can also create broad-brush buyer profiles, so you know you’re talking only to decision-makers at the mid-manager level or above, for example.
However, to gain a real competitive edge and maximize the value of the data you have, you need to up your analysis game to create predictive models. That requires more than just data; you’ll need intelligence and insight. Take the data you use to create a prospect list. You have firmographics, but what about behavioral information? What about psychographics? This is the kind of data – often drawn from external sources – that can make the difference between modest outcomes and stellar ones.
When you can target leads at specific points, you can:
- Create highly detailed buyer personas;
- Segment your leads and market at the micro level;
- Build tailored campaigns supported by relevant content; and
- Close more deals.
A model that incorporates behavioral triggers predicts which actions your prospects are likely to take at which times. Those triggers might include the hiring of a new IT leader – usually a green-light moment for purchase decisions. When you have that level of insight, you can reap greater returns on your investments across business units.
Predictive analytics: the new B2B table-stakes
B2B enterprises cannot afford to settle for raw data, nor can they continue to mistake that raw data for useful insight. In order to compete in a smarter marketplace, you need to view internal and external data sets as the starting point. From there, the ability to relate that disparate data will create the intelligence you need to draw actionable, profitable insights.
You must log in to post a comment.