Financial information infuses company activities. From budgeting to closing the monthly books, from measuring progress to managing cash flows, numbers are to business what the ABC’s are to our reading.

It’s no wonder then that companies large and small have invested heavily in their financial system, teams, and processes to generate financial information.  Let’s call this entirety the Financial Information System. Numerical data is collected, processed, and stored in data warehouses, where it is then used to develop insight and inform decisions. While the Chief Financial Officer is in charge of this system, all employees play a role, whether they fill out expense forms, develop budgets, or make investment decisions. And all decision-makers are encouraged to use the processes’ information  (e.g., making a presentation as a product manager about your category). A stellar CFO is like a great coach in winning the game of business.

But financial results are a lagging indicator of success. Kodak’s and Blockbuster’s numbers looked great for a long time while disruption of their business was underway by digital cameras and Netflix. Companies even make decisions that hurt future performance in the interests of shoring up current earnings and stock values. Kraft finally realized you could not cost-cut brands to greatness.

The real drivers of success—and better indicators of future performance—come from marketing and sales, not finance: how good you are at:

• Building awareness of your brand
• Understanding the market’s perception of our points of differentiation, if any?
• Knowing who is most likely to disrupt your offerings?
• Securing interest by prospects?
• Winning an order or trial?
• Keeping the customer for life, ideally as a fan and a large buyer?

In my experience, “market understanding systems” need to be given far more attention. Yet in many companies, processes for mastering this kind of information flow don’t even exist, and in others, they are far less robust than for financial information.

Sales representatives can exit the company with all the customer insight you wish you’d recorded. Marketing departments send out emails, not knowing if the right content is going to the right person.  Sales reps call on customers with no interest in being an early adopter of a new offering.

Thankfully, we’ve made progress in the last decade. Customer Relationship Management software and the Internet have transformed marketing from a creative branding and communication resource into a demand management activity, generating awareness and leads in measurable ways. (There are unintended consequences of this change, which I’ll discuss in my next blog.) Salesforce.com and its competitors have embedded sales representative customer information into company databases.

Best practices in this area are changing rapidly, as captured in Deepak Sharma’s interview of Jonathan Martin, the Hitachi Vantara Chief Marketing Officer. (See link here. Sharma is a Manager Director at Deloitte.) Hitachi Vantara is a provider of enterprise data solutions. Its sales cycle is long and complicated, involving multiple touchpoints within the prospect’s organization and inside Hitachi Vantara. The company’s leadership team decided to use its own data warehousing, analytics, and other offerings to improve its customer understanding process.

According to Martin, “For marketers, measuring campaigns and generating reports is just the start. The bigger challenge is to analyze that data, drive insights, and create a narrative. We want to engage customers and prospects in a more relevant and efficient way, which requires us to leverage data from multiple functions—not just marketing.”

Among the best practices in use are:

• Collecting, cleansing, normalizing, and centralizing selected data for all departments to use. (Marketing alone had over forty separate places where data was stored that needed to be consolidated.)
• A data governance/oversite team from across the company, led by the IT department.
• Using Artificial Intelligence software to mine insights from this data. Deploying these tools allows for financially scoring different marketing and sales activities so that the right sequence of actions is used for the right prospects to drive demand.
• Making data and its analysis a core competency of the organization.

Martin provided an example of the financial return from building a robust customer understanding process. “Last year, when launching a new storage product, we used information from the enterprise data platform to build a propensity-to-buy model, leveraging machine learning to identify the customers who were most likely to be early adopters. We analyzed historical data about customer engagement and surgically targeted specific accounts. Ultimately, we were able to predict with 97% accuracy which customers would buy. Marketing and sales teams have finite resources; analyzing the data in this way allowed us to focus on the activities that were most likely to drive outcomes for the company.”

How would you rate your organization’s financial understanding process? How does your process for customer and market understanding compare?