Business intelligence (BI) leverages data to create actionable intelligence that informs an organization’s strategic direction. In the last year, we have seen significant increases in the investments companies are making to develop business intelligence programs.
While I applaud this movement – it is a fundamental necessity in long-term business growth – the ongoing effort it takes to get BI right cannot be ignored. With a sound approach, business intelligence has the power to drive dramatic revenue acceleration. It has an equally dramatic detrimental effect when executed poorly.
To help you avoid catastrophe, I’ve outlined three common pitfalls we see companies make in their pursuit of business intelligence.
#1 Business Intelligence Is Used to Tell What Happened, Not What Should Happen
Strategy can’t exist without intelligence, and intelligence can’t be impactful without a strategic purpose. BI informs, substantiates and monitors strategy. When these two things become too siloed, you are unable to use business intelligence to its fullest potential.
Many high-growth, B2B companies do in fact have both strategy and a business intelligence practice within their organization, but too often business intelligence is used only to tell the story about what has happened already. Don’t get me wrong, that is a necessary and valuable function of BI, and can undoubtedly inform strategy. However, the true transformational value comes from the BI efforts that tell what you do and how you do it.
Let me provide an example. A B2B SaaS client was struggling with the pace at which it was penetrating a ripe market. They had all the BI output that told them this market was significant. Plus, their category was growing in that market. Customers saw value in their offering, and close rates were higher than industry benchmarks. Still, they weren’t making the progress they wanted.
The problem was that they were trying to be all things to all prospects. They were marketing to a diverse base and getting average results. With data they mostly already had, we built an account segmentation model that allowed them to target the segments of the market that had the highest return on their efforts. By understanding what accounts had the highest propensity to buy, combined with the highest potential to spend, the marketing team was able to focus their limited budget.
As a result, their efforts resulted in a 30% increase in qualification rate, a 22% increase in close rate and a 27% increase in average deal size. They accomplished these results with the same budget. The difference was that they had a strategy enabled by BI versus just using BI effort to give them visibility into results.
#2 Marketers Start with Answers Instead of Questions
“If a man will begin with certainties, he shall end in doubts, but if he will content to begin with doubts, he shall end in certainties.” – Francis Bacon
This is, by far, the most critical consideration. It is also, perhaps, the hardest to live by. As someone who has interpreted data for many years, I’ve heard “Can you use our data to show X” far too many times. The answer is usually “Yes!” But, why would you waste your money on that? If you are just going to make the data say want you want, then you might as well make it all up and skip the data entirely. Five years later, when you are suffering the consequences, you can run the project again properly.
In science, we start with questions and work our way to the answers. This is terrifying, and it is exhilarating. In business intelligence, this means casting a wide data net, rather than focusing on a few known Salesforce fields. And, it means keeping your eyes open to benefit from serendipity and optionality by identifying patterns in the data that tell you something that you didn’t know when you started. Not knowing is uncomfortable, but it is the only way to get to knowing.
#3 Marketers Are Too Reliant on BI “Tools”
Most data operations will soon be automated. In five years, not only will robots be running the analyses, they’ll be preparing the data and telling us which analyses to run. If you want to do a churn analysis, you won’t need someone to get your data in order, and you won’t need a statistician to run the analysis. You’ll go to your robot, tell it what you want to be done, and it will construct a workflow that matches your data assets and output a report.
However, these tools will never be enough, and marketers who rely on tools alone will fall short of creating true business intelligence. Effective business intelligence is a dance between the data on the page and the meaning in our heads.
Let me explain what I mean.
In marketing, we can distill hundreds of attributes and behaviors down to a few factors, which helps us predict business outcomes. For example, potential customers who don’t show up to meetings may be just that – known for not showing up to meetings. However, combined with other variables, such as being unresponsive to emails and not being a budget holder, allow us to create more significant meaning. In the case above, the three variables might create a segmentation called “Cold Feet.” Likewise, leads who are talkative, assertive, active, and energetic, might be called “extroverted.” The fact that someone is extraverted or “not worth our time” allows us to predict their behavior accordingly, and, adjust our strategy.
Methods like the above are not going to be co-opted by the robots anytime soon. This is because it is a search for meaning, and this meaning only exists in our heads.
The Bottom Line
Using business intelligence for transformational business results isn’t easy. It requires the practitioner to have both data science and business expertise, and it requires collaboration across an organization.
Do you have examples of how BI provided transformational value to your organization? Tweet @myleadmd to share your story!
Meet Eric Smith
Eric Smith is an alumni of the Visual Perception, Neuroscience, and Cognition Lab at Northwestern University. He gets excited about discoveries that lie at the intersection of perception and reality. Professionally, Eric bridged into business as a researcher at NU's Ford Center for Global Citizenship, where he caught the strategy bug. He spent 10 years doing private non-market intelligence and strategy as one of the founding members of Firstsight Group (formerly Diermeier Consulting), where he developed a "data science as intelligence" mindset. Along the way, he has met many interesting people with many interesting problems and has been lucky to be a part of solving them. At LeadMD, he aims to apply a scientific mindset to improving the state of marketing data science and intelligence.