This guest post is from Robert Reid, CEO of Battery portfolio company Intacct*, which was acquired by Sage for $850 million in July 2017.
Big data is a huge trend in business today, with companies across industries gathering, tracking and analyzing hordes of new metrics to improve their bottom lines. Many companies are hiring highly paid data scientists to parse all this data. According to job site Glassdoor, being a data scientist is a pretty good gig—one that offers a $110,000 median annual salary and has rapidly become one of the most sought-after jobs in the economy. (It’s No. 1 on Glassdoor’s “50 Best Jobs in America” list).
But what if you’re a small or midsize company that can’t afford to hire a dedicated data expert?
Turns out, you may not need to. Much of today’s new accounting and finance software—even for small businesses—is sophisticated and robust enough to turn your existing CFO, or head of finance, into a de-facto data scientist. You just need to know how to leverage it.
First, some quick background: In the past, legacy accounting software was set up mostly to record profit-and-loss metrics, and not to capture or manage much detailed, operational information. If you wanted to combine financial and operational data to create detailed analytics on your business, you’d have to purchase expensive business intelligence (BI) tools and pay a business analyst to make sense of that data for you. Another problem: The process of gathering and then analyzing your data would take so long that your insights often were outdated the moment you received them.
Today, by contrast, most modern financial software, often called ERP (for “enterprise resource planning”), captures very granular data about multiple aspects of your business, operational as well as financial. Often it offers something called “multi-dimensional tagging,” which allows businesses to surface valuable information about what really drives revenue. This means you can test new programs or launch new products, and learn in real time how to make your business more efficient and profitable.
Let’s say you’re running a hotel chain, and every location has a bar and restaurant on site. You’re considering adding minibars in your rooms, but you’re worried you might end up cannibalizing revenue from the hotel bar. With a legacy financial system, you’d have to add up revenue from the minibars and cross your fingers installing them was the right move. (Maybe crack a miniature vodka bottle if the results seem promising; open a few more bottles if not). It would be difficult to determine how much additional revenue came from minibars directly, versus some other factor, like a seasonal promotion. Similarly, any dip in bar revenues might or might not be attributable to people drinking from the minibars. Correlating the two factors would pretty much be guesswork.
With data-rich financial software, however, you can test those minibars in one location or even on a single floor, and instantly get a detailed picture, broken out by room, showing how many guests with minibars spend, or don’t spend, at the bar downstairs.
Take another question: whether or not to invest in an expensive piece of equipment. Will the additional revenue it drives justify the expense?
Recently I visited the dentist, who was in just this situation. I could have the procedure I needed done with the new equipment—it would cost more, but take less time and be less painful. I jumped at the chance. Assuming my dentist’s office uses financial software with a multi-dimensional general ledger, they’ll be able to track in real-time how many patients opt for the new equipment and pay more for it. They’ll easily see whether it’s worth buying the new machine for their other locations.
This kind of data analytics can also be used to test and refine promotions. For example, a restaurant dealing with low foot traffic on Tuesdays during the day shift might decide to run a salad promotion. With advanced financial software, that restaurant could dig deep into the results. Did customers who took advantage of the salad special also buy lunch entrees or drinks? Should staff be pushing to upsell them more? When were these salads purchased? Which ones were most popular? Could the promotion run for one hour instead of two? Modern financial software can answer all these questions.
This technology can help businesses get to know their customers better, too. Most businesses try to understand the attributes of customers who bring in the most revenue, so they can better target them and find more of them. But your biggest customers from a revenue standpoint could be buying low-margin products – they might not be your most profitable customers. Big data enabled by modern financial software can pinpoint the most profitable customers, how much it costs to acquire and service them, and where they came from. It can spur new ideas for how to attract similarly profitable customers and run promotions or offer discounts to make them more loyal and more likely to return.
An annual CFO Evolution Survey from consulting firm Armanino reveals most CFOs spend more than half of their time on accounting tasks that report on past performance. The same survey shows that CFOs would prefer to spend the majority of their time being a strategic advisor to the business, focused more of future-oriented analysis. Translation: your CFO really wants the data-scientist part of his or her job to grow.
This post originally appeared on TechZone360.
*For a full list of all Battery investments and exits, please click here.