Look at any study surveying trends within the IT industry and you’ll see increased demand for business analytics and intelligence. The Society for Information Management’s 2015 IT Trends Study, for instance, found that “analytics and business intelligence” were the biggest investments IT departments made that year.
But just because businesses are more open to exploring analytics and executives are dropping data science buzzwords in meetings doesn’t mean you don’t still have to sell your C-suite on investing in such technology. Analytics done right with the best tools and a skilled staff can get extremely expensive, and your C-suite isn’t just going to write you a blank check, especially if you can’t communicate how this investment will positively impact the bottom line.
As the co-founder of Soothsayer Analytics, which applies artificial intelligence to build analytics tools for companies, Christopher Dole has experienced firsthand how difficult it can be to sell senior leadership on the ROI of advanced analytics.
Since the founding of his company two years ago, he has continued to hone his pitch on prescriptive analytics, and he’s learned what information C-suite executives look for both before and after the launch of an analytics platform. He listed four pieces of advice for how to not only pitch an analytics program, but also ensure its continued success after its launch.
Do your homework
Prior to even scheduling a meeting with senior leadership, you must first arm yourself with the answers to every question that might get thrown your way.
“I would definitely plan on meeting with any relevant colleagues, peers, or other internal stakeholders about issues and opportunities that they’d like to address,” said Dole. “And once you have some ideas you should also, in advance, meet with your data team and identify any relevant data — preferably data that’s clean and comprehensive — so then when you’re actually in front of the C-suite or board you can start by clearly defining where you’re currently at in the analytics journey, whether it’s the descriptive, diagnostic, predictive, or prescriptive level. If leadership says that your company is already doing analytics, yet they can’t predict what will happen or what can be done to perturb it, then they aren’t really doing analytics, and you should clearly articulate that.”
It’s also important during your research to find examples of other companies’ experience with analytics solutions similar to the ones you’re proposing.
“Talk about the value it created for them,” said Dole. “So, for example, if you’re starting on an analytics initiative and you’re a telecom provider, talk about how a competitor tapped into their stream of customer data to reduce churn and provide millions of dollars per year of savings.” When generating a list of examples, he said, try to focus more on instances that generated revenue or prevented losses as opposed to reduced waste. “Making money is often seen as sexier than saving money.”
Start with the low hanging fruit
If you’re just starting out in the analytics game, it may be tempting to ramp up a state-of-the-art program. But it’s actually more important to get some early wins by capturing the low-hanging fruit.
“If possible, start with a larger problem that can be easily split into sub projects,” said Dole. “For instance, if you decide to focus on customer understanding, start with scientific customer segmentation. That way, once you know who your customers are, you can start to solve other problems that would require that understanding as a foundation anyway, whether it’s identifying opportunities for cross-sell and upsell, predicting and preventing churn, or forecasting customer lifetime value. These quick wins can typically be achieved within 12 weeks.”
Set the proper expectations
It can be incredibly tempting to hype the potential payoff of analytics, but overselling it can result in the C-suite viewing outcomes as failures when they would otherwise be considered wins.
“It may be a few month or two before any snippets of insight can be garnered, so it’s important that they are patient during the process,” said Dole. “A lot of what a data scientist is doing is identifying, collecting and compiling clean data into usable formats, and this can often take up to 60 percent of their time. Make sure they understand that a properly structured analytics project typically provides as much as a 13x ROI. There are many steps to achieving this, and everyone needs to be aligned on the ultimate goal.”
Use the KISS principle
Above all, you should keep it simple stupid. It’s all too easy for a data scientist to get bogged down into technical jargon and respond to questions with arcane answers.
“Use rich visualizations when possible because it’s much easier to understand a graphic than an equation or complex model,” said Dole. “Remove as much of the math and science as possible and just focus on the insights and the value that it’s going to create as well as all of the potential to expand upon it.”
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