Predictive analytics and machine learning (ML) are changing the game in terms of what toolsets CFOs leverage to predict financial outcomes. As with any progressive tool, some CFOs are not yet convinced of the broad benefits ML delivers day-to-day. While the evaluation of a predictive analysis tool or machine learning partner is an essential consideration of any business strategy, it requires some key conversations with your CFO.
Convey how the technology supports business efficiencies.
Although machine learning is increasingly being integrated into business strategies, the technology is still misunderstood by many. Hollywood has people believing that machine learning signals human-minded machines taking over the world. The most important thing CFOs should understand about ML (and be able to explain to decision-makers around them) is how ML will improve and automate previous manual processes. This shouldn’t be too challenging to communicate, as CFOs have likely already brought some level of automation to parts of the organization, in the form of data reporting, fraud detection, or even crafting investor reports.
Position the benefits of incremental time and cost savings.
The time has come when making profitable decisions boils down to leveraging big data the right way—and CFOs know this. Yours probably isn’t the only data-centric conversation they are having (or have overheard). It’s likely they feel bombarded with endless choices in terms of long-term, strategic, data-driven savings.
Cut to the chase and speak to customer service cost saving. ML savings include not only a lower cost to serve each customer, or lower per-ticket cost,—but more importantly, a reduced cost per customer as the company scales. Speak the CFO’s language and bring the dialogue back to customer service efficiency and future service team productivity.
Reiterate how ML crushes customer churn.
70% of buying experiences are based on how customers feel they are being treated (McKinsey). Implementing ML as part of a churn abatement strategy is an easy fix. As an extension of an automated customer support process, machine learning not only allows customers to enjoy a consistent support experience, machine learning scales the individual support experience. The technology ensures that customer service remains steadily unbeatable as a company grows, as new products roll out, as market environments change, and as customer sentiment shifts.
How does ML do this exactly?
Simply put, machine learning equips a company with artificial (but acute) senses when it comes to “looking” at data and “listening” to the data coming in and moving throughout an organization. Machine learning enables companies to turn subtle customer cues like words or actions, into insightful data. This data can then be leveraged to serve the customer faster, with consistent, accurate and relevant information.
Share how ML turns support organizations into profit centers
Historically, support centers, sales teams and marketing groups existed in three different silos. Machine learning makes it possible for organizations to make strides in sales, support and marketing efforts—at the same time. The ability to have real-time influence between the sales, marketing and support processes is an obvious precursor to additional profit. Because machine learning works from up-to-the-minute, granular information regarding customer actions and interactions, sales teams will always have real-time information on which customers are most satisfied.
And why is this so great? Consider this Insight Squared stat: 83% of customers are willing to refer others after a positive experience. People feel great after their problem gets solved. Having a system that enables sales teams to be constantly in touch with the most satisfied clients not only gives them prime real estate to upsell, it also gives the marketing team a list of people who can be used as part of a referral-based outreach strategy.
Convincing your CFO to invest in machine learning will most definitely not be a single-step process. Helping your CFO understand this predictive analysis tool better through education and sharing examples that apply to your industry is critical. From there, showing how ML can make financial decisions easier across multiple departments should prove very appealing.