How do Data and Analytics-Savvy CFOs Stay Ahead of the Game?

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How do Data and Analytics-Savvy CFOs Stay Ahead of the Game?

John Rodgers, COO & Managing Partner, SSA & Company And Nick Kramer, Senior Director, Advanced Analytics, SSA & Company

John Rodgers, COO & Managing Partner, SSA & Company

Digital disruption and the evolving business landscape have significantly changed the C-Suite’s directive. While all executives must understand how to embrace this new landscape, few play a role as critical as the CFO. In addition to overseeing multiple functions, CFOs must actively lead in overall strategy, transformation, and performance management. Forward-leaning CFOs leverage modern technologies to drive strategic value across the company, more efficiently run their function, and meet their expanded mandate.

"CFOs who find ways to effectively make use of new analytics and digital tools will drive stronger performance, create efficiencies, and steer their company’s innovation"

The question is “when” and “how”—not “if”—companies will adopt advanced analytics, AI, machine learning, robotics, and other technologies to drive advantage. Unfortunately, many view this transformation as a technology imperative versus a broader business initiative. For many leading companies, the CFO has become the “tip of the spear” for adopting digitization and analytics to improve business performance.

CFOs must lead efforts to dive deeper into performance metrics via real-time operations data, apply analytics to drive efficiencies, and enable transformation by effectively funding technology, training, and resources required to build analytics and digital capability.

Apply Analytics to Strategic KPIs

Strong companies translate their strategy into a set of simple, clear and measurable deliverables. Analytics and nontraditional data sources have changed the playing field on how leaders identify, measure, and view non-financial performance indicators that predict financial performance. CFOs must spearhead this new way of thinking, as they often hold responsibility for funding the investments that allow for capturing, and efficiently and effectively using, data. This information produces value in many ways. A few examples:

Improving Forecasting and Go-To-Market Speed

$1.4B electronics manufacturers struggled to find organic growth and began losing market share. Becoming better and faster at helping its customers go to market with their products was critical to increasing revenue and market share. Using an advanced analytics platform, machine learning, and natural language processing, we identified root causes for inefficiency and reengineered processes to optimize R&D, yielding annual top-line benefits of $40-80 million.

Nick Kramer, Senior Director, Advanced Analytics, SSA & Company

Managing Brand Reputation Risk through Analytics

Machine learning and advanced analytics applied to real-time data can help CFOs better predict and quantify bottom-line impact of risk, increase their company’s competitive advantage, and position their company to adapt to a constantly changing environment. A Fortune 100 hospitality company faced a public relations risk when an unknown concern escalated from mild chatter to extreme activism. Applying a predictive issue analytics tool to real-time, external data, enabled the company to predict significance and velocity of issues and react appropriately to protect its brand. Analyzing streams of previously uncollected risk data allowed the company to spot emerging trends and better assess risk and mitigate issues, leading to $6M in cost avoidance.

Understanding Customer Behavior to Drive Financial Performance

By tying customer behavior metrics to financial data, CFOs can access leading indicators of customer growth, retention, or churn to drive performance, forecasting, and efficient customer experience strategies. We helped a global retailer apply advanced analytics to identify specific thresholds for shipment pricing and promotions. These insights allowed the retailer to maintain customer service levels while improving bottom-line performance by better aligning pricing levels and promotions to customer needs and buying patterns. Notably, finance and marketing teams collaborated to identify the right measures and strike the balance between corporate objectives.

Applying Predictive Modeling to Customer Behavior to Improve Receivables

A $1Bn truck and trailer parts retailer had 30+ percent of its receivables past due every month. By applying machine learning to collections data, we helped the company identify factors that predict delinquent payments and create risk tiers and mitigation strategies, yielding 15-20 percent improvement in cash position. While B2C companies often apply sophisticated models to understand consumer payment behavior, many B2B businesses do not use this type of analytics to receivable and trade credit, leading to lost revenue, decreased cash flow, and under-informed decisions.

Enable Transformation through Digitization and Analytics

Today’s C-Suite faces increasing pressure from boards to articulate an actionable advanced analytics strategy. For CFOs with direct control over IT, this means driving an innovation agenda and strategically investing in technology, shifting the perception of IT as a cost center to a growth engine.

Often, the organization’s biggest gap falls in leadership’s ability to fully utilize these new tools and confidently guide analytics into decision-making and daily execution. CFOs must know how to apply rigorous measurement and continuous improvement to analytics and digital efforts to achieve sustainable ROI. We believe this transformation can be accomplished in three phases:

1. Identify today’s data needs. Too often, businesses manage today’s world with yesterday’s data. Advancements in technology and skilled professionals have made identifying and collecting today’s data to solve today’s problems far less expensive than ever before. To start, companies need a clear understanding of the specific data needs from the highest strategic level to the daily transactions. In our experience, companies best drive cross-functional collaboration and agreement through a series of focused workshops.

2. Develop the data “piping.” After agreeing to metrics, companies must set the data piping to ensure the correct data is collected and made accessible to the right people. Most firms we work with have 70-80 percent of this in place. As noted above, for those lacking the right infrastructure, the investment tends to be pennies on the dollar compared to prior years.

3. Equipping the right levels with the right tools and skills. The largest gap we typically see in driving an analytics transformation is with the skills and tools for the decision makers. Most often, data resides within technology or “data scientist” groups. Best-in-class firms up skill decision makers (typically mid management) with new tools and training to understand how to pull, cleanse, and visualize data to make real-time decisions. 


The stakes have never been higher for CFOs. Fortunately, the wealth of newly available data and technology creates significant opportunities for CFOs to assume a larger role in overall transformation. CFOs who find ways to effectively make use of new analytics and digital tools will drive stronger performance, create efficiencies, and steer their company’s innovation.

See Also: Top Data Analytics Companies For Capital Market

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