Finance has a role anywhere there is margin at play.

The AFP FP&A Case Study series is designed to help you build up key FP&A capabilities and skills by sharing examples of how leading practitioners have tackled challenges in their work and the lessons learned.

Presented at an AFP webinar, this case study contains elements that are anonymized to maintain privacy and encourage open discussion.

Insight: Finance can understand the decisions being made, the meaning of the data and the constraints of the business model.

Company Size:	Mid-cap
Industry:	Telecommunications
Geography:	North America
FP&A Maturity Model:	Analytics

Analytics: Predictive and prescriptive analysis facilitates the exploration and explanation of data, data science approach, computation and visualization.

Background: General Information About the Company

Finance had an opportunity to partner with operations. Customer attrition for this company was dramatic. It was a significant cost for the industry overall and could be significantly reduced through an AI retention program.

In this case study, we look at a telecom service provider with an annual revenue of $1 billion. Their previous retention efforts were expensive and unproductive, but they had to do something as it costs more than seven times as much to recruit a new customer as it does to retain an existing one.

Challenge: The Work or Difficulty FP&A Had to Address

There was skepticism in the company, serious doubt that AI could identify customers who were about to cancel their service. They were also working with siloed data.

Approach and Outcome: How FP&A Addressed the Challenge and What Came of the Efforts

The team started by combining data sets. From there, they created a series of AI-driven tests to uncover the actions that correlated with attrition within a 30-day timeframe. They also recommended customer touchpoints, spending $200 on the at-risk group, rather than $50 on every user. The AI models found customers more than three times likely to leave as the average customer, and reduced customer churn by 7%.

Q&A with Webinar Speakers

Why is this a finance use case?

Finance has a role anywhere there is margin at play. “Finance is going to dig for the root cause, discuss the five whys, and they're going to be able to bring everybody along in the conversation and tell them why this is so important and how this is going to benefit them,” said Amy Johnson, FPAC, former Senior Finance Manager at Amazon. “As we talk about finance being CFOs and having a significant role to play in this case, many times partnering with other teams or embedding ourselves with other teams is about communication and influence.”

“Finance, for better or worse, is the scorekeeper,” said Justin Croft, Vice President of Data Science & Solution Architecture for QueBIT Consulting. “Part of their role is holding people accountable for potential improvements. AI represents a significant opportunity for improvement across these and many other use cases.”

Are we talking about bringing extended planning and analysis (xP&A) to fruition?

Croft said, “xP&A is having operational and financial data together in one system and having operational and financial plans together in one system. Why wouldn't you want your operational plan to be reflected in your financial plan?”

“AI is based on historical data, and when that historical data is not representative of the future, you still need humans in the loop helping to augment and make decisions,” said Croft. “Let AI do what it does well: bringing in different variables and crunching numbers. And let humans bring common sense and flexibility. And you have to keep tending to the model with monitoring and fine tuning. This isn't a ‘set it and forget it.’ This is a living, breathing thing that can be changed over time.”

Who's leading this effort?

“If you are putting AI into place, this is something you need to decide and plan for your business: Who is leading it?” said Johnson. “It can be the operations team, the marketing team, or it can be the finance team. But there has to be an understanding, and there has to be a leader. But who leads it has to work with your industry, needs and the way your culture works.”

Do you get resistance when finance goes beyond its bounds? Do people say, “No, you play in your sandbox, this is my sandbox!” Or are people welcoming to the expertise?

“We get both,” said Johnson. “And that's where I say it's communication and influence — how do you win people over? I was working with my safety team, helping them walk through a logic model of how to get to an answer that had great financial repercussions, because it's a cost, right? You'd never want someone injured; you want to retain your employees, you want happy employees, and you want a safe environment. But nothing about their worksheet was finance.

“It's all about influence. It's working with people and making sure that you leave each person with a good impression of you because they will tell the next person. You continue to earn trust and get more buy-in along the way, and people begin to see your influence and your value, and then they start inviting you to the table. They start inviting you into their conversations, or just stopping by your door and asking, what do you think about this?”

You each offer two different models of how to manage the skills gap. Amy, you brought up the PhD expertise that you have on staff, whereas Justin, you are the outside expert. How can finance build up its AI skills given the environment that you’re operating in?

“I don't build the AI or the algorithms, but I have to understand it and I have to know it,” said Johnson. “We make sure that there are plenty of informational resources. We have online sources and modeling sources, we’ve talked to an expert and we have office hours. Also, we partner with other teams to say, hey, did you encounter this? What's going on? Did you already find this answer? We network among our departments and our peers to help ensure we don’t have that skills gap.

“The challenge in that is when you create all that information, somebody has to maintain those resources. We’ve built maintaining the models and data flow into a job description to make sure someone is taking that on and doing it.”

From his perspective as the outside expert, Croft said, “Companies hiring an outside consultant need to make sure they’re transferring knowledge and skills, that the consultant explains everything they build, people understand exactly how it works, and they understand the data going into it. They also need to understand the questions being asked, and the results coming out of it. We thoroughly document that with our clients.

“Finance can really shine because they understand the business model, they understand the decisions being made, and they know how the business works. Also, finance is the owner or keeper of a lot of historical data. They can move into the technology realm and have a better grasp of exactly what the data is saying, and the criteria, conditions and constraints that we're trying to make decisions or predictions under.”

---

Read the rest of the articles in this series:

• Four Rules for Your AI Journey
• Incorporating AI into Revenue Forecasting