Ten Key Considerations for Effective FP&A Communication

By Simone da Silva Collins, Finance Manager, Polycom

Communication is a key soft skill that FP&A professionals should master. Getting it right the first time enables FP&A professionals to be more effective and efficient. Effective communication enhances their integrity and credibility.

Communication is a two-way process.  This sounds a cliché but it is true. Communication is not a monologue, even when you are using the written form as I am here. There are the sender and the receiver of the message.

Here are some key considerations for effective communication that I’d like to share 

1.    Be prepared

It is common practice to prepare before going into a major meeting or presentation. However, you should always prepare when communicating. This is not only about the physical environment or the technical aspects of the delivery. You should prepare yourself by learning about your audience, understand what is the purpose of your communication and what you want to get out of it. In an informal context, try to use a few moments to think about what you want to say and why. You will discover that you are likely to take a slightly different approach to delivering your message.

2.    When

You know when you need to present the monthly results or a forecast. 

However, when you have important information that is outside of the normal meeting schedule, have you considered the element of “when”? A communication creates more impact if this is carried out at the appropriate time. There was an occasion when I was on holiday abroad when I received a message to reduce the budget target. Two options were available to me: to wait until the budget was finalized before communicating this change or to inform the business partner in the earliest possible instance. As I was away for a week, on the morning I received the change request, I emailed a brief message to my business partner this adjustment and also a point of contact with whom she can refer to in my absence. This allowed my business partner to change the campaign activities in good time in line with the reduced budget. This also enhances a trusting relationship with the business partner. 

When you request information, you should consider the availability and the priorities of our audience. The element of “when” comes into play. You should not leave it until the last minute before making the relevant requests. This does not only create unnecessary stress and frustration. It also creates a sense of distrust which can damage business relationship.

3.    Do unto others as you would have them do unto you 

Developing empathy and respect can lead to good working relationships. Empathy helps with effective communication.  This requires you to understand your audience, to put yourself in their shoes and understand that there are cultural differences, knowledge gaps and differences in experience that lead to different worldviews and how a message is interpreted. With empathy, you are more likely to use an appropriate gesture and choice of words to get your message across. You are also less likely to offend the other party. Without empathy, what may be intended as a harmless joke in an informal conversation can be interpreted and lack of sincerity and respect. Regular communication with your business partners can help you to learn about them and through this develop empathy.

4.    Don’t multi-task
How often are you responding to an email, text message or instant message while on the phone or video conference? Multi-tasking means you are not concentrating on the message that is delivered or only hear what you want to hear. This can lead to misunderstanding. It also shows a lack of respect and disinterest in the message.

5.    Listen (and observe)

Communication is not all about you delivering a message.  A one-way communication can lead to frustration as the sender can be making assumptions that may or may not be correct about the recipient.  You should not interrupt but give time for the others to speak. I was attending a team meeting via video where the leader was constantly interrupting me when I was speaking so the others could not hear me. This led to frustration on both sides and a valuable opportunity to communicate effectively. 

Listening is not the same as hearing. You can hear every word that is said to you but not necessarily listening and therefore retaining the message.  You can demonstrate listening through body language such as leaning forward and reflecting such as “Let me see if I understand you correctly. You mean ... “

6.    Eye contact 

Appropriate eye contact promotes a sense of interest and confidence.  This is not only true when you are physically speaking to another person but also when you are meeting using video technology. I was once on a video call where a director was looking down the whole time. He did not once look up to the video camera or make eye contact with the other participants.  The participants in the meeting were not sure if he was multi-tasking or was disinterested in the meeting or was not listening at all. This does not promote trust in a business relationship. Eye contact should be natural.

7.    Take advantage of technology (but choose the right one)

Technological advancements provide us with various forms of communication: nonvideo phone, video phone, mobile phone, email, text messaging, instant messaging, video-conferencing. Each has its benefits and disadvantages. It is important to choose the right form to deliver your message. If you want an instant or quick response, you may choose phone or instant message. Be aware that instant messaging can be intrusive. Email can be more appropriate for messages that require the audience to take some time to consider a response or for multiple recipients. Video conferencing can promote a sense of closeness as a form of face to face communication.

8.    Do not overload

Try to be concise and focus on one or two key points in the message.  Also, try to avoid message fatigue by sending too many follow up emails.

One of my previous employers promotes this by limiting the length of emails to fit within a screen size, including a digital signature. This discourages lengthy emails. Employees have to think about what they really want to say and put the essential information first.

For presentations, it is best to keep the number of slides to a minimum and use them as an aide-memoire. It is best to avoid the pitfall of “death by Powerpoint”.  Sometimes less is more.  It is advisable to present one idea per slide. This means the audience is more likely to listen to your presentation rather than reading the slide.

9.    Drop the emotional baggage

Emotion can distort the true meaning of a message. Emotion creates an invisible filter lens, much like the ones applied to cameras. Emotion can also lead to miscommunication. It is, therefore, important to take the emotion out of the equation. If you are frustrated and want to respond immediately to what is meant as a constructive criticism, try to visualise a traffic stop sign and take a deep breath. This will allow you to think more clearly and respond in a professional and courteous manner. 

10.    The bag that is behind you – get feedback

You have sent your email. You have presented the results. However, do you know what your audience thinks? You know what you want to say. However, how do you know if your audience understood you? 

During face to face communication, you can see the feedback instantly by observing body language, gesture, eye contact and listening to questions and comments from the audience. However, it is not advisable to directly ask “Do you understand?” The audience is more inclined to say yes rather than admit the opposite. One can try asking for their opinion or some kind of straw poll if appropriate.

It is harder to obtain feedback when you are not carrying out face to face communication. One way to do obtain feedback is to carry out follow-up through other channels of communication. For example, for me to obtain feedback on this communication, I can check with the administrator on the number of reads and some of the comments posted by readers. In other situations, you can carry out follow up via instant messaging or a phone call. However, it is a fine balance when carrying out follow up so that not to over-communicate.
I hope these key considerations will help you with effective communication.

Simone da Silva Collins is an FP&A professional working in Polycom, an industry leader in unified collaboration solutions.

She provides business partnering to various departments of Polycom in EMEA. She was previously the Group Finance Analyst supporting the Executive Team at Intec (now part of CSG), a provider of Business Support System (BSS) software and related services, primarily for the telecommunications industry. She also worked for Telewest (now part of Virgin Media) for over 7 years providing commercial and financial support to the Interconnect team.

Simone is originally from Macau in SE Asia. She gained her Masters at Manchester Business School. She has also recently achieved FP&A accreditation.

Create Eye-Catching Monthly FP&A Reporting Packages

Linda A. MacDonald, Finance Manager at Quadrant Health Strategies, Inc.

Linda MacDonald received her MBA from Salem State University in 1996. Since then, she has worked in Finance in global companies and is revered as a highly motivated, enthusiastic, finance professional with strong analytical and business acumen. Over the years, Linda has honed her financial analysis, budgeting, forecasting, and reporting skillsets. What sets Linda apart is her exceptional leadership style, which is based on the merits of promoting teamwork, innovation, and encouraging others in the workplace. Linda strongly believes in continuous learning and development that creates a culture of empowering employees.

LinkedIn account: www.linkedin.com/in/lindamacdonald/

FP&A Analytics, Visual Analytics FP&A professionals know that in internal finance, your “customers” are senior management, stakeholders and business leaders. 

As a finance business partner, whether a Director of FP&A, Finance Manager, Financial Planning Analyst, or Controller, you are the “SME” (subject matter expert), and monthly reporting is your time to shine.


As a trusted finance business partner, you are the person involved in the day-to-day details of the business, closest to the transactions, making sense of data sets, compiling information, making decisions surrounding revenue recognition and expense management in accordance with US GAAP.  You are heavily relied upon from your customers, the business leaders, to present accurate, transparent, timely and informative management reports, financial statements, metrics, KPIs, graphs and other visual representations of data that are relevant, pertinent and reliable.  The purpose of these reports is for leaders to make sound business decisions that ultimately provide direction for the longevity of the business.  A lot rests on your shoulders!


In my experience, whether your business leaders are financially savvy or not, solely presenting management reports, financial statements and long lists of generic information in a monthly reporting package has been proven ineffectual. Management reports and financial statements must be accompanied by visual aids, narratives and commentaries for optimal impact.

First, learn what your business leaders are most interested in.  Does their eye drop right to the bottom of the P&L to total net income or loss?  Are they most concerned with gross margin or contribution margin? The goal is to summarize data in easy-to-understand formats that show relevant information that prompts business leaders to put strategies in place to improve results and ultimately stakeholder value.


In your monthly reporting package, consider incorporating analysis such as what-if analysis, rather than the run-of-the-mill reporting expectations.  Since management reports are submitted in electronic format, be sure to lock spreadsheets and cells.  You can create “input cells” if you want to create an interactive or results-driven report that is dependent on certain criteria and variables.


There are a number of business intelligent programs that will automatically generate dashboards or you can use Excel to create attractive and eye-catching graphics that display results.  Bar charts, pie charts, and linear graphs can be designed to visually emphasize trends and results that are comprehensive, memorable and impactful. Data dashboards manage your metrics and KPIs in one snapshot.  Coloring graphs should be done in one tone or theme variation and should use no more than three to four shades of the same color.  You can even pull your company’s brand colors into your presentation.

When creating charts and graphs, remember that simpler is better.  Simplify graphs and charts by removing unnecessary axes and background lines.  Remove redundant labels and legends.  Clear, concise charts, graphs and infographics can provide a comprehensive overview of the business’s financial picture.  Use business metrics to gauge performance or progress toward a quantifiable goal.  Use KPIs to provide critical insight into “key” business measurements that best convey your business’s story.

When management reports are insightful, explanatory and convey a meaningful message, they are highly anticipated and referred to by senior management and business leaders throughout the month.  Be sure to give yourself kudos by incorporating your name into the presentation, so your name is seen regularly by senior management.

FP&A Analytics, Visual Analytics


There are 4 main types of charts and graphs:

  1. Comparative
  2. Relational
  3. Distribution
  4. Composition

Line graphs compare changes over the same period of time.

Pie charts compare relationships of parts of a whole or highlight proportions.

Bar graphs compare data between different groups or track changes over time.

Stacked column shows the parts that contribute to the total and compare change over time.

FP&A Analytics, Visual AnalyticsFP&A Analytics, Visual Analytics


Each financial ratio presented as a result of your in-depth financial analysis, whether profitability, efficiency, stability, liquidity, debt or investor ratios, should include an explanation.  For example, if including a quick ratio of 1.5, you want to provide a narrative that says something like, “This ratio gauges liquidity, meaning the business has $1.50 of liquid assets available to cover each $1.00 of current liabilities.”   By simply stating, in a few words, what the ratio is measuring and what it means, supports business leaders’ understanding and purpose for the measurement.  The additional narrative provides valuable information for business leaders to make informed decisions.


As an FP&A professional, you should also make recommendations for improvement and provide solutions.  For example, when supplying a profitability ratio such as a contribution margin, which measures a business unit’s or product’s portion of net revenue that contributes to the company’s profit, you want to suggest specific strategies for improvement.  Provide a commentary such as, “At this time, a review of the current pricing structure or reducing variable production costs may be required.”  This recommendation further provides an overall comprehensive picture to senior management on how to best move forward with decisions.

The FP&A professional’s part in leadership is vital and important to the decision-making process and direction of your business.  Make it your mission to provide the most relevant data to your “customers.”  Explain in non-finance terms, the results, implications, and impact of decisions.  This is your area to own, so be cool, calm and collected, and communicate with confidence!



Will You Be a Relevant FP&A Professional 5-10 Years from Now?

By Mark Gandy, Outsourced CFO, Principal at G3CFO

Edward Hess in his excellent book Learn or Die reminds us that today’s average tenure on the S&P 500 is 18 years and declining. In 1980, the average tenure was 30. 

Hess also states through his research that nearly half of the S&P was replaced in the past decade.

How’s that for company relevance?

Then he tells us the average tenure of the Fortune 500 CEO is just 4.6 years.

As an FP&A professional, you might be asking, “What does this have to do with me?” I believe the answer is everything.

Could it be possible that the role of the FP&A professional is just as much at risk as the large companies cited above or the CEOs of these same organizations? Shouldn’t we be asking, “How relevant is my role in the years to come?” Fair question?

Let’s unpack this FP&A relevancy question with a few others that might help.

Clayton Christenson on The Job to Be Done

Author and business consultant, Clayton Christenson teaches business owners that the key to selling more products or services is to know the specific job these goods perform for the customer. In this insightful video, Christenson explains that the job of the milkshake is to keep the driver occupied during a long drive to work while satiating hunger pains throughout the morning. Brilliant.

Let’s apply Christenson’s concept to FP&A. What job is being performed for every FP&A activity you perform? What’s the job of the historical marketing and sales analysis? What job is the driver-based rolling forecast being done for the users of this output? 

I’m sure you can add to this list of questions. Accordingly, I believe Christenson’s simple job-to-be-done framework aptly applies to the FP&A relevancy question.

The better you can answer Christenson’s questions in all aspects of your work, the more relevant you are likely to be over the next 5 to 10 years.

Two More Questions to Throw at the Relevancy Question

I have a theory. I provide CFO consulting services for small, growing companies in the U.S. My gut instincts tell me FP&A relevancy is not at risk, far from it. For me? That’s a different story.

Here’s how I personally attack the relevancy question. I do so with two other questions:

  1. How am I helping my clients to stay relevant in the long term?
  2. Additionally, how am I staying relevant myself?

I think I know the answer. I’m not even sure IBM’s Watson has the self-awareness to address this. My answer is that I stay relevant by keeping my clients relevant for a long, long time.

Similarly, I perceive that you, the FP&A professional, stay relevant by keeping your employer relevant. Now work backwards which comes instinctively natural for you — how will you do that?

As a homework assignment, I’d enjoy reading your feedback in our LinkedIn Group about how you are staying relevant by keeping the organization you serve relevant. It’s not about you. It’s about making the business you serve great. You have some of the greatest intellectual capital in the world — the creativity and capability that exists in your mind. Use it and you remain relevant.

Maximizing the Positive about Prediction

By Karl Kern, Founder/President, Kern Analytics LLC

FP&A Analytics, FP&A Trends, Predictive AnalyticsPrediction is an important work that FP&A practitioners do.  This work has many challenges. One way to address these challenges is by maximizing the positive.


1. Establishing a Framework

The first step in maximizing the positive is establishing a framework.  For FP&A practitioners a framework has been established by a quote from Bill James in his praise of the book The Signal and the Noise by Nate Silver: “Projection, prediction, assumption, trepidation, anticipation, expectation, estimation…we wouldn’t have eighty words like this in the English language if it wasn’t central to our lives.”  This quote establishes a framework because the vast number of words indicates the importance of prediction in people’s lives.  That means we have won half the battle! By winning this part of the battle we know that prediction will receive acceptance in organizations whether they are startups, small businesses, middle market companies, or large corporations.  That means we have a place at the table! The question is whether we can keep our place at the table.


2. Understanding the Objectives

In order for us to keep our place at the table, we need to take the second step in maximizing the positive, understanding the objectives within organizations.  I have had many opportunities to work with people who have had a variety of objectives. I have worked with entrepreneurs who want to create businesses through the ideas that they have about products or services.  I have worked with real estate investors who want to purchase properties that can be sold at a price which generates a high return on investment. I have worked with people in the construction industry who want to develop properties at a reasonable cost.  What these people have in common is this: they have goals that can be accomplished by maximizing the positive about prediction.


3. Developing Processes

After we understand the objectives within organizations the third step in maximizing the positive is developing processes.  Processes can vary based on the type of business. Startups need a process that guides entrepreneurs to think about they are going to earn revenue, employ people, promote their products or services, support their businesses, purchase and sell inventory, collect cash, and pay bills in order to predict revenues and expenses that determine valuation and predict cash flows that determine financing requirements which are two important parts in the effort that entrepreneurs must make in order to raise money.  Small businesses need a process that guides owners to think about their strengths and weaknesses in revenues, expenses, assets, liabilities, and equity that serve as the basis for predicting the valuation of their businesses should they decide to put their enterprises up for sale. Middle market companies need a process that guides executives to make decisions about capital expenditures like building new stores or adding new warehouses based on predictions from the cash flows from these proposals. Large corporations need a process that guides executives to make decisions about acquisitions or organic growth based on predictions from the returns on these ideas. Since processes assist the effort in making decisions from predictions based on financial numbers FP&A practitioners can influence not only how but also whether important decisions within organizations are made.

Financial Modeling Innovation: Predictive analytics vs Financial Modeling

By Lance Rubin, Cashflow Financial Modeler and Finance Innovator,  and Igor Panivko, CFO of Konika Minolta


Same Same but Different. After talking to over 300 Finance professionals over the past couple of years either face to face or online there still appears to be a lot of uncertainty and confusion out there relating to analytics and modeling.

Despite writing a blog on the differences a year ago (albeit FM vs Analytics), many were not clear on the differences.

It's almost analysis paralysis with all the jargon and especially when misused and twisted by software vendors around what it means to do modelling and analytics within the packages they are selling.

The purpose of this blog is really to go a little deeper into predictive analytics and financial modeling.

Now, I might be a financial modeling expert but I will be the first to say that I am not an expert in Predictive Analytics. Why not??? you probably asking, and that's because it's actually fundamentally a different skill set.

The software applications and languages / coding used at times are also significantly different to each other.

So, to give this blog a bit more credibility and balance I have decided to co-author this blog with Igor Panivko. Igor is the CFO if Konika Minolta and sits on the board of both the Ukraine and Russian entities.

He has impressively largely replaced the finance "human" team with automated BI and analytics capabilities meaning that he is then able to capitalise on AI and ML to perform advanced predictive analytics.

I have already learnt a lot from Igor, we both sit on an AI/ML committee alongside Larysa Melnychuk and Irina Zhuravskaya (amongst a few others) on behalf of FPA Trends. I hope those reading this will all also learn something from this blog and more of it to come.

For the purpose of this blog we are completely excluding generic data analytics terms and associated systems like PowerBI, Tableau, Qlik etc as none of these contain any native modelling or more complex predictive capabilities.


Our Personal Definitions

Financial modeling (FM)

I believe it's the overall skill of making better business decisions using well-constructed Excel model by applying business logic, accounting and problem solving.

At the basic end this might include revenue or cost analysis and dashboards with simple formulas and at the more complex end an integrated 3-way, structured references, advanced scenario manager and Monte Carlo simulation coupled with some VBA coding and advanced technology to enhance visual influencing dynamically and live.

Predictive analytic (PA)

Igor believes it’s a set of statistical tools designed for finding patterns in the behavior of the targeted outcomes by finding dependencies between dataset predictors and the target.

The PA process is based on the search for optimal statistical algorithms which fit the best into the prediction of an outcome through either the highest accuracy rate for a classification problem (discrete outcome prediction) or the minimum error rate for a regression problem (prediction of continuous values of an outcome).

Predictive analytics is actually the same as a supervised machine learning and classic statistical learning and any of these concepts can be used interchangeably.

Differences / Similarities

However, definitions don’t always make it clear as some words can still be confusing. So, to make this even easier to dissect we have summarised this into key segments namely

  • skills,
  • software,
  • computational complexity,
  • data requirements,
  • and computing power / hardware




Understanding of finance and economics of the key value drivers of performance.

Having an intuitive gut feel whether the projected results appear logically reasonable.

Having a healthy dose and professional scepticism on the projections. Irrespective which is used neither is 100% accurate, but some are pretty close.


PA has to deal with cleaned and well-prepared datasets whereas FM may be applied on very small pieces of unconnected data or without data at all.

PA has a very heavy reliance on statistics and mathematical calculations including complex algorithms and coding.  This is coupled with certain program languages like Python and R.

FM may require you to use more advanced systems and functions. For example, this might include writing or reading VBA and perhaps Index Match vs Lookups, offset, nested if statements, goal seek and randbetween etc. However, this isn't an explicit requirement unlike PA which simply isn't possible without complex mathematical algorithms and coding.

I think that FM more relies on the intuition of a modeler rather than patterns derived from data.



Both approaches use software tools such as data frames: data with columns and rows.

In FM language, it’s a spreadsheet with either simulated or manually prepared data.

In PA language it’s either a matrix or a data frame as in Excel uploaded from another system, database or another Excel file.

Both heavily rely on important software techniques as a data transformation, for example combining several tables, multiplying the whole table or one column (row) by either a number or another column, row or even table.

Both use cross-tabulation, like pivoting and unpivoting. All these functions are embedded in Excel and in both R and Python programming environments.


PA deals with the programming environment rich with embedded statistical libraries. For example, the R statistical language has more than 10,000 libraries.

FM, for those really true diehard fans and experts will still largely be Microsoft Excel. Yes, Excel on Apple, Google sheets and other knockoffs are just not up to scratch for experts. You certainly won't see any of the Modeloff competitors using these other spreadsheet programs.

You also won't be able to do any VBA automation, run Monte Carlo simulation and other complex FM techniques. You may even struggle to find goal seek which is incredibly useful for modeling.


Computational complexity


Not a lot.



Highly complex statistical based algorithms probably considered to be the highest form of complexity there is from a pure mathematical perspective.  This is why this article is co-authored with a expert, like Igor.

The level of complexity is sometimes already beyond human understanding as there are certain PA techniques which work like black boxes and do not show how they reached the highest level of prediction.

Certainly some formulas can get reasonable complex like deep nested if statements, or reverse engineered goal seeks or XMIRR calculations etc...but for the most part complexity can also be avoided by breaking out logic into simplified IF statements which are reasonably simple to write and follow. VBA can also be avoided completely by simply repeating a process with the F4 key.

Data requirements


Both require accurate data to provide any sense of reasonable insight to support/validate assumptions.

Most importantly would be actual data against which projections/forecasts can be compared to for confirming the accuracy of the predictions.

They both therefore need to be tweaked, updated, refined based on real live data to also gain increased confidence on the predictive outcomes.

The data collected should have a reasonable level of controls to provide assurances of its accuracy i.e. garbage in garbage out.


PA may work well both on small and big datasets, it depends on the skills of a modeler to choose the optimal algorithm. Sometimes, algorithms prepared on large datasets do not work better than smaller data sets that are well-prepared.

Many PA algorithms can work well on small datasets and show quite reliable results, but it’s not possible without any data.

Processing big volumes of data maybe be beneficial but is not always necessary. Usually, the dataset preparation takes 70-80% of all the research time and it may shrink the initial raw data ten-fold. Quite often, it is enough to run a test model on a subset of data before launching a powerful server for enormous calculations.

For the purpose of analysis, PA modeler often uses mathematical transformation of the raw data, like rescaling, logarithmic transformation of numbers, turning words into numbers, etc.

For FM, it is possible and very often the case that almost no data is required to build a financial model purely based on someone's professional judgement and business idea.

Many startups have great ideas but struggle to pull this succinctly into a story of numbers...sort of like paint by numbers. It is therefore quiet common to build a financial model forecast with no historical data.

This where the art vs the science of FM comes into play. 


Computing power / hardware


Both run better on higher performance machines and processors. Especially when the model contains multiple workbooks each 5-10mb (xlsb format) and all need to be opened at the same time...meaning sometimes even 8gig of memory isn't even enough.

It is possible, albeit not advised, to build and run a financial model on a Pentium 1 machine with Windows XP. I kid you not, someone attended one of my courses with this setup. I politely explained coming to a future of financial modeling course with such an old setup isn't going to be fun. It's like turning up to a cricket game with a baseball bat. It's possible to hit the ball, just don't know if it's the best idea.

It is possible to run most of the PA algorithms on a single machine, like a standard laptop with 8-12 RAM.  A dataset with size of 1-5 Gb can be run on most of personal laptops. It is quite rarely that such huge volume of data is needed to be analyzed for a research purpose. If the data is properly cleaned the required dataset can be downsized ten-fold and successfully be run on a standard laptop.


In some cases, PA might need to process huge volumes (many gigabytes or terabytes) of data then cloud solutions can be used.

Cloud based solutions for Excel based modeling is rare. There are some forecasting tools that use cloud, but these tools don’t provide the sort of complex financial modeling techniques like Monte Carlo simulation or multi-dimensional scenario management.



Although there are some substantial differences between FM and PA, both help us cope with uncertainties and make better decisions.

We can make financial models without data using our experience and business intuition and be very accurate with our forecasts and we can also make very good models based on proven predictive algorithms built on various data sources.

It would be nice to have both approaches working and supplementing each other.

There is no a competition between them, rather an opportunity not to rely heavily on only one way of decision-making but to combine and innovate.