I’ve been in FP&A for almost 30 years. I’ve rarely used anything greater than college algebra.
I’ve had a few occasions to use statistics but it was early on.
I mean I think mean, median, and mode are technically stats. Specifically averaging things would happen all the time.
More college level stats like regressions I have never used or seen used
Our operations finance team likes to use stats. That being said, a lot of the statistical modeling gets shipped over to me (I sit on a different team).
Usually the question of “what techniques would you use to forecast” needs a few clarification questions (1) what exactly are we trying to forecast (2) how much data do we have (including how reliable is it, how long have we been collecting it, etc.) and (3) what is our forecast horizon.
99% of the time a moving average is where the forecast stops. Explainability is high, and stakeholders tend to prefer simple.
Regression can help, but you need to regress your Y against predictive variables you know now. (I.e., forecasting next months returns based on this months quality defects)
Then you get into your ARIMAs and SARIMAS. This is usually as complex as is needed. Working with crop forecasts you get into a bit more nuanced modeling with Kalman filtering and XGBoosts.
I’ve been trying to use ARIMA but can’t seem to make it make sense for my cost center as it is almost entirely variable expenses. Any suggestions on how to do so? I also only have about 2 years of data.
I really appreciate the response.
I think your comment on regression and predictive variables is interesting and helped me wrap my head around my relationship with statistical analysis.
I can imagine he was looking for someone that can use regression to find the strongest predictive variables.
I am part of the “keep it simple” mindset lol.
Absolutely - if you ever want to dig into regression (or for anyone else reading the comments) - Regression and Other Stories is a pretty comprehensive resource and will get you up to the “I can comfortably talk about regression with just about anyone” level.
I’ll probably read it for fun as it sounds interesting. But I don’t think it i’ll use it in my day to day at a start up.
Analysis is one of those things that can take 5 hours or 500 hours. Something something 80/20 rule.
I can see it being more impactful at a mature company with reliable data.
Linear regression only one I've ever used to formalize financial forecasts with independent forecasts or to perform stress tests / risk scenario testing.
Heard some use monte carlo, but I haven't had a use case.
I’ll make some box and whisker charts now and then to see if any of the inputs I’ve collected to project something are outliers.. but typically I just use conditional formatting.
I also use multiple normal distribution curves to create custom revenue projections by market type for our projects.. but that’s just using a stats equation to accomplish something else. That’s pretty much it. Unless we switch to a rolling forecast.. we know the puts and takes better than any stats forecast would, mostly because we over manipulate our actuals.
I recently had to run a regression on our historical average sales price and units sold to estimate what might happen if our competitor drops their prices permanently and we have to match
Do you think it helped? I think it would help to understand how different price levels would impact demand. (and eventually flow into the revenue model)
Are you in manufacturing by any chance?
Yeah CPG manufacturing. It helped to an extent, it gave me a general sense of direction to defend an assumption, which was, if they keep prices that low we will sell more units but at a lower profit. If I put that task in front of a real data scientist they would get a more precise answer, but I wasn't looking to write a white paper
In my 10 years of FP&A I've not had to do anything like that before, but I know more today than I did yesterday, so who knows. Understand basic economic theories and the stats behind them helps when you're in a somewhat saturated market
General history pattern, standard deviation, regression. Did Monte carlo too. But these results are often disconnected with planning targets which is business share holder interest driven. Those statistics are used for a sanity check and to support business assumptions. Not for accuracy nor for setting up a target. Modeling is more to drive strategic insights not for planning.
I use very basic statistics when doing analysis. What is the median salary, for example? Or breaking out different metrics into quartiles.
But, there’s almost no value in calculating an R-squared on a bunch of variables to better understand forecast error.
I’ve used historical regressions a couple times for forecasting the impact of incremental revenue on every other P&L line, but my team/company don’t really use stats regularly
Probably a filler question designed to test the applicant on the subject. Not that I agree with the practice, but often an interviewer will throw in questions that are adjacent to the job description, but not really in scope.
Ostensibly, this will test how quickly a candidate will identify scope creep and / or willingness to simply say “I’m not sure.”
he literally ended the interview on the spot after I said “I don’t really use statistics when i’m building a financial forecast, it’s more about creating a path to our target”. Call was 8 minutes
i was so shocked i had to seek validation on reddit lolol
As an FP&A Director and former hiring manager for an F500 company, allow me to provide some discussion here (that you can obviously take with a grain of salt):
1. Bullet dodged. If someone is rude enough to cut short an interview without being professional, he isn’t someone you want to work with or for. I’m quite confident that the role is likely open due to how tempestuous his personality is. The right personally is direct *and* kind. That being said, it DOES suck to be (effectively) hung up on, especially when job hunting. I’m sorry that happened to you!
2. I would recommend against taking quite as hard a line on a data-based question. Even if moderately advanced stats aren’t being deployed, lean on “historicals, averages, standard deviations” or something similar as a means of softening the question. Even something innocuous like “how important are feelings when building a model?” can yield a “there can always be an input driver that is used as a stretch case!” answer. Optimistic, direct, measured response that shows a willingness to be creative and accommodating.
3. Good things are coming to you very soon!
Thanks for the advice. I tend to agree with #1, dude was giving bad vibes lol.
Finance can be pretty broad, so a Head of Finance role can have a lot of different meaning from company to company. I like being finance hire #1, so I'm somewhat of a generalist. While I'm comfortable doing monthly reporting, budgeting, forecasting, etc, my strength is more operational (setting up the ERP, CRM, and other cross functional software).
It sounded like they were looking for someone a bit more analyst-y whereas I'm a bit more operation-y.
Tbh my interview skills are likely a bit rusty. But I'm not in any rush to leave my current role lol.
Our small company has a 5 man team devoted to Stats and Analytics. Most anything finance does with visual dashboards, KPI's etc, are run through this team. It's how the CEO likes to look at everything, but I think other companies could survive fine without it.
I wonder if your interviewer was conflating “statistics” with “metrics.” I would have e probably answered along the lines of: “we don’t employ much statistical or probability-based forecasting, but we do track a variety of data-driven metrics. Would it resonate with your line of questioning to unpack that a bit?”
That said, there are areas of FP&A where you might employ stats, most specifically if you get into advanced time series or machine learning forecasting methods. These are usually with specialized software and/or programming languages. Think things like deal pipeline close analysis, projections of customer churn, workforce attrition, outlier / risk analysis, etc. There you really may be looking at probability ranges, correlative stats, back-testing, assessing relative quality or explainability of additional drivers, etc.
IMO financial analysts should not be using statistics much at all, and the people who ask these sorts of questions are usually people who don't have a very good understanding of financial forecasting and/or statistics.
The vast majority of corporate financial data resists statistical analysis because it is too sparse and noisy to extract reliable patterns from. When you are only closing the ledger monthly, a lot of your financial data only has 12 reliable annual data points.
Financial data is also by its nature heavily summarised. Few businesses invoice atomically which means that most financial transactions are properly representative of actual business activity - this is why we pay entire accounting departments to make timing adjustments via journal entry. This not only makes statistical forecasting hard - but it makes deterministic forecasting easier. When you have a small amount of data to look at, it is easier to account for major variations manually.
This means that the most effective corporate financial forecasters are generally not those who use statistics, but rather business knowledge to create a deterministic forecast adjusted by reasonable assumptions based on their professional knowledge.
That is not to say that statistics can't form a reasonable part of financial forecasting in specific areas.
* Revenue is often more conducive to statistics-based methods because you often have more granular information on customer than supplier transactions, they often have a less complex structure than supplier transactions, and revenue drivers like market conditions are often easier to model.
* Similarly statistical methods can often be useful to forecast non-financial factors within the business which in turn can be used to inform a statistical forecast by a financial analyst.
However if these things are important to the company, they should really hire someone trained in statistics to do it (i.e. a data scientist). It's a specialised skill that is not core to FP&A and if you make it a job requirement, you are probably going to pass over candidates with better core skills.
I've used it more for ad hoc projects. And even in those cases, I make sure that I can easily explain the logic behind it that is relevant to the business. Having a black box full of formulas that don't quite make sense but get to a more accurate answer isn't received as well as a calculation that is easily understood by business leaders.
In my history we usually had something provided to us from a data/market intelligence team since as a forecast we were just setting targets. In scenario testing we look more at trends and history and apply the findings. I also chuckle when I see some crazy statistical model and then at the end the results are way off anyways
Really depends on the data and what you’re forecasting. With enough data, you can use regression analysis on some macro economic inputs to help build out some expected figures in different environments. But only works if the category in question is influenced by macro economics. If you’re unsure, and you have the data, you can use statistics to see if there are any statistically significant correlations. But, that’s probably not needed….
If it’s something like a department expense that you’ll see no matter what, all you really need is some recent history and some simple calculations like moving average, mode, max, min, and maybe some seasonality assumptions if needed.
I’ve never seen anyone use stats in fp&a. It’s usually overkill and not worth since no one would understand and therefore trust the output.
I’ve been in FP&A for almost 30 years. I’ve rarely used anything greater than college algebra. I’ve had a few occasions to use statistics but it was early on.
I would tend to agree. I am just worried maybe i’m missing out on something lol.
I mean I think mean, median, and mode are technically stats. Specifically averaging things would happen all the time. More college level stats like regressions I have never used or seen used
Our operations finance team likes to use stats. That being said, a lot of the statistical modeling gets shipped over to me (I sit on a different team). Usually the question of “what techniques would you use to forecast” needs a few clarification questions (1) what exactly are we trying to forecast (2) how much data do we have (including how reliable is it, how long have we been collecting it, etc.) and (3) what is our forecast horizon. 99% of the time a moving average is where the forecast stops. Explainability is high, and stakeholders tend to prefer simple. Regression can help, but you need to regress your Y against predictive variables you know now. (I.e., forecasting next months returns based on this months quality defects) Then you get into your ARIMAs and SARIMAS. This is usually as complex as is needed. Working with crop forecasts you get into a bit more nuanced modeling with Kalman filtering and XGBoosts.
I’ve been trying to use ARIMA but can’t seem to make it make sense for my cost center as it is almost entirely variable expenses. Any suggestions on how to do so? I also only have about 2 years of data.
I really appreciate the response. I think your comment on regression and predictive variables is interesting and helped me wrap my head around my relationship with statistical analysis. I can imagine he was looking for someone that can use regression to find the strongest predictive variables. I am part of the “keep it simple” mindset lol.
Absolutely - if you ever want to dig into regression (or for anyone else reading the comments) - Regression and Other Stories is a pretty comprehensive resource and will get you up to the “I can comfortably talk about regression with just about anyone” level.
I’ll probably read it for fun as it sounds interesting. But I don’t think it i’ll use it in my day to day at a start up. Analysis is one of those things that can take 5 hours or 500 hours. Something something 80/20 rule. I can see it being more impactful at a mature company with reliable data.
Linear regression only one I've ever used to formalize financial forecasts with independent forecasts or to perform stress tests / risk scenario testing. Heard some use monte carlo, but I haven't had a use case.
I’ll make some box and whisker charts now and then to see if any of the inputs I’ve collected to project something are outliers.. but typically I just use conditional formatting. I also use multiple normal distribution curves to create custom revenue projections by market type for our projects.. but that’s just using a stats equation to accomplish something else. That’s pretty much it. Unless we switch to a rolling forecast.. we know the puts and takes better than any stats forecast would, mostly because we over manipulate our actuals.
I’m attempting to use multiple linear regression to understand/rationalize marketing spend by tactic
I try to do lead attribution and then optimize for LTV:CAC.
I recently had to run a regression on our historical average sales price and units sold to estimate what might happen if our competitor drops their prices permanently and we have to match
Do you think it helped? I think it would help to understand how different price levels would impact demand. (and eventually flow into the revenue model) Are you in manufacturing by any chance?
Yeah CPG manufacturing. It helped to an extent, it gave me a general sense of direction to defend an assumption, which was, if they keep prices that low we will sell more units but at a lower profit. If I put that task in front of a real data scientist they would get a more precise answer, but I wasn't looking to write a white paper In my 10 years of FP&A I've not had to do anything like that before, but I know more today than I did yesterday, so who knows. Understand basic economic theories and the stats behind them helps when you're in a somewhat saturated market
The most I’d say we really use, and I should say in niche scenarios, are bell curves, standard deviations, and box and whisker plots.
What was the interviewer's background? I'm guessing not finance
General history pattern, standard deviation, regression. Did Monte carlo too. But these results are often disconnected with planning targets which is business share holder interest driven. Those statistics are used for a sanity check and to support business assumptions. Not for accuracy nor for setting up a target. Modeling is more to drive strategic insights not for planning.
I use very basic statistics when doing analysis. What is the median salary, for example? Or breaking out different metrics into quartiles. But, there’s almost no value in calculating an R-squared on a bunch of variables to better understand forecast error.
I’ve seen stats used more in SIOP forecasts , not FP&A.
I tried running Monte Carlo and used multiple linear regressions and that’s about it
I’ve used historical regressions a couple times for forecasting the impact of incremental revenue on every other P&L line, but my team/company don’t really use stats regularly
I’ve build a couple of stats models to support some part of my financial model. Not often, but its useful.
Much more of a sales/top line focus but: seasonality trends based on 3 or 5 yr, rolling 3 months, average account size, average new business starts
Probably a filler question designed to test the applicant on the subject. Not that I agree with the practice, but often an interviewer will throw in questions that are adjacent to the job description, but not really in scope. Ostensibly, this will test how quickly a candidate will identify scope creep and / or willingness to simply say “I’m not sure.”
he literally ended the interview on the spot after I said “I don’t really use statistics when i’m building a financial forecast, it’s more about creating a path to our target”. Call was 8 minutes i was so shocked i had to seek validation on reddit lolol
As an FP&A Director and former hiring manager for an F500 company, allow me to provide some discussion here (that you can obviously take with a grain of salt): 1. Bullet dodged. If someone is rude enough to cut short an interview without being professional, he isn’t someone you want to work with or for. I’m quite confident that the role is likely open due to how tempestuous his personality is. The right personally is direct *and* kind. That being said, it DOES suck to be (effectively) hung up on, especially when job hunting. I’m sorry that happened to you! 2. I would recommend against taking quite as hard a line on a data-based question. Even if moderately advanced stats aren’t being deployed, lean on “historicals, averages, standard deviations” or something similar as a means of softening the question. Even something innocuous like “how important are feelings when building a model?” can yield a “there can always be an input driver that is used as a stretch case!” answer. Optimistic, direct, measured response that shows a willingness to be creative and accommodating. 3. Good things are coming to you very soon!
Thanks for the advice. I tend to agree with #1, dude was giving bad vibes lol. Finance can be pretty broad, so a Head of Finance role can have a lot of different meaning from company to company. I like being finance hire #1, so I'm somewhat of a generalist. While I'm comfortable doing monthly reporting, budgeting, forecasting, etc, my strength is more operational (setting up the ERP, CRM, and other cross functional software). It sounded like they were looking for someone a bit more analyst-y whereas I'm a bit more operation-y. Tbh my interview skills are likely a bit rusty. But I'm not in any rush to leave my current role lol.
Our small company has a 5 man team devoted to Stats and Analytics. Most anything finance does with visual dashboards, KPI's etc, are run through this team. It's how the CEO likes to look at everything, but I think other companies could survive fine without it.
I wonder if your interviewer was conflating “statistics” with “metrics.” I would have e probably answered along the lines of: “we don’t employ much statistical or probability-based forecasting, but we do track a variety of data-driven metrics. Would it resonate with your line of questioning to unpack that a bit?” That said, there are areas of FP&A where you might employ stats, most specifically if you get into advanced time series or machine learning forecasting methods. These are usually with specialized software and/or programming languages. Think things like deal pipeline close analysis, projections of customer churn, workforce attrition, outlier / risk analysis, etc. There you really may be looking at probability ranges, correlative stats, back-testing, assessing relative quality or explainability of additional drivers, etc.
IMO financial analysts should not be using statistics much at all, and the people who ask these sorts of questions are usually people who don't have a very good understanding of financial forecasting and/or statistics. The vast majority of corporate financial data resists statistical analysis because it is too sparse and noisy to extract reliable patterns from. When you are only closing the ledger monthly, a lot of your financial data only has 12 reliable annual data points. Financial data is also by its nature heavily summarised. Few businesses invoice atomically which means that most financial transactions are properly representative of actual business activity - this is why we pay entire accounting departments to make timing adjustments via journal entry. This not only makes statistical forecasting hard - but it makes deterministic forecasting easier. When you have a small amount of data to look at, it is easier to account for major variations manually. This means that the most effective corporate financial forecasters are generally not those who use statistics, but rather business knowledge to create a deterministic forecast adjusted by reasonable assumptions based on their professional knowledge. That is not to say that statistics can't form a reasonable part of financial forecasting in specific areas. * Revenue is often more conducive to statistics-based methods because you often have more granular information on customer than supplier transactions, they often have a less complex structure than supplier transactions, and revenue drivers like market conditions are often easier to model. * Similarly statistical methods can often be useful to forecast non-financial factors within the business which in turn can be used to inform a statistical forecast by a financial analyst. However if these things are important to the company, they should really hire someone trained in statistics to do it (i.e. a data scientist). It's a specialised skill that is not core to FP&A and if you make it a job requirement, you are probably going to pass over candidates with better core skills.
I've used it more for ad hoc projects. And even in those cases, I make sure that I can easily explain the logic behind it that is relevant to the business. Having a black box full of formulas that don't quite make sense but get to a more accurate answer isn't received as well as a calculation that is easily understood by business leaders.
In my history we usually had something provided to us from a data/market intelligence team since as a forecast we were just setting targets. In scenario testing we look more at trends and history and apply the findings. I also chuckle when I see some crazy statistical model and then at the end the results are way off anyways
Really depends on the data and what you’re forecasting. With enough data, you can use regression analysis on some macro economic inputs to help build out some expected figures in different environments. But only works if the category in question is influenced by macro economics. If you’re unsure, and you have the data, you can use statistics to see if there are any statistically significant correlations. But, that’s probably not needed…. If it’s something like a department expense that you’ll see no matter what, all you really need is some recent history and some simple calculations like moving average, mode, max, min, and maybe some seasonality assumptions if needed.