《会计分析》学习财务报表数据和非财务指标如何能与财务业绩相联系。在这门课中，有了沃顿商学院著名会计学教授的教学，你将会学到如何使用数据来评估驱动财务业绩表现的因素并预测未来财务背景。尽管许多会计和金融组织公布数据，会计分析有效利用这些数据来洞察内情，这堂课会涉及很多领域，在这些不同学习领域会提供数据使你能洞察其他商业领域，包括顾客行为预测、公司策略、风险管理、优化组合等等。学完此部分课程，你将理解财务数据和非财务数据是如何与预测、优化操作以及决定策略等相互影响的。这堂课为了帮助你能更好地做出商务决策，因此你能应用你所学知识来自己做出商务决策并使用财务数据t提出策略。
Incorporating Analysis Results in Financial Models 4.5
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來自 University of Pennsylvania 的課程
会计分析
1782 個評分
從本節課中
Linking Non-financial Metrics to Financial Performance
Linking non-financial metrics to financial performance is one of the most important things we do as managers, and also one of the most difficult. We need to forecast future financial performance, but we have to take non-financial actions to influence it. And we must be able to accurately predict the ultimate impact on financial performance of improving non-financial dimensions. In this module, we’ll examine how to uncover which non-financial performance measures predict financial results through asking fundamental questions, such as: of the hundreds of non-financial measures, which are the key drivers of financial success? How do you rank or weight non-financial measures which don’t share a common denominator? What performance targets are desirable? Finally, we’ll look at some comprehensive examples of how companies have used accounting analytics to show how investments in non-financial dimensions pay off in the future, and finish with some important organizational issues that commonly arise using these models. By the end of this module, you’ll know how predictive analytics can be used to determine what you should be measuring, how to weight very, very different performance measures when trying to analyze potential financial results, how to make trade-offs between short-term and long-term objectives, and how to set performance targets for optimal financial performance.
與講師見面
Brian J BusheeThe Geoffrey T. Boisi Professor
Accounting
Christopher D. IttnerEY Professor of Accounting
Accounting
Ultimately, if we're gonna link non-financial metrics to financial
performance, we gotta figure out how do we take this analytics results and
put them into a financial model?
Cuz ultimately, what we're trying to do is predict if these non-financial
metrics go up and down, what's gonna be the impact on financial results.
So here's an example of this.
We've got a computer hardware manufacturer, major supplier of equipment.
And again senior-management, they wanted to know whether these customer initiatives
these company was putting in actually paid off or not.
And more important than that, it's not just that they pay off in the past,
if I need to develop action plans that will increase value in the future,
what action plan should I take on?
And obviously what I like is called the biggest bang for the buck.
If I put a dollar in something, where am I gonna get the biggest return,
which non-financial metrics do I wanna improve, and by how much?
Now, the first thing you need to think about is, when you do these,
what are the financial outcomes that you care about?
Because I need to predict that, and it doesn't have to necessarily be profits,
there could be intermediate financial results you want.
So if you take something like customer satisfaction,
there's always a lots of reasons why you want higher customer satisfaction,
you think it's gonna keep the current customers you have, or retention.
You think they're gonna recommend your product and
not too bad recommendations, are there any good, get a bigger share of wallet,
a bigger proportion of the expenditures by your customer.
I mean there's various reasons why and you need to lay out,
when you're gonna estimate these models, what are the outcomes that you care about?
And ideally, what you like to do is what are the intermediate outcomes?
If you think about customer satisfaction again, there is no reason that higher
customer satisfaction necessarily leads to higher performance.
If only, if satisfaction goes up and you can retain customers, and
they buy more and you get bigger share of wallet, and
they make positive recommendations, that's when it pays off.
So you need to set up,
what are these intermediate outcomes that you want if these things go up?
So this is what the company picked.
Now, the idea here is, yes, customer satisfaction is going to
impact these things, we're gonna try to estimate what the impact is so
we can build a financial model.
But if you're a manager,
customer satisfaction actually doesn't really mean anything.
[LAUGH] Okay, they're satisfied.
What does that mean to say a customer is satisfied?
What can you, as a manager, do to actually change satisfaction and
which dimensions do customers really care about.
So here's where you have to put the front end of the model on this.
Customer satisfaction is in the middle.
What are the things that customers care about that's gonna increase their
satisfaction in such a way that they buy more from you, that they stay with you,
that they make positive recommendations.
So here, what the company did is, let's go out and do some marketing research.
Let's do some focus groups with customers.
Let's see what they say would cause them to actually buy more from us,
and there's various things that could happen, right?
Do I think I've got a good relationship with my supplier?
Do I trust them?
Do I think the value is high?
Something like a personal computer, right?
Do I think the thing actually works, right?
Can I change over when I put the new software onto this program?
One of the big things is can I take my old files and push it over there?
So what you want is a manager when you estimate these models.
Yes, I want the financial results.
But what I wanna know is what are the specific actions I can take?
Are these over on the other side that will impact
satisfaction in such a way that my financials go up?
And where is the biggest bang for the buck, right?
Of these many dimensions I have over on the left hand side, which one of those,
if I had a dollar to invest,
which one would I want to invest in such that I get the biggest return?
And that's the model we're gonna estimate.
So what we have here is basically a regression model, right?
First of all, we're gonna say if satisfaction goes up,
how much does retention go up?
How much does word of mouth go up?
How much do these ultimate financial outcomes go up?
On the other end you've got, which one of these dimensions or drivers
actually cause customer satisfaction to move up and down and by how much?
So these are the estimates we have here.
Now if you look at this diagram, we got two things.
We got things inside circles and we have little numbers next to arrows.
So here's what these things are, the numbers in a circles, that tells you how
is this company doing on a 0 to 100 scale in that dimension, 100 being the highest.
Right, so if I get a 70, I'm doing 70 out of 100.
If I get 80 on that dimension,
I'm doing 80 out of 100 where 80 says I'm doing better.
The numbers next to the arrows, those are the coefficients from a regression model.
Basically, what it says is if I can increase the score on those drivers to
the left by one unit, here is the impact I'm gonna have on my dependent variable.
Either on customer satisfaction or if I'm estimating economic outcomes, here's how
a one unit change in satisfaction impacts these economic outcomes.
So here's an example.
If you take those numbers, if you increase the relationship score by ten.
What these says is the customer satisfaction scores gonna increase by 3,
right, you take that 0.3 times 10, so it's gonna go up by 3.
Now if I can increase my customer satisfaction score by 3,
what does predicts is it's gonna increase my retention score by 3, or from 73 to 76.
Now based on that, if I can figure out what's the financial benefit from
keeping a customer, I can come up with a financial model.
Now again, if you go back to the diagram, something I always ask my students is
if you had a dollar, where would you invest it, given this?
Now a lot of times what people say is, well invest it in trust because that
coefficient, that number next to the arrow, is biggest for trust.
It's 0.8 which says if I could increase my trust score by 1 point,
my customer satisfaction score is gonna go up by 0.8.
That seems to make sense, but then the question is do you really believe you
can increase your trust score by one point, or
ten points, and how much is it going to cost you?
It may be the case, remember, these are personal computers,
they trust you as much as they're gonna trust you, okay.
They don't trust anybody perfectly, it may be extremely hard to move the trust score
above 0.8 where you're already doing fairly well.
It also maybe really expensive.
It, alternatively, if you look at your shopping score, you're doing so
badly on that one, right?
Something in the 60s and it may actually be easier to go after that because
if you're doing badly, it might be easier and cheaper to improve that.
So even though it has this coefficient of 0.1,
if it's easy to move that one by 10 points and very hard to move trust by even 1
point, you should probably be going after the shopping.
So, what you need is both that model we just estimated which
tells you the benefit from moving the shopping or trust score.
And what do you think the cost would be to actually move this and
how easy is it to do that?
Now based on that,
you can start coming up with a financial model which is ultimately what we want.
If I increase trust or if I increase shopping,
what do I think the ultimate impact is on retention, or recommendations, and
the other financial outcomes that we care about?
Now, again, this is looking at straight linear relationship.
It just keeps going up in a nice straight line.
But we've found from some of the earlier analyses that a lot of times,
there's non-linearities.
It doesn't keep going up in a straight line.
In fact, maybe you wanna be really good at customer satisfaction, or
maybe you wanna stop somewhere like 75.
So here's where you wanna dig down deeper.
So here's what the company did.
Let's not stop there.
Let's look a little further.
Do we have any of these non-linearities?
It turns out this is one of those companies where you really wanna increase
satisfaction.
Cuz what they've found out is if you could increase that retention score to 90 or
above, the customer bought the same brand again 56% of the time.
If it was below 90, they only bought it 30% of the time.
That suggests you really, really wanna spend money to get customers above 90.
That's when they start buying a lot more of this stuff and they stick with you.
Same thing with the recommendation score.
If it was 90 or above they recommended about 1.5 times.
If it was below 90, it was less than once.
Those are fairly big changes.
So here's a company where it would say, if I can push my customers above 90,
there's a huge payback.
Because there's a big difference once you get passed this 90,
in terms of whether you're gonna keep them or recommend them.
So now the question would be, I've got to pick action plans, cuz ultimately,
that's what I want.
Can I use analytics to pick out which action plans are gonna
result in this higher financial performance?
So based on this, what you might say is, let's try to pick action plans that
are gonna move customer scores above 90 cuz that's where the big payoff is,
if I can move them above 90 on here.
So let's make an assumption.
And you would have to do some market research to figure this.
Let's assume that you could probably move a customer score by a maximum of ten
points and that's probably true because customer satisfaction scores are really
hard to move.
They're easy to move down if you had a disaster, people get less satisfied,
but they're really fairly hard to move up.
So let's say the most on a 100 point scale you could move them is 10.
But what that would say is if I want to move people above 90, there's only a small
number of customers that I could move 10 points that are gonna push me above 90.
So at that point,
what you wanna do is figure out which customers do I wanna focus on?
It's not all the customers.
What I want are the ones that I can do something that might move them 10 points,
and move them over this 90 threshold,
that's where you're gonna get the biggest bang for the buck.
So here's what you're gonna do.
If you start looking at all their customers, and
here's just some diagrams of how many customers you have at each score.
The people you wanna focus your action plans on, the one based on analytics,
you're gonna get the biggest bang for the buck are the people in the red bars.
Those are the people where I could move them about 10 points
over that 90 threshold and based on my analytics,
I believe that they're gonna recommend a lot more and they're gonna stick with me.
So roughly,
it's about 16% of the customers you wanna focus on, not all of them.
Okay, now based on that, let's see if we can come up with the financial model.
So what we're gonna do is say,
okay, if I focus on these 16% of the people, how much is it gonna
cost me to put a customer initiative in that's gonna move them up 10 points.
If I move them up 10 points, what do I think the financial benefit is
from retaining them, and from having them recommend people more?
Well, the way we can do that now is take the analytics, and basically,
we're gonna do a little Excel spreadsheet.
We're gonna come up with what's called a net present value.
Again, net present value because some of the benefits are gonna come out in
multiple years.
And I need to discount those back because I would rather have a dollar
now than a dollar in the future.
So here's the little spreadsheet we're gonna have here.
So here's some of the assumptions we're gonna make, okay?
First of all, and again you would have to base this on what your company does,
let's assume we got a five year time horizon.
What happens now is satisfaction's gonna have no impact after five years especially
with something like technology, right, new players come in, new technology.
Let's assume we have a 15% discount rate.
There's a cost to capital in your organization,
how much do I have to go out and borrow the money from,
how much did I have to pay to get money from my shareholders.
Let's assume 15%, which right now is very high, but we use it for
arguments purposes.
Now the margins on a personal computer are pretty small.
Let's say on average, you could sell a $2,000 high end personal computer.
The margin you're gonna get out of that's $145.
You're not gonna make much on each of one
of these sales relative to the selling price.
So let's assume what we're thinking about is, can we invest $5 million
in some kind of customer satisfaction improvement initiative,
where we get a five year horizon and 15% discount rate?
Let's see what the benefit would be there.
Now here's the estimates we did before.
We estimated that 25.99% improvement in retention if I could move people over 90,
we estimated a 0.65 change in recommendations if you could move them
above 90.
Now you need to think about is, of those people,
how many do you think you could actually move ten points?
All right, cuz obviously you're not gonna be able to move all of them.
So here's where you wanna do market research.
Based on some more analytics that you do with market research,
what's the odds that we could move somebody up at least ten points?
If you think it's 20%, 20%, if it's 30, it's 30%.
Cuz that's gonna have an impact on whether you think this works or not.
So based on those assumptions,
which are in the spreadsheet, we're gonna compute the net present value.
Again, with the net present value, you've got cash going out.
Well that's the $5 million right now.
Then I've got cash coming back in.
Well, the cash comes in either because I stick with you and I buy one of your
computers later, or I recommend to somebody else and they buy it.
And again, based on this analytics and market research, we can estimate that.
And based on that, we can come back and
just with this simple scenario, you get a huge payback.
Now obviously, these are all estimates.
But the nice thing here is you use the analytics to start the estimates.
Based on that, you come up with a spreadsheet.
Now you can start doing a lot of what if analyses.
What if I change my discount rate to only 5%?
Which is getting closer to what it is right now,
where you have almost no interest rates.
What happens if my margins go up to $200?
What happens if they go down to 100?
So once you set up this spreadsheet that's based on the analytics, you can do a whole
bunch of what if analyses by changing some of these parameters,
saying this is an estimate.
How comfortable are you if this estimate is off by 10% positive,
5% at negative and come up with a comfort zone?
Based on that,
how comfortable are you investing $5 million to improve this dimension?
But it all starts with the analytics cuz you have to come up with some parameters
to start estimating these financial models,
then you can start doing the what if analyses on this.
