The Business Analytics Capstone Project gives you the opportunity to apply what you've learned about how to make data-driven decisions to a real business challenge faced by global technology companies like Yahoo, Google, and Facebook. At the end of this Capstone, you'll be able to ask the right questions of the data, and know how to use data effectively to address business challenges of your own. You’ll understand how cutting-edge businesses use data to optimize marketing, maximize revenue, make operations efficient, and make hiring and management decisions so that you can apply these strategies to your own company or business. Designed with Yahoo to give you invaluable experience in evaluating and creating data-driven decisions, the Business Analytics Capstone Project provides the chance for you to devise a plan of action for optimizing data itself to provide key insights and analysis, and to describe the interaction between key financial and non-financial indicators. Once you complete your analysis, you'll be better prepared to make better data-driven business decisions of your own.
Steps to Linking Non-financial Metrics to Financial Performance (Accounting Analytics)
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来自 University of Pennsylvania 的课程
商业分析毕业项目
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从本节课中
Module 4: Effects of Your Strategy/Measuring these Effects
Module 4 was designed to give you the opportunity to focus on the effects of your strategy. Effects and Measurement can be often overlooked in strategy development; creating a thoughtful and thorough plan for measuring the effects will improve your final project tremendously. In this part of the project, you will describe two events: what you think will happen and how you will measure it. Look to the courses in the Business Analytics Specialization to see what kind of data companies use to measure effects to create a measurement plan of your own. You are strongly encouraged to complete and include your responses to Application Exercises 3 and 4 as part of your Effects and Measurement components. You may create a scenario (Operations Analytics) to predict some of the intended effects of your strategy, either following the outline of Application Exercise 3, or of your own design. Once you submit your own plan for effects and measurement, please review the work of at least three of your peers. You may find new ideas, or new ways of looking at data and measurement from this exercise. We encourage you to incorporate what you've learned into your final submission!
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The Wharton School
So let's go through some of the steps on linking non-financial metrics to financial
performance, okay?
Again, here's Some Fundamental Questions.
We're going to have this causal business model.
So the first question is, to what extent does this business model actually
incorporate the right drivers of financial success?
Obviously, they're predictions, that's what a strategy is,
it says, if I do this, this is how I think financial performance is actually going to
end up happening, right?
Do we have the right measures in there?
Again, you can have a measure that you think is good,
it just doesn't capture what you think it's capturing on this.
Th second is, okay, given that, now that I've got this business model,
I think I have some measure that I want to use.
How can I use that business model to allocate resources?
because ultimately, that's what we need to do.
Now allocation resources could come through I said, budgets based on this.
It could come simply because this is how I'm going to evaluate managers and
if I evaluate you on something, that's what you're going to give me.
I don't specifically have to tell you what to do.
And finally, as I've said before we have to worry about how you set targets for
these measures.
Which is going to be one of the most difficult problems that we have with non
financial measures because more is not necessarily better.
So let's start with Step 1, Identifying the Right Drivers.
So here are the steps: we're going to develop this causal business model first
of all again, it has to be linked to organization strategy.
What are you trying to achieve?
What the causal business model is trying to do is saying,
here's what I'd like to achieve.
What are the steps that I need to take to successfully implement this strategy,
see if it works, and see if ultimately it leads to financial performance?
And the way we're going to do that is we're going to articulate these
hypothesized drivers.
This causal thing A leads to B leads to C, those are hypothesis.
For each one of those A, Bs, and Cs we're going to come up with financial or
non-financial performance measures.
And see if ultimately do they translate through the way we thing it's going to.
Second thing we have to do is construct what are called reliable and
valid measures of the key drivers.
Basically, do this measures pick up, would you think they're picking up?
Out strategy says, okay, do A, do B, do C, the question is, how do I measure A?
Again, this is not always such an easy thing to do onness,
so here's a little pop quiz.
If you think about fast food hamburger chains in the United States, there's lots
of ways I could evaluate are you satisfied with the fast food hamburger chain.
I could say is it the quality to the hamburgers,
I could say is it the cleanliness, I could say is it how easy is it to get in and
out of the parking lot.
And if you think about most customer satisfaction surveys you've seen,
they ask you a whole bunch of questions.
Well, I can answer all of those, but
what I really want to know as a company is not just, are you satisfied?
Are you satisfied on a dimension that's going to make you come back and buy more?
And that's the key thing, well, it turns out, for
fast food hamburger chains in the United States and
some research we've done the quality of hamburgers are actually quite low.
Now it turns out the quality of French fries is really important, right?
But it has very little predictive ability when it going to come back.
Number one and
only those of you who actually have small children are going to understand this.
The number one predictor whether somebody says are going to come back
is was you child happy with the toy in a happy meal last time you went there.
because did you going to realize once you a small children the only time you go
out for fast food is when your kids want you to.
Now, if you think about that if I ask you something about did you like
the hamburger?
And I'm using that to evaluate managers,
there going to improved the quality of the hamburger but
if that was the cause you to come back and buy more that's not a good strategy.
So what we need are constructs or measures that are reliable at capturing
what you think they're going to capture and valid.
They are going to be predictive whether you come back.
The other thing that you want is you don't want measures that bounce up and
down all the time onness stuff.
And what you get it a lot of times with these measures is because you use
like very small sample sizes, they bounce up and down.
They don;t actually tell you much, so what we want is a measure that's valid and
reliable.
Predicts what you wanted to predict and it's not what's called too noisy
which means it just bounces up and down we don't know why on this.
Next step.
I've got a casual business model, I has some constructs of measures that I think
It actually captured the dimensions I care about.
Let's verify the linkages, let's see if it's actually true,
because again, we're guessing here, right?
The strategic plan maybe based on your intuition or whatever, right?
It is a hypothesis about how things are going to work.
Let's verify, if I said A should lead to B should lead to C, let's verify this.
Let's do some analytics on this to see if these linkages that you
hypothesized actually show up.
And again, I want to know why because if they aren't,
either I gotta change my measures, change my strategy, or figure out what
are the organizational barriers that are blocking this from happening?
Okay, so here's an example, we've got a major fast food chain.
The company's got 6,000 plus stores in the United States, and
the offer both in-store purchases.
They also offer delivery.
Now their overall profitability wasn't growing fast enough to meet either their
internal or external expectations.
So like a lot- >> Can you say that again-
>> Yeah.
>> One more time.
>> So their overall profitability was not growing enough to meet either their
internal or external expectations.
Like a lot of companies, this is public, you've got the analysts saying here's
an earning per share target that we have to hit.
They weren't hitting that and
they weren't hitting their internal targets either, so what happens?
They have a series of meetings, we got the senior level executives,
all the functional areas.
We're going to get together and we're going to build a consensus business model.
We're going to actually go through this process.
Let's take our strategy.
Let's lay out what we think the consensus business model is, right?
And then we're going to come up with performance measures based on that
consensus business model, right?
Now like a lot of companies, and even you in a lot of cases,
you have to start out somewhere.
So this consensus business model was developed using only management intuition.
There really wasn't any data analysis upfront,
we've been in this fast food business a long time, we know our customers,
we know our competitive environment.
Here's what we think is going to take to meet our financial objectives, so
here's their consensus business model.
Well, they're going to use something called the customer service profit chain
which is very common in retailers.
The idea is, I've got to start out looking at my employees, right?
So, if I've got very good employees, the idea is well,
that is going to lead to a better customer experience.
If I get a better customer experience, what's going to happen?
Well, customers are going to buy more, they're going to come back,
they're going to tell a friend.
And ultimately, that is going to get me the financial results I want
which in their case, was growth and financial returns.
Now if you look at this, there is actually no direct link
between employee selection and staffing and financial performance.
Lots of intermediate things have to happen.
So if I was just going to look at employee selection and staffing and try to use
predictive analytics to predict financial results, it's going to be very difficult.
There's too many other things that are going to have to happen
before you get that end result.
The other thing is its going to take a while for this to actually go through,
between me hiring employees, and actually getting better financial performance.
But in the inner room, if I get better employees that's going to predict that
there's more productive, the employees are going to add more value.
If I've got better employees who are more productive and work harder,
that's going to lead to a better customer experience,
which ultimately is going to lead to the financial performance they want.
Now again, think about this, it's a fast food company and we've got delivery here.
So here be an example, say you've got a pizza delivery company, how many
times have you actually seen that same pizza delivery person come to your house?
Not very often.
So you need to start out with the intuition,
why might it be the case that if I have better employees it's going to
lead to a better experience and lead to better financial performance?
Well, it could be the case that if the person knows my neighborhood,
my food actually arrives on time.
They get the order right on this, right?
The person taking the order on the phone is actually more productive,
can get the order taken faster, they get their right things down, right?
If I got brand new employees, they're probably very unproductive,
I have to train them so I probably don't want to have to hire new ones, right?
So all of that should lead to a better experience if I like the pizza delivery
person, I might order more from them.
I might tell my friends this is a great pizza delivery firm and ultimately,
that's when you're going to get what you want at the end.
So that's the idea behind a causal business model.
The ultimate financial results were growth and financial returns but
to get there, how exactly we're going to get there.
Now if you look at this diagram,
under each one of these round circles which is the basic causal model.
There is some more specific things that we think capture that action,
that's where the performance measures are.
So if you look in under these, you could come up for
each one of the things in the rectangles at the bottom.
You could come up with a performance measure that is related to the business
model, which is the round circles there, and we can test that, right?
If I start gathering measures for the things in the rectangles,
let's see as the measures in the blue buckets go up.
What happens to the customer measures?
Do I see them going up or not?
Because that's what we're hypothesizing will happen, so
here's what actually happened based on this model.
First of all,
if you believe this model, they key thing I care about is employees first.
If I don't get that right, my entire strategy is going to fall apart, so
what they did was, take employee turnover as one of their primary metrics for
decision making and performance evaluation.
And again, this is based on intuition, we just know this must be true and again,
think about employee turnover.
It's costly, you've gotta hire new employees, I have to do the recruiting.
They're very unproductive when you first start,
you might have to buy them a uniform, so employee turnover is very, very expensive.
And, it impacts the customer experience,
because if I have an employee who doesn't know the business or is not trained or
doesn't do it well, I'm not going to like going there.
So that became a very important performance measure,
it also entered into the bonus plans for the store managers,
we're going to evaluate you on overall employee turnover.
In addition to that, we're going to put in these human resource programs,
retention bonuses to keep people in place.
because if what we believe is employee turnover is one of the key drivers of
financial performance, we want to figure out how to keep them there.
Well, what they did is if you stayed there for
at least a year they started paying some of your educational expenses for you.
So now there's an incentive for you to stay there, so
we're going to put the performance measure in, that's a bonus for people.
We're going to put in this expensive program of retention bonuses, and
based on this casual model, and that's how we're going to run our business.
Well, they did that, spent a lot of money on these retention bonuses right?
Gave bonuses to store managers base to turnover and then they said, well,
why don't we go back and actually see if this model works?
Because we're basing all of this on a prediction that if these measures,
the rectangular boxes at the bottom of those circle at the bottom of that diagram
is they go up and down.
We expect that to translate all the way through to the end to the financial
performance.
Well, they found out some very interesting things.
First of all, you had stores that had very different turnover,
that had absolutely no correlation with financial results.
Well, that was our prediction on some of this, you had some stores that were
extremely profitable with very high employee turnover.
And you come to the east and
turn over really was not all that predictive when you started doing this.
Now, here's where you want to be careful about we're going from data analysis
right to interpretation.
Now, it turns out, I was one of the consultants working on this and
their first response.
And when you'll get a lot of times when it doesn't match intuition It's gotta
be wrong.
And what they did was they hired a statistician to replicate our work and
the statistician came back and says well that's exactly right,
that's what the statistics stay.
Now the question is, and a key part of analytics, let's try to understand why.
Well, when they actually went out and did some qualitative assessments,
which I would encourage you to add here.
It turns out that one of the reasons why stores with higher turnover might
have better financial performance than better managers.
The better managers were in a store more often, they were actually looking at their
employees, making sure they were following all the procedures.
They were more willing to fire people because they knew whether they were doing
a good job or not, that was part of it.
So it's not necessarily if you've got a good manager,
maybe they will fire people faster because they know let's get rid of him
and replace him.
The other big issue was, it depends on what location you're at, and
whether you can actually replace employees.
The turn out in a lot of low income areas, you don't anybody to replace them with.
So why would I fire them when it's not necessarily true, that I could pick up
an employee who's going to be at least is good or better than one I currently have.
So part of this is going to depend on your labor market, so
what actually what it had to happen then was, let's get two set of analysis.
One for stores for low income earners and one for stores in a high income earners.
So part of predictive analytics is you need to keep pulling back the onion.
Here's the initial results I get.
Let's try to understand this which may lead you to more analytics
work you need to do.
And here it was, let's estimate separate models for different socio-economic
areas because it has a big impact on what you think the relationship is.
It has a big impact on what you'd set the bonuses at and a big impact
on how you would spend money on this stuff, so what actually happened here.
The management intuition was partially correct
because it turned out after further analysis,
what they found was it's actually not overall employee turnover that matters.
It's turnover of the supervisors of the store,
those are the people that do all the scheduling.
They're the ones who are actually making sure people are following the procedures.
So turnover was right but only turnover of a specific type of employee, which was
supervisors, so this is where you want to make sure you got the right measure.
Your model is kind of right, turnover impacts
satisfaction impacts other things but what measure of turnover do you want.
Here the measure was turnover of supervisors.
So based on that, again peeling back the onion, they changed this performance
measure to not overall turnover, but turnover by employee category.
And that's what we're trying to get here, it's not just performance,
not just turnover.
It's which measure of turnover actually predicts something we care about here.
Further analysis, led them to say, okay,
now that I know the relationship between supervisor turnover and financials.
I can use that to estimate what the benefit might be from the size of this
retention bonus.
How much am I willing to spend on it?
I don't want to spend too much but based on this relationship, I can say, okay,
if I can increase my supervisor retention or lower the turnover by 10% say.
How much am I willing to pay them in a bonus to actually make that economically
feasible?
So it not only changes the performance measured,
it changed your estimate of financial performance.
And finally, after that, again if you think about that causal model,
it's not just employees to financial's, it's employees to satisfaction,
to financial performance.
After that, then they started estimating this bigger business model,
where you start looking at all these linkages.
But it's usually a good idea to start small,
let's look at one linkage at a time before you start trying to estimate all of this.
