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Let's discuss a Strategy Analytics.

Strategy analytics refers to the conversion of data to gain insights.

Perhaps calculations, maybe even a little analysis that might be done.

This is also a critical part of a well done robust strategic analysis,

and complements the data collection research that you might be conducting.

Now there's not just one size-fits-all types of tools that we can give when

we think about analytics here.

Often it requires what I would call creativity and exploration,

maybe iteration, to be able to get good insights from the data.

We often talk about making the data speak.

What does it actually say at the end of the day?

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Now there's a number of tools that might help with this.

Spreadsheets are the first to come to mind in various ways we can represent data and

spreadsheets to try to understand it.

But there are other things like statistical software such as Stata or

R, that can help manipulate data.

And then finally there's a growing number of data visualization tools such as

Word Clouds which can be very helpful for taking data and

manipulating it in a way to provide these types of insights for making decisions.

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So let's talk about some just common measures that can be created

based on some underlying data.

So here we see a list of Industry Structure Measures,

different ways of conceptualizing what is happening in a broader industry.

First we have the Compound Annual Growth Rate.

How fast is this industry growing over time?

What is its growth rate over a number of years?

Elasticity of demand.

This refers to the price sensitivity of customers within the given market.

Cross price elasticity refers to the price sensitivity from one product to another or

one class of products to another.

We've raised this before as a way of thinking about

when are substitutes more of a threat or less of a threat within an industry.

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Another good metric are various measures of the concentration within the industry.

To what degree is the industry dominated by a few large players.

The concentration ratio is simply a measure of the four

largest firms' market share within an industry,

which we often referred to as the CR4, or concentration ratio 4.

So in one extreme, we have monopoly industries in which the CR4 would be 100%.

The top 4 firms, really the top 1 would have 100% of the market.

We can think of other markets where there is a diffuse number of competitors,

where maybe the four firm concentration ratio is less than 20%.

The Herfindahl-Hirschman Index is just another measure of concentration.

It is the sum of the squares of the market shares within the industry.

And similar to the concentration ratio, the higher that index, if it's one,

that suggests that it's a monopoly, anything less than that

suggests that there are some competition within the industry.

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Last but not least, you might want to calculate something like the economies of

scale within the industry, which can be done if you have the cost data to

understand how large a production releases the cost within the industry.

We can use regression analysis and

the like to try to calculate those types of curves.

Now it's beyond our scope in this little module here to talk about how you

calculate each of these different metrics, but if you go to the strategist toolkit,

it does talk about each of these and gives you the formula to calculating them.

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Let's consider some others.

These are some standard financial performance measures.

First and foremost you would think about profitability, or just simply earnings,

though earnings can be represented in a number of different ways.

I mentioned EBIT here, which is just Earnings before interest and taxes.

This is at least one accounting way of measuring earnings.

What might be more fruitful so look at the ratio, take those earnings,

take that income, and divide it by some measure of scale.

So I have three listed here.

Return on assets.

Return on equity.

And return on sales.

One could imagine, for example, a company who has a million dollars in earnings or

net income.

However, it is important to recognize that they make that they million dollars of

a 10 million in sales or they make it of a 100 million in sales.

Because it's a lot less attractive to have 100 million in sales and only one million

in profitability as compared to the 10 million in sales the other company has.

In essence what we're getting at here is the margin.

How big is the margin that's created in the organization?

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Some other metrics we might want to think about, price earnings ratio.

This in essence reflects the stock price versus the earnings per share

that are given by a company here.

And that's a common metric that you see,

especially representing tech companies and the like.

To understand what the future growth potential and

earnings might be in that organization.

Discounted cash flows is a critical measure that looks

at cash flows generated by the organization today, and moving forward and

discounts back based on a discount factor.

Market to book ratio looks at the market valuation of a firm and

compares it to the book value of assets.

With the assumption being the higher market to book ratio,

the more the market thinks that this company is going to create value.

Tobin's Q is a related measure that replaces the book value with

the replacement value of assets, and yet another measure of what the market expects

versus the current position of an organization.

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Now this financial performance metrics might not be

sufficient to completely give us a picture of the organization.

So there's a number of other things we might want to look at.

We of course want to look at revenues maybe the cost side like the cost of

goods sold.

Growth within the company both top line and

bottom line, market share is just a metric for the success of the organization.

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Leverage refers to the degree to which the company has taken on debt.

This is important from a strategy perspective because the more

leveraged you are, the higher your debt load,

the less likely you are able to pursue certain strategic opportunities.

Turnover refers to the degree to which employees are turning over.

And that could be a good indicator for whether you're creating any type of

advantage in terms of your staff and the like.

And then the final analysis two other metrics here, R and D intensity and

advertising intensity, which take your R and D expenditures and

divide it by sales or your advertising expenditures and divide it by sales.

And it gives you the sense of strategic direction of the company in terms,

are they investing heavily in marketing,

are they investing heavily in innovation and the like?

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Finally we want to think about inference tools.

Ways in which we can manipulate data to get further insights.

In many ways, the strategist's toolkit is filled with inference tools here.

Five forces, capabilities, analysis, all of these are to increase inference.

There are some others that are list here that also are more commonly used across

different fields of business and can be useful in strategy analysis.

Break even analysis.

Given capital expenditures how much

sales do we need to break even from that initial capital expenditure.

Decision trees highly related to our discussion of game theory.

This the notion of giving two strategic options what are the pay

offs associated with them.

Sensitivity now is also important no matter what type of analysis you're doing.

This is looking at,

if we vary the parameters in a model, what happens to the outcomes?

A variety of approaches for doing that, that I list tornado charts,

Monte Carlo simulation or Monte Carlo analysis.

Very critical that no matter what data we bring to bear, what analysis we bring to

bear, we look at the sensitivity to the results to that data.

We mentioned regression analysis briefly before.

It's beyond our scope to really go into depth about regression analysis, but

it's a way of looking for relationships between sets of data.

Finally I mentioned before data visualization, and again,

there's a growing number of techniques and tools out there to help you visualize data

to once again make inferences and ultimately make good recommendations.