0:01

Let's discuss a Strategy Analytics.

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

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

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

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

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

Â we think about analytics here.

Â Often it requires what I would call creativity and exploration,

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

Â We often talk about making the data speak.

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

Â 0:40

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

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

Â spreadsheets to try to understand it.

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

Â R, that can help manipulate data.

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

Â Word Clouds which can be very helpful for taking data and

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

Â 1:07

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

Â based on some underlying data.

Â So here we see a list of Industry Structure Measures,

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

Â First we have the Compound Annual Growth Rate.

Â How fast is this industry growing over time?

Â What is its growth rate over a number of years?

Â Elasticity of demand.

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

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

Â one class of products to another.

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

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

Â 1:52

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

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

Â The concentration ratio is simply a measure of the four

Â largest firms' market share within an industry,

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

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

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

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

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

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

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

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

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

Â suggests that there are some competition within the industry.

Â 2:47

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

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

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

Â We can use regression analysis and

Â the like to try to calculate those types of curves.

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

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

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

Â 3:16

Let's consider some others.

Â These are some standard financial performance measures.

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

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

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

Â This is at least one accounting way of measuring earnings.

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

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

Â So I have three listed here.

Â Return on assets.

Â Return on equity.

Â And return on sales.

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

Â net income.

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

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

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

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

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

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

Â 4:15

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

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

Â that are given by a company here.

Â And that's a common metric that you see,

Â especially representing tech companies and the like.

Â To understand what the future growth potential and

Â earnings might be in that organization.

Â Discounted cash flows is a critical measure that looks

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

Â discounts back based on a discount factor.

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

Â compares it to the book value of assets.

Â With the assumption being the higher market to book ratio,

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

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

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

Â versus the current position of an organization.

Â 5:08

Now this financial performance metrics might not be

Â sufficient to completely give us a picture of the organization.

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

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

Â goods sold.

Â Growth within the company both top line and

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

Â 5:27

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

Â This is important from a strategy perspective because the more

Â leveraged you are, the higher your debt load,

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

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

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

Â advantage in terms of your staff and the like.

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

Â advertising intensity, which take your R and D expenditures and

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

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

Â are they investing heavily in marketing,

Â are they investing heavily in innovation and the like?

Â 6:10

Finally we want to think about inference tools.

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

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

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

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

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

Â Break even analysis.

Â Given capital expenditures how much

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

Â Decision trees highly related to our discussion of game theory.

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

Â offs associated with them.

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

Â This is looking at,

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

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

Â Monte Carlo simulation or Monte Carlo analysis.

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

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

Â We mentioned regression analysis briefly before.

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

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

Â Finally I mentioned before data visualization, and again,

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

Â to once again make inferences and ultimately make good recommendations.

Â