August 15, 2009

Sizing, Segmenting, and Forecasting Markets Paul Teich

Business Strategy

Market research

Quantitative – what, where, when Facts Ask people closed questions (yes/no, how

many, what date, …)

Qualitative – why, how Opinion, attitude Ask people open-ended questions

Then ask to follow them around for good measure…

Quantitative

Primary (do it yourself) Design survey to find out exactly what you

need to know Compromise on subset that you can afford

Secondary Someone else has already gathered some or

all of the data you need as a product or service It’s probably less expensive than DIY

Quantitative Sources

Ask people in your industry who they use Market analyst data sets and PR Competitor PR Governments Libraries

Especially school librariesLike UT, for instance

Qualitative

Expert opinion Ethnography Focus groups Talk to people

No, really, find some people to talk to…

Qualitative Sources

Ask people in your industry who they use Market analyst reports and press releases Charities Political action committees

Segmentation

People (B2C) Demographic – population characteristics Behavioral – loyalty, purchase patterns Psychographic – personality, values, attitudes,

interests, lifestyles

Organizations (B2B) Firmographic – characteristics of orgs

Successful segmentation

Segments are measurableQualitative data can be economically collected

Segments are substantialMarket is usefully subdivided

Homogeneity within each segment Constituents are enough alike that they behave as a flock (for key attributes)

Heterogeneity between segments Flocks are different enough to tell apart

Market sizing

TAM – Total Available MarketHow many potential customers are there?

SAM – Served Available MarketHow many (of the total) are already buying a similar or competing product?

SOM – Share of MarketWhat % of the market does each product or competitor account for?

Market forecasting

In order to forecast your market…

…you must have a history

Historic time series

More than two data points Buy or find secondary market data… It must be appropriate to your segmentation

Ideally, history is >2x the timeframe you want to forecast5 year forecast implies 10 year history

Look for patterns in historic dataYes, this is statistics + art

Insert your forecast here

What are you forecasting?How far are you willing to go?

History and forecast must look like a continuous time series No “miracle occurs here” jumps Humans are very good at pattern recognition,

they will call BS if the curve looks wrong

Technology and product adoption

Large body of existing knowledge and best practice

But it’s mostly pay per view Start with Wikipedia

“technology adoption lifecycle” “forecasting”

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