Who will give you the best return on your investment?

Market segmentation or dividing the market into groups with similar wants or needs under segmentation bases that include:

- Geographic factors (location, climate)
- Demographic variables (gender, age, household income, occupation, education, religion, nationality, family size, marital status, presence of children)
- Psychographic variables (lifestyle, need for status, role of money, ethics and morals, risk taker vs conservative, spendthrift vs thrifty, AIO’s – attitudes, interests, opinions)
- Behaviouristic or user status (nonuser, ex-user, potential user, first-time user, regular user)

Take a look at the Nielsen Claritas PRIZM customer segmentation system and try to know in which cluster will you be clasified from Low-Rise Living to Upper Crust.

Market segmentations will help us to discover the appropriate target market(s) for our product or service. Targeting consists of knowing the target audience (audience targeting)

By understanding the audience: What are they reading? What sites and pages are they looking at? What are they shopping for? Which search terms are they using? What behaviors are they exhibiting that represent intent? we can position how our product or company is perceived in the minds of consumers.

Predictive analytics encompasses a variety of techniques from statistics, data mining and game theory that analyze current and historical facts to make predictions about future events, identify risks and opportunities.
It uses the theory that “birds of a feather flock together”, the approach is based on the assumption that active customers will have similar retention outcomes as those of their comparable predecessors.
It can be used to make predictions about active customers regarding:
* Whether they are at high risk of canceling their service
* Whether they are profitable to retain
* What retention tactics are likely to be most effective
One of the better known applications of predictive analysis is credit scoring, used in financial services, in order to rank-order individuals by their likelihood of making future credit payments on time.