Empower Your Marketing with Predictive Analytics
Why is Predictive Modeling all the rage in marketing management circles?
It’s because Predictive Modeling helps marketers trim down who they spend money to reach with an offer, to those most likely to respond.
What does that mean?
Suppose you have already done some analysis to determine geographic and psychographic market segments for your product or service. Depending on the size of the community in which you operate, there may still be tens of thousands of ‘qualified’ prospects.
But you don’t have a communications budget big enough to effectively reach tens or hundreds of thousands of prospects repeatedly. After all, it may take several exposures before consumers respond. Perhaps, when you tried it, the response rate was terrible, so you backed off. This is when Predictive Modeling can help.
Predictive Modeling takes three main forms:
- Predicting who in a group is likely to do something
- Predicting what, among an array of products, a customer is likely to buy next
- Identifying trends in consumer behavior and product preferences
Predictive Modeling that identifies who is likely to do something, also referred to as response modeling in direct marketing circles, is the technique called for when the list of initially qualified prospects is bigger than the budget will support.
How does it work?
It has been found that people behave in consistent patterns. Consequently, a statistical method called regression modeling can be used to take in data collected about people and determine their likelihood of doing something. The output from this process is a score; which allows all of the people to be ranked in terms of their likelihood of behaving as hoped in response to an offer… by buying something.
The result is that a list of the most promising prospects can be made. With this list in hand, a marketer can then work to determine the most efficient and effective way to reach these people with a message about a product or service.
Everyone in a targeted market segment is more likely to respond to a product than people in groups that have shown little buying interest in that product. Predictive Modeling makes it possible to go inside market segments and concentrate marketing communications, messages, offers, sales methods, even product features to satisfy the most likely customers.
The 80-20 rule often applies even within market segments. 80% to 90% of the expected response can be achieved by targeting only 20% to 30% of the pool of customers or prospects.
A small investment in marketing analysis on the front end is likely to yield a big payoff by reducing campaign costs, increasing sales, and leaving other prospects untouched and fresh for a future campaign.
- Models for Marketing Planning and Decision Making
- Role of Customer Response Models in Customer Solicitation Center’s Direct Marketing Campaign
- Dr. Philip Kotler Answers Your Questions on Marketing
Tags: Marketing, Marketing Communications, Predictive Modeling, Regression Modeling, Response Modeling, Targeting
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