Predicting Customer Loyalty & Customer Churn To Maximizing Profitability

Some customers are loyal and others are not.  Rather than spending money to attract any customer, companies could focus their efforts on getting the right type of customers.  Is it possible to identify customers with a higher propensity to defect at the drop of a hat?  Sure, credit card companies often use predictive risk scoring models to evaluate potentially risky customers.

You can probably think of a number of customer loyalty factors that can predict customer churn and customer loyalty.  Some factors that are often mentioned include transience, youthfulness and price sensitiveness.   As you might imagine, the return on your marketing investment will be much greater if you start with potentially loyal customers as opposed to potentially disloyal customers.

How Do I Profile Potentially Loyal Customers?

The best place to start is by building a database that contains both loyal and defecting customers.  A careful analysis of the loyal customers and disloyal customers can reveal some valuable insights.  While this is a good first step, THE MARKETING ANALYSTS recommend further analysis using predictive analytics for customer segmentation.  Once behavior based segments are identified, profitable strategies can then be developed to maximize your Marketing ROI.

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Developing Customer Profiles: The First Step in Market Segmentation and Target Marketing

Target Marketing Through Predictive Segmentation

Customer Profiling, Target Marketing & Predictive Segmentation

 

Modeling Customer Behavior for Segmentation and Target Marketing

Before you design your next marketing campaign, you should consider the ideal customer that you want to target.  To maximize effectiveness and ROI, your marketing collateral should target the right audience, with the right offer and be delivered through the right medium.   However, knowing the best offer, storyline, theme, length and medium to use requires in-depth knowledge about your target.  Gaining this level of knowledge often requires marketing research and the aid of predictive marketing analytics.

Where do I start?

Without much effort, many companies can identify a number of good marketing segmentation variables.  For example, direct mail marketers might identify demographic and psychographic factors.   Marketers that are more sophisticated might apply a crude RFM model.  RFM models have been around for years.  In short, RFM is based on the premise that the people who bought from you recently are more likely to respond to new offers than people who made a purchase in the distant past.  Large financial institutions will likely take the RFM approach a step further by combining data purchased from external suppliers.  Next, genetic algorithms are trained to classify customers into groups based on their propensity to use a particular product or respond to specific types of offers.

Unsure Where You Should Start? . . . Spend A Day With Your Customers

If you are unsure where to start, then you need to connect with your customers.  You need to be in front of your ideal customers as much as possible to understand them.  While you can easily conduct a survey, it is often better to start by “spending a day” with your customers.  Try to figure out what your customers do in the morning, noon and at night.  Can learn more about them by follow them online through social media circles?

Surveying Your Customers

If you decide to conduct a survey, you might consider collecting the following types of data.

  • Demographic Factors: Age/lifecycle, income, disabilities, mobility (in terms of travel time to work or number of vehicles available), educational attainment, home ownership, employment status, and even location
  • Socioeconomic: Disposable Income, Memberships to clubs, Vacations, Entertainment patterns, Assets owned, Investments
  • Customer Behavior and usage patterns
  • Risk profiles
  • Profitability of customer
  • Customer tenure

However, collecting this data through surveys will be challenging.  As an alternative, you might consider purchasing equivalent data from information suppliers or coming up with creative ways of capturing this information on your own.

Once you have completed your research, you should know enough about your customers to start developing customer profiles.  When you can start answering the following questions, you are close to developing a segmentation strategy and a predictive model for target marketing.

  • Who is your “perfect customer”?
  • Why does a customer buy (or not buy) your product?
  • Do customers buy from you (or your competitor) for any particular reason?
  • Do customers tend to buy specific products at particular times?
  • How much consideration do your customers give when making these types of purchases?
  • What benefits do they see in your company (or your products)?

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Marketing Forecasting and Segmenting using Neural Networks

Predictive Data Mining & Neural Networks

Predictive Data Mining & Neural Networks

What is Predictive Data Mining?

Predictive Data Mining combines data warehouse with advanced multivariate statistical analysis and artificial intelligence.The artificial intelligence is gained through the use of a neural network, a powerful data modeling tool that is able to capture and analyze complex input/output relationships for the prediction of future customer behavior, classifying customer segments and forecasting events.Today, predictive data mining has become an essential tool for strategic decision making at many mid-size companies and large corporations.

Like a human brain, a neural network acquires knowledge through a training process and its knowledge is stored within inter-neuron connection strengths known as synaptic weights.When trained and deployed correctly, perceptron (MLP) neural networks, sometimes used by THE MARKETING ANALYSTS provide superior results when analyzing linear and non-linear relationships.

How are Marketing Neural Networks Being Used?

Right now, there are vast databases and powerful technologies crunching numbers about your lifestyle and the lifestyles of millions of other Americans.They know the value of your home, the type of car you drive, the ages of your children, your credit rating and more.This data is being mathematically processed to determine if you are the best target for the latest gadget to hit the market.

While this sounds like something from a George Orwell novel, it describes the predictive modeling power behind neural networks and modern data mining technologies.While data mining conducted at this magnitude is limited to certain government agencies, the price of this technology has dropped substantially due to new mathematical discoveries, lower technology costs and improved processing power.As a result, many corporations are now embracing the power of predictive data mining to gain competitive advantages through customer segmentation strategies, predicting customer behavior and by making projections about the future.

Here are a few additional ways that predictive data mining is being used today.

Marketing Predictions

Producing accurate sales forecasts is an important part of measuring your marketing strategy.Inaccurate forecasts lead to missed opportunities, avoidable costs, inefficiencies and many other problems.Although Microsoft Excel provides certain forecasting tools, its forecasting tools fail when non-linear relationships and missing data are present, this is often the case when analyzing marketing data.In these cases, neural networks provide superior forecasting accuracy.

Market Segmentation

When neural networks are correctly designed and deployed, they can accurately identify people who will be most receptive to a product, promotion or advertising campaign.Some of the most frequent methods of segmentation with neural networks combine metrics such as recency of purchase, frequency of purchases and amount spent.Other factors include age, sex, income, location, education level, occupation and household status.Today, neural networks are a primary method for highly predictive marketing segmentation.

Prediction and Classification

Neural networks are a proven technology for solving complex classification problems.Credit companies often deploy neural networks to spot fraudulent credit card activity and identity theft.Other companies deploy neural networks to identify defecting customers in order to maximize their customer retention.

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