Today, the hype surrounding data analytics has converted into real, documented returns for companies of all sizes and across all industries. Many companies have achieved double-digit returns on their investments (ROI) in analytics for several years now. Partly because of this, the software analytics space is more crowded than it has ever been. Standard ETL-solution providers are adding analytics to their multitude of offerings. Many of these new players in the Master Data Management (MDM) field have BI platforms that combine integration, preparation, analytics and visualization with governance and security features. Such standard analytics processes as column dependencies, clustering, decision trees, and recommendation engines are all included in many of these new software offerings. Instead of forcing clients to purchase modules on top of modules on top of modules, new software companies are creating packages that contain many built-in analytical functions. Thanks to built-in connectors, open source products like R, Python, and the WEKA collection can easily be slotted into many of ETL, MDM, BI, CI, CX and MA software solutions, thereby reducing costs and the need for expensive translation layers.

The Analytic Value Escalator below shows how analytics can be used by an airline.

Customer Acquisition

Just like every other business, airlines are always looking for new customers. With the travel market becoming more and more competitive and saturated by the day, there is always a constant need to attract new customers. Customer segmentation models can be used to build predictive models that identify key characteristics of attractive customers.

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Customer churn

Arguably, customer retention is both one of the cornerstones of any CRM system, as well as being the most important component of the customer lifetime value (CLV) framework. There are indications that companies have problems managing customer retention. Airlines are unique from many other industries in that their customers are tied into contracts, but many of the retention metrics relevant for contractual firms are also relevant for non-contractual firms. A simple 0/1 indicator of transaction, and a measure of recency are appropriate for both types of companies. 

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Customer conversion model

The Customer Conversion model can be used to score customers based on information contained in the casino’s source systems as it would only be applicable for customers who had pre-booked their room (as opposed to walk-in customers). Historical information would be extracted from the casino’s IT systems around desirable customers. This would include spending patterns and profitability.nTo identify the relationships that may exist between how the customer comes to the casino and his or her desirability metric, information would be extracted from the casino’s source systems. For a casino, this would include information such as source of betting, channel of betting, lead-time for betting and the incentives offered to attract the customer. Basically, anything that can be attributed to the initial transaction the customer has with the casino would be used as a potential input.

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Customer segmentation

A customer segmentation model provides a view of the casino from a customer perspective: such models have many and varied applications. Customers are segmented according to what they present to the casino. Views include:

  1. Game preference
  2. Day of week
  3. Time of day
  4. Length of session
  5. Size of stake
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Optimizing offers

In addition to predicting the future worth of patrons, it is important to know which marketing campaigns are the most effective for driving response, revenue, and profit. In general, certain offers are better than others, and specifically certain offers will be better for certain patrons.

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Propensity to respond model

A Propensity to Response model is the theoretical probability that a sampled person (or unit) will become a respondent in an offer or survey. They are especially useful in the marketing field. A response likelihood model can have substantial cost savings as it can lead to lower mailing costs by identifying patrons who are very unlikely to respond to a particular offer. After segmenting these people out, the casino can then focus on only those most likely to take up the offer. A casino can identify the likelihood of response from all eligible patrons. After that, it can identify the most valuable patrons that are most likely to respond. This allows the casino to estimate the expected response from the most valuable patrons and eliminate mailing(s) to the patrons that are of lower worth and/or are unlikely to respond.

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RFM model

RFM is a method used for analyzing customer value. It is commonly used in database marketing and direct marketing and has received particular attention in the casino and retail industries. RFM stands for:

  1. Recency—How recently did the customer purchase?
  2. Frequency—How often do they purchase?
  3. Monetary Value—How much do they spend?
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