Analytical marketing

With predictive analytics, gaming organizations can easily segment their customers and coordinate marketing campaigns to effectively target each segment across each outbound channel. For example, if a casino customer is scheduled to receive all of his or her event promotions via e-mail, the predictive analytics solution will automatically remove him from concurrent campaigns being run through other channels. This ensures consistency and also improves customer satisfaction, since the organization respects the customer’s contact preference and does not inundate him or her with multiple offers. Moreover, a predictive analytics solution monitors channel capacity and usage to eliminate overload, while distributing campaigns equally across the various channels. If one channel is at risk of overload, the solution automatically shifts the remainder of a campaign to a different channel to ensure completion. This enables organizations to maximize the capacity and value of each channel without resorting to time-consuming manual monitoring.

Specifically for lotteries and sports betting companies, predictive analytics can help to:

• Identify the most valuable players
• Predict a player's future worth and/or his or her future behavior.
• Plan the timing and placement of advertising campaigns.
• Create personalized advertisements.
• Define which market segments are growing most rapidly.
• Segment players into groups based on their behaviors and then create marketing campaigns to exploit those behaviors.
• Determine a player's level of gambling skill.
• Identify the likelihood a player will respond to an offer as well as identify the offer(s) to which player's are most likely to respond to.
• Predict when a player is likely to return.

By utilizing data from past campaigns and measures generated by the predictive modeling process, casino operators can track actual campaign responses versus expected campaign responses, which often prove wildly divergent. Additionally, casino operators can generate upper and lower 'control' limits that can be used to automatically alert campaign managers when a campaign is over or under-performing, letting them focus on campaigns that specifically require attention. This enables gaming organizations to maximize the capacity and value of each channel without resorting to time-consuming manual monitoring.

Data Mining

Unlike traditional statistical analysis, which relies heavily on hypothesis testing, data mining tries to identify relationships and inter-dependencies that affect a marketing-related opportunity or problem. While traditional multiple regression methods can only use a limited number of complexity levels, neural networks and decision trees can easily handle up to 200 predictor variables, allowing them to do much more complicated computations.

Normally, with statistical modeling, an analyst poses a simple question such as: “Are higher-income people prone to be more loyal to a gaming player card than those with lower income levels?” The hypothesis would elicit two responses, either “yes” or “no.” Data mining, however, can reveal factors that contribute to player loyalty; factors that the analyst might never have thought to test for.

Predictive Analytics for Tatts could discover:

• Which games have contributed most to growth.
• What types of product and marketing innovations appear to be driving growth.
• What are the key player attitudes in relation to lottery players and how could those attitudes be leveraged to drive growth?

A crosstabs and

Chi-square analysis is used to research which games have contributed most to growth and which marketing innovations have contributed most to growth. We start out assuming that all contribute equally and then using Chi-square show that some contribute more than others.

By using player

demographics or selling

station demographics. For

example, in the US, convenience

stores in poorer neighborhoods

sell more individual tickets and

more tickets overall than

they do in more affluent

neighborhoods.

Applications for Predictive Analytics

• Cross-sell/Up-sell – 47%
• Campaign Management – 46%
• Customer Acquisition – 41%
• Budgeting and Forecasting – 41%
• Attrition/Churn/Retention – 40%
• Fraud Detection – 32%
• Promotions – 32%
• Pricing – 30%
• Demand Pricing – 30%
• Customer Service - 26%
• Quality Improvement - 25%

From Wayne Eckerson, “Predictive Analytics: Extending the Value of Your Data Warehousing Investment,” TDWI, 2007. Based on 166 respondents that had implemented predictive analytics.

Successful marketing is about reaching a consumer with an interesting offer when he or she is primed to accept it. Knowing what might interest a player is half the battle to making a sale and this is where customer intelligence and predictive analytics comes in. Customer analytics has evolved from simply reporting customer behavior to segmenting customers based on their profitability to predicting that profitability, to improving those predictions, to actually manipulating customer behavior with target-specific promotional offers and marketing campaigns.

How can analytics increase gaming revenue?