Revenue Management

ANALYTICS

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 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.

Slot Floor Optimization

Part art, part science, Slot Floor Optimization can help a casino understand the relationship between its machines and their profitability. Slot Floor Optimization helps managers determine the best mix of machines, the best prices on the machines, and the best utilization of the floor space to maximize returns. When it comes to slot floor planning, bad decisions will lower returns on deployed assets and can have a direct impact on the patron experience. Ensuring the right mix of games, at the right price, aligned to customer demands and preferences can help the casino generate incremental revenue, all the while improving the customer experience.

 

When deciding which games to offer or replace, casinos may look at historic results and surmise reasons why games which were popular in the past will continue to be so in the future. Advanced analytics can help casinos with slot floor planning to forecast the right mix of gaming choices, denominations, and machine placements to optimize customer interest and use.

When it comes to slots, casinos must ask the following questions:

HOW MUCH GROSS REVENUE WILL EACH GAME GENERATE NEXT YEAR?
MUCH WILL A NEW GAME GENERATE?
WHICH GAMES NEED TO BE REPLACED?
WHICH ONES SHOULD BE ACQUIRED?
WHEN SHOULD WE PURCHASE NEW GAMES TO REPLACE POOR-PERFORMING EXISTING GAMES?
WHAT DO WE NEED TO DO TO MAXIMIZE GROSS WINS, GIVEN BUSINESS CONSTRAINTS SUCH AS SPACE, BUDGETS AND TIMELINES?
Optimizing the slot floor then becomes much more complex than replicating past and patron behavior with future slots that might be variations on a theme. Optimizing becomes much more complex than looking narrowly at daily win per unit (WPU), without regard for other factors that affect machine performance.

A unique combination of data profiling, statistical forecasting and optimization can improve slot floor performance from both a placement and replenishment standpoint. A multistep analytic process can use available data – machine attributes, transactions, player behaviors, business parameters, current state and more – to create optimized shopping lists and deployment plans for new slot machine purchases.

The first step is to identify machines that should be decommissioned. “No single attribute will explain all of the variation in performance,” claims SAS. “In a sample case from a Canadian casino company, seven variables were influential, and some of the findings were surprising,” says SAS. “Some rules of thumb that were seen as instrumental in previous decisions are not as important as one might expect."

The next steps is choosing the replacement machines. “Having generated a revenue forecast for all the machines, we now understand where the revenue is going to be, based on where a machine is in its life cycle, as well as where demand is expected to migrate as new machines are deployed,” said Carothers. The goal, of course, is to select more machines that replicate the performance of the good machines.“The next step is to gather data from manufacturers to identify which new machines are more like those best-performing machines, both in gross wins and patron experience,” says SAS.“ These new machines – candidates for purchase – become analysis surrogates for comparable machines and replacements for others that have lost their luster,” explains SAS.

The third step is to deploy the new units within some constraints. Forecasting predictions should be used to determine the best mix of machines for the floor through optimization. “Optimization is a prescriptive type of analytical work that will generate specific recommendations and timelines for purchasing new machines, decommissioning others, and reconfiguring the machine mix,” says Ivan Oliveira, Director of Advanced Analytics at SAS.

 

Step Four is to assess the effects of the replacements. “A closed-loop process ensures that forecasts of future revenues are appropriately calibrated in the models, cannibalization is understood and accounted for, and the analytic tools are optimized and fine-tuned along the way,” argues SAS.

In summary, SAS says, “the slot floor optimization process starts by categorizing machines and generating revenue forecasts for existing and new machines. Since there are many permutations of potential changes to the slot floor, these forecasts are pushed into an optimization process that generates an action plan. The results of implementing those recommendations are continuously monitored, and that information is rolled back into the process to refine predictive models for ever greater accuracy.”

Diagnostic

Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” It attempts to understand causation and behaviors by utilizing such techniques as drill-down, data discovery, data mining and correlations. Building a decision tree atop a web user’s clickstream behavior pattern could be considered a form of diagnostic analytics as these patterns might reveal why a person clicked his or her way through a website. Diagnostic analytics takes a deeper look at data to attempt to understand the causes of events and behaviors. 
 

Predictive

Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavioral patterns. Predictive analytics is the use of statistics, machine learning, data mining, and modeling to analyze current and historical facts to make predictions about future events. Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavioral patterns. Often the unknown event of interest is in the future, but predictive analytics can look into past behavior as well.

Prescriptive

Prescriptive analytics tries to optimize a key metric, such as profit, by not only anticipating what will happen, but also when it will happen and why it happens. Prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually ingest a mixture of structured, unstructured, and semi-structured data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options every minute of every day.
 

ANALYTICAL MODELING

Intelligencia can implement complex analytics solutions from the ground up, remotely, or at your location. We help businesses understand their data in ways that will simplify their analytics journey as well as help with data governance. Our experienced architects, data integration experts, and experienced modelers can build and maintain models to ensure they are up-to-date and capturing the most relevant business data.

SERVICES

Intelligencia's consultants can help you get your data house in order, then work with your team to build the models, or we can build them alone, then deploy and maintain them on your systems. Or ours. The choice is yours.

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