Analytical Engine

Ian Ayres, in his book Super Crunchers, questions decisions made through intuition and advocates decisions made by optimal utilization of data. In the book, Ayres gives examples of how data driven decision making is impacting businesses, education, sports, government, etc. One such industry where a lot of data is collected but not necessarily used in decision making is the gaming industry. Despite Harrah’s being an analytics poster child, not a lot of gaming companies leverage analytics to its potential.

Casinos collect revenue and customer data from a variety of sources, such as player cards, slot machines, and gaming tables. They can also gather information from non-gaming sources: through call centers, surveys, hotels, restaurants, and events. Harrah’s is a pioneer in collecting detailed data on its customers’ activities. Harrah’s team sliced that data into finer segments ”identifying unique customer groups and targeting each group with pitch-perfect marketing strategies.” The results -  tremendous increase in customer loyalty that has turned Harrah’s from a relatively unremarkable player into an industry giant.

Recently, we worked on an engagement for one of the large gaming companies in the world, where the casino wasn’t able to capitalize the rich data it had on its players and slot machines. Direct marketing campaigns and slot machine floor layout optimization, were two big initiatives that were performed but without leveraging the rich information in the data. An analytics driven approach was needed to identify high potential players and optimize slot machine layout. In this post, we will talk about the approach we used to segment players and identify changing behavior patterns of players.

Though a lot of advanced concepts of customer analytics can be applied here, we used a basic RFM (Recency, Frequency, and Monetary) segmentation to effectively target players. RFM was tweaked to LFM (Latency, Frequency and Monetary) for a simple reason- unlike a website or a grocery store, where a person’s recent visit shows that he/she is likely to visit again in near future, a casino trip happens after some interval. So it makes more sense to calculate whether days between the consecutive trips are changing significantly. In an LFM cube the cutoff of each cell of cube can be determined using basic business rules based on frequency distributions.

One of the KPI that was tracked was average spend per trip, and due to this, some players with higher number of trips and medium spend are undervalued and under-targeted. The LFM cube gives us the visibility into such customers and helps us evaluate the true potential of such players. Such a cube can be run at regular intervals to capture the movement of players between the cells, and such movement is a good predictor of changing behaviors and can be an effective lever for targeting marketing campaigns.

In next post we shall talk about how analytics can be used around slot machines and how such inputs can be used to design the floor layout in a better manner.

Avinash summarizes the interesting insights from the recent Emetrics- the online marketing conference. One graph that particularly caught my attention, from a presentation by Tim Hart of J. Paul Getty Trust, is an analysis to determine content consumption of a website.

The blue bar shows the type of content on the site: Education, Research, Collections etc. The red bar shows the percent of Visits to that content.

To put it another way “what are the large chunks of content on the site, what are visitors to our website looking at”.

I am sure the insights will scream out at you. 86% of the content was being consumed by 23% of the visitors. For 25% of the visitors were looking at 4% of the content (Research).

This analysis helps to set the baseline for content consumption at the highest level and informs about the needs of the online visitor to the website. If your website uses any web analytics tool, it should be pretty simple to create the above graph.

Two useful links if you are in the market for web analytics solutions.

Pat McCarthy has put together a great summary of all the major web analytics solutions (both free and paid) available in the market. The list includes all the usual suspects and some niche solutions as well.

Avinash in his insightful post recommends a radical process of choosing a web analytics solution for companies. Instead of following a major requirement gathering exercise followed by an RFP, he recommends implementing a free tool, deepening the analytics skill set in the organization, fixing the internal data capture issues before starting the RFP process.

Incidentally, Google Analytics (which is free) is the solution of choice for the websites of the most of the current Presidential candidates websites.

Our client was losing revenue from 68% of potential customers who were sold the service by the call center channel but did not complete registration process, which meant that they could not be billed.

One of the initial analysis we did was plotted the percentage of ‘sold but not active’ customers against the number service agents and found that a small number of the agents were disproportionately responsible for making the sales.

This led us to investigate the efficacy of the sales process itself and whether some agents were abusing the system for commissions. To answer the former, we did a customer survey while for the latter we started generating an agent-level report on a weekly basis. The report was able to identify the precise subset of agents who were trying to game the system and our client was able to take corrective action.

As the example illustrates, agent-level reporting is a very actionable tool and can be used for:

Metric Tracking:
Strategic metrics like efficacy of saves, sales quality etc. should be tracked at an agent level

Auditing:
For companies outsourcing a large portion of their call center operations, such a report can serve as an auditing tool

Compensation design and reporting:
It also can help to design and report a performance based incentive structure for agents