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.

Mobile phone operators are facing a rapidly saturating market, thus facing customer retention as the primary challenge.  Mobile operators, like almost all big retail operations, have their own predictive churn models. Moreover, mobile operators have the luxury of having a wider array of variables. The “Know Your Customer” questionnaire and the usage behavior of the mobile phone typically provide access to a few thousand variables.  Modelers frequently add some off-the-shelf segments (from providers such as Claritas or Acxiom) in the mix to make a better sense of and add more predictive power to the model.

But the large number of variables adds at least two complexities.  First, even after performing variable reduction, modelers usually are still left with a high number of components/ factors, a number high enough to make a concise business presentation difficult.  Second, the high number of variables also affects the actionability of model insights.  No single variable affects churn in isolation, so it is hard to determine the independent impact of individual variables in customer churn.

An alternative approach to comprehend and act on the model recommendations will be to bundle the variables in logical groups.  For example, the variables related to time can indicate when a customer is more prone to churn and can be grouped as a ‘when’ group of variables. This ‘when’ group will not necessarily tell us why a customer is more prone to attrition, and would rather tell us when she is more prone to attrition.  Similarly, the variables’ indication of issues faced by a customer (her interactions with call center might be a good source for this) such as handset problem or network issues can be grouped as ‘why’ variable.  The demographic variables that are significant in predicting churn help the modeler to profile the customers likelihood to churn and can be grouped as ‘who’ variables. An integrated view of these three groups is more palatable, and from a modeler’s perspective, it is easier to convey to the business ‘who’ are more likely to churn, ‘why’ they are more likely to churn and ‘when’ they are more likely to churn.

The above approach does not only make the models easier to comprehend, but also increases actionability by providing a structure to the model findings.

What once was a good ole’ boy sport that relied on tribal knowledge buried deep inside a crew chiefs’ head is becoming a hi-tech, data rich, and analysis driven sport. More and more teams, crew chiefs, engineers, and even pit crews rely heavily on granular data elements that are used to feed complex models to extract the slightest bit of knowledge that can be used to save thousandths of a second on the track or in the pit. The sport of NASCAR is no longer defined by car lengths that won the race rather it’s becoming a sport defined by hundredths and in some cases thousandths of a second that decides between the winner and the first loser, a.k.a. “second place”.

After having an interesting and thought provoking conversation with Ken Brown, former engineering director at Dale Earnhardt Inc. and lead engineer for GM’s Corvette Racing program, I found myself drawing parallelisms between what I do in marketing science and statistics at Diamond for our clients and what NASCAR teams are doing to extract value from information. Not in the ROI sense but in the thousandths of a second sense. However, this in the end does translate into ROI for the thousands of sponsors that make the sport go round and round, no pun intended!

According to Ken, granular data elements such as wedge, nose weight, spring rates, tire pressures, camber, and gear ratios (just to name a few), all play a critical role in determining the how good a car will perform at a particular race. However, the key decisions are often made long before the car arrives at the race track. Essentially through track testing, wind tunnels, shaker rigs, and simulation exercises, the engineer and crew chief can decide which and how much to change each variable to make their car the best on the track. In marketing science I call these things covariates!

So how are teams doing this? Although the details of the process are unique to each team, the general process that goes into setting building and setting up a stock car is as follows:

1. Know the track (flat track, road course, banked)

2. Define/identify the significant set up parameters (or covariates) relevant to that track

2. Gather test data (prior race data, testing, wind tunnel, etc) and validate its quality

3. Analyze preliminary data via data visualization techniques (histograms, pareto charts, and cross-tabs)

4. Build models (e.g., DOE, Pareto Analysis)

5. Analyze results of simulation techniques

6. Set-up car for the race

7. Test and refine!

This process that is followed by the top teams is not unlike the process we follow in our information and analytics practice at Diamond when trying to identify the key variables that will attract and retain the customers or segments a business is going after. Moreover, the quality of the testing data is one of the most critical components to the entire process.

So the next time you see a professional stock car race, it’s important to know that before there was the driver in victory lane, there was a data geek in the background making it all possible.

Defining the hypotheses and doing the analysis is sometimes a lot easier compared to selecting the right graphic to present your results, which help to convey the message without confusing the audience. The multitude of charting options available in Excel does not help matters.

We recently came across a wonderful post by Andrew Abela, which very clearly lays out the various chart options and helps to choose the right one for your analysis based on the message you want to convey. It’s a must for anyone who works with data analysis.

Click on the thumbnail to see a larger image of the graphic from Andrew’s blog

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.

One of the most popular search term that brings people to this blog is “marimekko chart”. People are mostly looking for a template to easily build a marimekko charts and stumble upon our related postings on churn and a new way to represent at marimekko charts. In order not to disappoint them, I am adding a nifty excel template (Mekko maker.xls) which makes creating standard marimekko charts a breeze.

Jay recently posted a comment asking us to elaborate on our churn heat map posting. The best way to explain is to show an actual churn heat map which we created for a client.

• Height of each row represents % of all activations
• Each row split into three parts –returns, voluntary churn, and involuntary churn, from left to right, with the % of total churn in that row determining width
• Particularly high or low levels of each type of churn are indicated by color, providing a “heat map” for where the largest churn issues reside

Depending upon your organization, you can choose what segments/sub-segments and products to use to create the heat map

For an executive this churn visual gives a starting point for generating hypotheses and serves as a useful reporting tool to see the efficacy of churn reduction activities

Marimekko chart is a powerful way to communicate up to three levels of data (such as Sales, by Competitor by Market Segment) in a single, two-dimensional graph. Diamond has developed a variant of the Marimekko chart that is suitable for comparing data sets for multiple buckets within the same category, e.g. a comparison of the volume and efficiency of the different sales channels over a period of time (see chart), for a dashboard for one of our clients.

Each color area represents a channel and consists of 24 individual bars (one for each month). The width of the bar indicates the volume for that month and height of the bar indicates the conversion rate for that month. The trends in height and width of bars provides detail of the trend within the channel and comparison of the overall height and width of each colored area provides the reader quick attention to the importance of the channel in terms of volume and effectiveness of the channel in terms of the conversion rate. The jaggedness of the profile of each colored area also gives a visual indication of variance in the performance of the channel.

PS: If you are interested in interesting ways to represent data, check out some recent postings from Juice Analytics (1,2) and Information Aesthetics blog.