Saturday, January 28, 2012

Basic Data Mining for Customer Segmentation using Logistic Regression

Logistic regression is a data mining technique that is used in banks to determine various things like the risk factor associated with a person. If a customer is above a certain risk limit than services like overdrafts are not extended to the customer. Similar data mining can be done by any marketing company to find out which of their customers are going to pay or are capable of paying for their products - for example for a gaming company that would be paying for playing the game. A combination of a number of variables could lead to giving out this important information – age, sex, location, salary, education etc. Each variable would have a different weight associated with it, where higher weights (coefficients) would represent more important variables. A part of the historical data can be used as training data to get a good estimate of the coefficients and the results can be tested against the rest of the historical data to check the accuracy.

When the results are accurate enough, logistic regression could be used to determine the probability of a customer paying for virtual currency in the next one day/one week/one month etc. Customers could also be segmented according to their paying probability and paying capacity so good decisions could be made on spending on acquisition of these customers. This can also lead to saving a lot of money by cutting down on paying for acquisition of non-paying customers. Similar technique can be used to find influencers and influential people and attract them to play games thereby helping games go viral. The process is simple and saves a lot of money by recognizing which customers are capable of paying and targeting all your campaigns to attract this user base. A lot of segmentation is possible using this simple method of data mining.

Example: Suppose the variables that are most important in finding paying are – Age, Education and Salary.
β0 = 1 (the intercept)
β1 = 2
β2 = 3
β3 = 4
x1 = Age
x2 = Education in years above high school
x3 = Salary in dollars above 50000

The model can hence be expressed as:
Probability of conversion to paying customer = 1/1+e^-z (Z= 1 + 2 x1 +3 x2+ 4x3 )

With increase in age, education and salary the probability of paying increases.
So, for a customer who is 24 years of age, has studied 7 years after high school and has a salary of 100,000 dollars the probability of conversion to paying customer would be 1/1+e^-z (Z= 1 + 2*10 +3 *7+ 4*50000 )

After understanding the segmentation of the customers and the probability of conversion to a paying customer, informed spending decisions can be made. Advertisements can be targeted to only to the segment desired and games can be designed to cater to the paying audience.

Thursday, January 19, 2012

Marketing Analytics - The Mobile Advertising Models & Ad Networks

I have been reading a lot about marketing and the gaming industry lately and feel its something really cool to blog about. Anyone into marketing or into analytics will find the next few posts quite interesting. Well, at least that is what I hope for!

Every mobile company needs to advertise. Now there are number of ways of doing this. Either have direct partnerships with other companies which own apps, go to Google or Apple or let someone else decide how to get those ad impressions for you.


There are number of ways to advertise - Display CPM, CPC, CPA/CPI, Search CPC are all online advertising models. Each of them varies in their cost structure.

1. Display CPM: CPM is an acronym for Cost per Mille or Cost per thousand. In such a setup the cost of the advertisement is calculated for 1000 page impressions each time the advertisement is displayed. A company which decides to use CPM advertisements will be quoted a guaranteed number of page impressions for an advertisement. The cost structure will be based on the decided number. The cost structure is independent of the visitors clicking the advertisement. Publishers get a share of the revenue.

2. CPC: CPC stands for Cost per Click. In this advertisement model, the publisher is paid each time a visitor clicks on the displayed advertisement. It does not matter what the visitor does after clicking on the ads. These types of ads are monitored to ensure that the publisher does not artificially inflate numbers.

3. CPA: CPA stands for Cost per Acquisition or Action. In this model, the advertiser pays when a certain action criteria is met after a visitor arrives at the advertiser’s link. For, a gaming company this criteria could be a visitor clicking on a link to reach the iTunes store and downloading their game. This model tends to be costly because of the action guarantee associated with it.

4. Search CPC: In this type of mode, advertisers pay a fee for displaying their content shown on search engines. Sometimes the natural results and the paid search results can be easily differentiated by visitors due to the display structure. An advertiser pays only if a customer clicks on an advertisement. The cost associated is higher than content CPC.

CPM is useful for a company that is already established or is in the early stage wherein they have a huge market and want to ensure market visibility. CPC ensures that a visitor at least looks at an advertiser’s link and this would be useful for a gaming company if the visitor is browsing using a mobile device. However, in my opinion the most important of these models for app companies would be Cost per Action since this could ensure that a visitor clicks on a link and downloads the app. As the number of people that download the app increases, the app ranking rises on iTunes and Android Market Search, the number of reviews increase helping the app(a game maybe?) go viral. The higher the k-factor the more successful the adoption of this model would be.

Now how do you get your ad in there??

The two leading mobile ad networks(in my opinion) for iOS would be AdMob and iAd.

1. AdMob: According to AdMob’s website 1,107,009,356,188 global impressions have been served by AdMob. Google AdMob offers a variety of services like Text Ads, Ads with Offers, Click to download etc. They are very well established and these services gel very well with other services offered by Google like AdWords Reporting and Google Analytics. Google AdMob is doing very well and has worked with a number of big brands. One disadvantage Google has is that its competitor Apple owns the iOS platform which might cause problems in the future. But its Google!

2. iAd: iAd is owned by Apple which makes it a good choice for user acquisition on iOS. According to Apple, iAd has installed more than 15 billion applications, it’s audience has activated over 225 million iTunes accounts, spends, on average, 73 minutes per day using apps and engages with iAd ads for an average of 60 seconds per visit. Apple vs Google - Its tough to answer that one!

Both iAd and AdMob have had complains from users and no one network seems significantly better than the other. The spend would be evaluated by looking at the Clicks, Fill Rate, Impressions, eCPM (Effective Cost Per Thousand) and Revenue. In my opinion, the best way to advertise would be to implement “mobclix” or something similar wherein multiple ad networks could be used.