Research has shown that customers who have abandoned shopping carts, when subjected to retargeting email campaign, often come back and in many cases end up buying more than what was originally in the shopping cart.
There are many attributes of such email campaigns. In this post, we will find the attribute values that produce the maximum effectiveness for such retargeting campaigns, by including some of those attributes. A Hadoop based decision tree algorithm will be used to mine existing retargeting campaign data. The solution is part of my open source project avenir.
Abandoned Shopping Cart Email Campaign
This post contains an interesting list of 7 most effective email campaigns. One of them is for abandoned shopping carts to recover potentially lost sale.. There are various parameters as listed below that contribute to the effectiveness as listed below
- Timing of the campaign following abandonment
- Products on the cart
- Amount being spent
- Any promotion in the email
- Any product recommendation in the email, based on what’s in the shopping cart
- Campaign creative
- Customer age
- Customer sex
We will be considering only two of the attributes listed; timing and product recommendation. I don’t mean to imply that the other attributes are not as important. The idea is to show the usefulness such tools based on decision tree. In the real data mining project all the attributes above will be taken into account and a full blown decision tree will be built.
Campaign Timing and Product Recommendation
I have combined two attributes into one categorical attributes. The first attribute campaign timing defines the time elapsed since the abandonment event and email sent to the customer. We will consider 3 cases: 1 hour, 2 hours and 3 hours. According to research, most customers go cold within a short time after the abandoning the cart.
As additional enticement, the email may include product recommendations based on what’s in the shopping cart. That’s our second attribute. We will consider the cases: 1. Cross sell recommendation 2. Social recommendation, based on what other users have purchased 3. No recommendation.
The 9 values of the categorical attribute that we will call campaign type are obtained by taking all possible pairs of the following values.
|Time elapsed||1 Hr||2 Hr||3 Hr|
Quick Note on Decision Tree
Decision trees have been around as a very successful and popular machine learning technique. One reason for it’s popularity is that it yields interpretable result, so that you can extract rules like if featureAttr1 y then classAttr = z. It can also be thought of as a rule mining system.
The basic idea is to split the data into different sub population based on splitting some attribute, such that each sub population is as homogeneous as possible with respect to class attribute. This process is repeated for various attributes to break the sub populations into smaller sub populations ina iterative way.
There are different statistical and information theory based criteria for defining the homogeneity of the data e.g. entropy, gini index etc. Details can be found in my earlier post.
For numerical attribute, we can create multiple split points between min and max range of the attribute. If you choose n split points, you will have (n+1) segments or splits. For categorical attributes we can create splits by breaking the values (cardinality of the categorical attribute) into mutually exclusive sets of 2 , 3 or more sets.
Our feature attribute campaign type has a cardinality of 9 distinct values. We are going to use a split size of 2. The set containing all 9 values will be split into al possible sets of 2 mutually exclusive sets, where each one of them is a split. Generally, given a a maximum split size n, we will create all splits of size 2, 3 all the way to n.
Our decision tree algorithm will find the split that results in most homogeneous sub populations. Once that is done we will be able conclude that when campaign type is one of several types corresponding to the optimum split, the customer is most likely return and make purchase responding to the retarget campaign.
Decision Tree Map Reduce
Here is some sample input, consisting 1. customer ID 2. campaign type 3. Cost of items in shopping cart and 4. Whether customer returned and completed purchase. In our analysis the only feature attribute we will use is campaign type. The last attribute is the class attribute which is binary i.e. whether customer returned and purchased the items left in the shopping cart.
1988527,1N,119,Y 1225473,3S,316,N 1713851,1C,275,N 1876866,2C,182,Y 1970338,3N,212,Y 1805869,3N,30,N 1852129,2N,310,N
The map reduce implementation is in the class ClassPartitionGenerator. My earlier post contains details of the implementations. The mapper key output is a tuple consisting of 1. attribute ordinal 2. split 3. split segment index and 4. class attribute value. The mapper value output is the count of 1.
We end up having 255 possible splits. Each split is consists 2 mutually exclusive sets of the 9 values of the categorical feature attribute. The general problem of generating splits is ated as follows. If there are n items that need to split into k mutually exclusive sets, then how many such cases are there. There is factorial based formula to compute this. It works out to 255 for our case. The mapper processes each record for all the splits. The reducers gathers all the count values
We are using gini index as the splitting criteria. Among all the splits, the one with lowest gini index will produce the most homogeneous sub populations. Actually we calculate information gain which is defined as the decrease in gin index. We make 2 runs of the MR. For the first run we calculate gini index for the whole population without any splitting attributes being involved. In the second run, we consider different splits and calculate gini index for each split.
Then we take the difference between the results of the first and second run, which is called the information gain. The split with the highest information gain in the best split i.e the most discriminating. For such splits, one of the split sets will correspond to mostly successful campaign, and the other one mostly unsuccessful campaign.
Combiners Combat Combinatorial Explosion
In each run of the map reduce, we could find candidate splits for multiple attributes as specified through the configuration parameter split.attributes. This results in large fan out from the mapper. Each mapper input record may generate several hundred records as we we process each record for all the splits for all the attributes and all the possible class attribute values.
For example, an input of size 1 million records may generate several billion mapper output records. To combat the problem, I have used a combiner, which will significantly cut down the amount of of data to be shuffled. If there are n attributes, the number of records generated for each input record is as follows.
With combiner, each mapper will be emitting m number of records which is significantly less. It’s most likely to be even smaller than the input size.
Here is some sample output showing the best split along with some other. The fields are 1. attribute ordinal 2. split 3. gain
|1,[1C, 1S, 1N, 2C, 2S, 2N]:[3C, 3S, 3N],0.04878071070622809|
|1,[1C, 1S, 3C, 3N]:[1N, 2C, 2S, 2N, 3S],9.734830700108467E-4|
|1,[1C, 1S]:[1N, 2C, 2S, 2N, 3C, 3S, 3N],0.05306572868938034|
|1,[1C, 2C, 2S]:[1S, 1N, 2N, 3C, 3S, 3N],0.026436046040040653|
The highlighted row shows the best split with a gain of .053. The campaign is most effective when the types are 1C and 1S i.e. when the campaign emails are sent 1 hour after the shopping cart is abandoned with cross sell or socially recommended products. The result is not surprising and agrees with our intuition.
Since we used 2 parameters as input among many, we have simplistic model and got results that we expected. Had we considered other input parameters, we may have found different and interesting result e.g, for certain customer segment, 2 hour delay produced most recovery of abandoned shopping carts. Here is the tutorial to run this example.
Although I have the core splitting logic of decision tree implemented along with various splitting criteria,what is missing is a driver that that will generate the full tree by running multiple Hadoop jobs iteratively by repeatedly finding new optimum split and splitting the data accordingly. When that is done I will be back with another post.
For commercial support for this solution or other solutions in my github repositories, please talk to ThirdEye Data Science Services. Support is available for Hadoop or Spark deployment on cloud including installation, configuration and testing,