offer recommendation methodology for vito's mobile app

Offer Recommendation Methodology

-Dipesh Patel

Problem Statement• As Internet population is growing exponentially

need for monitoring web user’s traffic is useful for future business growth of ecommerce players and other retailers.

• Online shopping platform generates huge amount of log data with transactional data of users. What products should be offer to target customers and customer acquisition requires appropriate Data analysis and prescriptive analytics to convert results into actionable insights and recommend products accordingly .

Objective

Require to build a recommendation engine for new and existing users. Each users should be recommended top five offers on basis of their likings and preferences

Available Data• Following Data files are available:- Customer Details Seller and Merchant Bank and payment Offer and category• Event data:- Text log file consists of user’s interaction

data with app including preference and likings.

Data Relationship Model

Data Insights

Customer Vs. State

Customer vs. Merchant Type

Customer Age Distribution

Age vs. Customer

Customer Vintage(months)

Customer vs. Categories

Offer Tenure(months) per Category

Hypothesis Statements • Customer who spent more than average session time on

Navigating app may buy items.• Discount offers may have significant impact on buying

pattern of customer.• The people who bought only item1 but they have similar

characteristics (in terms of variables) to the people who bought both item1 and item2, are more likely to buy item2 also.

• Customers who are younger than or equal to average Age of population are potential buyers.

• Customers who does transaction frequently are regular buyers.

Next Steps

• Feature Engineering• Hypothesis Testing• Model Building• Model Evaluation

Feature Engineering• Offer Tenure:-It is difference in offer start date and

end date• Age of Customer• Vintage:-calculates period of customer being active

on system starts from acquired date.• Transaction count:-Total unique count of transaction

done by customer• Session Time:-Difference between user logging time

and exit time on application.

Hypothesis testing Alternative hypothesis:-Customer who spent more than average

session time on Navigating app may buy items. Null hypothesis:-No significant impact on buying due to session

time . Calculated variables:-a. population mean(mu0)b. Sample mean(x bar)c. Population standard deviation(sigma)d. Level of significance (alpha=5%)e. Sample size(n)f. Test statistics(Z test)g. Critical Value(Z critical)

Ztest < ZcriticalConclusion:-Null Hypothesis Accepted

Z Test Result

Recommendation model

Collaborative Filtering• The process of identifying similar users and

recommending what similar users like is called collaborative filtering.

Basic assumption and idea • –Users buys items (implicitly or explicitly) • –Customers who had similar tastes in the past,

will have similar tastes in the future

Content Based Filtering• A content based recommender works with data that

the user provides, either explicitly or implicitly (clicking on a app). Based on that data, a user profile is generated, which is then used to make suggestions to the user. As the user provides more inputs or takes actions on the recommendations, the engine becomes more and more accurate.

Important Terms

• Find the Term Frequency(TF): TF is simply the frequency of a word in a document.

• Find the Inverse Document Frequency(IDF): It is the inverse of the document frequency among the whole corpus of documents.

• Vector space model: In this model, each item is stored as a vector of its attributes (which are also vectors) in an n-dimensional space and the angles between the vectors are calculated to determine the similarity between the vectors.

Dataset Training data: We have user, offer id and

category id, transaction count and demographic details .

• –For example, (125 , 13942 , 4) means that the user with ID 125 shops with offer ID 13942 transaction count of 4 .

• 1430 users and 1430 offers.

Popularity Model Result

where all the users have same recommendation based on the most popular choices. We’ll use the graphlab recommender functions popularity_recommender for this.

Item similarity ModelThere are 3 types of item similarity metrics • Jaccard Similarity: – It is typically used where we don’t have a numeric

rating but just a boolean value like a product being bought or an add being clicked

• Cosine Similarity:– Similarity is the cosine of the angle between the 2

vectors of the item vectors of A and B– Closer the vectors, smaller will be the angle and

larger the cosine• Pearson Similarity– Similarity is the pearson coefficient between the two

vectors.

Item similarity Model(cosine)

Model Evaluation

• Recall:– What ratio of items that a user likes were actually

recommended.– If a user likes say 5 items and the recommendation

decided to show 3 of them, then the recall is 0.6• Precision– Out of all the recommended items, how many the

user actually liked?– If 5 items were recommended to the user out of

which he liked say 4 of them, then precision is 0.8

Thank You