Allegro Boosts Online Click-Through Rates by 500 Percent with Web Analysis The Allegro Group is headquartered in Posnan , Poland, and is considered the largest non-eBay online marketplace in the world. Allegro, which currently offers over 75 proprietary Web sites in 11
European countries around the world, hosts over 15 million products and generates over 500 million page views per day. The challenge it faced was how to match the right offer to the right customer while still being able to support the extraordinary amount of data it held.

In today’s marketplace, buyers have a wide variety of retail, catalog, and online options for buying their goods and services. Allegro is an e-marketplace with over 20 million customers who themselves buy from a network of over 30 thousand professional retail sellers using the Allegro network of e- commerce and auction sites. Allegro had been supporting its internal recommendation engine solely by applying rules provided by its re-sellers. The challenge was for Allegro to increase its income and gross merchandise volume from its current network, as measured by two key performance indicators.
• Click-Thru Rates (CTR)

• Conversion Rates.

The online retail industry has evolved into the premier channel for personalized product recommendations. To succeed in this increasingly competitive e-commerce environment, Allegro realized that it needed to create a new, highly personalized solution integrating predictive analytics and campaign management into a real-time recommendation system.

Allegro decided to apply Social Network Analysis (SNA) as the analytic methodology under- lying its product recommendation system. SNA focuses on the relationships or links between nodes (individuals or products) in a network, rather than the nodes ‘ attributes as in traditional statistical methods. SNA was used to group similar products into communities
based on their commonalities; then, communities were weighted based on visitor click paths, items placed in shopping carts, and purchases to create predictive attributes.

Statistical classification models were then built using KXEN Infinitelnsight Modeler to predict conversion propensity for each product based on these SNA product communities and individual customer attributes. These conversion propensity scores are then used by Allegro to de fine personalized offers presented to millions of Web site visitors in real time. Some of the challenges Allegro faced applying social network analysis included:
• Need to build multiple networks, depending on the product group categories

– Very large differences in the frequency distribution of particular products and their popularity (clicks, transactions)
• Automatic setting of optimal parameters, such as the minimum number of occurrences of items (support)

• Automation through scripting

• Overconnected products (best-sellers, mega- hub communities). Implementing this solution al so presented its own challenges including:
• Different rule sets are produced per Web page placement

• Business owners decide appropriate weightings of rule sets for each type of placement / business strategy

• Building 160k rules every week

• Automatic conversion of social network analyses into rules and tableization of rules

As a result of implementing social network analysis in its automated real-time recommendation process, Allegro has seen a marked improvement in all areas.

Today Allegro offers 80 million personalized product recommendations daily, and its page views have increased by over 30 percent. But it’s in the numbers delivered by Allegro ‘s two most critical KPIs that the results are most obvious:

• Click-through rate (C TR) has increase d by more than 500 percent as compared to ‘bestseller’ rules.

• Conversion rates are up by a factor of over 40X. Web Analytics Metrics


1.1 Elaborate on the following website analytics concepts: Click-through rate and Conversion rates. (5)

1.2 Elaborate on the challenges faced by Allegro. (5)

1.3 Using your own research, elaborate on the concept of Social Network Analysis and how Allegro used it and if they encountered any challenges applying it.
1.4 Clusters are one of the four methods used in datamining. Elaborate on this concept and state how it could have been used to help Allegro improve their click-through and/or conversion rates. (25)

1.5 State the results of implementing the proposed solution in the study. (5)


Use the information below to answer the questions below.

A dataset on Kaggle contains student achievement data for various high schools. The data were collected using school reports, IQ assessment reports and questionnaires.
Here is the modified metadata:

1. average_mark – average of subject marks (percentage mark saved as a decimal/flot)
2. sex – student’s sex (“F” – female or “M” – male)

3. age – student’s age (integer ranging from 15 to 22)

4. address_type – student’s home address type (“Urban” or “Rural”)

5. family_size – total number of people in household (integer)

6. parent_status – parent’s cohabitation status (“Living together” or “Apart”)

7. household_head_education – (“none”, “primary education (4th grade)”, “5th to 9th grade”, “secondary education” or “higher education”)
8. Household_head_job – (“teacher”, “health” care related, civil “services” (e.g. administrative or police), “at_home” or “other”)
9. class_failures – number of past class failures (integer)

10.family_relationship – how strong is your relationship to your family (0 – weak to 5 – very strong)

11.temperature – average temperature in area measured in degrees Celsius (float variable)
12.household_income – total household income (measured in rands and cents) – IQ score (integer)

14.romantic_relationship – with a romantic relationship (binary: yes or no)

15.absences – number of school absences (integers from 0 to 93)

2.1 Elaborate on the concepts of discrete numeric data and continuous numeric data.
2.2 Classify the variables in the study as either categorical or numeric.

For categorical variables, further state whether the variable is nominal or ordinal.
For numeric variables, further state whether the variable has interval or ratio data. Give a brief reason for your choice
Answers to Above Questions on Case Study Above

Answer 1: Click through rate is an important metric that is utilized in case of digital marketing whereby it is used to evaluate the ways in which the advertisement is performing. Its calculation is done by dividing the number of Clicks on an ad with that of the number of impressions that are generated by the ad.

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