Big Data Challenges for an Ecommerce

- Gaurav Tiwari

Lead, Business Intelligence,

Lazada Malaysia 

Lazada Group in a nutshell : Lazada was launched early 2012 to target >500m customers…

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What is big data for us

What do we want to do

Challenges that lie ahead of us

What are we doing

How are we doing it

Future of BI and Big Data at Lazada

Wrap up

Agenda for today

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What is Big Data for us ?

The “Google” definition :

“Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.”

For us, this corresponds to :

Increased amount of transactions.

Increased repository of customer buying data

Increased data about our sellers

Increased quantity of feedback from our customers

Challenge lies in : how to make sense of this data

1a

What is Big Data for us ?

In Last 2 Quarters :1. 44% of Total Revenues2. 47% of Total Orders3. 49% of Total Items

1b

What is big data for us

What do want to do

Challenges that lie ahead of us

What are we doing

How are we doing it

Future of BI and Big Data at Lazada

Wrap up

Agenda for today

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What do we want - Be the best at what we do!!

Steer through massive amount of data with ease

Ability to make real time decisions and support cross department decision making

Understand our customers buying patterns to provide “Effortless Shopping” experience

Provide insights and infer suggestions based on statistical data analysis

Empowering decision makers to sieve through data easily without wasting time on processing

2

What is big data for us

What do want to do

Challenges that lie ahead of us

What are we doing

How are we doing it

Future of BI and Big Data at Lazada

Wrap up

Agenda for today

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Business Challenges

Actively migrating from a stack (less powerful tool set) to sophisticated, high performing BI tools with increased data handling and analysis capabilities

Multiple experiment with new BI tools and technology with comparison analysis

Results in same information coming from multiple sources with different assumptions and methodologies

Difficult to develop bridges to align on different data sources with different methodology and fill in the gap between the reports

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3a

Technical Challenges

Migration between Platforms (Old to New)

Data integrity issues

Data security issues

Ever changing Data Warehouse and Data Marts

Cost Consideration : Have to get the best out of limited spending capabilities on new servers and tools

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3b

What is big data for us

What do want to do

Challenges that lie ahead of us

What are we doing

How are we doing it

Future of BI and Big Data at Lazada

Wrap up

Agenda for today

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Reporting Needs

• Commercial• Marketing• Operations

Processing Needs

• Alteryx• Powerpivot• R• Python• Excel

Analytics Needs

• Tableau• Alteryx• Qlikview• Google

Analytics

CRM Needs

• Salesforce• Zendesk• Mailchimp• Seller

Center

We love to try new things!!

We experiment with new skills, tools and strategy which makes our business better Helps develop ourselves as well

We solve for the needs of the business :

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4a

Reporting Needs

Developed Centralized reporting system in Qlikview

One Stop shop for integration of data sources and reporting

Aligned with Lazada SEA to set up clear-cut methodologies for each metric and its derivation

Marketing Analytics team

Dedicated team to constantly monitor & act on the performance of marketing metrics and marketing channels

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4b

Processing Needs

Migrated to strong ETL tool Alteryx, Qlikview and Tableau

Full automation of data preparation for reports

Easy integration of data sources that helped in creating a dynamic data warehouse

Development of local reporting server to make the data available to Sales, Marketing and Operations teams in an excel based environment

Currently processing data to the sizes of up to ~100 million rows seamlessly to sieve through our customers, their transactions and their buying behaviour

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4c

Analytics Needs

Biggest focus for Lazada after setting up the proper reporting tools.

Statistical Data Modelling using R and python

Predictive Regression modelling using Alteryx and Tableau

A/B testing on our product performance towards a new initiative

Natural Language Processing (NLP + Text Mining to create Assortment Police Actively checks the product descriptions and flags the products not suitable for sale

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4d

CRM Needs

Salesforce : Helps manage sellers in terms of their

Performance Management

Product performance

Automated Communication with the sellers

Zendesk :

Record, Understand and Solve customer’s grievances

Analyse customer’s feedback to identify and start solving the biggest pain point

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4e

What is big data for us

What do want to do

Challenges that lie ahead of us

What are we doing

How are we doing it

Future of BI and Big Data at Lazada

Wrap up

Agenda for today

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Lazada Data Warehouse Architecture

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What is big data for us

What do want to do

Challenges that lie ahead of us

What are we doing

How are we doing it

Future of BI and Big Data at Lazada

Wrap up

Agenda for today

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What is big data for us

What do want to do

Challenges that lie ahead of us

What are we doing

How are we doing it

Future of BI and Big Data at Lazada

Wrap up

Agenda for today

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Thanks