This document discusses challenges and opportunities for developing future e-commerce platforms (E-commerce 3.0). It outlines how customer data collection and analysis can be used to personalize recommendations, marketing, and other aspects of the customer experience. Key points include: 1) Tracking extensive customer data from on-site, off-site, and mobile interactions to better understand customers. 2) Analyzing the data to generate product recommendations, bundles, and optimized marketing campaigns. 3) Scaling systems horizontally using service-oriented architectures and asynchronous messaging to handle increasing data volumes.

1. Strictly private and confidential
2014 - All Rights Reserved
E-Commerce 3.0
Challenges for a future prove e-commerce platform
02.06.2014, Christian Zacharias

2. Strictly private and confidential 1
Customers starts discovering the web and the shopping possibilities
Pull behavior – Customer looks for a specific product in the shop
Easy to sell Products
Agencies building the fist ecommerce systems
Rise of Intershop and big integrated player (Broadvision, Art Technology Group)
XTcommerce and osCommerce as free alternatives
The Gold Rush
E-Commerce 1.0

3. Strictly private and confidential 2
Customer gets more importance
More Content and Multimedia to engage Users
Customers Voice – Reviews
Pull approach – try to get the customer on the page
Stimulated impulse shopping – Deal-shopping
Integrated Platforms (Hybris, Magento, Oxid)
 Monolithic approach
 Hard to scale
 Caching helps (Varnish)
Switch to own solution after hitting the performance wall
OpenSource and Hard to Scale
E-Commerce 2.0

4. Strictly private and confidential 3
Cross-device ecommerce experience
 Start buying process mobile finish on desktop
 Increase reach through API´s and Apps
Know everything about you customer
 Better recommendations and prediction systems
 Act data-driven and deal with BigData
Service oriented Approach
 Decouple you platform
 Single responsibility principle
 Easier to scale
What is this about
E-Commerce 3.0

5. Strictly private and confidential 4
Agenda

6. Strictly private and confidential 5
Agenda

7. Strictly private and confidential 6
 Each Customer Contact is important
 Onsite
 Visited Pages
 Products watched
 Baskets
 Checkout
 Orders
 Logins
 Offsite
 Adwords, Affiliates, Newsletters and Mails
 Marketplaces (Daparto, Idealo, …)
 Banners placed through Marketing-Services
 Retargeting
 Mobile
What should I log
Get to know your Customers

8. Strictly private and confidential 7
 Create a Logging System
 Integrate Logging system
 Track and Mark every Visitor
 Create VisitorId for each new visitor and mark him trough a Cookie
 Identify existing Visitor through this Cookie
 Correlate all action from the visitor to this VisitorId
 Integrate your logging system into your Application Platform
 Controller Logic
 Database connector
 Background Processing
 CronJobs
 Add Analytics Solutions used to track OnPage Actions and additional Information about the
Visitor/Customer
 Track inbound URL´s to track marketing efforts and campaigns
Track everything!
Get to know your Customers

9. Strictly private and confidential 8
Logging Architecture
Get to know your Customers

10. Strictly private and confidential 9
Agenda

11. Strictly private and confidential 10
Reports and direct access
Get Insights from you Data
DW
H
HDFS
Data Stores
Reports, KPI´s, Mails
Adhoc Queries, Direct Data Access
 Predefined Reports for quick
insights
 Summary-Mailings
 Dashboards for specific
Business Areas
 ! Report Analytics
 Direct access to prepared Data
 Only Read Access
 Dedicated Instance
 Promote often used queries to
Reports
 ! Add Metrics about Data Usage

12. Strictly private and confidential 11
Business Inteligence
Get Insights from you Data
DWH
HFS
Data Stores
Business Intelligence
ETL Processes
Aggregate
Data
Calculate
statistical
Datasets
Data Driven Shop
Recommendations &
Bundles
Intelligent CRM
Marketing Control
 Aggregate and implement statistical functions to calculate co-occurrence matrices of
Products2Products and Products2Visititors
 Collect Marketing- and Campaign-Performance Data
 Analyze Customer Behavior and correlate with Product and Order Data to improve CRM
 Use existing OpenSource Solutions like Apache Mahout to do the heavy mathematics

13. Strictly private and confidential 12
Agenda

14. Strictly private and confidential 13
Co-occurrence Matrix for Product2Products Recommendation
 Upselling Potential
 Boosts by Margin-, Sales- or Stock-Business Rules
Recommend Cross-Selling Products
Provide Products-Scores to improve Product Listings
 Scores based on Stock, Margin, Revenue, Sales ..
 Calculate final Sorting-Score based on specific formulars
Generate statistical Bundles
Personalize the full shop experience
Recommendations & Bundles
Data takes over your shop
Data Driven Shop
Recommendations &
Bundles
Intelligent CRM
Marketing Control
How to measure your success: TEST TEST TEST

15. Strictly private and confidential 14
Improve Customer Mailings and Transaction-Mails
Newsletter Optimizations and Personalization
BI generates Recommendation of Products for the
Recipients
Recommend Products based on historic customer
behavior and insights
Intelligent CRM
Data takes over your shop
Data Driven Shop
Recommendations &
Bundles
Intelligent CRM
Marketing Control

16. Strictly private and confidential 15
Marketing tracking provides insights into marketing
performance
Marketing Dashboards and Reports from DWH
Control your marketing spending's based on
campaign-performance
Recommend Marketing Material Placement based on
Customer intends and behavior
Banner generation
Automate Bidding and Campaign Management
Marketing Control
Data takes over your shop
Data Driven Shop
Recommendations &
Bundles
Intelligent CRM
Marketing Control

17. Strictly private and confidential 16
Agenda
Get to know your
Customers
Get Insights from
you Data
Data takes over your
shop
Get ready for Scale

18. Strictly private and confidential 17
Low information content
Not enough significant data for statistical
analysis
Deal with the data
Get ready for Scale
A
B
Data Usage
f(P,T)
Product-range
Exponential increase of data usage within
the increase of the product-range and
traffic
Significant also increase exponential
Scale Processing and Storage in the same
way

19. Strictly private and confidential 18
 Scale horizontal through virtualization of you platform and Service Oriented Architecture
 Collect messages non-blocking and asynchronous
 Receiver is a central service accessible by all components
 Flexible message-distribution and -processing
 Different message consumers with different purposes
• Direct log analysis (e.g. Kibana, ElasticSearch)
• Permanent store all Log-Messages for long-term analysis (e.g. Hadoop & Hive)
 Data Storage needs the scale as well
 HDFS, self hosted or cloud
 S3
 Base for further Data aggregation and processing
Prepare your System

20. Strictly private and confidential 19
E.g. Tirendo Platform Architecture

21. Strictly private and confidential
Questions
20