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Tirendo - eCommerce Platform

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Tirendo - eCommerce Platform Tirendo - eCommerce Platform Presentation Transcript

  • 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
  • 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
  • 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
  • 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
  • Strictly private and confidential 4 Agenda
  • Strictly private and confidential 5 Agenda
  • 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
  • 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
  • Strictly private and confidential 8 Logging Architecture Get to know your Customers
  • Strictly private and confidential 9 Agenda
  • 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
  • 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
  • Strictly private and confidential 12 Agenda
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Strictly private and confidential 19 E.g. Tirendo Platform Architecture
  • Strictly private and confidential Questions 20