1. Traffic Optimization Partners (TOP) Kick-Off meeting ??/01/2015

2. Background Meeting Goals  Context  Business objectives: Goals and KPIs  Business Structure  Project structure  Project Team  Project Management  Schedule  Next steps 2

3. Context (1/2) Through different channels (API, shopping box and channel), our partners generate up to 40% of merchants traffic 3 55 Partners Merchants LG Sites 40%60% 15 APIs (70%) 40 Partner sites (30%) Shopping box (20%) Shopping channel (80%)

4. Context (2/2) Partners traffic is taking a larger part in Leguide.com traffic and revenue mix. But the merchants are complaining receiving a bad quality traffic originated from our partners :  Merchants could withdrawn from our platform Evolutions have been implanted over the years to improve the quality :  A larger panel of shopping boxes (product rotation, retargeting, etc.)  Offer and merchant shuffling in result pages  Partner capping We are suffering from a lack of visibility on our traffic quality. Currently we monitor the partner traffic only from a “click-outs” approach:  80% originated from a Shopping channel page  20% from a shopping box: retargeting or product boxes 4

5. Business objectives: Goals and KPIs The Business objective is to secure and increase LG revenue from the partners websites 5 Goals Increase partners traffic to reduce Google dependency Increase merchant satisfaction by optimizing partners traffic quality Maintain/increase partner satisfaction Key Performance Indicators Partners traffic (# clicks) Merchant cost of sales Merchant loyalty/terminations Identified problems LG’s dependency on SEO traffic (Google) Possible loss of merchants due to the low quality traffic Possible loss of partners for higher revenue sources

6. Business Structure (1/2) The first step taken towards this goal was using user behavior analysis through the retargeting process 6 BillingClicksOffersShops Display Strategy PartnersLG sitesMerchants Clicks Valo User behavior Key words Retargeting Processing Capping

7. Business Structure (2/2) The targeted business structure will massively use BI and predictive algorithm to ensure more and better qualified traffic to our clients 7 BillingClicksOffersShops Display Strategy PartnersLG sitesMerchants Clicks Valo User behavior Key words Retargeting Processing Capping External data Predictive algorithm Business Intelligence KPIs

8. Project structure (1/2) Due to the amount of work to do, the stream is divided into interdependent sub-projects 8 Data collection Merchant tagging External data collection Business Intelligence Predictive performance/risk algorithm New Billing Sales evolutions Business/strategic decisions

9. Project structure (2/2) The Business Intelligence will lead to possible new strategic decisions that must be anticipated in order to amend the new billing project Brainstorming on possible changes:  Developing new offers:  “High visibility”: more traffic but lower quality  New grid system to allow multiple grid options:  A lower price for some categories  A higher price for others  Developing a real time valorization 9 To be discussed during workshop(s)

10. Project Team The Business Development team is leading the stream that impacts the whole partners revenue stream 10 RoleActor Business Owner Christophe Gares Yves Guerreiro Project Manager Tom Bienvenot Renaud Moulas Sponsors Executive Committee Stakeholders Cyril Bouskila Nadège Chagne Franck Jaminet Technical team Christophe De Kerviller Thomas Lehoux Bruno Cadu

11. Project Management Our traditional methodology will be used to conduct the project  Governance  Stand up : daily  Sprint planning : each 2 weeks  30’ Weekly stream leaders sync (PMO + Business owner + MB)  Steerco meeting (Sponsors + Busniess owner + PMO + MB)  Tools  Dedicated Wiki  Dedicated workspace on Jira (to be defined)) 11

12. Schedule The predictive algorithm is the last step after completing the data collection, the BI tools and the New Billing project 12 Q1Q2Q3 Data collection V1 : Merchants tagging External data: TradeDoubler Business Intelligence V1 New Billing Sub-subject 2 ? Predictive performance algorithm New offers, new deals with partners

13. Next steps All bricks are being investigated and defined simultaneously in order to pin out the dependencies Data collection:  Internal data : Work in progress  External data: Work in progress Business Intelligence:  Purchase negotiation for Pentaho software  Data collection/normalization  Tool implementation Predictive algorithm:  Guidance workshop to be scheduled (January) Sales evolutions  Guidance workshop to be scheduled (January) Billing:  Brainstorming on possible dependencies 13