Title: How Leroy Merlin Uses Elasticsearch to Drive Sales and Increase Revenue on their E-commerce Nowadays the success of any e-commerce business depends on many moving parts. Understand how Leroy Merlin improved their product promotion on its e-commerce website using Elasticsearch, and implemented a content search approach to increase annual sales turnover.

1. How Leroy Merlin uses Elasticsearch to
Drive Sales and Increase Revenue on their
E-commerce
Rui Velho – Leroy Merlin
Tiago Fonseca - Syone

2. Agenda
• Context
• Initial Problem
• Challenges
• Solution
• Benefits

3. Context

4. Leroy Merlin Portugal –
Adeo Group

5. 410
MILLION
CUSTOMERS
SERVED
23.1
TOTAL
TURNOVER
BILLION €
114000
LEADERS
14
COUNTRIES
800
STORES
N°
EUROPEAN PLAYER
onthe global DIY
market
ON THE GLOBAL
DIY MARKET
1
N°3
WORLDWIDE
BRANDS
and banner
brands
3
USEFULTO INHABITANTS
ALL OVER THE
WORLD

6. SOCIAL
MOBILE
INTERNET
ANALÓGICO
[1923]
DIGITAL
The Speed of Change

7. Time2Market
oriented
Open Agile
Product
Design Thinking
With end users
Mobile-first
approach
Internal team
involved
Start SmallFeedback
Modular
Architecture
Build & Run
Fast
Continuous
delivery short
cycle
Drivers

8. Initial Problem

9. Initial Problem
• SaaS Solution Implemented
• No optimized Search Results
• Expensive Software Maintenance Costs
• Lack of Product Support

10. Challenges

11. Challenges
• New Search functionalities needed
• Start small (MVP Product)
• Build Fast (AGILE)
• Innovate (+ Use Cases)
• You Build It You Run It

12. Solution

13. Solution
MVP Product:
Implementation of search capabilities for the Project Kiosks for physical stores.
• Elastic Search:
• 3 x Master / Data Nodes
• Logstash:
• 1 x Logstash Node;
• Data ingestion:
• 1 x Elastic Pipeline configuration:
• Data Collection, through the Logstash HTTP REST input plugin;
• Data parsing;
• Implementation of two indexes that will swap on each update, in order to avoid
service outage

14. Solution
DATA LAYERPRODUCT
INFORMATION
INGESTION LAYER
Elasticsearch
Master/Data Node 1
Elasticsearch
Master/Data Node 2
Elasticsearch
Master/Data Node 3
KIOSK
Logstash Node

15. Solution

16. Solution
2nd Project:
E-commerce Search Engine – Implementing an Elasticsearch solution, in order to
provide an efficient product search engine to increase sales.
The Search component used a Elasticsearch Cluster deployed on Azure with the
following characteristics:
• Elasticsearch:
• 3 x Master / Data Nodes
• Two indexes that will swap on each update, in order to avoid service outage
• Logstash:
• 1 x Logstash Node

17. Solution
• Search Engine Requirements:
• Research Term;
• Search Criteria;
• Sorting Criteria:
• Clicks Number;
• “Promo” Price;
• Etc..
• Tiebreaker;
• Research Weight;
• Ordination Weight;
• Total Product Weight.

18. Solution
DATA LAYERINGESTION LAYER
Elasticsearch
Master/Data Node 1
Elasticsearch
Master/Data Node 2
Elasticsearch
Master/Data Node 3
Website
Logstash Node
Product Information
Physical store Sales
Information
Online Store
Sales Information

19. Solution

20. Solution
• Implemented with Open Source Solution and Syone expertise
• MVP + Project had a combined effort of 65 FTE’s
• A small investment (only services).
• The elastic cluster is also used to infrastructure logging
• Data integration layer for the website(data + content)
Start 3rd Month1st Month 2nd Month

21. Benefits

22. • Personalized search (250K products within Kiosks and the Website)
• Fully deployed on Azure with "unlimited scalability"
• Enhanced performance (response time around 100ms on the website)
• Cost reduction around 40% for overall business case
• Introduce innovation
Benefits

23. Next Steps

24. Next Steps
• Subscriptions acquisition to take advantages of the following features:
• Graph;
• Machine Learning;
• APM;
• Security;
• Alerting;
• Analytics;
• Support;
• Innovative Use Cases
• Next Best Offer
• Similar Products Presentation

25. THANK YOU!

26. supported by
2019
powered by syone