Madrid, 11th of November MDM as Foundation to an Exceptional Customer Satisfaction

Transcription

1 Madrid, 11th of November 2013 MDM as Foundation to an Exceptional Customer Satisfaction

2 Index 1. Who is Printemps / Who is Arhis 2. The Challenge 3. The Business Approach 4. The Project 5. Key Conclusions

3 1. Who is Printemps Paris, Where Tradition Meets Innovation Tradition Innova&on

4 Printemps Haussmann, more Than 140 Years Of History 4

5 Printemps at a Glance ü 17 stores in France 1,200 m² to 47,000 m², including: 1 Flagship store: Printemps Haussmann brings 50% of the revenue with 600 POS 16 additional stores located mainly in large cities with over 500,000 people (500 POS) 3,500 employees + similar number of Concession people +1 Billion Euros in revenue Fashion, Beauty, Luxury, Home Equipment, and Accessories 1,500 brands 5

6 Strategy: The 5 Pillars of Our Success The Best Fashion Luxury Beauty (FLB) Offering of the City Unique shopping environment and excep?onal stores A UNIQUE AND EXCITING SHOPPING EXPERIENCE, FOR CUSTOMERS WHO LOVE FASHION Personal and unique long- term rela?onship with our customers, built on services Best in class organiza?on Strong and consistent leadership image 6

7 Who is Arhis Arhis a Swiss Leader : Informatica strong expertise and a global presence

8 Informatica and Arhis 1. One Team & Unique Model 1. Informatica provides a dedicated MDM practice providing strategic support and oversight on Informatica MDM engagements 2. A strategic deliver partner - Arhis - delivers experienced implementation skills and Informatica technical eminence 1. Pure focus on MDM 2. Strong Informatica experience and technical eminence 3. Track record of successful MDM project delivery 3. Implementation best practices combined with a proven MDM project methodology 2. Our Focus: Customer Satisfaction 1. Speed of execution 2. Reliability of Quality and Delivery 3. Strong Commitment 4. Flexibility and Adaptability

9 2. The Challenge 1 9 Printemps Haussmann

10 Challenges Strong competition in Customer attraction / Loyalty Impact in effectiveness Differentiation is quickly copied Customer more exigent New Shopping and Interaction Channels (Web, mobile, Social ) Growing number of systems that support customer interaction Customer expectations of a seamless/homogeneous experience Limited access to complete Customer s information Due to the lack of identification / different identification models Due to data quality complexity (changes in Name, Spelling issues, ) Multiple Customer master data sources A Single Customer View becomes critical to continue providing Customer satisfaction and profitability"

11 3. The Business approach 1 11

12 MDM Scope Business assumptions Customer Master Data consolidation and Address cleansing Marketing analytics to be performed on quality data Campaign management to be feed from all customer sources Customer Master Scope Create a single view of the customer, taking data from all systems in batch and real-time De duplication of customer data, creating and maintaining relationships between customers and Printemps Establishment of indicators and typology to define customer groups Connection to campaign management : for mail, and sms-text Enable personal relationships with customers in the store 12

13 MDM Scope MDM & CRM: Identification, Knowledge, Analysis and Communication P. Com P. Listes Internal Customer Files POS Transactions MDM Customer Datamart SMS Delivery ing Delivery Postal Mailings FRONT STORE AND WEB CONSOLIDATE AND QUALIFY CLIENT MASTER DATA MARKETING ANALYTICS CAMPAIGN MANAGEMENT = NEW SOLUTION OR RENEWED TOOLS

14 10 DIFFERENT SOURCES MDM Scope MDM : An overview of a single customer in relation with Printemps Single Contacts One to one relationship Brands contacts Salesmen contacts Loyalty POS (services in store) Tax Free Foreigners contacts in store VIP contacts in store Non Loyalty Customers P Listes / Store Front P Listes / web site Printemps. Com Employees - MDM - OVER 1,5 MILLION CONTACTS AFTER MERGE VIP welcome Lead International desk Typology «A potentiel» «Guest relation» Prospects Personal Shopper «VIP» Clients Main typology 1 Main typology 2 Main typology 3 Main typology 4 Main typology 5 Main typology 6 Main typology 7 Main typology 8 Main typology 9 Main typology 10 + Specific relationship Services Détaxe 14

15 Benefits 1 A 360 view of Printemps Customers (relationship, life cycle..) A single and full view : All customers Personal Relationship : Loyalty programs, different Printemps targets: VIP, Personal Shopper, International... Status : prospect, client.. active or not Best targeting and dedicated message in communication tools New groups identified for the marketing campaign Marketing analytics from customers and not only payment methods 2 Data quality and governance (name, addresses, cards...) De duplicated and cleansed Master Data : less bounce back better use of marketing resources for campaign management optimize customer relationship include international specificities 3 Enrichment (personal data ) Personal data to be shared in different applications : Preferences, sizes, One to one relationship : Personal Shopper, guest relation... A shared view of all contact and relationship activities with Printemps, not only using POS tickets A foundation to develop one to one relationship" 15

16 4. The Project 1 Printemps Deauville 16

17 Project solution 1. Data Model : Unique Rowid MDM : A data model for a new customer Datamart Match and merge with trust scores and validation rules 2. Use of Powercenter as a heavy duty ETL to manage batch loads Cleansing and transformation of the data Enrichment of deduced information. Example : opt in for mail address is Yes if there is a validated address 3. The Core: MDM Hub + IDQ: MDM Mapping to feed the BO tables Advanced data profiling with IDQ IDD as a User interface only for the datasteward team : manual deduplication, manual update in case of customer mails 4. Batch and real-time interfaces : Web services implemented for P Com subscriptions and P Listes registration Re-use of actual batch interfaces 17

18 Additional functionalities Integration with external solution : Address cleansing : Uniserv For French addresses and some foreign countries, using WS and SaaS depending on Uniserv certification Files exchange : Import brand or any other XL files fulfilled in the store XML File built from recruitment forms collected in the store, including personal information Enrichment using external databases Opt-in rules unique and reliable In phase with legal constraints Set new Opt in collect in current application : tax-free, P Listes... New rules to manage addresses When shared by different contacts Unique principal Recruitment information Employees & brands that managed the initial contact 18

19 Data Sources BRANDS INTERNATIONAL CUSTOMERS BRANDS ; BRUMMELL PRINTEMPS CARDS Loyalty SAP TAX-FREE CUSTOMER FILES XL in stores Recruitment Enrichment EMPLOYEES HR solution SERVICES POS MDM DWH ing Delivery solution STOP SMS SMS SOLUTION P Com Batch Uniserv NEWSLETTER SUBSCRIPTIONS Extract ADDRESS CLEANSING P Listes Real-time BENEFICIARIES GIFT GIVERS 1 st phase 2 nd phase

20 Project Plan 5 main phases to achieve the objectives Discover / Initiation Analyze Design Build / Test Deploy Activities Activities Activities Activities Activities Initial data analysis Evaluate sources of data Identify high level requirements & architecture Conceptual Data Model PROFILING : Detailed source data analysis & requirements definition Local data modeling Services and realtime requirements Refining the solution architecture Physical data modeling Business rules for cleansing, matching & merging Interface design Security, policies & standards Test cases Implementation of data model and rules defined in the design phase Develop interfaces between Siperian, source and target systems. Tuning matching rules and merge Testing (System Integration & user acceptance ) Migrating the fully built, tested and accepted solution into a production environment Processing initial data loads Wrapping up the project Handing over the system for appropriate support team Provide Arhis Support for system information.

21 Several Go Lives / Several teams Project Delivery Gradual deployment: 1. MDM Go Live : 1. Batch load of the existing sources 2. Initial load and Scheduling (via PWC) 3. Merges 2. Datastewards manual cleansing 3. Communication data + new Datamart + new ing 4. New data : Customer files Both external and internal teams to be involved: Functional / Technical Experts Project management, SME important involvement Involve P Com P Listes... Team User testing by sql data requester

22 5. Key Conclusions Tradition 22

23 Key Success Factors q q q Business Involvement Capability to manage SME development Coordination of many departments Need to convince marketing, even if it seems very technical, to maintain high priority on the project as a stakeholder for the CRM. Arhis : Right Partner with strong solution knowledge Able to explain functionalities like matching rules, trust scores, cleansing rules... and advise Propose a data model related to company s assumptions Autonomy to profile and manage data quality Different skills in ETL, IDQ, MDM, address cleansing tools.. Multiple expertise required Profiling, Data Modelling, Data Quality, Matching best practices Web services, XML, network architecture, delivery tools... 23

24 Lesson Learned Business Complexity Data Protection and Privacity (Optin/Opt out) Each MDM project is singular and need specific design Business process need to be adapted / new ones may be defined Data Quality management is an art Important component of trial and error Continuous improvement Important time consumed in Design, profiling, cleansing... Testing Rigorous testing is very time consuming Full run of matching and merging rules of the full db can be very long Gradual deployment Starting with limited user impact (batch) Test the technical complexity and the business rules before going critical (on-line) 24

25 Change Management Easiest than expected and high level: Marketing / Board Maturity of the concept of SCV (single view of customer) Marketing is aware of the existing limitations The Stewarding, a new formal role Not previously formalized / with a clear ownership Key in the long term success As Transparent as possible for the end user On line search and proposal of similar / matching records only select / reject. Associate marketing analysts in testing to make them confident with the data quality and merge 25

26 Thank You! Key figures Informatica MDM Hub 9.1 Power Center 9.1; IDQ 9.1 Uniserv Address Cleansing 1 year project 10 sources 1,5 million contacts 3-5 Printemps Staff FTE Implementer: Arhis (5-7 FTE) Printemps Parly 26