Customer data quality: Roadmap for growth and profitability



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Customer data quality: Roadmap for growth and profitability (Aberdeen Group) (1)
Customer data quality

Beschrijft maturity class framework + kenmerken van elke groep



  • 3 niveaus van maturity in data quality

>< 5 in MSQ

  • Bij elk niveau de eigenschappen: metrics, processes and technology

  • In deling in niveaus op basis van 5 domeinen (4 dezelfde + 1 extra)

>< 4 in MSQ

 ook PACE framework beschreven

+ verband tussen PACE en maturity framework
Eigenschappen van goede customer data quality:


  • data collection, cleansing and analysis

  • data process management

  • accountabilitiy strategies for CDQ

  • technology enablers

  • measurment / testen

Verbeteringen door goede customer data quality:



  • customer data integrity

  • usability of customer data

  • time necessary in preparing customer data

stappen te ondernemen wanneer je in een bepaalde klasse zit

 vgl met stappen uit MSQ model
Tackling the multichannel marketing challenge (SAS) (2)
Geeft de problemen en oplossingen weer van multichannel marketing


  • integratie van alle customer data (data warehouse)

>< data fragmentation across silo’s

  • gebruik van predictive analytics voor voorspellingen over customer gedrag

  • real-time analyses om tijdige boodschappen te kunnen afleveren

>< bij outsourcing: krijgt de analyses veel te laat terug

  • automated processes

  • verschil tussen traditionele BI en CI aangehaald (p7)

BI: enkel info op geagregeerd niveau, over business

CI: info over de individuele klant vanuit multiple views

zorgt voor: deep customer insight + customer centric, cross-channel strategies
gebaseerd op de drie I’s: (+ uitleg vd cirkelvormige figuur van SAS die daarbij hoort)


  • insight: via analyse

  • interaction: strategieën afstemmen op goede customer interaction

relationship based strategies ipv transaction based

  • improvement: via metrics / reports / …

 = 4 P’s op individueel niveau (one-to-one marketing)
 link met MSQ: technologische mogelijkheden om meer relevantie te bereiken
Developing effective Multi-channel marketing strategies (Banta Corporation) (3)
Evolutie van ‘media mix’ naar multi-channel marketing

Shift in focus from transactions to customer relationships

Increase in the number of channels
Overzicht van de verschillende kanalen en hoe je er gepersonaliseerde boodschappen over kan versturen + het effect van gepersonaliseerde boodschappen
Einde: 5 key strategies for effective multichannel marketing:


  • customer data is king

  • get customers to opt-in

  • invest in personalizing communications

  • leverage more effecive customer channels

  • simplify the transition by using service providers


multi-channel marketing (McKinsey Marketing Practice) (4)
developing a multi-channel action plan: 4 stappen worden uitgelegd hiervoor

  1. decide on Multi-channel vs multple channel strategy and design Multi-channel value proposition

cross-channel benefits, linkages among multiple channels

core benefits:



  • greater convenience

  • more targeted and actionalble information

  1. develop a multi-channel network architecture

no fragmented customer experience

view multple channels as a network >< seperate things



  1. retool your ability to deliver target customer experience

enterprise wide metrics

common cross-channel vocabulary



  1. build the requisite organization, marketing and IT skills

CRM/ Customer information capabilities enable multi-channel management

Web-analytic tools


+ test and learn, phased approach >< big bang
Beyond web analytics for retail banks (5)
Problemen en SAS-oplossingen voor web analytics

The relevance revolution (Portrait software) (6)
Nieuwe uitdaging: relevance

One-to-one marketing

Personalization wordt nu individualizaion
Ontstaan van de relvance revolution:


  • challenging marketing conditions: meer en meer competitive

  • audience “ad fatigue”: only the most relevant messages will be heard

  • demanding customers: increasingly high expectations

  • proliferating media and channels: new communication media

  • shriking marketing budgets: accomplish more with less

battleground of the relevance revolution:



  • mass personilization of outbout messages

event triggered marketing automation

analytic approaches



  • limited insight undermindes inboud interactions

  • misaligned cross-channel comunications

need unified customer image

  • islands of disconnected data

individualized customer campaings: new weapon = customer intelligence



  • higher responses and conversion rates

  • reduces opt-outs

  • increase cross-selling and up-selling

  • strengthen retention and loyalty

  • boost marketing performance

  • turn contact centers into profit centers

steps to undertake:



  • event-triggered marketing

  • touch point across inbound and outboud channels

  • 360° view of the customer

  • Analytics

  • Experience based differentiation


Information is the foundation for customer intelligence (CSC) (7)
Recommendations:

  1. customer data integration

  2. 1 persistent customer ID: platform for analysis

!! Customer intelligence maturity model : 4 maturity niveaus, 3 domeinen



  • customer information integration (cfr technical)

  • customer insitghts: segmentation & modelling (cfr strategy)

  • customer insitghts operationalization (cfr process)



Customer Intelligence Diagnostic Survey (CSC) (8)
Survey over customer intelligence

op basis van het CSC customer intelligence maturity model (zie ander artikel)

samenvatting van bevindingen + bij elke vraag apart de statistieken

? Amerikaanse bedrijven

6000 enquêtes uitgestuurd  58 antwoorden
Komt weinig overeen met de vragen uit onze survey maar kan wel nuttig zijn om vragen uit te halen voor de interviews
Yearly marketing survey (the house of marketing) (9)
Enquête bij meer dan 700 belgische bedrijven over marketing

Interessant stuk over Customer intelligence in de Belgische markt!

Nuttig om onze resultaten mee te vergelijken en op te nemen in ons rapport
SAS Customer Intelligence (10)
Reclame paper over de mogelijkheden van SAS customer intelligence

Uitleg over die figuren van Mieke


Knowledge management and data mining for marketing (Michael J. Shaw et al, Elsevier, 2001) (11)

IT: heeft marketing verandert:

Grote volumes data  zorgt voor opportunities en challenges

Opportunities: internet als data collector

Data zorgt voor betere decision support

Challenges: meer mogelijhkeden  meer competitie  meer druk op marketing


CRM: gebaseerd op begrip van de customer

Bekomen dmv data mining tools

 kennis transformeren in effective marketing strategies
Mogelijk gemaakt door ontwikkelingen in: database processing, data warehousing, machine learning, knowledge management
 in de customer centric environment is er nood aan een eenvoudig en geïntegreerd framework voor het systematisch management van customer knowledge
Uitwijding over verschillende data mining technieken en hoe ze voor marketing gebruikt kunnen worden
consumer centricity takes merchandising into new territory (forrester, 2007) (12)
problem huidige systemen: inflexible, product centric

>< merchandising: customer centric, dynamisch

Nood aan integratie, data-intensieve analyse en optimizatie

Nood aan betere ondersteuning van processen door technologie



Customers at the core (Marketing Management – Robert Schiffer and Eric Leininger, 2008) (13)
“Customer-insight driven companies continually strive to integrate all customer insight into a knowledge base, and widely share this customer knowledge throughout the organisation.”
5 niveau’s van customer input integration:

negeren meeste customer input

unfocussed marketing strategy



  • customer curious:

beginnende interesse in input van customers die op basis van willekeur wordt toegepast en voornamelijk anekdotisch is

unfocused marketing strategy and lack of customer insight



  • customer-input driven: investeren in klwaitatieve en kwantitatieve data van klanten

alle customer input moet aandacht krijgen

segmented view of customers

moeilijkheden om data in inzicht om te zetten


  • customer-insight driven: gedreven door deep insights into customers

best opportunity for profitable sustainable competitive advantages

  • customer controlled: zijn te ver gegaan en laten zich dicteren door de willekeur van de

customers; verliezen hun focus op customer segmenten

laten het maken van beslissingen over aan de klanten zelf of salespeople; geen marketingstrategie meer

“forget about customer insight, give customers what they ask for”
The quest for customer focus (Harvard business review, 2005) (14)
Customer focus: learning all there is to know about your customers

Creating a comprehensive picture of each customer

Share customer picture throughout organisation

Use the insight to guide decisions


Stage 1: communal coordination

Creation of a centralized repository of customer information which records each interaction a customer has with the company

Stage 2: serial coordination

Gaining insight into customers from past behaviour

Stage 3: symbiotic coordination

Developing an understanding of likely future behaviour

Stage 4: integral coordination

Real-time response to customer’s needs


CRM and customer-centric knowledge management: an empirical research (Constantionos J Stefanou et al; 2003) (15)
Conceptual model of CRM development stages

  1. Non-IT assisted CRM

    1. Customer surveys, manual recording systems, non-IT-asisted processing

    2. Defensive relationship marketing

  2. IT-assisted CRM

    1. Call centers, fax, mail, spreadsheets, databases, statistical packages, internet pcresence: manual process that uses IT to analyse data

    2. Customer satisfaction – complaint management

  3. IT-automated CRM

    1. ERP, EDI, E-commerce, operational CRM  emphasis on customer interaction automation + tracking customer purchase patterns, …

    2. Customer accounts – orders management

  4. i-CRM

    1. analytical CRM / SCM, DSS: integrated CRM: dynamically monitor customer preferences, analysis technologies (data mining, rules engines, …)

    2. customer personalization management

 e-mail survey in Greek organisations


Marketing automation (destinationCRM.com, 2008) (16)
Overzicht van de tools die op de market zijn voor marketing automation
Putting one-to-one marketing to work: personalization, customization, and choice (Neeraj Arora et al, 2008) (17)
One-to-one marketing = tailoring a firm’s marketing mix to the individual customer

  • personalization: firm decides what marketing mix is suitable for the individual

 invasion of privacy ?

 expensive: data and software needed



  • customization: customer proactively specifies one or more elements of his or her marketing mix

 greater customer satisfaction

 ultimate product differentiation

 costly

 complex purchase decision for customers: too many choices to make


 one-to-one marketing is extreme form of segmentation: segment size one
3 stappen voor one-to-one marketing:

  • collectiong data

customer data integration + collection of data at all touchpoints (360° view)

for how many customers do you collect data ? (all >< %)



  • transforming data into insights

statistic modelling

  • operationalizing the results

coordination between marketing, IT and production needed
challenges in personalization:

reliance on statistical analysis of customer data

right marketing effort to richt customer

? cost of misclassification: disctracting and annoying the customer by recommending a series of books which he has no interest in may be worse than making no recommendations!


3 levels of customization:

- mass marketing

- segmentation

- individual


Spatial data mining method for customer intelligence (Bo Fan et al, 2003) (18)
e-business  harder competition  focus attention on customers instead of production only

provide more individualised and efficient services


customer intelligence = a decision analytical method which includes customer identification, customer selection, customer acquirement, customer improvement and customer maintenance

data mining: tool to detect underlying pattern of customer behaviours

+ deepen customer insights

 use spatial and non-spatial data for data mining on customers


Intelligence CRM: a contact center model (M.J. Tarokh et al, 2007) (19)
Overzicht van CRM en CI en de link ertussen

Verschil CI en BI

Customer intelligence = an integration of customer knowledge based concept, method, process, data and software, which can provede a fully integrated solution for gathering customer information from every touch-point in an enterprise
Multple communication channels:

Web, call centers, field sales, dealers, partner networks

+ multiple lines of business

Challenge: make it easy for customers to do business with the organisation any way they want at any time, through any channel in any language or currency

 a signle, unified organisation that recognizes them at every touchpoint
Application of data mining techniques in customer relationship management: a literature review and classification (e.W.T Ngai et al, 2009) (20)
CRM: strategy / set of processes and enabling systems for long term relationship with customer. Foundations: customer data and IT tools


  • operational CRM

  • analytical CRM

 use of data mining tools for better analysis

Inability to discover valuable information hidden in the data prevents the organisation from transforming these data into valuable and useful knowledge


Application of data mining techniques in CRM: needed in a customer-centric economy

The importance of analysis and planning in customer realationship marketing: verficiation of the need for customer intelligence and modelling (Javier Gonzalez Alvarez, 2006) (21)
Marketing innovations:

Acquiring customer information

Creating business models based on that information

Customer centric approaches


Approaches to CRM:

  • based on analysis and planning

  • collection and maintenance of data

  • segmentation of the data

 ensure that customer information can be converted to data

! need the right data for analysis

! quality of the customer data

Customer information structure


Rest vh artikel: over Datasteps, een analytical CRM solution van Marketing DataBasics
The Cambridge container company: managing customer-centric information integration (Amy W. Ray et al, 2002) (22)
Implications of moving to customer-centric operational decision making
Problems:

  • before: few sharing of information cross-departementally : internal networks don’t facilitate such sharing of data

>< now: web-based technologies enable companies to seamlessly collect and share qualitative and quantitative information internally and externally with customers and trading partners

  • managerial and behavioural hurdles





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