Marketing Information Systems:
part 3
Course code: PV250
Dalia Kriksciuniene, PhD
Faculty of Informatics, Lasaris lab.,
ERCIM research program
Autumn, 2012
Timetable
Part 1: Oct.22 Mon 14:00–17:50 C525
Part 2: Oct.23 Tue 8:00–11:50 G101
Part 3: Nov. 05 Mon 14:00–17:50 C525
Part 4: Nov. 05 Tue 8:00–11:50 G101
Part 5: Dec.10 Mon 14:00–17:50 C525
Part 6: Dec.11 Tue 8:00–11:50 G101
Assessment session: 1-2nd week of January
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Customer Relationship Management
Customer relationship management (CRM) is a broadly
recognized, widely-implemented strategy for managing
and nurturing a company’s interactions with clients and
sales prospects
The overall goals are:
- to find, attract, and win new clients,
- nurture and retain those the company already has,
- retain former clients back,
- and to reduce the costs of marketing and client service
(Pepper, Rodgers, 2004)
The spectrogram principle of the
customer analysis
The success of the enterprise
highly depends on the
“prism” as analytical model
which can convert “white
light” of information to the
swath of colours with
different brightness: identify
compounds of customer
portrait by characteristics,
their importance and effects
to the financial results of the
enterprise.
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Components of CRM Systems
• The software producers understand the structure of
CRM differently
• You can find CRM, which mean different goals: sales
module, communication module, performance of sales
personnel, distribution channel analysis, loyalty “point”
systems, etc. (what type is Sugar CRM?, MS CRM ?,
SAP CRM?)
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Customer Relationship Management
(CRM) Systems – general understanding
• Provide information on existing customers, their
loyalty and churn
• Identify and target new markets
• Enhance customer’s satisfaction
• Manage relationships with partner organizations
• Marketing: cross-selling, upselling, bundling
• Customer service
• Partner relationship management
• Internal marketing (making enterprise attractive
for its workers for keeping their knowledge) 6
What’s hot from Gartner 2012
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What’s hot from Gartner 2012
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CRM- is a philosophy of management
enterprise resources (4+1 main types).
Traditional parts of
enterprise resource
capital:
• Financial
• Material
• Human
• Intangible
• Information
CRM explores new
types of resource
capital
• Knowledge & info
• Customer capital, where
share of each customer is
explored (different
approach is market
share)
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10
Customer capital management goals
Get:
... Profitable
customers
Keep:
... Profitable
customers as long as
possible
... Win them back
from competitors
... Convert notprofitable
customers
to the profitable
Enhance:
... incentives to get
additional products
.... Positive reference
from existing
customers to win new
... Customer service
programs
CRM information needs
CRM goal Information need Capability of accounting
systems to supply info
Profitable customers New and old customers
Profit per customer
No
Profit calculation per unit
Keep profitable customers
as long as possible
Communication history Sales info is available
Limited info about reaction
to promotions
Win profitable customers
back from competitors
Customers of the
competitors
Who were won back
No
Convert not-profitable
customers to the profitable
Expenses per customer
Sources for turnover
No
Provide incentives to get
additional products
Know individual needs No
Enhance positive
reference
Opinion, referrals, impact No
Enhance customer service
programs
Effectiveness of
programs
No 11
IDIC model for CRM
D. Peppers ir M. Rogers (2004)
IDIC model
Analytic:
Identification Differentiation
Operational:
Interaction Customization
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Application of IDIC model
• Identify customers- explore individual characteristics.
Needs variables for identification: tel.no. address, email,
psychographic characteristics, preferences, habits
• Differentiate customers- searching for different
characteristics which enable segmenting. Definition of
similar segments helps to focus attention to best (most
profitable) groups, and create scenarios evoking specific
behaviors
• Interact with customers- search for tools and technologies
for creating perception of the enterprise to its customer in
attractive way, get feedback, avoid information distortion
due to attitudes (e.g. caused by resistance to spam)
• Customize treatment- maximize profit due to meeting
individual needs 13
Two tasks for managing CRM
OPERATIONAL CRM:
How to collect
information about
relationships
Surveys, registering
calls, visual observation,
loyalty cards, promotion
responses
ANALYTICAL CRM:
Ho to evaluate and
use information
Evaluation by creating
meaningful CRM
indicators
Reporting, statistical
methods, analytic tools,
intellectual computing
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What is indicator?
• Indicator is a common language among managers
• Instead of evaluations “good”, “bad”, the numeric
evaluations, rankings, graphical visualizations, etc. could
be more effective
• Indicator is a lever which we have to envisage, and use
proper impulse of sufficient power to make impact on it.
• Indicator reveals influences which affect enterprise. It is
important to notice these influences, to know how they
are created, what efforts are needed to make them serve
to the enterprise needs.
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Integrated approach- CRM perspective
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Problems of getting right data for analysis
Accounting information is limited, there is need for contact
points, where customer information can be recorded
(loyalty cards, personalized access points, transaction
terminals, call centres, web pages or social networks)
The best descriptive is qualitative data, but it is collected in
inconsistent way (surveys), or stated by subjective
judgments, or classified by subjectively extracting
characteristics of communication
Therefore our challenge is to apply the historical purchase
data, utilize information from access points and capture
qualitative data consistently
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How to create indicators ?
• Traditional commonly understood marketing indicators?
• What is missing? What direction should be followed in
order to enhance power of indicators?
• How to understand gap?
• Common rules for creating indicators : absolute
(turnover, profit), relational (EBITDA), percental (impact
of marketing for “bottom line” in accounting), complex
interpretation (RFM), formulas (LTV), ranking (loyalty)
• Analytical report types : summarization, queries, trends,
anomalies, extremities.
• Textual, numerical, color, graphical
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Information for evaluation
• CRM evaluation based on accounting information
• Defining loyalty and its relationships to sales
• Using non-financial information
• Balanced scorecards
• Internet technologies based indicators
• Social network analytics
Gap of the indicators
20Source: Zumstein, D.
How to fill the gaps to final indicator
• Making qualitative indicators. Negative side- hard to
transform to measurable
• Creating lead indicators which are going to influence
factual results in (lag indicators). Negative side- some
relationships between them are missing or misleading
• Proxy indicators try to created intermediate links leading
to final values Proxy—Financial—Statistical
• Creating indicators similar to financial philosophy : Return
on Customer Investment (ROCI); Return on Relationship
(ROR); – similar to ROI (return on investment in finance)
• Longitudinal metrics – involve dynamics
• Refining indicators by learning relationships philosophy
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CRM variable types
• Simple transactional variables – purchase value,
frequency
• Derived variables- CLTV- customer lifetime value
• Survey-based: satisfaction, knowledge, preference
• Event-based: churn, complaint
• Expert-evaluation-based: loyalty
• Compound variables – RFM
• Proxy variables- compound-weighted-ranking based
• Models: Pareto, Whale curve, custom designed models
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Promising variable types
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CRM indicator and metric samples
Customer profitability metrics
• Cross-sell change
• Process and operation cost change
• Credit usage level
Change of number of customers and their structure:
• attrition,
• churn rate,
• Naming groups by character: “vintages”, “cohort”, “VIP”
• satisfaction changes according to survey data
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CRM indicator and metric samples
Value of customer
• Evaluation in monetary terms by assumption that
customer is the asset of enterprise
• NPV-net present value
• Potential value (IRR)
• Current and potential value according to survey data
• ROI – return on investment to customer
Cycles among purchases:
• Cycle duration (shorten, lengthen, regularity)
• Buyer trajectory – characteristics accumulated during
purchase history
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CRM indicator and metric samples
Evaluation of purchase structure:
• Large purchase buyers
• Petit purchase buyers
• Frequent purchase return makers
Grouping, segmenting metrics:
• Decile analysis (divide by 10% segments)
• Pareto principle
• Whale curve
• Share of customer (e.g. VISA uses share of wallet)
• Share of personal consumption, expenditures
• Customer satisfaction
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CRM indicator and metric samples
Life cycle value
• Most valued customer segment- MVC
• Relationship value
• Relationship duration
• Migration
Loyalty metrics
• Specific behavior: “bought in past and will buy in future”
• Attitude, brand preference
• Tenure functions
• Ranking according loyalty strength
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Loyalty categories –their variety
• Loyalty pyramid expresses levels of loyalties
• No loyalty– first level of loyalty when it is simply absent
The user freely searches for product by changing
suppliers, not bonding to them. If he bought during
promotion period, the sales of this loyalty group return
back to previous level
• False loyalty: customer does not feel any difference
among products of suppliers, but he has no need to
change them –behavior by inertia
• Hidden loyalty- customer has preference to some
product or supplier but not always keeps buying it
• Real loyalty- the customer has clear preference and
uses it even when there exists sufficient choice
Loyalty categories –their variety
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Compound variables – RFM
Variable R (Recency) show the number of days since
the last visit till the date set for analysis
Variable F (Frequency) indicator is equal to the number
of visits of the customer.
The M (Monetary value) is equal to the total value of
purchases during all the history of communication.
• CRM task lays in defining RFM combination matrix for
decisions. E.g. how we treat recent customer who comes
often, pays much ? How do we treat if he comes rarely?
Do we change opinion if he comes only during holiday
time? If we waited for his holidays and he missed – did
he chose competitor?
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“Whale curve“ analytic visualization
Customers are sorted by descending order of their turnover
(or profit) values, in order to compute thier cumulative
percent values and to plot to Y axis.
In X axis you plot the cumulative percent of the number of
customers (e.g. if the enterprise has 10 customers, each of
them makes 10% of the enterprise customers, second line
will show cumulative of 2 customers which make 20
cumulative percent, etc.
The Whale curve shows what percent of total number of
customers in X axis are able to generate their part of the total
enterprise turnover (profit) (plotted in % in Y axis).
The final point of curve means total turnover by all customers
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“Whale curve” of profit, red line denotes loss
Using “Whale curve”
Define visually the areas with same growth, split customers
to segments accordingly
• Ask questions by analyzing behaviors of segments: what
we can do in order to convert “second best” customers to
the “best”
• How we can convert customers who bring “loss” to
“profitable
• Do we have different rules and personnel for segments?
We can split cumulative curve to “deciles” as well
Pareto “law” is visible in “Whale curve” at 20% in X axis
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CRM for changing customer indicators
• Cross sell- offering additional products, which are
compatible to those already bought
• Up-sell- improvements of the product already bought
• Bundling- complex product /service/subscription
• “Churn rate” measurement. No precise methods to define.
The goal is to elaborate indicators which could make early
prediction of churn
• Mass customization- exploring customer choices,
segmenting them and offering as most popular of them as
standardized solutions for best-fit segments (improves
costing, reduces waste and stock)
• Using strategic games for capturing rules of behavior (e.g.
putting advertisements to Second life game)
Proxy – creating cause-effect linked indicators
Indicator Measure Weight %.
Average income Average of present and
forecasted income
20
Change of income Annual change 25
Relationship features Duration of contract 15
Tenure of history
Technologic involvement System integration 20
Reporting system
Tele-Web
Email
Parrnership value Contact level 10
Refferal
Future value
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Top 5
customers
Ranking by „proxy“ Ranking by
monetary value
Rank difference
A 1 1 0
B 2 22 +20
C 3 62 +59
D 4 4 0
E 5 3 -2
Customer portrait Analytical aspects:
1 The percentage difference of each
characteristics of the customer
compared to the best value existing in
the customer base of the enterprise.
2 customer portrait can be expressed
as the area plot of the radar chart.
Bigger normalized percentage values
of each variable of the customer
portrait form larger area plot, which
can show, that the particular
customer falls among the best
customers of the enterprise.
3 possibility of tracking each
customer over time by dynamics of
each variable and the compound
index as well.
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Source:Kriksciuniene et al 2012
Customer index
Customer portrait index
is computed as a mean
value of all normalized
variables included to the
customer portrait
If we assume that each
variable has different
importance we include
including weighting of
the variables
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Source:Kriksciuniene et al 2012
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Web-page based indicatorsIndicator Title Meaning
CPM Cost per
thousand
Thousand shows of banner
CPS Cost per sale Cost of one web transaction
CPV Cost per
visitor
Price for one visitor who made click
CTR Click/through
ratio
Ratio between showing and clicking
Traffic Number of visitors per time period
Site reach Number of visitors per time period
Site
frequency
Number of returning visitors per time
period
Burn out of a
banner
Fall of response to banner when it is
shown to the defined visitor segment
Ad views
(also
impressions
Number of reaches of banner
Banner click Number of reaches of banner
Hit Data request for download 1 „hit“- 1
request .“Qualified hit“- successfuly
sent
Visits Visit of one customer for series of
activities. Rules how to recognize same
visitor as new (time limit).
Application of computerized solutions for
CRM
• www.sugarCRM.com –registers activities related to
customers (contacts, commercial offers, negotiations,
sales). Analytic tools. System is cloud based,
customized
• www.microstrategy.com system for intelligent analysis:
aggregation of data, drill-down principle slice-and dice
• Campain management- dynamic workflow based
solution by microstrategy – provides wizard based,
responsibility- based process management analytic
support
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CRM campaign research (Microstrategy)
1. Sales situation is evaluated
2. Loyalty level is evaluated
3. Problem is explored in detail (see the following
example of wrongly selected promotion delivery
channels (pre-campaign analysis)
4. Campaign is planned, the target group is selected by
analytics
5. Post – campaign analysis
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Example :microstrategy campaign analysis workflow
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Specialized CRM systems and
integrated solutions
• Siebel
• SAP
• Oracle
• Baan
• Peoplesoft
• Microstrategy
• Microsoft CRM
• Microsoft Attain
• Microsoft Axapta
• SAS
• Remedy
• Goldmine
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Axapta CRM main menu
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AXAPTA CRM modules
• (Sales Force Automation):
• Business relationships information (communication with
sales partners, customers
• Sales orders and quotes information
• Preparing customer data according to their relationships
to business areas, price quoted, processes or contacts
• Managing correspondence of business relationships
• Contact management- task management, their status
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Business relationship definition
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Business relationships
• It is the centralized place where you can find any
information registered in Axapta about any
business relationships filtering by any instances
describing them: contact data, persons, etc.
• Business relationship window has three parts:
• Filter window
• Main area
• List area
• Menu button areas
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AXAPTA CRM module functions
• Sales Management –information for effective
management
• Graphic evaluation of marketing personnel members
• The ordering probability is computed, time forecast of
probable orders is designed
• Real results are compared to forecasted
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Sales Quote window
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CRM quote preparation and transfer to
orders
• Initial contact
• Confirmation of interest
• General evaluation
• Sending offer
• Preparation for negotiation in written or meeting
• Setting status: (“in progress”) allows editing. “Final”
status includes archiving and non-editing modes
• As soon as relationship status meets order, the functional
button allows to design order for sales
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AXAPTA CRM modules
• Marketing automatisation, telemarketing
• Synchronization to Microsoft® Outlook® about contacts
and meetings
• Each marketing worker has the individual workbook
where his activities are registered
• Not complex business prospects evaluation tool
• Mailing list creation, Microsoft® Word bookmarks
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Managing correspondence
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SAP integrated system: CRM module
Module is composed of various functional blocks.
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Analytic scenarios
Analytic scenarios- multi purpose analysis
• Customer analysis – value analysis per
customer
• Product analysis – observation of product,
promotion optimization
• Communication channels – analysis of regular
and e-channels
• Marketing analysis- allows to select new
markets. Cross-sell scenario design
• Sales analysis – extensive reports “win or lose”
analysis for competitive evaluation
• Customer oriented business management by
differentiating approaches to customers
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Structure of analytics scenario
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“Best practice” application in SAP
• The analytic scenarios idea is to evaluate them at all
enterprises which implemented SAP solutions.
Successful scenarios are standardized and implemented.
Benchmark of scenario effectiveness is provided
CRM tasks related to social media analyticsnew
source for deriving value indicators
• Development
from Web1.0 to
Web4.0
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Social networks: nine most popular (2010)
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The Web 2.0 characteristics:
Social Media, and Industry Disruptors
• The ability to tap into the collective intelligence of
users
• Data is made available in new or never-intended
ways
• Relies on user-generated and user-controlled content
and data
• Lightweight programming techniques and tools let
nearly anyone act as a Web site developer
• The virtual elimination of software-upgrade cycles
makes everything a perpetual beta or work-inprogress
and allows rapid prototyping
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Online Social Networking: Basics and
Examples
New Business Models
social network analysis (SNA software)
The mapping and measuring of relationships and
information flows among people, groups,
organizations, computers, and other information- or
knowledge-processing entities.
The nodes in the network are the people and groups,
whereas the links show relationships or flows
between the nodes. SNAs provide both visual and
mathematical analyses of relationships
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Business and Enterprise Social Networks
• social marketplace
The term is derived from the combination of social
networking and marketplace. An online community that
harnesses the power of one’s social networks for the
introduction, buying, and selling of products, services,
and resources, including one’s own creations. Also may
refer to a structure that resembles a social network but
is focused on individual members
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Commercial Aspects of Web 2.0
and Social Networking Applications
• Consumers can provide feedback on the design of
proposed or existing products etc.
• Word-of-mouth (viral marketing) is free advertising
• Increased Web site traffic brings more ad dollars
• Increased sales can come from techniques based on
personal preferences such as collaborative filtering
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CRM in virtual community
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Web 2.0 data types
• Rating
• Tagging
• Forum content
• Blog
• E-newsletter
• Video materials
• Competitions
• Search engine analysis
• Shopping in social networks
• Feedback from customers: conversational marketing
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Advertising using social networks, blogs
• Viral (Word-of-Mouth) Marketing done by bloggers
• Classified Ads, Job Listings, and Recruitment
• Special Advertising Campaigns
• Mobile Advertising
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The Future: Web 3.0 And Web 4.0
• Web 3.0: A term used to describe the future of the www.
It consists of the creation of high-quality content and
services produced by gifted individuals using Web 2.0
technology as an enabling platform
• Semantic Web: An evolving extension of the Web in
which Web content can be expressed not only in natural
language, but also in a form that can be understood,
interpreted, and used by intelligent computer software
agents, permitting them to find, share, and integrate
information more easily
• Web 4.0: It is still an unknown entity. However, it is
envisioned as being based on islands of intelligence and
as being ubiquitous 71
Blog record preparation for analysis
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Internet pages analysis: data preparation
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Hot to use ontologies
• By interlinking information from various sources, it is
possible to define if “the person knows book author”
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Links among individuals and their types
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Internet query analytics
• Grouping by
topics
• Defining group
sizes
• Detailed
information of the
query success
• The suitable
formats and
algorithms for
queries can be
designed
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How it spreads when in need:
Katrina PeopleFinder
Hurricane
2005
1.1 M people
were on
search
Blogger
initiative for
search
PeopleFinder
Information
Format PFIF
system was
implemented
during 24 hrs
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Peoplefinder query sample
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Project scope
• 7,000 records on Sunday. 50,000 records on Monday
evening
• 4000 volunteers
• Total 640,000 records
• ShelterFinder – other project where all shelters for
people were registered
• Katrina PeopleFinder project data was passed to Google
and used together with American Red Cross and
Microsoft for finding people
• Project is now closed for preserving sensitive data
• The processes can be transferred from non-profit to the
commercial area for analysis of referral information
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Assignment 2
Tools &software: Sugar CRM, MS Excel pivot
module, Statistica advanced models,
Viscovery SoMine
2nd team assignment and lab work training:
•Operational CRM (Sugar CRM)
•Analytical CRM (CRM performance
analysis by applying business intelligence
approaches (pivoting, visualisation) and
computational intelligence methods
(neural networks, fuzzy rules, Kohonen
self organizing networks)
8080
Assignment 2 – Task description
• The data file for analysis CRM_data_for_analysis.xls
• The task description is in file 2_assign_CRM_task.pdf
• The outcome – report, Excel data file with pivot analysis
and final data table for computational analysis
• In part 4 the task is further developed by analysis while
applying Statistica and Viscovery software tools
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Literature
Berry, M.,J.A., Linoff, G.S. (2011), "Data Mining Techniques: For
Marketing, Sales, and Customer Relationship Management", (3rd
ed.), Indianapolis: Wiley Publishing, Inc.
(Electronic Version): StatSoft, Inc. (2012). Electronic Statistics
Textbook. Tulsa, OK: StatSoft. WEB:
http://www.statsoft.com/textbook/
(Printed Version): Hill, T. & Lewicki, P. (2007). STATISTICS: Methods
and Applications. StatSoft, Tulsa, OK.
Sugar CRM Implementation
http://www.optimuscrm.com/index.php?lang=en
Statsoft: the creators of Statistica http://www.statsoft.com
Viscovery Somine http://www.viscovery.net/
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