118'708 CRM: Session 7 Data warehousing mining to Knowledge Management

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118.708 CRM Session 7 Data warehousing mining
to Knowledge Management
Learning Coach Dr. Mohan Agrawal Principal
Strategy Consultant Marketing Aims
Inc. Canada www.marketingaims.com Email
magrawal_at_marketingaims.com
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Power point link

• www.marketingaims.com/files/um/

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Learning agenda for today

• Data in relationship building
• Knowledge management
• Data warehousing
• Data-mining
• Customer profiling

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The Overriding WisdomData and CRM

• Relating to the customer is the essential result
  of gathering data from interactions and
  transactions combined with knowing customers
  through using relationship technologies..
• .Britton Manasco and Bill Hopkins,
• Marketing Optimization Solutions

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1 Customer data includes

• Hard and soft research data (Information,
  Insights, observations, experimental, experiences
  etc.) about the behavior, preferences,
  demographics and psychographics of customers
• Transactional data and communication data before,
  during and after the sales.
• Includes purchases, usage, profitability,
  satisfaction, retention, loyalty and referrals.

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1a Data is Boring ... Until you talk dollars

• 75 reported significant problems as a result of
  defective data.
• More than 50 had incurred extra costs due to the
  need for internal reconciliation.
• 33 had been forced to delay or scrap new systems
  for want of data.
• 33 had failed to bill or collect receivables.
• 20 had failed to meet a contractual or
  service-level agreement.
• The biggest problem for e-businesses was lost
  sales.
• The biggest problem for traditional businesses
  was the need for internal reconciliation.

Source PWC Survey of 600 major enterprises in
Australia, United Kingdom and United States
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1b What do you call this database?

• The database where one man had been pregnant
  three times and one woman had 97 children.
• The database where 80 percent of customers had no
  children, and where entering 0 meant both
  zero and dont know.
• The database where hundreds of customers had the
  same unique Social Security Number.
• The database where many babies and children had
  mortgages.
• The database where 30,000 people were born on
  1/1/00.
• The database where Mickey Mouse had registered
  40,000 times.
• The database where some customers were born in
  the future.

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1c Hence a great customer data is

• One that tells you about a customer that was
  unknown before, usable and reliable for relating
  better with the customer.

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2 Data Management is Knowledge Management

• Data management is turning to be more like a
  knowledge management rapidly.
• Knowledge management (KM) is an iterative
  process that stores data and converts it into
  information facilitating interaction with
  customer.
• KM is thus process of collecting and analyzing
  customer information to identify specific market
  opportunity and investment strategies.

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2a KM facilitates the following

• Customer identification
• Customer segmentation
• Customer profiling
• Customer behaviour prediction

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2b KM helps

• Collect massive amount of information for
  relationship campaigns
• Improve response rates with the help of data
  mining tools
• Customize offers
• Reduce costs by offering a targeted campaign
• Integrate multiple marketing activities
• Change consumer behavioral pattern.

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2c KM Tools

• Three basic tools
• Data warehousing
• Data mining
• Customer profiling

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3. Data warehousing

• The marketing system receives data from a
  variety of sources like point of sale systems,
  internet access, automatic teller machine,
  customer care applications, complaint files,
  direct marketing contacts and denials, third
  party prospect information, government and
  industry data.
• The need is to warehouse it for a future use.

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3a. Data warehousing

• A data warehouse is location and process of
  storing large amounts of information about
  customers from sources internal to the company,
  from customers and third party resources.
• Data warehousing begins with data collection via
  Customer Information file.
• Information technologies facilitate the creation
  of data warehousing.

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Interactive reflection

• Review the Customer Information File of Khivraj
  Motors.

• Review the key points of the Information File of
  a B2B customer
• Identification
• Background
• Presale contact
• Buying criteria
• Purchases
• Decision-makers
• Decision-making
• Purchase cycle
• Post purchase behavior
• Distribution channels used
• Pricing
• Creditworthiness

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3b Data Warehouse Facilitates

• Building up customer information, revenue,
  behavior, cost data.
• Accessing information by the analysis
  applications.
• Broadcasting the KPI information throughout the
  organization.
• Assisting marketing campaign applications both in
  terms of providing information for specific
  campaigns and results of the campaigns
• Providing a 360 degree view of the business.
  For example revenue and profit by product line,
  geography, division and customer segment.

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4 Data mining

• The Process of discovering actionable and
  meaningful patterns, profiles and trends through
  the technologies.
• The data mining technologies include neural
  networks, machine-learning and genetic algorithms.

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4a Data mining- another view

• The Process that employs information technology-
  both hardware and software, to uncover previously
  unknown patterns of behavior, trends and issues
  from the assessment of warehoused data.

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4b simply put, Data mining

• Differs from other marketing research and other
  data analysis methods in a fundamental way
• It discovers hidden structures, ratios, patterns
  and signatures.

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4c Data mining An Illustration

• IF A Customer reads NY TIMES
• AND customer Gender is MALE
• AND customer Age is 37-42
• AND customer is from Manhattan
• THEN the customer will Purchase..

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4d The Data Mining Algorithms

• Associations (75 of customers who buy Coke will
  also buy potato chips 65 of customers who buy
  Coke and potato chips also buy peanuts)
• Classification or profile generation (Customers
  with excellent credit history have a debt/equity
  ratio of less than 10)
• Sequential patterns (60 of customers buy TVs
  followed by hi-fi audio systems 90 of the time
  whenever the sales of Coke goes up, the sales of
  potato chips also goes up.)
• Clustering (multiple answer patterns as a whole).

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4e Data Mining Tools

• -Neural Network
• -Decision Trees
• -Rule Induction
• -Data visualization

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4f Data MiningTen Steps

• Identify your objective Profile your customers?
• Select your data Form the database?
• Prepare the data Append demographic
  information?
• Evaluate the data Visualize?
• Format the solution Segment predict? Contd

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4f Data Mining Ten Steps

• 6. Select the tools Single or suite?
• 7. Construct the models Train and test?
• 8. Validate the findings Share with teams?
• 9. Deliver the findings Provide report, code?
• 10. Integrate the solutions Marketing campaign?

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5 Customer profiling

• Customer differentiation
• Customer segmentation
• Customer Lifetime Value

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5a Customer Differentiation

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5b Customer Segmentation

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5c Customer Lifetime Value

R annual revenue received from a loyal
customer i the relevant interest rate or
opportunity cost of money per period N the
number of periods in which a customer makes
purchases
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5c Customer Lifetime Value
www.benchmarkportal.com to download the excel
spreadsheet to calculate Customer Life time Value
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5c Customer Lifetime Value

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5 In sum, KM helps us know

• Who are my most profitable customers?
• How do I increase their wallet share?
• How do I customize my interaction with them?
• How do I proactively and profitably serve them?

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THANK YOU