Blog Questions
What
is your understanding of CRM
Customer
relationship management (CRM) involves managing all aspects
of a customer’s relationship with an organisation to increase customer loyalty
and retention and an organisation's profitability. CRM helps companies make the their
interactions with customers seem friendlier through individualisation and create economies of SCOPE.
Compare operational and analytical
customer relationship management
- Operational CRM supports traditional
transactional processing for day-to-day front-office operations or systems that
deal directly with the customers. Focuses on organising and simplifying the
management of customer information. It uses a database to provide consistent
information about a company’s interaction with a customer.
- Analytical CRM supports back-office operations
and strategic analysis and includes all systems that do not deal directly with
the customers (big chunks of data)
Describe
and differentiate the CRM technologies used by marketing departments and sales
departments
- Sales force
automation (SFA) functions provide such data as sales
prospect and contact information, product information, product configurations
and sales quotes.
- Cross-selling
is the marketing of complementary products to customers.
- Up-selling
is the marketing of higher-value products or services to customers.
- Bundling
is a type of cross-selling in which a vendor sells a combination of products
together at a lower price than the combined costs of the individual products.
How does CRM help businesses find
and retain their most valuable customers?
Customer
relationship management (CRM) is an enterprise wide effort to
acquire and retain customers. The customer life cycle: engage, transact,
fulfil, and support. It includes a one-to-one relationship between a
customer and a seller.
One simple idea “Treat different customers
differently.” CRM helps keep profitable customers and maximizes
lifetime revenue from them while analytical CRM uses data mining to provide
strategic data about customers. Data mining uses various modelling and analysis
techniques to find patterns and relationships to make accurate predictions.
Predictions might include;
- Which customers to market to
- Up selling / Cross selling
- Retaining good customers
The customer life cycle has three main
phases; Acquiring Customers, Increasing the value of the customer, and Retaining good customers (Analytical CRM data can help at each stage).
1. Acquiring new customers
By using analytical CRM data, companies can
be more accurate in the ways they obtain new customers.
For instance, a bank may have traditionally
emailed 1 million customers with a credit card offer at $1 per mailout. They
may get 60 000 responses, with only 10 000 serious customers. By applying data mining models, they may only
need to target 250 000 customers. A clear saving in marketing costs.
2. Increasing the value of existing customers
Cross Selling – using analytical CRM data an
organisation can predict where cross selling will be effective. Cross selling
is offering customers information about other complimentary products. Up selling- this involves using data to look
at buying patterns of customers and knowing when to offer more product. The CRM
could show times when customers are likely to buy more product, could be
seasonal or related to an event.
3. Retaining good customers
Acquiring new customers exceeds the cost of
retaining existing customers. Good customers are those that are profitable or
have ‘lifetime’ value. By using analytical CRM’s to profile good and bad
customers, more effort can be spent keeping good customers.
USING I.T. TO DRIVE ANALYTICAL CRM
- Explore customer behaviour
- Develop a personal customer profile– when a
web site knows enough about a person’s likes and dislikes that it can fashion
offers that are more likely to appeal to that person (personalisation)
- Analytical CRM relies heavily on data
warehousing technologies and business intelligence to glean insights into
customer behaviour
These systems quickly aggregate, analyse, and
disseminate customer information throughout an organisation.
Analytical
CRM information examples:
1.Give
customers more of what they want
2.Value
their time
3.Over-deliver
4.Contact
frequently
5.Generate a
trustworthy mailing list
6.Follow up
Describe
business intelligence and its value to businesses
Business intelligence applications and
technologies are used to gather, provide access to, and analyse data and
information to support decision-making efforts.
BI includes simple MS Excel Pivot tables to highly
sophisticated software that fetches data from the different front-an
back-office systems. Many Businesses are finding that they must identify and
meet the fast-changing needs and wants of different customer segments in order
to stay competitive in today’s consumer-centric market.
Explain
the problem associated with business intelligence. Describe the solution to
this business problem
Usually
organisations are data rich and
information poor. Companies can have a lot of
data, however they are not able to benefit from levering this information and
turning it into useful data for analytical and strategic decision making.
The issue most organisations are facing today
is that it is next to impossible to understand their own strengths and
weaknesses, let alone their enemies, because the enormous amount of
organisational data is inaccessible to all but the IT department. Data mining is the
answer. This is the application of statistical techniques to find patterns and
relationships among data and to classify and predict.
What
are two possible outcomes a company could get from using data mining?
CLUSTER ANALYSIS
Cluster analysis
is a technique used to divide an information set into mutually exclusive groups
such that the members of each group are as close together as possible to one
another and the different groups are as far apart as possible
CRM systems depend on cluster analysis to
segment customer information and identify behavioural traits
Examples include:
- Consumer goods by content, brand loyalty or
similarity
- Retail store layouts and sales performances
- Corporate decision strategies using social
preferences
- Industry processes, products, and materials
- Character recognition logic in OCR readers
- Data base relationships in management
information systems
ASSOCIATION DETECTION
Association
detection reveals the degree to which variables are
related and the nature and frequency of these relationships in the information
- Market basket analysis – analysis web sites
and check out statistics to identify buying behaviour and predict future
behaviour. This is used for cross selling / up selling.
STATISTICAL ANALYSIS
Statistical
analysis performs such functions as information
correlations, distributions, calculations, and variance analysis
- Forecast
– predictions made on the basis of time-series information
- Time-series
information – time-stamped information collected at a
particular frequency
