Page 334 - DMGT308_CUSTOMER_RELATIONSHIP_MANAGEMENT

Unit 12: Customer Privacy

In reality, far too many organizations don’t even know who their customers are or, at the very Notes
least, how many they have.
Finding out who your customers are, what they want and providing it seems to be a task fraught
with difficulties. Ask any experienced marketing manager and they will confirm that not only
providing the product or service but understanding and managing consumer expectations are
key to creating satisfied customers. Despite their efforts, many will find that their customers
still act indecisively, unpredictably, unproductively and often find they dissatisfied and
disappointed with the result of their transaction. What’s the solution?

Data – The Unknown Diamond

Knowledge focused businesses collect data on their customers to provide them with a framework
to build an understanding of their market. Improvements and innovations in technology, a key
enabler of customer data collection, have provided organizations with the ability to store,
share, analyse and transfer vast amounts of data at low costs. Growth in the use of sophisticated
databases, data warehouses and data mining software applications make it possible for companies
to analyse customers’ behavioural patterns, individual levels of profitability and the lifetime
value of their customers.
However, most businesses underestimate the wealth of customer knowledge within their own
organizations. It is not untypical to find website interactions in one database, lease agreements
in an administration system, call centre history in another, and payment history in an accounts
system.
Integrating such a mass of information can prove difficult but can also provide valuable insight
into your customers’ behavioural patterns likes and dislikes. Like a diamond, there are many
perspectives that are in existence in even the most basic of data sets, and approaching your data
analysis from a different angle can produce startling results. Advanced data analysis techniques
can produce statistically grounded behavioural models that more accurately project customer
behaviour in a variety of situations:
 The likelihood of purchase of specific product or service,
 The best next offer, or
 The probability of defection.
These models are specific to that organisation’s existing customer base and are not based on
assumptions about lifestyle characteristics or demographic profiles that account for the
segmentation principles applied to the majority of direct marketing activities in organization.
As your CRM strategy progresses, considerable ongoing performance measurement is required.
Although this can be built, at least in part, upon the consolidated customer data used in the
original strategy development, in reality, numerous new collection processes will be required
to ensure appropriate metrics are captured on an ongoing basis. For more comprehensive
analysis, these internal information sources will be needed to be coupled with external feeds
such as customer surveys.
Measurements and Analysis Support may include:
 Customer Segmentation – Cost-to-serve

 Customer/Product/Channel Profitability – Revenue Optimisation
 Demand Forecasting – Customer Lifetime Value (LTV)
 Campaign Analysis – Customer Churn/Propensity to Churn
 Channel effectiveness – Customer Satisfaction
 Sales Analysis – Customer Complaints

LOVELY PROFESSIONAL UNIVERSITY 329