Week 11, Chapter 9: CRM & BUSINESS INTELLIGENCE

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