Customer Relationship Management

CUSTOMER RELATIONSHIP MANAGEMENT

Weekly Questions – Week Ten

What is your understanding of CRM?

CRM is the most valuable asset a company can acquire, making the company better off and ahead of competition as they can establish loyal and devoted customers. CRM involves managing all aspects of a customer’s relationship with an organization to increase customer loyalty and retention as well as an organisation’s profitability. As organizations move from the traditional product-focused organization towards customer-driven organizations, they are recognizing their customers as experts, not just revenue generators. This ultimately leads organizations to realize that without their customers, the organization would not exist, making it critical for an organization to do everything in their power to satisfy customer needs.

NEEDS FOR CRM INCLUDE

It costs six times more to sell to a new customer than to sell to an existing one.

—A typical dissatisfied customer will tell 8-10 people.

—By increasing the customer retention rate by 5%, profits could increase by 85%.

—Odds of selling to new customers = 15%,  compared to the odds of selling to existing customers (50%)

—70% of complaining customers will remain loyal if problem is solved

This video describes the golden rules for CRM.

Compare operational and analytical customer relationship management.

Operational CRM supports traditional transactional processing for day-to-day front-office operation systems that deal directly with the customers. Analytical CRM supports back-office operations and strategic analysis and includes all systems that do not deal directly with the customers. The primary difference between the two management systems is the direct interaction between the organization and its customers.

Describe and differentiate the CRM technologies used by marketing departments and sales departments.

The operational CRM technologies used by marketing departments are list generating, campaign management, and cross-selling and up-selling technologies, whereas the operational CRM technologies used by sales departments are sales management, contact management, and opportunity management. Marketing companies try to sell multiple products to one customer, rather than sell one product to multiple customers.

Operational CRM helps marketing departments to gather and analyse customer information to deploy successful marketing campaigns. Marketing success is directly proportional to the organisation’s ability to gather and analyse the right information.

List generators compile customer info from a variety of sources (website visits, questionnaires, surveys, flyers, etc.) and segment the info into different marketing campaigns for potential customers according to the criteria gathered (income, educational level, age etc.). List generators provide the marketing department with a solid understanding of the type of customer it needs to target for their campaigns.

Campaign management systems guide users through marketing campaigns performing such tasks as campaign definition, planning, scheduling, segmentation and success analysis. They also calculate quantifiable results for return on investment (ROI) for each campaign and track the results in order to analyse and understand what to do in future campaigns.

Cross-selling is selling additional products to the customer, while up-selling is increasing the value or the sale. CRM systems help marketing departments to identify these campaigns by offering the department info about their customers and products.

This video goes thourgh step by step instructions for a manager to use CRM technologies in their business, simultaneously explaining the benefits of each aspect of the technology.

How could a sales department use operational CRM technologies?

Sales and operational CRM technologies combined makes for very good business. the two primary reasons sales departments track customer sales electronically are firstly; sales reps were struggling with the overwhelming amount of customer account info they were required to maintain and track; secondly,

The three key operational CRM technologies a sales department can use (as mentioned earlier) are:

-       Sales Management CRM Systems, which automate each phase of the sales process, helping individual sales reps to coordinate and organize their accounts. Features include calendars (for planning), alarms (for reminders) etc.

-       Contact Management CRM Systems, which maintains customer contact info and identifies prospective customers for future sales. Features include maintaining organizational charts, detailed customer notes, and supplemental sales info. E.g. if a customer calls, their caller i.d. along with notes detailing previous conversations will appear.

-       Opportunity Management CRM Systems, which target sales opportunities by finding new customers for future sales. They determine potential customers and competitors and define selling efforts, including budgets and schedules. Opportunity management deals with news customers, whereas contact management deals with existing customers.

Describe business intelligence and its value to businesses.

Diagram of Business Intelligence.

This diagram further explains Business Intelligence simply. 

Business intelligence (BI) refers to applications and technologies that are used to gather, provide access to and analyse data and information to support decision-making efforts. For one to succeed, one should have full knowledge of their strengths and weaknesses, as well as that of one’s competitor. BI involves collecting information, discerning patterns and meaning in the information, and responding to the resultant information.

NEEDS FOR BI

—Determine who are the best and worst customers thereby gaining insight into where it needs to concentrate more for its future sales

—Identify exceptional sales people

—Determine whether or not campaigns have been successful

—Determine in which activity they are making or losing money.

Explain the problem associated with business intelligence. Describe the solution to this business problem.

The problem associated with business intelligence is that businesses are rapidly accumulating vast amounts of data, and for most organizations to assess this data is very time consuming. Information has to be requested from different departments or IT, who must delegate staff to pull together various reports. This process can take a long time, which by then the info may be outdated, resulting in the problem – data rich, information poor. Organisations are challenged to transform data into useful info so that employees gain knowledge that can be leveraged to increase company profitability.

To improve the quality of business decisions, managers can provide existing staff with BI systems and tools that can assist them in making better, more informed decisions, resulting in the creation of an agile intelligent enterprise. The solution of implementing BI systems and tools allows business users to receive data for analysis.


Click this link to find out more problems and solutions associated with Business Intelligence...

What are two possible outcomes a company could get from using data mining?


Click this link for a definition of Data Mining...

Data mining is the process of analyzing data to extract info not offered by the data alone. It is the primary tool used to uncover business intelligence in vast amounts of data. Data-mining tools use a variety of algorithmic techniques to find patterns and relationships in large volumes of info, approaching decision making with the following activities in mind:

-       Classification: assign records, to one of a predefined set of classes.

-       Estimation: determine values for an unknown continuous variable behavior or estimated future value.

-       Affinity grouping: determine which things go together.

-       Clustering: segment a heterogeneous population of records into a number of more homogeneous subgroups.

Analysts use the output from data-mining tools to build models that perform a variety of info analysis functions. The analysts then provide a business solution by putting together analytical techniques and the business problem at hand, which often reveals important new correlations, patterns and trends. Forms of data-mining analysis capabilities include cluster analysis, association detection, and statistical analysis.