Customer Relationship Management (CRM)

Customer Relationship Management (CRM)

• It encompasses the strategies, technologies, and practices used by businesses to manage and analyze customer interactions throughout the customer lifecycle
• The ultimate goals are to improve customer relationships, enhance customer retention, and drive sales growth

Relationship Orientations

• Businesses develop customer relationships in four different ways:
  • Mass Marketing
  • Differentiated Marketing
  • Niche Marketing
  • One-to-One Marketing

Production vs. Marketing Orientation

• Production orientation focuses on making products available and highly affordable
• Marketing orientation prioritizes understanding consumer needs and delivering value better than competitors

CRM and Repeat Business

• CRM serves customers over the long term, generating repeat business through multiple buyer-seller interactions
• There are three levels of interactions:
  • Transactional selling (Low)
  • Relationship selling (Medium)
  • Strategic partnerships (High)

Mechanisms that Govern Exchanges

• Transactional Selling focuses solely on making a sale, emphasizing competitive pricing, social proof, and purchase urgency, resulting in short sales cycles and low customer lifetime value
• Relationship Selling prioritizes building a connection with customers to close sales, focusing on customer interactions
• Strategic Partnerships are mutually beneficial relationships that operate with a long-term view, aiming to build a stable foundation for future engagement

CRM, Customer Loyalty, and Lifetime Value

• Sources of sales revenues are:
  • New customers
  • Current customers
• Finding new customers is expensive but crucial for expansion
• Selling to existing customers is less costly and builds customer loyalty

Computing the Value of a Customer

• Businesses use CRM software and processes to develop long-term customer relationships through customized products, services, and rewards programs
• Sales managers can quantify Customer Lifetime Value (CLV)
• Customer Lifetime Value (CLV) is the total profit a business can expect to earn from a customer throughout their relationship, helping businesses decide how much to invest in acquiring and retaining customers

How to Calculate CLV

• To Calculate CLV you need to know:
  • Lifetime of a customer
  • Amount purchased and contribution margin
  • Discount rate
• You can predict the lifetime of a customer by:
  • Known lifetime (ex: Subscriptions to a service like Netflix or the internet)
  • Gathering and analyzing primary and secondary data (use survival analysis)
• Predict customer purchase amount (in future) based on:
  • Previous purchases (History)
  • Analyzing market demand and trends in purchase behavior
• To know what discount rate to use:
  • Base it on interest rates for borrowing
  • Or use announced rates by the government or authorized entity

Application of CLV

• A customer spends $1000 at a store for 3 years, assuming a 10% discount rate and 30% profit margin
• Profit for each year = $300
• NPV 1st year profit = 300
• NPV 2nd year profit = 300 /(1+i)
• NPV 3rd year profit = 300 /(1+i)^2
• CLV = $820.66

The Salesperson and Managing Customers

• Value is often narrowly used as a synonym for low price
• Its more correct definition is an individual's perception of a product's worth
• A salesperson can create value by:
  • Gathering important data about the customer and the market
  • Identifying the types of data needed to give the customer better service
  • Effectively managing the relationship between the firm and the customers

Types of CRM Software

• Operational CRM: Automates and streamlines company processes for customer relations
• Analytical CRM: Collects, analyzes, and offers decision insight about customer data
• Collaborative CRM: Connects company department communication in real time
• Strategic CRM: Prioritizes long-term customer engagement and segmentation
• Examples of CRM Software
  • Salesforce CRM automates sales pipelines, lead tracking, and customer interactions
  • Zoho Analytics provides insights into customer behavior, trends, and business performance
  • HubSpot CRM connects teams and integrates email, chat, and social media interactions
  • SAP CRM helps businesses develop customer engagement strategies and loyalty programs

Marketing Automation and CRM programs

• Marketing automation and CRM data analysis systems are advancing rapidly
• AI refers to cognitive-like functions such as problem-solving and learning performed by computer systems
• Machine Learning is the ability for computer systems to take actions without being specifically programmed to do so
• Al and machine learning transforms CRM with:
  • Better data integrity - Machine learning processes replace manual data entry
  • Better data intake - Software captures and codes all customer communication, enhancing salesperson effectiveness
  • Account-specific recommendations based on output help salespeople take specific actions, resulting in more customized service and trust

Technological Components in a CRM

• Sales Force Automation
• CRM Software
• CRM Hardware

Sales Force Automation (SFA)

• It automates a company's sales processes by doing repetitive and boring tasks
• This helps reduce sellers' time, effort, and errors
• An integrated SFA system often integrates with:
  • Generate expense reports
  • Sales forecasting
  • Performance analysis
  • Lead management
  • Managing territories
  • Generating proposals

Technology and CRM Programs

• The most important CRM hardware components include:
  • Data Mining
  • Data Warehouse
  • Personal digital assistant (PDA)
  • PDAs, laptops, and servers
  • Database Marketing
• Benefits of CRM software include:
  • Advanced analytics
  • Easy data access
  • Flexible sales
  • Effective processes

Multivariate Statistical Tools for Sales Data Analysis

• Automatic Interaction Detection
• Multiple Regression
• Cluster Analysis
• Discriminant Analysis

Tools Explained

• Cluster Analysis is a method to group similar data points based on shared characteristics
  • Segmenting customers into groups based on purchasing behavior
  • Its used for market segmentation, customer profiling
• Discriminant Analysis differentiates between predefined groups based on predictor variables
  • Predicting whether a student will pass or fail based on attendance and grades
  • Its used for credit risk assessment, customer classification
• Multiple Regression is a statistical method that predicts a dependent variable based on multiple independent variables
  • Estimating sales revenue based on advertising spend, pricing, and competition
  • Its used for business forecasting, customer lifetime value prediction
• Automatic Interaction Detection (AID) is a decision tree-based technique that identifies interactions between variables and segments data accordingly
  • Identifying key factors influencing customer churn
  • Its used for market research, customer segmentation, predictive analytics

CRM Successes and Failures

• Despite its popularity, CRM adoption doesn't guarantee success
• CRM is not recommended for firms using a transactional approach
• CRM is also not recommended for firms whose customers won't realize any added value from the benefits of the process