CRM Based Marketing Strategy Functions ETL RFM Primary Key Granularity Data Mart Olap Cubes Fact Tables Hierarchy Navigation Customer Side Company Side Customer Selection Increase C. Number Keeping Existent C. RetentionC. Customer Satisfaction, Keeping Existent Customer, Customization, Service Range Segmantation Targeting Posititioning Costs & Benefits CRM Based Marketing Strategy Functions Database How? Software Errors Aim Information & Relational Marketing Datamining Prediction or Description CRM? Loyalty Retention, Winback & Acquisition Theory (Remzi Grocer) or Software Cust. Pyramid Loyalty Types Reason of Loy. Rel. Management CRM System Sales Force Automation Implementing Crm Systems Trust Conflict Handling Commitment Communication Measuring Customer Satisfaction

CRM FUNCTIONS Customer Selection Segmentation Campain Modelling Brand Management New Products Gain Customer Order Man. Demand Analysis Logistic Man. Complain Man. Retain Customer Market Leadership Necessity Analysis Increasing Customers Analitic CRM Cross Selling

COMPANY SIDE - CUSTOMER SIDE BENEFITS - COSTS COMPANY SIDE - CUSTOMER SIDE Benefits; Customized Products More Income Long Term Income Cross ‐selling Up ‐selling Bundling Customer Loyalty Costs; IT Costs (server, software, training, security, labor organization), Framework change , Re-engineering Resistance by employees Benefits; Regularity (Barber - no risk) Touch point (Banks - ques ) Customized product or service Enhances service (Holiday Inn) Costs; Sharing your personal informations Opportunity costs(Underestimating the other companies’ offers)

LOYALTY – Customer Pyramid Potential Customer; There is a possiblity to be Customer of the company in the future Customer;Who bought servise or product from the company Regular Customers;Who buy servie or product in regular time interval Supporting Customers;Regular customer but do not recommend the company to other customer Loyal Customers;Regular customers and recommend companys’ product or services to other customers Behavioral Brand Loyalty: Undivided Loyalty :A,A,A,A,A Divided Loyalty :A,B,A,B,A,B Switched Loyalty:A,A,A,B,B,B Indifference Loyalty:A,B,C,D,E,F,G,H Which Factors Effects the Customer Loyalty???

Which Factors Effects the Customer Loyalty? Before Sales During Sales After Sales Expectations Performance/Quality Judgements Expectations and Judgements Pleased Satisfied Disappointment

CRM BASED STRATEGY Marketing Strategy CRM SYSTEM Customer Data INFORMATION MANAGER Marketing Strategy

SALES FORCE AUTOMATION “The application of digital and wireless technologies to personal selling” is known as SFA. Sfa software; organizes and manages data about sales touch points and customer’s history with company. SFA may use data mining to integrate the pipeline data with other CRM data and make suggestions to sales representative. Automation tools describe where prospects /customer are in the sales cycle. “EXPEDIA EXAMPLE” SFA tools have risks and costs.??? Identfy Leads Suspected interest Qualify Leads Value Estimates Contact Buying Centers Recognize Key players Re-contact Winback interest Develop Relationship Develop interest Negotiate Terms Longterm View Close Ask for the sale NO YES

SALES FORCE AUTOMATION TASKS Benefits of SFA : Increase sales productivity and efficiency. Offers high quality customer service. Increase customer satisfaction. Creates customer loyalty. SFA PROBLEMS: Planning Communication Measurement Source: Earl D. Honeycutt, Tanya Thelen, Shawn T. Thelen, Shoren T. Hodge(2005); “Implements to Sales Force Automation”, Industrial Marketing Management, Volume 34, pp.313-322. Definition state - aim Collecting feedback Connection errors between different departments Lack of sales person education Focusing on organizational profits - sales person? Resistance to downsizing - automation Lack of process definitions Reward mechanisms

SALES FORCE AUTOMATION TASKS Contact and Time Management Opportunity or Lead Management Knowledge Management Price Quotes and Order Configuration Follow-up Management Analysis and Reporting Tools

Contact and Time Management Opportunity or Lead Management Communication with potential customers and existent customers (e-mail, sms availability) Date update and integration customer name, address, phone number and etc organization chart, decision tree Providing information to sales representatives about customer data (birthday, hobies, interests,location maps) Sharing information between departments and sales team members Time management tool, meeting arrangements Opportunity or Lead Management Determine potential customers of the company Product focus service Correct order control Purchasing precisely Defining correct communication point with customers Opportunity management = Leadership + Process and information management Sales Forecast Customer Value Calculation Future sale forecast

Price Quotes and Order Configuration Knowledge Management Easy managing, managing organizational information Intranet, network inside the company Extranet, sharing information, B2B - suppliers Ordering effectively Administrative facilities Price Quotes and Order Configuration Price lists,discount forms, price quotes information Product configuration is important for sales represantatives Decreases offering time Cross-selling Establishing self service with Extranet support – e-commerce Follow-up Management Communication is a part of Management Organization of communication and delay follow-up Product delivered on time? E-mail send Complaint system

Analysis and Reporting Tools Providing access to call and sales reports iformations Providing Analysis for sales managers Time –customer - location based Providing information for Managers Ratios, percantage information about the sales Call Centers Direct access to customers Classical call center hyergy: Menu options Waiting customer representative Identication process Providing iformation about customer to customer represenatative Problem definition Informing customer about his/her problem result Call Routing Interactive Voice Response Caller ID Systems Automatic Distribution System Trouble ticket Caller Note

Description or Prediction ??? Enabling Technologies Data Mining Description or Prediction ??? Automated prediction of trends and behaviors. Automated discovery of previously unknown patterns. Evolutionary Step Business Question Enabling Technologies Product Providers Characteristics Data Collection (1960s) "What was my total revenue in the last five years?" Computers, tapes, disks IBM, CDC Retrospective, static data delivery Data Access (1980s) "What were unit sales in New England last March?" Relational databases (RDBMS), Structured Query Language (SQL), ODBC Oracle, Sybase, Informix, IBM, Microsoft Retrospective, dynamic data delivery at record level Data Warehousing & Decision Support (1990s) "What were unit sales in New England last March? Drill down to Boston." On-line analytic processing (OLAP), multidimensional databases, data warehouses Pilot, Comshare, Arbor, Cognos, Microstrategy Retrospective, dynamic data delivery at multiple levels Data Mining (Emerging Today) "What’s likely to happen to Boston unit sales next month? Why?" Advanced algorithms, multiprocessor computers, massive databases Pilot, Lockheed, IBM, SGI, numerous startups (nascent industry) Prospective, proactive information delivery

Data Mining Decision Support Systems Functions: Churn Analyse: Which customer will switch – To which rival Cross-Selling: Which customer – Which product Fraud-Detection: Which customer is planning to cheat (assurance) Risk Management: Crediy card apply –Yes or No Customer Segmentation: Who are my customers? Targeted ads: Which advertising? To which customer? Sales Forecast: How many products will be sold in the next month

Description or Prediction ??? Data Mining Description or Prediction ??? RFM (Recency, Frequency, Monetary) Functions; Classification – Modelling – fuction- test set (Predictive) Clustering (Descriptive) Association Rule (Descriptive) Regression (Predictive) Deviation Detection (Predictive) Sequential Pattern Discovery (Descriptive)

Data Mining Information Discovery Methods; Decision Trees Genetic Algorithms Neural Networks 1 ad 12 ads 25 31 First Generation 6 15 22 28 Second Generation 11 16 19 Third Generation

Data Mining Problems; Measurable ? Data Size (Enormous) Disordered Data Data quality Securing Personal Data Multimedia Data -Streaming

Customers’First Sales CUSTOMER CYCLE Retention-Holding Gain Regain Suspect-Potential Loyal Customer Repeat Customer Customers’First Sales Unactive Customer Losy Customer

Gaining by Satisfying Customers; CRM SYSTEM Recognize; Name &Past Gaining by Satisfying Customers; Performance & Quality AIM; One to One Relation Result; Customer Loyalty Develop; Customer Relation

LOST CUSTOMER Lost Customer Which Customer is about to leave? Life Time value of Customer Why he/she is leaving? Which contact way is the best? How much time is needed to activate the customer? Lost Customer First Conversaton After Problem With Customer; Thanks for trusting to our company Competely Lost Customer; Thanks for Past support to our company