Customer Satisfaction Keeping Existent Customer Customization Service Range

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

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

BENEFITS - COSTS COMPANY SIDE - CUSTOMER SIDE Benefits; üCustomized Products üMore Income üLong Term Income üCross ‐selling üUp ‐selling üBundling üCustomer Loyalty Benefits; üRegularity (Barber - no risk) üTouch point (Banks - ques ) üCustomized product or service ü Enhances service (Holiday Inn) Costs; üSharing your personal informations üOpportunity costs(Underestimating the other companies’ offers) Costs; üIT Costs (server, software, training, security, labor organization), üFramework change , üRe-engineering üResistance by employees

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 Divided Loyalty : 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 Pleased Judgements Performance/Quality Satisfied Expectations and Judgements Disappointment

CRM BASED STRATEGY Customer Data CRM SYSTEM INFORMATION Marketing Strategy MANAGER

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 Re-contact Winback interest Qualify Leads Value Estimates NO YES Close Ask for the sale Contact Buying Centers Recognize Key players Develop Relationship Develop interest Negotiate Terms Longterm View

SALES FORCE AUTOMATION TASKS Benefits of SFA : üIncrease sales productivity and efficiency. üOffers high quality customer service. üIncrease customer satisfaction. üCreates customer loyalty. SFA PROBLEMS: üPlanning üCommunication üMeasurement 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 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.

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

Contact and Time Management üCommunication with potential customers and existent customers (e-mail, sms availability) üDate update and integration Ø customer name, address, phone number and etc Ø organization chart, decision tree üProviding information to sales representatives about customer data (birthday, hobies, interests, location maps) üSharing information between departments and sales team members üTime management tool, meeting arrangements Opportunity or Lead Management üDetermine potential customers of the company üProduct focus service ØCorrect order control ØPurchasing precisely ØDefining correct communication point with customers üOpportunity management = Leadership + Process and information management üSales Forecast ØCustomer Value Calculation ØFuture sale forecast

Knowledge Management üEasy managing, managing organizational information üIntranet, network inside the company üExtranet, sharing information, B 2 B - suppliers ØOrdering effectively ØAdministrative facilities Price Quotes and Order Configuration üPrice lists, discount forms, price quotes information üProduct configuration is important for sales represantatives ØDecreases offering time üCross-selling ØEstablishing self service with Extranet support – e-commerce Follow-up Management üCommunication is a part of Management üOrganization of communication and delay follow-up ØProduct delivered on time? ØE-mail send ØComplaint system

Analysis and Reporting Tools üProviding access to call and sales reports iformations üProviding Analysis for sales managers ØTime –customer - location based üProviding information for Managers ØRatios, percantage information about the sales Call Centers üDirect access to customers üClassical call center hyergy: ØMenu options ØWaiting customer representative ØIdentication process ØProviding iformation about customer to customer represenatative ØProblem definition ØInforming customer about his/her problem result üCall Routing üInteractive Voice Response ØCaller ID Systems ØAutomatic Distribution System ØTrouble ticket ØCaller Note

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

Data Mining Description or Prediction ? ? ? RFM (Recency, Frequency, Monetary) üFunctions; ØClassification – Modelling – fuction- test set (Predictive) ØClustering (Descriptive) ØAssociation Rule (Descriptive) ØRegression (Predictive) ØDeviation Detection (Predictive) ØSequential Pattern Discovery (Descriptive)

Data Mining üInformation Discovery Methods; ØDecision Trees ØGenetic Algorithms ØNeural Networks First Generation Second Generation Third Generation 1 ad 6 ads 12 ads 15 ads 16 ads 25 ads 22 ads 19 ads 28 ads 25 ads 31 ads 11 ads

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

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

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

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