Data Mining Techniques for CRM Group 1 組員 9534608 謝岱高 9634524 廖鎔熠 9634532 黃雅莉 9634543 郭奇龍 老師跟各位同學大家好 嘿嘿 今天準備介紹的題目是：資料探勘技術應用於顧客關係管理 從前幾年片面認識資料探勘這個嶄新的名詞的時候，剛開始以為這個技術是十分強大， 能做到挖掘預測，以為，幾乎能應用在各種地方，且能滿足各種需求， 但經過這篇文章的介紹，卻瞭解到資料探勘也是有所侷限，尤其在利用之前， 要清楚如何用，用在何處，才能真正體現這方法論的價值， 以下將會介紹資料探勘運用到顧客關係管理這塊領域的功能與作用。

Data Mining in CRM ... “ ...through data mining – the extraction of hidden predictive information from large databases – organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions.” 在文章ㄧ開始導言裡所講， 資料探勘最主要的目的是希望能對以未來導向的資訊做出可能的分析，也就是預測 這必須要藉靠過去所發生的代表性事件，以過去推測未來的支撐決策支援系統，這樣才是可行。 而ＤＭ的價值就藏在分析過去資料的能力上： 搾取出藏在極大量的具有預測價值的雜亂資料 以定義出關鍵客群，預測未來消費行為，並給於的能提早準備的決策建議。

Agenda Introduction, Definition: Paul The Evolution & Apps. of Data Mining: Eneida Internal Considerations & Data mining techniques: Ximena Data mining and CRM – relationship & customer privacy: Lester Case Studies (Neural Networks, CHAID): JPG CHAID vs neural nets; Conclusions: Edith 而這邊列出我們所要報告的內容： （（用英文快速唸過去，唸錯也別管，心情好的話也能跳著唸。。。））

Introduction Product-oriented view VS. Customer-oriented view Design-build-sell VS. sell-build-redesign One-on-one marketing VS. mass marketing Goal of revolution: Establish a long term relationship with each customer The advent of the Internet and technological tools accelerate modern CRM revolution CRM is important for B2C or C2B, and even more crucial in B2B environments 因為大環境的變化，我們都很清楚消費模式已經有很大的改變， 從產品導向轉到顧客導向。 從傳統的設計－製造－銷售，反過來先搞清楚顧客要什麼，再重設計。 從只著重產品的賣出，到維持主顧的長期關係，這些現象我們都很清楚在身邊ㄧ度上演。 尤其近幾年的網路發展，消費者已經不想以前那麼盲目，上網比較、看心得、看推薦， 面對這種文化的產生，店家比以前更重視顧客關係，ㄧ句網路傳開的負面抱怨，比什麼都具殺傷力。 而除了面對企業對顧客的關係外，顧客關係管理更須重視企業對企業，還有顧客對企業等關係。

Why Data Mining? Between businesses and customers… Collecting customer demographics and behavior data makes precision targeting possible Helps to devise an effective promotion plan when new products developed Creates and solidifies close customer relationships Between businesses… Helps to smooth transactions, communications and collaboration Simplifies and improves logistics and procurement process

What is Data Mining? “…a sophisticated data search capability that uses statistical algorithms to discover patterns and correlations in data.” “…another way to find meaning in data.” Data mining is part of a larger process called knowledge discovery

What Data Mining is ~NOT~ Data mining software does not eliminate the need to know the business, understand the data, or be aware of general statistical methods. DM does not find patterns or knowledge without verification DM helps to generate hypotheses, but it does not validate the hypotheses

Evolutionary Stages of Data Mining Collection Access Navigation Mining (1960’s) Retrospective, static data delivery Summations or averages Computers, tapes, disks IBM, CDC (1980’s) Retrospective, dynamic data delivery at record level Branch sales at specific period of time RDBMS, SQL, ODBC Oracle, Sybase, Informix, IBM, Microsoft (1990’s) Retrospective, dynamic data delivery at multiple level Global view or drill down OLAP, multidimensional databases, data warehouses Pilot, IRI, Arbor, Redbrick (2000’s) Retrospective, Proactive information delivery Online analytic tools, feedback and information exchange Adv. Algorithms, multiprocessor, computers, massive databases Lockheed, IBM, SGI

Breakdown of Data Mining from a Process Orientation Discovery Predictive Modeling Forensic Analysis Conditional Logic Affinities and Associations Trends and Variations Outcome Prediction Forecasting Deviation Detection Link Analysis

Applications of Data Mining Retail Banking Telecommunications Performing basket analysis Sales forecasting Database marketing Merchandise planning and allocation Card marketing Cardholder pricing and profitability Fraud detection Predictive life-cycle management Call detail record analysis Customer loyalty 零售商 透過店裡信用卡消費還有銷售點系統可以收集顧客的消費紀錄，這些紀錄可以讓零售商了解各式各樣顧客的消費行為。 至於在實際的應用上 大致上有四種 菜籃分析、銷售預測、資料庫行銷、商品的規劃分類 菜籃分析就是透過消費者在購物的過程當中，從購買的物品當中找出商品之間的購買跟消費者之間有何關係，，可以藉此改善庫存以及對商店佈置策略。 大賣場例子 銷售預測即是判斷消費者在距離下次購買商品的時間是多長，藉此可作為庫存的策略。 資料庫行銷就是透過資料庫裡找出消費者購物的傾向 也許是特別針對某某牌子會購買的是哪些人等等藉由這些資訊做成本節省跟集中 商品規劃跟配置當要擴充店面時，可以根據其他銷售據點的特性，例如說人口分布、消費者類型、週遭的環境…..等等，藉由找尋跟新擴充的銷售點相似的店面，透過記錄做新店的佈局安排。 銀行 銀行利用這項技術可以做很多方面的應用例如：信用卡行銷、持卡人定價和利潤率 、欺詐偵測及預防、預測生命週期 信用卡行銷 持卡人定價和利潤率 發卡人可以利用數據

OTHER APPLICATIONS Customer Segmentation Manufacturing Discrete segments by adding variables Warranties Customize Products. Predict features Frequent flier incentives No. clients who will ask for claims Identify groups who can receive incentives

INTERNAL CONSIDERATIONS Data mining Decision-making process Skillsets and technologies must be available to integrate them Sell to and service customers Manage inventory Supervise employees Work to correct and prevent loss Knowledge gained through DM An algorithm for scoring A score for particular customer, employee An action associated with a customer, employee or transaction

DATA MINING TECHNIQUES Nearest Neighbor Data Retained Case-Based Reasoning DM Approaches Logical Cross Tabulational Equational Numeric and Non-numeric Numeric Data Data distilled They are applied to tasks of predictive modeling and forensic analysis They extract patterns and then use for various purposes

Pros and cons to data mining approaches

CUSTOMER RELATION MANAGEMENT CRM: Development of the offer 3 Which’s Know Target Sell Service Definition 顧客關係管理要求公司去了解市場和顧客，去選擇最佳顧客進行一對一行銷，以及去辨別那裡不值得進行行銷。 要賣那一個產品，那一個顧客 要從那一個管道進行行銷(which product to sell to which customers and through which channel) 在”賣” ，用一些宣傳手法增加marketing的管道。 第四項是透過良好的服務像是call center 的設立 去保留舊有之顧客。注重客戶需求勝過於產品之特色。 將重心、焦點建立在客戶之上 ，在那一個崗位上，都可以思考客戶與供應商之需求，由外而內思考產品之行銷方式。在激烈竸爭的環境下，只有靈活面對外在環境變化的企業，才能得以生存發展。 1 - From product to customer orientation - Market Strategy from outside-in 2 Stage Concept 2 Push the development of customer orientation Innovating value proposition

Campaign Execution & Tracking Components of CRM Billing Records Surveys Web logs, Credit Card records Customer Information Internal Customer Data Outside Source Data Customer Data External data sources Current Address, Web page viewing profiles. Data Warehouse Historical Data Analyze the Data Data Mining Techniques + Customer Oriented Interactions between MKT, information, Tech and sales channels Campaign Execution & Tracking

Data Mining & CRM The Relationship Customer Life Cycle Prospects Respondents Active Customers Former Customers Inputs What information is available Data Mining Output What is likely to be interested 4 stages in customer lifecycle 有望成為顧客的人－他們還不是顧客，但是他們是我們的目標市場 Prospects他對產品或所提供的服務有興趣 目前正在使用或享受我們所提供服務的人 可能是一些不好的顧客，有可能使用不付費，導致成本過高， 因為他們不會成為我們的目標市場，或是他們轉換到購買敵對品牌之商品

Neural Networks vs. CHAID Case Studies Neural Networks vs. CHAID

Case #1 Neural Networks

Neural Networks The exact way in which the brain enables thought is one of the great mysteries of science

Neurons

NeoVistas Solutions’ Decision Series For retail, insurance, telecommunications, and healthcare. Includes discovery tools based on neural networks, clustering, genetic algorithms, and association rules

The problem Large retailer Over $1 billion in sales Overstocked on slow-moving products Under-stocked on most popular items at critical selling periods.

Solution With Clustering and Neural Network: Review point-of-sale history and equate store groupings to sales patterns. Forecast stocking requirements on a store-by-store basis.

Results Management is able to forecast seasonal trends at the store-item level. The Decision Series tools showed that clustering similar items into actionable groups streamlined the ordering process. Revenues increased by 11.6%

Case #2 CHAID

Applied Metrix Uses a combination of CHAID segmentation and logistic regression response probability modeling to establish predictive models that are deployed over a proprietary Internet system

The problem Home equity marketer that extended home equity lines of credit at the national level. The client’s goal was to increase the efficiency of targeting current mortgage customers who might be interested in the client’s service.

The Solution CHAID identified 16 distinct market segments. In particular, one particular segment accounted for 65% of responses to the mailing.

Results The highest-rated group from the predictive model had by far the highest response rate to the equity line of credit campaign—85% above average for the direct mailing, The goal of the program was a 10% increase in response rate, but the actual response rate increased 30%. The firm was able to increase profits by over one million dollars in the first year after implementation.

Case #3

PNY Technologies, Inc Oct. 2007

PNY Locations PNY - New Jersey Sales Office Manufacturing & Sales PNY – Norway PNY –UK PNY – Benelux PNY –Germany PNY – China PNY – Italy PNY - California PNY –Spain PNY –France PNY –Miami PNY – Taiwan *All US product ships from NJ location 13 Locations worldwide. PNY Products are sold in over 50 countries 482 Employees Worldwide 33

PNY Product Mix Shift 53% Flash Flash = Flash Cards & Drives, Mobile Memory = Consumer & OEM Graphics = Consumer & Professional 34 34

Consolidated Revenue by Channel

Current U.S. Channels of Distribution

Revenue Growth +21.8% +23.8% +23.0% +17.3% +8.7% +6.7%

US - 2006 Market Share:

US INDUSTRY OVERVIEW BY CATEGORY - UNITS PNY INDUSTRY TOTAL 2004 vs. 2005 Units (in thousands) 2004 vs. 2005 Units (in thousands) +55% +69% +48% +57% +1% -3% -15% -4%

Market Share – Month of August PC Memory Unit Share - Aug USB Unit Share - Aug SD Unit Share - Aug #3 #2 PC Memory Unit Share - Aug VGA Unit Share - Aug #2 #2

Flash Drive Overview – YTD Aug 2007 USB Unit Share – YTD Aug 2007 Observations PNY holds the #3 share position YTD 1GB represents the largest segment within the category with 40% of the unit sell-thru 2GB represents 31% of YTD sell-thru

Secure Digital Overview – YTD Aug 2007 SD Unit Share – YTD Aug 2007 Observations PNY holds the #2 market share YTD at 21.8% Secure Digital accounts for 55% of Flash Card sell through YTD 1GB is the highest selling capacity at 41% followed by 2GB at 38%

Memory Overview – YTD Aug 2007 Memory Unit Share – YTD Aug 2007 Observations PNY holds the #2 Market Share in Memory 7 of the top 10 selling SKUs in the industry are DDR Notebook Memory accounts for 25% of Memory sell-thru YTD

Graphics Unit Share – YTD Aug 2007 VGA Overview – YTD Aug 2007 Graphics Unit Share – YTD Aug 2007 Observations PNY holds the #2 overall share in the Consumer Graphics category YTD PNY has 5 of the top 10 selling SKUs in the industry 512MB represents 18% of the sell-thru YTD

CHAID v.s Neural Nets CHisquard Automatic Interaction Detector/Detection Clarity and explicability Implementation/Integration Data requirements Accuracy of model Construction of model Cost Application

Clarity and Explicability CHAID較易理解的 Neural Nets模糊的 Easy to explain to a domain expert or business user CHAID wins!!!

Implementation/Integration 實行困難度：CHAID < Neural Nets The risk of missing code by an IT department：CHAID < Neural Nets Performance：CHAID > Neural Nets(significantly faster) CHAID wins!!!

Data Requirements CHAID : more data must be provided 資料皆須進行前置作業 Neural Nets : binary format CHAID : continuous independent variables bust be banded

Accuracy of Model Neural Nets provide more accurate (powerful & predictive) models  complex problems Have risks Neural Nets wins!!!

Construction of Model CHAID  easier and quicker to construct Neural Nets  many parameters that need to be set 很難應用 v.s 易於偵測錯誤 CHAID wins!!!

Costs High cost(Neural Nets) Time & High level of building skills CHAID wins!!!

Applications 顧客忠誠度、購買傾向、顧客終身價值 Neural Nets > CHAID(both direct and undirected ways) Continuous independent variables v.s Categorical with high cardinality(performance) Classification problems v.s Estimation problems Easier to build and implement and less costly(CHAID)

THANK YOU!!!