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Home About Us Contact Us Testimonials Spam-Filter- Proofingİ (SFP) Service Search This Site Search Our Complete Listing of Data Cards Download Our Free Direct Mail Guide Corporate Training Programs | Web Learning Center Building and Managing Your Marketing Database, Cont. Lesson 3: WHAT DATA SHOULD YOU COLLECT AND TRACK? The best way to determine the information you need to maintain in your marketing database is to develop an understanding of what data you need to market your product or service to your clients more effectively. What information will help you determine the kind of customer that will most likely buy your product or service? What information do you need to know about your customers that will help you more effectively express your marketing propositions? Below we have listed some common data elements that you might want to consider tracking (of course each company's marketing needs will vary). Keep in mind that you don't necessarily need to ask for all of this information -- much of it can be obtained through data appending services (we'll address data appending a little later in this section). Types of Data to Consider Tracking Name and address and Contact Information: Self explanatory. Demographics: Consumer and business-to-business companies have very different marketing requirements (for a more complete list of demographics see Database Layout). For business-to-consumer, demographic information includes Gender, Date of Birth, Family Status, Occupation, Ethnic Group, Education, Marital Status, Household Income, and other lifestyle data. For business-to-business, demographic information might include Start Date of Business, Employee Size, Sales Volume, Professional Office Size, SIC Code, Function/ Title, Business Status, Franchise, Public or Private Company, Stock Exchange and stock symbol, Fortune 1000 Status if applicable, Credit Rating Scores, Yellow Page Advertisement, and Population Code. Acquisition information: How the lead or customer found out about your company (see Promotional Source Codes). Transactional history: This is one of the easiest and most important types of data to track in your database and should include every transaction a customer or lead ever makes with your company, including response to surveys or promotions, phone call inquiries, returns, method of payment, the medium used to transact with you, etc. You may be in the process of cleaning up your marketing database and you need to determine what data you should keep and what data you should get rid of. Of course if you don’t know whether you really need to keep the information or not, and it’s not going to increase your costs significantly to keep it, then by all means, keep it! Just keep in mind that every piece of data you collect will cost you money – in both storage and maintanance costs. How Do You Collect Demographic Data About Your Leads and Customers? Obviously, the more demographic information you know about your leads and customers, the easier it will be for you to get them to make a purchase (or repurchase) in the future. And the easier it will be to target the people that are qualified to buy. But as every marketer knows, it's not easy getting information, especially with the increasing privacy concerns that people have regarding the Internet and email spam. The important thing to keep in mind is that you don't have to ask your customers in order to collect a lot of the demographic information listed above. You can get that information through data appending services. Data Appending Data appending companies make their money by collecting and reselling information that is available in the public domain. Below, we have listed some examples of the kinds of business and consumer information you can get from data appenders. Data appenders get their information from public domain sources like the U.S. Census bureau, so while the data is a good indicator of general demographics, you need to be careful when making assumptions about some of it. For example, household income is an average. It is not the exact income of the people in that household. The Census bureau collects the household income by sub-cells within Census tracts, so the household income is an average for an entire block. Let's take the San Francisco Bay Area as an example. In San Francisco, a block might have people living on it who just purchased their home for half a million dollars with a combined household income over $100,000 a year. Well, that same block will have people living on it who are 3rd generation and purchased their home many years ago or inherited the home from their parents. Their household income might be under $40,000 a year. Data Appending Example 1: Consumer Household Incomes Dwelling Type / Size Unknown 1 unit - single family Under $15,000 2 units - duplex $15,000 - $19,999 3 units $20,000 - $24,999 4 units $25,000 - $29,999 5-9 units $30,000 - $34,999 10-19 units $35,000 - $39,999 20-49 units $40,000 - $44,999 50-99 units $50,000 - $74,999 100+ units $75,000+ Education Levels Age of Children Some high school Male (age 0-6) High school diploma Female (age 0-6) Some college Unknown (age 0-6) College graduate Male (age 7-12) Graduate degree Female (age 7-12) Unknown (age 7-12) Occupation Type Male (age 13-18) Homemaker Female (age 13-18) Retired Unknown (age 13-18) Student Blue collar White collar Professional Data Appending Example 2: Business Employee Size Business Status 1-4 Headquarters 5-9 Branch 10-19 Subsidiary headquarters 20-49 50-99 Stock Exchange 100-249 Private corporation 250-499 New York 500-999 American 1000-4999 NASDAQ 5000-9999 Other 1000+ Credit Rating Scores Professional Office Size Very good 1 professional Good 2 professionals Satisfactory 3 professionals Unknown 4 professionals Professional 5-9 professionals Institution 10+ professionals Yellow Page Ad Size Population Codes Regular listing 24,999 Bold listing 25,000-49,999 In-column ad 50,000-99,999 Display ad 100,000-249,999 250,000-499,999 500,000+ Data appenders generally charge a setup fee, plus a fee per thousand names appended. Of course when you go to get anything data appended on your file, you should only pay for the hits. So if you’re going for phone numbers in a file of 100,000 names for example, and they can only data append 40% or 40,000 names, you should only have to pay for the 40,000 phone numbers that they give you. So if it’s ten dollars a thousand to put a phone number, you’re paying 10 times 40,000, you’re not paying 10 times 100,000. If you’re going to do data appending, you definitely want to go through an intermediary like Name-Finders rather than going direct. It doesn’t mean you have to use us -- you can go to any company that acts as an intermediary -- but keep in mind that good brokers like us will get a better price based on the business that we give the data appending companies all year long. So even if your broker marks up the data appending service, it will still be cheaper to you than if you went directly to a data appending service. Plus, you'll benefit from the broker's wisdom about what kind of data appending information you really need to have in order to increase your direct marketing effectiveness. The data you get back from the data appending service will be numerically coded. So for example, if you request "dwelling type" from the service, you'll get a column of codes corresponding to the various dwelling types available. That way you can easily sort on the data Data Appending Information Is Coded Below we have listed a few of the decode sheets supplied by the data appenders that we work with. As you can see from the examples below, the data you get back from your data appender will take the form of coded information. The first table shows the US decode sheet for business-to-business data appending provided by Info USA. The second example shows the US decode sheet for business-to-consumer data provided by Donnelly. US FIELD DECODE SHEET: BUSINESS (Source = Info USA) Title Codes Employee Size 1 Owner A 1-4 2 President B 5-9 3 Manager C 10-19 4 Executive Director D 20-49 5 Principal E 50-99 6 Publisher F 100-249 7 Administrator G 250-499 8 Religious Leader H 500-999 9 Partner I 1,000-4,999 A Chairman J 5,000-9,999 B Vice Chairman K 10,000+ C Chief Executive Officer (CEO) D Director Sales Volume/Assets E Chief Operating Officer (COO) A Less than $500,000 F Chief Financial Officer (CFO) B $500k-$1 million G Treasurer C $1 million-$2.5 million H Controller D $2.5 million-$5 million I Executive Vice President E $5 million-$10 million J Senior Vice President F $10 million-$20 million K Vice President G $20 million-$50 million L Administration Executive H $50 million-$100 million M Corporate Communications Executive I $100 million-$500 million N Data Processing Executive J $500 million-$1 billion O Finance Executive K Over $1billion P Human Resources Executive Q Telecommunications Executive Ad Size R Marketing Executive A Regular Listing S Operations Executive B Bold Listing T Sales Executive C In-Column Listing U Corporate Secretary D Display Ad V General Counsel W Executive Officer Credit Rating Scores X Plant Manager A Excellent Y Purchasing Agent B Very Good Z Auditor C Good U Unknown Gender Code I Institution M Male F Female Public Code 1 Public Company Industry Specific Code Stock Exchange Code & Symbol For certain SIC Codes, size and classification information appears in the Industry-Specific Code Field. 1 New York NYSE 2 American AMEX 3 NASDAQ NASDAQ 4 Other Other Individual/Firm   1 Individual Population Code 2 Firm 1 1-24,999 5 25,000-49,999 Business Status 6 50,000-99,999 1 Headquarters 7 100,000-249,999 2 Branch 8 250,000-499,999 3 Subsidiary 9 500,000+ Match Level Franchise/Specialty 0 Site - Level Refer to our catalog 4 Zip + 4 Centroid 2 Zip + 2 Centroid Hospitals (# of Beds) X Zip Centroid A 1-24 B 25-49 Nursing Homes (# of Beds) C 50-99 A 1-19 D 100-199 B 20-99 E 200-299 C 100-249 F 300-399 D 250-499 G 400-499 E 500+ H 500+ Hotels/Motels (# of Rooms) Schools/Colleges (Enrollment) A 1-24 A 1-299 B 25-49 B 300-499 C 50-99 C 500-999 D 100-299 D 1,000-9,999 E 300-499 E 10,000+ F 500-999 G 1000+ Office Size A 1 professional B 2 professionals C 3 professionals D 4 professionals E 5 - 9 professionals F 10 or more professionals Restaurants (Cuisine Codes) A Bistro P Pizza B Brew Pub Q Swiss C Chinese R Middle Eastern D Deli S Spanish E Barbecue T Thai F Indian U Continental G Cajun V Mexican H Soul Food W Vietnamese I Italian Y Oriental J Japanese Z Seafood K Korean 0 Steak House L Caribbean 1 French M Irish 9 Greek N Kosher US FIELD DECODE SHEET: CONSUMER (Source = Donnelly, 2002) Adult Age Range Household Income Mortgage Amount A 18-24 A $0-$5,000 A Under $25,000 B 25-29 B $5,001-$10,000 B $25,000-$49,999 C 30-34 C $10,001-$15,000 C $50,000-$74,999 D 35-39 D $15,001-$20,000 D $75,000-$99,999 E 40-44 E $20,001-$25,000 E $100,000-$149,999 F 45-49 F $25,001-$30,000 F $150,000-$199,999 G 50-54 G $30,001-$35,000 G $200,000-$249,999 H 55-59 H $35,001-$40,000 H $250,000-$499,999 I 60-64 I $40,001-$45,000 I $500,000-$999,999 J 65+ J $45,001-$50,000 J $1,000,000+ K 65-69 K $50,001-$55,000 L 70-74 L $55,001-$60,000 Mortgage Type M 70+ M $60,001-$65,000 F FHA N $65,001-$70,000 V VA Marital Status O $70,001-$75,000 S Single P $75,001-$80,000 Finance Type M Married Q $80,001-$85,000 A Adjustable Rate U Unknown R $85,001-$90,000 F Fixed Rate S $90,001-$95,000 V Variable Rate Gender T $95,001-$100,000 O Other M Male U $100,001-$105,000 F Female V $105,001-$110,000 Residence Type U Unknown W $110,001-$115,000 H House X $115,001-$120,000 A Apartment Credit Card Limit Y $120,001-$125,000 0 Unknown Z $125,001-$130,000 Owns/Rents 1 $101-$300 1 $130,001-$135,000 O Owns 2 $301-$500 2 $135,001-$140,000 R Rents 3 $501-$1,000 3 $140,001-$145,000 4 $1,001-$3,000 4 $145,000+ Loan To Value Ratio 5 $3,001-$5,000 A 0.00 6 $5,001-$9,999 Home Value B 0.01-0.09 7 $10,000+ A $1-$14,999 C 0.10-0.19 B $25,000-$49,999 D 0.20-0.29 Bank Credit Card Type C $50,000-$74,999 E 0.30-0.39 1 Retail Card D $75,000-$99,999 F 0.40-0.49 2 Bank Card E $100,000-$124,000 G 0.50-0.59 3 Oil Card F $125,000-$149,999 H 0.60-0.69 4 Specialty Card G $150,000-$174,999 I 0.70-0.79 5 Upscale Retail/Specialty H $175,000-$199,999 J 0.80-0.89 6 Finance Card I $200,000-$249,999 K 0.90-0.99 J $250,000-$299,999 L 1.00-1.09 Expendable Income / Net Worth Rank K $300,000-$349,999 M 1.10-1.19 A Top 6.6% L $350,000-$399,999 N 1.20-1.29 B Second 6.6% M $400,000+ O 1.30-1.39 C Third 6.6% P 1.40-1.49 D Fourth 6.6% Time Zones Q 1.50+ E Fifth 6.6% E Eastern F Sixth 6.6% C Central Match Code G Seventh 6.6% M Mountain 0 Street Address Level Match H Eighth 6.6% P Pacific 2 Zip + 2 Centroid I Ninth 6.6% A Alaskan 4 Zip + 4 Centroid J Tenth 6.6% H Hawaii X Zip Centroid K Eleventh 6.6% L Twelfth 6.6% Nielsen Population Rank Land Use M Thirteenth 6.6% A 810,000 + HH T Mobile Home N Fourteenth 6.6% B 85,000-809,000 HH O Fifteenth 6.6% C 20,000-84,999 HH D Under 20,000 HH Ethnic Code Ethnic Code Cont. Ethnic Code Cont. 1 Arabian 21 Vietnamese 41 Indonesian 2 Armenian 22 Tagalog (Filipino) 42 Hebrew 3 Asian 23 Chinese 43 Laotian 4 East European 24 Czechoslovakian 44 Latvian 5 Indian 25 Norwegian 45 Lithuanian 6 English (British) 26 Dutch 46 Malaysian 7 French 27 Greek 47 Manx 8 German 28 Hungarian 48 Portuguese 9 Hispanic (Spanish) 29 Middle Eastern 49 Singaporean 10 Irish 30 Russian 50 Sri Lanka 11 Italian 31 Ukranian 51 Swiss 12 Japanese 32 Welsh 52 Thai 13 Pakistani 33 Bangladesh 53 Turkish 14 Polish 34 Bulgarian 54 Yugoslavian 15 Scandinavian 35 Burmese 55 Danish 16 Scottish 36 Cambodian 56 Hindu 17 Swedish 37 Estonian 18 Mandarin 38 Ethiopian 19 Cantonese 39 Finnish 20 Korean 40 Icelandic Vehicle Group Vehicle Group, Cont. Vehicle Group, Cont. 2 Entry Level (Car) 22 Mid Luxury (Car) 42 Compact Pickup 4 Basic Economy (Car) 24 Prestige Luxury (Car) 44 Midsize Pickup 6 Lower Middle (Car) 26 Heavy Duty Station Wagon 46 Full-Size Pickup 8 Upper Middle (Car) 28 Minivan (Cargo) 48 Full-Size Van 10 Upper Middle Specialty (Car) 30 Minivan (Passenger) 55 Cutaway Van 12 Traditional Large (Car) 32 Window Van (Passenger) 59 Motor Home 14 Basic Sporty (Car) 34 Mini Sport Utility 69 Step Van 16 Mid Sporty (Car) 36 Sport Utility 70 Van Camper 18 Prestige Sporty (Car) 38 Full-Size Utility 20 Basic Luxury (Car) 40 Luxury Pickup Building and Managing Your Marketing Database Introduction Planning Your Database Database Applications What Data Should You Collect and Track? Tracking Transactions Promotional Source Codes Database Layout and Data Formatting Case Study: US Media Company Back

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