The DATA Difference Targe.ng for Stronger ROI!

Transcription

1 The DATA Difference Targe.ng for Stronger ROI! Presented by: Dr. John Leininger Department of Graphic Communica<ons Clemson University Today s game plan Whether working with personaliza<on in print, or PURLs, the value of personaliza<on con<nues to grow. What got the reader s ajen<on last year is just average this year. You need to mine the data from customer records, merge it with census data, and link it to mul<ple digital assets based on the personality of the customer. The reality is to compete in the market you have to up the ante, and this means you need to be developing a plan on how your company will make use of the massive about of data that is now being collected every day. Ul<mately, the company that controls the data is going to be the one making the money. If that moves upstream to the designer's arena, the print output becomes a commodity. 1

2 Database Workflow Why use a workflow tool? Improve produc<vity Reduce turnaround <me Improve ROI and value- added services (as long as you do not give it away) Improve consistency in the process Reduce mistakes Build on past experience and become more powerful over <me Mul<ply use op<ons to offer greater gains as new customers are brought in Working with Data Where data comes from What type of data can you work with: Customer s records List company s demographic data Listserve marke<ng analy<cs Working with demographic, psychographic, and geographic indicators (to determine the capacity to buy and the propensity to buy) Rela<onal data from outside sources 2

3 Where should you be working with your data? Microso[ Excel A true database applica<on Microso[ Access Mailing so[ware FileMaker hjp:// Pro- 12- Bundle- Tutorials.htm MySQL Oracle etc. What data do you need? Look at the objec<ve of the piece, campaign, marke<ng plan, etc. What is presently available? (How clean and reliable is this data?) What data can you purchase to add to the customer profile? What data can you manufacture from exis<ng data? Focus the message to the needs of the customer 3

4 What Do You Need to Correct in an Address? (Where is the best place to correct it?) Missing or wrong street, direc<onal, prefix, suffix Missing gender indicator City, state, or ZIP Code missing or in conflict Missing secondary address informa<on Misspelled words like street or avenue ( steet or aveneu ) Improper state abbrevia<on Trailing space a[er a name or address Inconsistent filling of fields Company legal name and reference name What Do You Need to Correct in an Address? (Where is the best place to correct it?) 4

5 This was more than a Blooper One mistake was not enough, there were three MAJOR Bloopers on one mailing. The First Blooper (and how they tried to fix it) This is a real , other than blocking out the names nothing has been changed. The author of the lejer and is trying to make a joke of the mistake and never says it was their fault. 5

6 What was the Event and Mailing? It was a conference on Variable Data Prin<ng The mailing was an 8- page 8 1 2" by 11" Mailed first class at $.60 per piece 5,000 pieces printed and mailed Blooper #2 What the printer saw on the first screen is boxed in black. 6

7 Blooper #2 The first window of records in the database had only 3 line addresses, so the printer designed the label informa<on with 3 lines address. Almost half of the names had 4 line addresses. What do you think happened to the 2nd line of the address? Blooper #3 Address Correc<on Requested No one planned on 2,500 pieces being returned. This was an extra cost no one had budgeted for in the ini<al plan. 7

8 The Final Results No one knows how many people actually got a mailing piece. It was a very low ajended conference. The budget was shot to pieces. What can be done in mail processing sotware (it is just a group of rela<onal databases) Correc<ng addresses in mail prep so[ware Appending Geocoding informa<on based on addresses Formasng op<ons Genderizing Concatena<ng address and postal informa<on Addressing Move- update with NCOA Wri<ng rules and sor<ng data files De- duping NCOA process AEC I and II 8

9 How big is BIG? A sample of collec<ng data through smartphones people that sign up for the GPS tracking to find their phone give the company the rights to track their phone Tracking the loca<on of the phone for 50 million subscriber takes 26 bytes of informa<on for each loca<on, checking once a minute for 24 hours = 2 terabytes per day Let s turn to BIG data it is really all about Business Intelligence (BI) It is called BIG data because of the volume of informa<on It is also very diverse, which is what allows companies to mine for rela<onships It comes from internal records, real- <me contact (ex. smartphones), third- party vendors, social media, census data, etc. Working with BIG data requires a partnership approach between the customer and service vendor 9

10 Working with BIG data (the world is changing and you have decide if you are going to adjust your strategies) Gathering Data Basic analy<cs Advanced analy<cs (how are customers reac<ng/behaving) Discovery analy<cs Exploratory analy<cs Analy<cs doesn t require big data Analysis Analy<cs is the tool to give the marke<ng team the metrics to predict or project a plan of ac<on Some examples of BIG Data Target customer analysis works New York Times ar<cle on predic<ng pregnancy hjp://bit.ly/target- predic<on hjp://bit.ly/target_1 City of Boston using Big Data to iden<fy potholes hjp://bit.ly/boston_ph 10

11 A printer s opportunity is working with SMALL data using BIG data prac.ces Recognizing the opportunity: Many companies that are working with BIG data are large companies with a large IT and BI departments. Printers need to target the smaller customer that needs the benefits of BIG data management, but does not have the staff. Big Data find out what it can do HP Big Data Service hjp://bit.ly/hp- data WhatTheyThink.com Whitepaper Marke<ng: Print + Data = Results hjp://bit.ly/wtt- whitepaper Working with big data Whitepaper hjp://bit.ly/big- data2 LiJle Data vs. Big Data hjp://bit.ly/lijle- vs- big Bitly links are caps sensi<ve! 11

12 Big Data find out what it can do Big Data Why Transac<on Data is Mission Cri<cal to Success (from IBM) hjp://bit.ly/big- data- cu- 1 Big Data Sourcebook, 2 nd Edi<on hjp://bit.ly/big- data- cu- 2 database Trends and Applica<ons Succeeding with Big Data Analy<cs hjp://bit.ly/big- data- cu- 3 Big Data Quarterly Bitly links are caps sensi<ve! Seeing the big picture of data Volume Terabytes Records Transac<ons Tables, files Batch Near <me Real <me Stream 3 Vs of BIG Data Structured Unstructed Semistructured All of the above Velocity Variety Big Data Analy<cs, by Philip Russom, published by TDWI The Data Warehousing Ins<tute,

13 Establishing a base- line Two books to use as a resource to develop a data management strategy for your company Strategic Database Marke/ng, 4 th Ed by Arthur Hughes and Managing Your Business Database by Theresa Kushner and Maria Villar What is a Script? Scripts are used in many tasks we do every day It is an automated set of steps that can be set up to perform a single task or a set of tasks on command or automa<cally It can be built into the applica<on or driven by an outside program It can be created ahead of <me, or as you use the so[ware it can be recorded to be used again in the future Ul<mately, a script is a tool you can choose to use it or not, but the person using the right tool at the right <me will always perform bejer than someone with no plan and only basic tools 13

14 Scripts Clean up the database Clean out unwanted elements (spaces, symbols, punctua<on) Reformat data Automate rules and search func<ons Applying rules from a standard format to each database file Search for target data convert and append as needed De- dupe your database Create new and relevant data from exis<ng data Complex Calcula.on It is a building block for conver<ng, merging, extrac<ng, rela<ng factors and issues, personalizing data, or crea<ng relevance It can be one line or hundreds of lines of text It can be embedded into a script (allowing it to be automated or triggered) It can save <me and money Once developed they can be reused, repurposed, or evolve into other calcula<ons 14

15 Live Demo Script Removing leading and trailing spaces Rules What you can do with a date Random number generator Reformasng data Selec<ng an appropriate background for the job Now You Have to Pull it All Together Rela.onal database management allows you to: Bring together all the outside list and mail prep files into one master database file Create unique rela<onships with crea<ve input to enhance the personaliza<on and design Build on exis<ng knowledge and combine related elements across mul<ple databases 15

16 Rela.onal Databases (Why?) Profiling the customer s customers Working with list companies They can clean up and append the list with relevant informa<on They can use data analy<cs and algorithms to determine common parameters of the customer They can deliver a list of people with similar characteris<cs 16

17 5/17/15 A Lot of Players in this Market hjp:// hjp:// hjp:// hjp:// Let s purchase some data: Know what you are looking for: Gender Income Rent/Own Newcomers Home Market Value Age Ethnicity Educa<on etc. 17

18 5/17/15 Maps on- line hgp://maps.locr.com What can you do with personalized maps? Banks/Financial service providers Insurance companies Customer loyalty programs Car manufacturers and dealers Retail chains Service centers Parcel shops Chari<es/Fundraising All kinds of branches, many others If you are new to data analy.cs Consider working with a reliable op<on to predict customers purchases from historical data (this means the customer has shared this data with your company) RFM Analysis Recency, Frequency, Monetary value Downloadable white paper: RFM Migra<on Analysis A New Approach to a Proven Technique, hjp:// May 12,

19 Scripts and Rules Scripts Automate repe<<ve tasks Clean up lists Reformat content Complex Rules 350 complex calcula<ons for $49.95 (approximately 20 rules targeted for print, but it really depends on your IT staff) hjp:// References to consider: Big Data at Work: Dispelling the Myths, Uncovering the Opportuni<es, by Thomas H. Davenport Big Data, Analy<cs, and the Future of Marke<ng & Sales, (free ebook) hjp://bit.ly/big- data- ebook 19

20 Thanks for listening! Contact Information Dr. John Leininger Department of Graphic Communications Clemson University G-01 Tillman Hall Clemson, SC (O) 864/ (F)