If your organization is like many Altair works with, the amount of customer information you have is restricted by the retrieval/input method.  For instance, your point of sale (POS) only captures name address, phone and/or email.  Even then you are counting on the sales associate to correctly spell and enter these elements.  Now you have two problems, a limited amount of data on the customer and potentially incorrect data from the POS.  Many times you would like to have additional variables to help drive decisions, or selections for mail campaigns/promotions but you can’t get there with what you have.

I worked in the banking world for over 15 years, and many of those years in the marketing/analysis side.  We always wanted to know more about our customers, but we didn’t always have the cleanest data or the most complete data.  I learned not only how to clean the data but get more matches by matching at the level of the corresponding data elements.  Given the amount of information you have you can get more data than you think.  There are levels you can match at that will help this.

Overlay match levels:

Individual – First Name, Last Name, Address

Household – Last name, Address

Address – Address only

Some data elements need be done at the Individual level (age, gender, etc).  However, many variables can be overlaid at the household level (Household income, Number of Children, etc), or the address level (house square footage, lot size, summarized credit data, etc.).  So even when the retrieval/input limits the information you have, you can add data to your customer file that enhances the capabilities for analysis and selection for marketing.

There is a full range of things that can be done to make your customer file work for you.

• Hygiene such as NCOA and Delivery Point Verification (DPV) can prepare your file for mailing.  This not only benefits marketing but billing and collection as well.
• Data append as described above can enhance decision making with additional information outside what’s been given.
• Profiling is the next step to learning more about your customer.  A profile is different than an append in that you are now comparing your customers to their peers in the same geographic footprint.  See an example of our profile here : Customer Profile
• Modeling outcomes are much more robust with outside data.  The combination of your data and the third party data allows for the prediction of outcomes such as booking a loan, responding to an offer or leaving you as a customer.

To learn more contact Troy Blackman at tblackman@altairci.com or 615-468-6821

Most of you have probably heard the term “Big Data”.  It is a trendy buzzword at many conferences and there are a number of articles and blogs that discuss it as the next big thing.  What exactly is “Big Data” and why should you care?   Most define it as the collection and analysis of large amounts of data to make better decisions and/or provide better direction.   To me, the term speaks to the growth in transactional and other data processing over the past 10-15 years (ex. – Amazon’s recommendations for similar products).   There is much more data available today, which can allow for more precision in BI (Business Intelligence) and other types of analysis.

The problem is that “Big Data” is not the answer.  Quality Data is the answer.

Quality does not seem to be a focus in any of the articles I’ve read recently.  “Small Data” can be even more powerful than “Big Data” depending on the quality of the data being captured.  While it is true that more data can lead to more precision in analysis, the data elements must be clearly defined and validated.  By clearly defining what is important in terms of data capture and developing mechanisms to validate the data, you will ensure that quality data flows through the process, no matter the size of the data.

Altair has 70+ years of combined processing and analytics experience and a scalable network infrastructure so we can help your company navigate any data considerations.  The average company processes 63 Terabytes of data annually.  As a data-centric company, Altair processes nearly as much data on a monthly basis.  Please contact me at tblackman@altairci.com to learn how Altair can help.

I think one of the best lessons I learned from my college computer programming classes was that no matter how well you thought your program worked you had better be running your final QC on the output file itself.  Your program may run without an error and your output may have been created but do you know it is correct?

You’ve done your QC along the way and everything is checking out.  You expect the output file to be correct but simple practice of opening the file and looking at it can sometimes expose errors that no one thought would slip by in the processing.  This is especially true when processing systems run in one format and output files are created in another.

Performing some simple analysis on the output file such as running min and max frequencies on the output fields can determine if correct criteria was used in the selection process as well as validate the data in that field.   Looking at each frequency with the expectation of what is possible provides the verification necessary to ensure proper validation.  At Altair we require our Operations team to supply our QC team with output reports that are used in the verification that file was run correctly.  Having this focus on the output, as one of many QC points, adds an extra level of security for both Altair and our clients.

In the data processing world mistakes happen.  It’s just a fact of doing business.  Knowing that, implementing a quality control (QC) procedure is a must. Volumes can be written about QC.  Here are a few highlights from our perspective.  Putting processes in place to catch errors during the order fulfillment process keeps Altair from those embarrassing moments when the client gets something other than what they asked for.  This leads us to the first and most important step in our quality control process, reviewing the order with the client to ensure we are giving them what they want.  All of us in the data processing world have delivered jobs that are exactly what the client asked us to do but not what they wanted.  Clarification up front keeps this most detrimental of error types from happening.

Second, it is not enough to put “one size fits all” error detectors in place when custom orders are involved.  This is a twofold QC step. Any time you add, change, delete, or create there is opportunity for error.  If this is coming from the client it needs to be in writing for clarification.  This creates the documentation for the processing as well as the QC.  If the manipulation is happening to produce a specific output it must be verified.  At Altair we incorporate custom QC at the key processing steps to identify errors and to prove to ourselves that the processing is running smoothly.

Last, take the time to actually do the QC.  It does take time and when meeting deadlines that are tight, time is of the essence.  Our philosophy is we’d rather be a little behind than deliver an error. Our clients appreciate the diligence and when involved in the development of the process with us become even more aware of the importance.

Processing time is money. The sooner we identify an issue, the sooner it can be resolved.  Rerunning a completed order because of even the smallest of issues is still rerunning the entire order.  What’s the old saying, “If you didn’t have time to do it right, when will you have time to do it over.”… Experience has taught us to sweat the details upfront.

Tools we use:

• Word Forms: check lists for gathering important information
• Work Orders: gathers details from clients and documents the process for both sides
• Visio: creates a flow of the process for, allows for efficient processing and QC design
• Access: Brings all work orders together for effective communication and multiple project handling
• Six Sigma process control
• QC Focus groups.
• ENTERPRISE LEVEL INVOLVEMENT! Not just operations.

What is the match rate? This is the first question I hear when appending data for a customer. But is it the most important question?

Assuming your vendor is standardizing the names and addresses on both datasets to ensure the most matches, how do you know the appended data is matched properly? There are many possible match levels – address only, last name + address, full name + address or phone number. The type of data determines how to match.

In most cases, matching should follow this loose guideline:

-          Deed/Mortgage/Demographic matching  should be done at the household level

-          Tax/AVM/home value matching should be done at the address level

If your vendor is matching at different levels to the same file, you could be receiving false matches depending on the type of data you’re using. Ask your vendor to flag the different types of matches so you can determine yourself which appended elements make sense and at which match level. Ultimately, the match rate is important, but the way the match is done is paramount.

Altair has several enhancement databases available to supplement data element population totaling more than one billion current and historical records. We have the experience and data to assist in providing the highest quality matching for overlay/append projects. Please contact us to learn how Altair can help.

Altair Customer Intelligence, a leader in delivering data driven customer insight, shows that having fresh data is well worth the investment.  Using basic criteria and our shelf “in the market models” for Home Equity, FHA Refinance, Prime Refinance and Reverse Mortgage we show the cost of wasted mail for records that no longer qualify and the missed opportunity cost of what should have been mailed but was not.

All too often direct marketers will use data that is not up to date in the effort to affect a cost savings.  In reality the outcome is a loss.  In our study we took four of our shelf models and compared the marketable population from one quarter to the next.  We looked at December 2010 and March 2011.

Prime Refinance

In a nationwide review we found within our Prime Refinance model the available population was 17.9MM in December of 2010 and 18.6MM in March of 2011.  On the surface it looks like a 3.5% change.  In reality 1.1MM no longer qualified and 1.7MM newly qualified.  The effective change was actually 15.5%.  To put it in dollars mailing the incorrect 1.1MM would have cost over half a million dollars using a conservative $0.48 per piece in the mail cost.  The missed opportunity cost on not mailing the newly qualified is 10 times higher.

FHA In the Market

In a nationwide review we found within our FHA model the available population was 16.7MM in December of 2010 and 17.1MM in March of 2011.  Between Q4 2010 and Q1 2011 922K consumers no longer qualified and 1.3MM newly qualified.  This gap of over 2MM is a 14% change in the population.

Home Equity Model

In a nationwide review we found within our Home Equity model the available population was 5.8MM in December of 2010 and 6.2MM in March of 2011.  The consumers were between 25-55 in age, had an income >$50K and their LTV was <=80%. This population saw a shift of 29.7%, likely attributed to changes in job status and equity in the home.

Reverse Mortgage Model

In a nationwide review we found within our Reverse Mortgage model the available population was 9.5MM in December of 2010 and 9.6MM in March of 2011.  These numbers are deceivingly close with only a 1.5% change overall.  The underlying change with 485K no longer qualifying and 632K newly qualifying tells a different story.

New Medicare rules in 2011 will make it more challenging to reach enrollment goals.  The marketing window determined by the government has been shortened and your reimbursements could be lower depending on your rating.  To succeed in this changing landscape, Medicare Advantage marketers will have to develop a comprehensive strategy for prospecting, conversion and on-boarding; be more flexible and better prepared than in the past in order to meet their marketing goals.

Here are three key tactics to include in a successful strategy to  maximize your Medicare Advantage enrollments this season:

1.  Increase your universe—make sure you have every possible prospect in your market area.  We’ve recently mystery shopped all of the major data companies.  Through this research, we found 20 to 40% variances in the number of qualified Medicare prospects.  By using the right sources, you can increase your prospect universe by 20 to 40% thus increasing your new enrollments.

2.  Integrate your marketing—there is a misconception that 65+ consumers are not online when in fact this is the fastest growing online age group.  An integrated campaign with direct mail, email and targeted online ads will greatly improve your Medicare enrollments.

3.  Analyze, measure and report—with the shortened marketing window, it is imperative that you have a reporting tool that is updated daily with your prospect, application and enrollment data.  You will have a very small window of time to make decisions and access to a consolidated 360 degree view of your application and enrollment data will be vital to your success.  Altair has  a very powerful business intelligence tool that will access data across all of your databases and provide online reports to you very affordably.

Medicare marketing can be challenging.  Unlike traditional marketing where you can implement a crawl, walk, run strategy, Medicare is ready, set, GO!!  By using these tactics within your strategy, you can be ahead of the pack.

You’re wondering how all three of these ties together.  Facebook and my upcoming alumni weekend quickly proved data timeliness as a relevant and real world thing.  I recently started using Facebook and quickly found many college friends that I lost touch with.  Our 20^th reunion is coming up and looking in Facebook I realized that while some of us are what we set out to be in college more of us are not.  Over the years our paths have diverged for many reasons and this is true in a much shorter time period on data used for direct marketing.

As a person who has been on the data side of the business for over 15 years I extol the importance of data quality with frequent updates of said data upon all of my clients.  It falls short in many cases because they see me as selling.  I regularly prove the value by doing research.  One such example using the following criteria; Age 25-55, Income >$50K, LTV <=80%: 

As you can see there was a drop in available records but what are more noticeable are 3.5MM actually failed criteria with 2.6MM replacing them.

As you can see this was in 2008.  In 2011 with home values and income as volatile as the are you bet I’ll be updating this in a future blog.

Customer service begins with the first message that you send to your prospective client.  In the age of Google, Amazon, Droids, iPhones and eBay consumers expect you to know things.  In fact they expect you to finish their thought while not wanting you to be too intrusive.   A great example is Pizza Hut.  When we call from our house they’ve stored our address, our name and only need to know the type of pizza we want.  This Intelligent Acquisition is split into two pieces, Basic and (not Basic, Platinum, Mensa, Smart??).  If you don’t get the Basic the other won’t matter.

Basic Intelligent Acquisition

It boils down to data hygiene and common sense.  Get the offer to the correct person at the correct address.  It sounds simple but weekly my house gets offers sent to my wife in her maiden name which she hasn’t used in 11 years or an offer to replace my current mortgage with company “ABC” with their mortgage and company “ABC” is wrong or an offer from a company we have a relationship with for the same product we already have.  In each instance the customer service rating in my mind for this company goes down.  How do you avoid this?

• Standard postal hygiene; CASS, DPV and NCOA
• Proper purge of customer files in a timely manner
• A reliable source for your prospect files that get regular updates and consult on usage

Not Basic Intelligent Acquisition

With the hurdle to basic behind you the fun can really begin.  You want to finish the consumer’s thought without being intrusive.  This is where data comes into play.  The data within your walls helps you understand more about your customers.  You know how you acquired them, what their purchase history is and how profitable they are, which allows you to classify or stratify them.  Once you bring external data into the fold you will be able to use that data to predict like behavior in potential customers.  Intelligent Acquisition is using data and analytics to:

• Make an offer relevant to the consumer’s needs
• Use a message that motivates action
• Appeal to intellect as well as emotion

Once acquired the true customer service experience begins.  Use your Intelligent Acquisition to have Great Customer Service and keep gathering data internally and externally to deliver the experience that makes them loyal.  A perfect example of this is Zappos.  They have figured out how to create loyal internet shoppers with their post-acquisition customer service.

While doing some research for one of our clients, questions came up about zip code facts.  The United States Post Office (USPS) operates one of the largest infrastructures in the world but finding information on the system can be difficult.  In the spirit of sharing, here are some of the facts that Altair uncovered.

Do P.O. Boxes have their own zip+4?

YES

How many zip+4s are in the United States?

33,182,405

What is the average number of zip+4s within a zip code?

863.5

What is the lowest number of zip+4s within a zip code?

1

What is highest number of zip+4s within a zip code?

7,284

What are the average number of households within a zip+4?

4.5

What is the highest number of households within a zip+4?

4,584

What is the lowest number of households within a zip+4?

1