By Mike Hattub, Chief Operating Officer, AnalyticsIQ

Email entered the marketing industry in a ‘wild-west’ fashion several years ago.  Initially, there were very few rules and regulations around email marketing and, as expected, this attracted many different types of businesses.  Unfortunately, the types of businesses that capitalized on this new marketing channel had varying degrees of business ethics.

Over time, higher quality players have emerged and regulations have also been developed for the email marketing environment.   Known information is now available to help validate and verify the quality of email addresses for marketing.  There are also compiled files of other types of suppressions so that email marketers do not send emails that will black-list them or their Email Service Providers (ESP).

These known sources of information are very important but only tell part of the Email Optimization story for marketing.  The one issue with known data is that it only can be useful when it matches to a database (for example, if I have a list of 100,000 email addresses and I match this against a Suppression/Invalid file then maybe 30,000 of my emails are identified as bad.  The other 70,000 could be good but there is no way to know for sure).

Analytics has evolved tremendously in this area so that companies can also infer ‘bad’ emails which should also be excluded.  Additionally, analytics can also determine which of the ‘good’ emails have the highest propensity to Open.  Lastly, analytics can drive which Opens will be the biggest fans of a product in the Social world as well as who has the most Discretionary Spending for making purchases.

All of these dimensions together, and also some custom analytics, brings email marketing much further down the path toward an above-board, SPAM-free channel.

By Mike Hattub, Chief Operating Officer, AnalyticsIQ, Inc.

As data becomes more and more readily available in the marketing world, the ability to sift through everything to find value is a never-ending process for both end-users and data compilers/resellers alike.  Not only is the sheer volume of data constantly growing, but the depth/breadth of data is expanding as well.  One can easily get lost in data very quickly.  When this happens, frustration is not far behind.  While it is said that ‘all information is good’, it is also true that ‘prioritizing and analyzing information in an optimal manner is imperative’.

As businesses dive into the vast oceans of data, some of the questions one may ask are:

• What data should I be looking at first?
• How do I translate this raw data into something meaningful?
• Can I get to the outcome I need from the data I have?

Having the tools to analyze data assets from both purely statistical and pragmatic business perspectives is the key to being able to take on the data that exist today.

Obviously, end-users need these tools to run their business effectively.  However, data and analytics companies also need to use these tools to continuously find additional value in their overall data assets.  While value can often be bought by acquiring data sources, great value can often be uncovered with assets already in-house.

These types of discoveries can be hugely valuable since the additional cost is usually negligible.  So, with all of this in mind, never stop looking for value because it is there if you know where to look.

“The importance of accurate home value”

By Marc Sabatini, SVP Business Development, AnalyticsIQ, Inc.

When you are in the market for clean and accurate data (the foundation of any good marketing program), Home Value plays a vital role in the quality of the data you use to market your product or service to customers and prospects alike.  You may think that Home Value is only valuable when it comes to financial services marketing, but keep in mind:

• Disposable income, Discretionary Spending, Net Worth, and Credit Risk are highly correlated to Home Value as well as the change in Home Value over time.  High initial response rates to marketing programs are useless if they do not have the ability to qualify or afford your offer (i.e. mortgage, insurance, retail, investment).  Furthermore, determining which hand-raisers or targets will convert ultimately comes down to economics – can they afford to buy or do they need your help.  For the vast majority of the US households this comes down to accurate Home Value and the change in Home Value over time.

In the continuously fluctuating housing market, Home Value has been one of the most difficult data elements to accurately track and predict.  Over the past 5 years, Home Value has been one of the data points that has either made or broken marketing campaigns….mostly broken.  Since Home Value is a major contributor to loan-to-value ratio and debt capacity ratio, many marketing programs have been missing the mark due to inconsistent and faulty data.

Many of the major data providers have been crippled in their data compilation efforts by not being able to update or provide accurate home valuation statistics due to a variety of factors; staying on top of the rapidly changing real estate market, monitoring and applying changes at the neighborhood and county levels, and the lack of commitment to acquire and maintain the necessary resources.

By Gregg Weldon, Chief Analytical Officer, AnalyticsIQ, Inc.

 In recent years, the United States has been hit hard by the “Great Recession”.  Since then, we’ve been noting how specific areas are being affected vs. the country overall.  This report looks at Las Vegas and the state of Nevada, a true “boom-or-bust” area over the last 5 years.  Comparisons will include home price changes, unemployment rates, and retail sales for the CBSA, the state, and the U.S. for July 2006 and April 2011.

 In July, 2006, the recession was still yet-to-come, and most areas of the country were in good shape, economically.

In 2006, Nevada was on top of the world, with homes increasing in value at 2 to 2.5 times the national rate, strong increases in retail sales, and low  unemployment.  In Las Vegas, there was a boom in the construction industry, as new casinos were being built in record numbers (and for record prices).  In fact, Las Vegas was one of the fastest growing large cities in the country.

What a difference 5 years makes.  Home prices, which had been 15% above the national average, are now 32% below average.  Not only that, but home prices still haven’t bottomed out, as they continue to fall.  Meanwhile, the  unemployment rate has more than doubled and continues to worsen.  Las Vegas has several partially completed casinos on the Strip, with no timetable for these hulks ever being finished.

By Mike Hattub, Chief Operating Officer, AnalyticsIQ, Inc.

Lead Scoring is growing more and more critical in order to achieve and maintain success in the online direct marketing space.  This ‘Lead Scoring’ concept takes on many interpretations related to real-time online services.  The interpretations range from:

-  Validating lead information;

-  Minimizing fraudulent leads;

-  Adding identifying information for additional marketing;

-  Appending descriptive characteristics;

-  Applying segmentation for optimizing products/offers;

-  Applying algorithms predicting events like conversion.

Having a one-stop shop that can execute all of the above services is extremely valuable since every lead buyer needs one or more of these solutions to manage their business effectively. Additionally, being able to have one platform to deliver these services creates an even more powerful data asset for end-users by having a complete data repository for ongoing analysis, validations, modeling, etc.  The more you know and can learn about leads at every stage of their lifecycle, the better decisions can be in the future.

The online marketing world is maturing rapidly and new companies are entering the market constantly.  As this phenomenon continues, the need for advanced analytics will grow exponentially; companies will have to adapt to survive and stay ahead of the pack.

By Dave Kelly, President/CEO, AnalyticsIQ, Inc.

AIQ has just added another seasoned executive to our team, Marc Sabatini.  Marc will be focused on business development and will establish our new office in NYC (OK….actually New Jersey).

Marc has a great track record in our market space, and I have been fortunate to work closely with Marc over the last 13 years.  He takes an entrepreneurial approach to the business and appreciates the value of analytics.  Our kind of guy!

This is a significant evolution for AIQ. We have grown a great business through
partnerships and viral marketing.  While we will continue down the same path, it will be nice to actually reach out to ‘strangers’ to let them know about some of our great products and services.

Feel free to reach out to Marc:  marcs@analytics-iq.com.

By Dave Kelly, President/CEO, AnalyticsIQ, Inc.

Although we typically avoid overt self-promotion with these blog entries, I thought I would use this space to list out some of our newest products:

• Social-IQ: This is an individual-level score that assesses the degree that an individual engages social-networking sites such as Facebook and Twitter.  The score identifies 14 million influencers on these networking sites.

• AIQ Political Suite: AIQ has developed tools to identify donors to conservative and liberal causes.

• Improved AIQ Home Value: AIQ’s home value is sourced (and optimized) from seven unique home values, as well as other related data.  The end result is a much more accurate home valuation and better coverage.

AIQ’s consumer database is compiled (by AIQ) from over 100 distinct sources and consists of 500+ proprietary attributes.

By Gregg Weldon, Chief Analytical Officer, AnalyticsIQ, Inc.

In recent years, the United States has been hit hard by the “Great Recession”.  It’s interesting to note how specific areas are being affected vs. the country overall.  This report looks at Atlanta, GA, as well as Georgia itself.  Comparisons will include home price changes, unemployment rates, and retail sales for the CBSA, the state, and the U.S. for July 2006 and April 2011.

In July, 2006, the recession was still yet-to-come, and most areas of the country were in good shape, economically.

In 2006, homes in Atlanta were increasing at a slower pace than the US (or Georgia overall) and the unemployment rate was dropping at a somewhat slower pace.  Retail sales, however, were quite strong in comparison.  The state of Georgia was closer to the U.S. averages.

Five years later, Atlanta and Georgia have lost ground when compared to the country.  Home prices are dropping much faster than average, retail sales increases are half the rest of the country, and Atlanta has a faster rising unemployment rate than the U.S. (Georgia is slightly better).  In addition, Atlanta and  Georgia have fallen behind in home prices and unemployment rates vs. the median U.S. values from where they were 5 years ago.

Overall, home prices are down, unemployment is up, and retail sales are anemic everywhere when compared to 5 years ago.  However, Atlanta and the entire state of Georgia have fared much worse than average, and continue to fall behind the rest of the nation.

By Mike Hattub, Chief Operating Officer, AnalyticsIQ, Inc.

When looking at all of the marketing data available today, it is difficult not to get intimidated or frustrated with the vast number of options.  There are large, national data sources that collect data yet they also turn around and license this data to resellers.  There are small, niche data sources that bring value to specific slices of the marketplace.  Then, there is every size, shape, and type in between.   At the end of the day, there are so many options that there truly is an optimization that can be performed when assessing data sources for your business.

Leveraging an analytical, and business-based, approach to data acquisition can add extremely valuable profit to your bottom-line.  Revenue that currently was being eaten up as expenses paying for unneeded data sources can now flow through and be used for better opportunities for growth, research, testing, etc.

However, it is critical to understand how best to proceed down the path of data source optimization.  Some of the dimensions in this type of process center around data licensing costs, marketing costs, number of consumers, accuracy of consumers, numbers of data elements, types of data elements, and others.

Once all of this information, and more, is gathered then a tiered/stage analysis exercise determines empirical value in each data source.  Combining the empirical information with business-based observations brings this process to the finish line.  The result is an optimized set of data that brings the best overall value to your business.