Posts Tagged ‘ EDU LEADS ’

3 Reasons Why “Big Data” Isn’t Really All THAT Big

Quality Payday Leads

Over the last couple of years, Big Data has been unavoidable. It’s not just big, it’s massive. If you throw a stone down the streets of London or New York, you’ve got as much a chance of hitting a big data guru as you do a social media guru.

Undoubtedly, there is great power in data, but is Big Data all it’s cracked up to be?

50% of my brain thinks Big Data is great, and 50% of me thinks it’s a neologism. I’ve found it difficult to reconcile all of the varying information out there about it.

So join me for the first part of a two-part series looking at Big Data. In part one, I’ll look at Three reasons why Big Data is a big load of baloney. And next week in part two, I’ll look at Three reasons why Big Data is awesome.

1. Big trends are trendy

My pet rock still hasn’t moved, and my Tickle-Me-Elmo still won’t shut up. And also, Big Data is big, at least according to Google Trends:

Targeted Data

Some other terms once synonymous with the inter-web were pretty trendy too. Remember this one?

Auto Finance Leads

The adoption curve of the term “web 2.0” looks quite similar to where we are now with Big Data. And yet, if you still use the term “web 2.0” in your job, then you probably think the Fresh Prince still lives in West Philadelphia. (He doesn’t.)

The thing about Big Data is that it really isn’t anything new. Cluster analyses, propensity modelling, neural networks and the like have been in use in the marketing sphere for quite some time.

The phrase used a few years ago for this sort of stuff was ‘business intelligence’


But now, we don’t care about business intelligence anymore. Who needs intelligence? It’s over-rated. Like Goethe said, “All intelligent thoughts have already been thought”.

And yet, Big Data is everywhere. Why shouldn’t it be? It’s BIG. However, you ask 10 people what Big Data means, you’ll get 10 answers, none of which make much sense.

Maybe it’s because of this:


We’ve all seen Moneyball and read Nate Silver’s blog. There are people out there who are better at statistics than you. And this is scary.

So what’s the solution? Throw a bunch of money at Big Data, whatever it is, and sleep soundly knowing that you’ve gainfully employed a math graduate.

And therefore, Big Data is a big load of baloney.

2. Missing one V

Gartner defines Big Data as requiring Three V’s: Volume, Velocity, and Variety. So let’s look at this a bit deeper.

Volume of data: for sure, there’s loads of data out there. Huge amounts. Check.

Velocity of data: yep, data is moved around in large quantities faster than ever before. Check.

Variety of data: in most digital marketing ecosystems, there are the following types of data (yes, I know there are more, but for the sake of argument bear with me):

  • Site stats.
  • Email engagement stats.
  • Mobile/SMS stats.
  • Past purchases.
  • Demographics, preferences etc.

And within each of these, the options are finite. For example, in email, most people measure (at the very least) opens, clicks and conversions. That’s three types of data.

And for all of the other areas above it’s the same. For the sake of argument, let’s say that we’ve got 30 types of data in total.

This is the thing. 30 types of structured data. Processing this data doesn’t require a super-computer, it simply requires robust statistical methodology.

So, if you’re a digital marketer, what you actually have is ‘a few sets of structured, small data’, not ‘Big Data’.

And therefore, Big Data is a big load of baloney.

3. You can perfectly predict the past

With the beginning of the National Hockey League’s 2013-14 season fast approaching, I’ve been spending a lot of time lately trying to determine the best bets to place on the eventual winner.

And of course, it seems Big Data is the best route to my next million dollars. (Btw if anyone is interested in joining my hockey pool then drop me a line – go-live is 1st October!)

I downloaded as many team statistics as I could from last season and embedded them into a spreadsheet. It included rudimentary statistics such as Goals For and Goals Against, right through to Winning % when trailing after two periods, CORSI 5v5, and defensive zone exit rate.

Then I ran a multiple regression and removed non-causal variables. I perfected the model such that the formula spat out expected point totals that were on average within 0.5 points of the actual result.

When I plugged in the raw data from the previous season, the outputted expected results weren’t even close to the actual results.

This is a perfect case of what is called ‘over-fitting’.

When you have a lot of data, the urge is to use all of it and create an uber-complex, bullet-proof formula. Take all of your data points and find the trendline that touches everything. But there’s an inherent problem with this – all you’ve done is create a formula to perfectly predict the past.

The risks that come with an over-fitted model are twofold:

  1. You are assuming that the future will      be the same as the past.
  2. Adding or removing variables becomes      extremely difficult and risky.

So despite there being lots of data out there, the dominant strategy is to focus on the causal variables. In the hockey allegory above, while I won’t reveal my secrets, two of the stronger predictors of eventual success are goal differential and shot differential.

Not rocket science, I know – if you take more shots than your opponents you’ll generally score more goals than your opponents. However, I did learn to remove strictly correlative variables (such as Faceoff Win %, PDO and punches thrown).

Instead of focusing on Big Data and its billions of variables, I’m instead focusing on a small amount of variables that actually matter.

Within your organisation, what are your causal variables? By looking at all the Big Data available to you, you run the risk of the truly valuable signals being obfuscated by irrelevant correlates.

And therefore, Big Data is a big load of baloney.


I do too. Well, 50% of me does. Feel free to elaborate on your point of view in the comments section below.

Parry Malm is Account Director at Adestra and a guest blogger on Econsultancy. Connect with him on LinkedIn or Google+.

Topics:Data & Analytics

by caesararum

WWW.APEXDM.NET  “Your Turnkey Solution to Leads & Data”


Optimizing the Call Center through Improved Targeted Data Analytics

Are you confident that your call center’s lead generation activities are targeted to reach out to the prospects that are more likely to respond positively? Often times, the answer turns out to be “What is targeting?” Let’s take a look a case study featuring call center lead generation efforts for commercial banking loan products.

In this case study, among the available prospect data records, only half were contacted each month, leaving the other half of the prospect data records untouched. The initial list selection was based on annual sales/revenue, which succeeded in eliminating the poorest performing prospects. However, those prospective customers were not further prioritized for their call center representatives to focus on the best prospects.

Adding marketing analytics to the mix improved lead generation results. Here’s a snapshot of the data analysis and recommendations made with the intent to increase the lead generation conversion rate:

Added filters to the prospect data to combine any call disposition history,

Created metrics that would track and measure lead conversion data,

Introduced third party demographics into the data to determine if prospect record prioritization
based on predictive modeling could improve their lead generation rates.

This analytical approach focused on leveraging important customer/prospect data history that the client maintains for each business. The historical data they were already capturing included: call outcome detail by month and lead disposition outcomes. As with any call center, leads could not be generated until a sales rep initiated a live discussion with a decision maker or buyer.

By incorporating an estimate (score) of each business’s likelihood to generate a live contact, the sales conversion model expected performance (aka “model lift”) to improve. The resulting scores enabled ranking that was not only reflective of the best prospective businesses but also of those most likely to generate a connection to a live person (instead of voicemail, ring/no answer, wrong number, and the like).

The initial results were quite encouraging, with a projected one-year increase in profits of $1.5+ million from the lead generation efforts. While maintaining consistent staffing and call activity levels, lead referrals for this client have increased 28%. In addition, the successful close rate of those leads has improved 10% and is expected to climb higher with additional time to book pending business. While a traditional method for building a customer look-alike model or a conversion model would have enhanced results beyond random calling, additional improvements were achieved by turning call disposition data into additional insights.

This is just one method of marketing analytics you can apply to your customer data to increase ROI through your call center or sales efforts. Optimizing your customer and prospect data before reaching out and scoring your prospects based on their interaction history and likelihood to respond can create efficiencies and enable your sales force to work more effectively on targeted lead generation efforts.

Paul Raca is the Vice President of Marketing Analytics at SIGMA
Marketing Group

Marketers Plan to Allocate More Resources toward Online Marketing Next Year to Boost Student Prospecting, Enrollment and Retention


Survey: Higher Education Marketers Credit Online Lead Generation for Half of Qualified
Inquiries, Yet Most Invest a Fraction of their Budgets in Online Marketing

Marketers Plan to Allocate More Resources toward Online Marketing Next Year to Boost Student Prospecting, Enrollment and Retention

Higher education marketers, in large part, have been slow to invest in online marketing; however an imminent paradigm shift looms according to a new survey, the Study of Institutional Goals for Student Recruiting and Retention. The survey conducted by a leading interactive marketing company focused on
helping institutions find, enroll and retain students, shows that 42 percent of respondents credit
online lead generation for the majority of their qualified leads. However, 65 percent invest less
than 20 percent of their budgets in online marketing. Realizing a need to adopt new technology,
half of respondents say they intend to increase their commitment to online marketing in the
coming year.

“In an industry that each day becomes more competitive and complex, higher education
institutions are looking for proven methods of recruiting students, and are embracing technology as
the solution,” says Steve Isaac, EducationDynamics Chairman and CEO. “Emerging technological
marketing platforms allow institutions to reach out to prospective students in their space on their
terms, in a way that actively engages them, creating a personalized experience.”

Nearly 100 not-for-profit higher education marketers attending the American Marketing Association
conference in San Diego November 11-14, 2007 participated in the survey, designed to capture the
current trends and practices in prospecting, enrollment and retention. Survey highlights include the

Student Prospecting

• Only nine percent of respondents devote more than 40 percent of their budgets on online
• While most survey respondents (50%) say they plan to increase their online marketing
budgets in the coming year, no respondents anticipate decreasing their investment.
• Surprisingly, tools to engage parents throughout the student lifecycle were attractive to this
crowd with 55 percent of respondents saying that online parent communication and
networking would be helpful in both converting inquiries into students and reducing attrition
among students.
Student Enrollment
• While 41 percent of institutions gauge marketing success by the rate of inquiries to
enrollment, 9 percent of schools have no way of measuring this success.
• In what is becoming a more metrics-driven industry, only 26 percent of respondents know
their student cost per start.
• When asked about technology solutions, 81 percent suggest that customized online
academic and social networks would be helpful in enrollment efforts.
Student Retention
• Respondents overwhelmingly (76%) indicate that customized online academic and social
networking that commenced before or during orientation would be helpful in retention
• 82 percent would find an early warning alert system that notifies administrators when
current students are struggling or dissatisfied to be helpful.
• Among institutions that offer freshman seminar classes (61%), marketers report that the
biggest obstacles are lack of hard data showing the course effectiveness and participant
satisfaction(34%), lack of student interest in taking the course (13%), and faculty
disinterest in teaching freshman seminars (14%).

Our Leads Are Looking for Your School

ApexDM’s On-Line Education Network is focused on the continuing education marketplace and focuses its resources on serving the specialized needs of its customers.
Because of our diligence, ApexDM has become an expert in delivering highly qualified student enrollments to our college and university customers while increasing our client’s geography and demographics.

Creating Partners, Not Customers

ApexDM provides our clients with turnkey lead generation solutions to fit their unique needs. We strongly believe that having a customer focus is the key to success and in turn has contributed to building our business by establishing strong partner relationships.

Why choose our Education Leads?

Real Time Delivery ensures higher closing.
All our leads are 100% Exclusive.
No Setup fees.
No long term commitments.
Genuine lead replacement policy.
Highest quality maintained and assured

ApexDM specializes in all types of EDU Leads. Contact us at to get started.