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News Tribune mines Big Data for reader acquisition, retention


Big Data is a buzzword that so many use. But do we really understand what it means and how to use it?

According to Webopedia’s definition:

“Big Data is ... used to describe a massive volume of both structured and unstructured data that is so large that it’s difficult to process using traditional database and software techniques. In most enterprise scenarios, the data is too big or it moves too fast or it exceeds current processing capacity. Big Data has the potential to help companies improve operations and make faster, more intelligent decisions.”

But how do we use these huge databases we have compiled over the years at our organisations?

We have leveraged this data in several ways here at The News Tribune, and it is starting to pay off for us with audience growth.


Several months ago, I wrote about a carrier sampling programme we were starting, but we didn’t really have results to speak of at that time.

The programme started on May 20, and, to date, we have started 10-week sample subscriptions at 3,5000 homes in our primary market. The first homes that we started samples at are just ending their subscriptions.

So far, we have closed about 1.5% of those as new paid subscriptions, and that is before we have had door crews or telemarketing reach out to close the sale.

We accomplished this programme through a programme called MMS from Marketing Solutions Group, and we were able to segment non-subscribers from a group of our top 10 ZIP codes in the market based on pre-print revenue. We then further segmented these by those that had the best reliability of address data to be sure that we would have reliable data.

So far, the programme has been a success for advertising as an AAM-approved home delivery targeted programme, and it is looking to be a win for our audience team as well on the acqusition side.


We have not focused on retention as much as we should, but we partnered with INKA Solutions earlier this year to measure retention data for us.

The results have been interesting thus far.

One thing we found in the last few months is that our Easy Pay orders, which one would think would have the best retention, do not. Instead, our best 26-week retention is our 10- or 12-week order at the kiosk. This has caused us to re-evaluate the way we sell, not only at the kiosk, but through all channels.

Another finding could lean toward the acquisition side, but we found it through the use of the same retention tool. Retention for telemarketing at 13 weeks on the paid side was 85%, and on the billed side was hovering around 50%.

We found that if we worked with our telemarketing vendor, it may make sense on a cost-per-order basis to actually sell more billed orders, and increase our volumes, while not affecting our total solicitation spend. We have not pulled that trigger yet, but it’s an interesting idea.

That is really the key of having these tools to anlayse your audience data. You may not always hit a home run, but a lot of singles can win the game.

Social data

This is one of the more interesting tools that I have not really explored fully yet, but the dabbling I have done leads me to believe there can be some great uses.

Facebook has a custom audience tool that allows page administrators to upload e-mail addresses from their own database. One can then analyse an audience that has matches with a Facebook profile to the profile of all Facebook users in general.

Facebook will also then find users in the market that match that same demographic information, and we can target them with subscription offers.

When testing this, I uploaded all of the e-mail addresses that we had on file for The News Tribune readers.

Our audience skewed older than the total Facebook user age, but that was not really a surprise. Our audience also tended to be married twice as much as the average user. How they accessed Facebook was another interesting stat, and leaned toward desktop. 

Useful information gleaned from this study for our advertising team was that our readers also tended to own a home, use credit cards, and shop at high-end department stores much more than the average Facebook user.

I also uploaded our non-subscriber data into Facebook and got data on those users as well, which differed in many cases significantly from our current audience.

Many may think that this is all information that we already know. The key is that this data is free. We didn’t have to pay Facebook to upload my list and were able to get this quick analysis in a matter of minutes.

Big Data doesn’t have to be scary; you just have to know how to break it down in small chunks and then use it accordingly. That is what we are doing, and that is what other media organisations need to focus their efforts on as well to continue to compete.

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