Great data — like great content — should not be free
Satisfying Audiences Blog | 21 July 2015
The news media industry has beaten the drum on the importance of charging for great content. The argument goes as follows:
First, great content has a tangible cost — after all, the story does not write itself! Further, if readers believe that content has zero value, they will treat it as having zero value. This only perpetuates the very phenomenon (readers turning to awful Web sites purporting to be “news” outlets for free news, which may or may not be accurate) that news media organisations are striving to avoid.
So if the customer will not value the content unless the news organisation charges for it, the only way to be properly compensated (and keep on the office lights) is for there to be a real charge for great content.
Make sense to me.
However, if this is true, why do news media organisations refuse to pay for great circulation data? After all, the very same arguments can be made for the critical importance of great data, yet news media organisations repeatedly react to data vendors the very way that they chastise readers for reacting —news media organisations would rather pay nothing for awful data (which may or may not be accurate) than pay something for quality data.
Why is this? What exactly is high-quality circulation data? What should high-quality circulation data cost? And once that data is available, what should be done with it to extract the largest possible return on investment from it?
Why do news media organisations resist paying for great data?
News media companies engage in this type of self-defeating behaviour largely out of what I refer to as “The Two Hs” — habit and hubris.
News media executives believe they can generate great data on their own, much like the sick patient who believes he should be able to self-diagnose his cancer … after all, he knows his own body better than anyone, right?
But our experience is that news media executives cannot do it. As someone who has generated quality data for clients for more than 20 years, I know from experience that I can do it more quickly than you and I can do it more cheaply than you, namely because I have a massive head start over you (For more on this important topic, see my prior article “When outsourcing Big Data makes sense”).
But most news media executives choose to ignore this reality and instead rationalise that great data is simply too important to rely upon externally. Big mistake.
What is high-quality data?
Simply put, circulation data is only valuable to the extent that a news media executive can do something valuable with it. This means that, at a minimum, the data must possess the following characteristics:
- The data should calculate and track the right metrics: Rate of return? Net margin? Net margin surplus? Net margin surplus per copy? Copies per dollar invested? Knowing which specific metric and having that data available for each specific situation is critical.
- The data should be holistic, incorporating all of the economic drivers of performance, e.g., circulation revenue, advertising revenue, newsprint and ink expense, delivery expense, acquisition expense, etc.
- The data should be accurate: It should avoid averages, or at least provide an understanding of the distribution around each average. It should be updated on a monthly or weekly basis.
- The data should be detailed: High-quality data needs to be collected at the right level of granularity such that the data will provide accurate insights as to how to change business strategy. In the case of subscriber acquisition, this typically means that data must be collected by individual subscriber and then bundled into micro-targeted segments — by acquisition channel, delivery frequency, and original subscription term, at a minimum.
If your data repository does not possess each and every one of these characteristics, then you don’t have high-quality data.
What should high-quality data cost?
Our experience is that the cost associated with collecting, processing, and interpreting the right kind of high quality circulation data costs approximately US$0.03 to US$0.05 per paid start per month, or US$0.36 to US$0.60 per paid start annually.
Think about that for a moment. Our experience with top 50 newspapers is that their average direct cost is US$30 to US$40 per paid start, in the form of vendor commissions, bonuses, direct expenses, etc. If we add indirect expense, such as labor costs from circulation staff, costs could increase by another US$5 to US$10 per start.
Thus, if the average fully loaded cost of a paid start is US$35 to US$50, the incremental cost associated with optimising those costs in the most efficient and productive way possible is less than 0.7% — 1.5% of the total acquisition expense budget.
Is this amount significant? It depends on how you view the value of data. If high-quality data generates annual improvement in circulation cash flow that is 10 to 50 times the expense (which is what our experience has shown us to be the case at many newspapers), then an investment of 0.7% to 1.5% of the total acquisition expense budget is miniscule, especially when one considers that it can be recouped in as little as one month.
Still skeptical? Let’s consider the architect.
An architect designs buildings and in many cases supervises their construction. The architect has a vast reservoir of knowledge related to the intricacies of creating and refining a home’s blueprint, taking into account cost, differing materials, time, feasibility, rules and constraints (e.g., labour cost, scheduling) and a myriad of other factors.
Could a builder or even a homeowner take on the responsibilities of the architect, since those costs are “not needed?” Theoretically, of course he could… but why would he want to do that when an architect can do it more quickly and more cheaply than a homeowner can?
Architects typically charge 5% to 15% of total construction expense, all while never touching a brick or piece of wood. Yet news media executives complain when asked to spend no more than 1.5% of their total budget for a detailed blueprint that allows them to maximise their return on circulation investment. Simply put, 0.7% to 1.5% is a lot more affordable than 5% to 15%, all for the same function.
Why is data alone worthless?
Here is perhaps the most important question of all. Now that we have established how critical high-quality data is toward optimising circulation, it is time to acknowledge it is worthless on its own. This is because high-quality data is not an end in itself. It is a means to a much more important end, which is knowing how to interpret the data and knowing what to do with it. A recent experience helps to highlight this important distinction.
Impact Consultancy operates in an industry where other consulting firms purport to compete with us. Some of these competitors tell news media clients that their service is identical to Impact’s offering, since – they assert – that they extract similar data, calculate similar metrics and create similar reports.
However, when it comes time to use that data to offer concrete analysis and recommendations, one prominent “competitor” recently offered one of our newspaper clients a recommendation for a retention initiative that was the exact opposite of the recommendation Impact had made, even though we were using the same data and calculating similar metrics.
How could this be? How could anyone look at similar data and reach different conclusions?
Because data is effectively a Rorschach test. It is subject to interpretation, and we see what we want to see in data. Anyone who believes that the right data will simply illuminate the sole optimal strategy is misunderstanding the true value of data.
High-quality data is absolutely necessary but not sufficient. The data in and of itself is not valuable. It is the recommendation based on the data that is the real value proposition.
Going back to the example of the firm “competing” with Impact, within about 90 days it became clear that the retention initiative Impact had advocated was a substantial success, which only not only demonstrated that the “similar” competing service was not similar at all, but it also highlighted the reason why high-quality data is worthless on a stand-alone basis.
Bottom line
In sum, what is high-quality data worth? By itself, not very much. But if the data is used to derive the proper insights for business change, it is invaluable, worth far more than its cost. Consider this the next time you fall in the trap of “The Two Hs.”
Don’t let this happen to you. Recognise that if you want great data, it is not free.
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