As I covered in my recent blog, homebrewed analytics can solve a world of problems. However, your media company may not have the vast resources of News UK to go build yourself INCA (their analytics toolsuite) or build yourself a STELA (the NYT’s own toolsuite). But a smart publisher can allow commercial tools to provide some good, everyday utility and focus their effort on money questions (literally and figuratively).
For example, La Nación in Argentina presented at our last master class series this spring how they created a score that could speak to the quality of an article to understand how it would contribute to further habituating users in their journey to a subscription.
Victoria Riese, the company’s chief data officer, said that beyond helping the newsroom understand the immediate value of articles, it also helped the newsroom identify new opportunities in areas where they could tell appetite was untapped.
Now, I love when analytics provide good insight on what happened, but I like them even more when they point the way forward.
At Groupe Les Échos-Le Parisien in Paris (disclosure: I was once the chief digital officer of Le Parisien), Violette Chomier, their chief data officer, explained what led them to focus on building a score for their subscribers — not so much focusing on content analytics, but rather, bringing content analytics back to their CRM data to identify users who needed to be sustained in building healthy habits so they would stay on as subscribers.
Violette identified one important characteristic of a good opportunity for a homebrewed metric or tool: working on something where enough data is known. The reason Violette and her team went after churn first, she explains, is that all users are known and in the CRM.
Of course, a similar score for users who may just be on their way to subscribe would be interesting, but many such users are not logged in and working with anonymous cookies is full of pitfalls. But cross-referencing CRM and content analytics were two reasonably clean inputs and a good base to build on.
This type of effort can come together in a reasonable amount of time.
For example, Gazeta Do Povo in Brazil worked on a propensity to churn score as part of the Meta-supported audience analytics accelerator that INMA ran for a good part of 2021 with publishers based in Latin America. Much like Les Échos-Le Parisien, they used their Salesforce data, with their content analytics to modelise what may signal a future churning user. The whole project, once well defined and the team assembled, took four weeks to build end-to-end.
Of course, as with any new useful insight acquired, your “problems” begin once you’ve eaten the Fruit of Knowledge. It’s likely going to be a good deal harder to put in place the various remedies and plans to try and buttress these flagging users. But Gazeta Do Povo now has a way to identify what behaviours tend to be associated with a future churning user.
These types of metrics are, of course, the most opinionated of all. The specific blend of signals that worked for La Nación to determine quality is specific to their content offering, voice, and audience. Both in terms of the design of the blend (what goes into it) but also the calibration of it (how we refine models), there is something that is going to be hard to put into a box for all to use and sell commercially.
So if you think of how to orient your data resources to identify where homebrewed analytics could be most useful to you, there are at the intersection of:
[where you care most to impact] For example, subscription; for example, for a publisher with a significant ad business, the trade-off between ads and subscriptions.
[where your own qualities as a publisher are the most unique or the most tied to your brand] If you’re a long-form publisher, you know that is hardly the most common type of publisher out there. What are metrics that would speak to how users relate to your flagship content? What are edge cases of behaviour that are uniquely prevalent in long-form content (likely: article reading completion may look quite different than for general news publishers).
[where you know multiple systems in your company have partial information on the question, but you need to see the intersection of this information]
There are a million questions one can ask when you start to look at the specifics of any one publisher. But your understanding of your own product will necessarily lead you to identify that where you are the most unique is where you’d have the most opinions for how analytics should be obtained, parsed, and contextualised.
As it happens, where you are most unique is probably your competitive advantage, too. This is the place to look for discrete questions you could not only want to do data analysis on, but imagine what it would mean if such information was readily available by heading to an always-on tool?
If you keep such questions finite enough for a first pass, I’m sure you’ll find some great opportunities for some simple homebrewed tools to shine light where commercial tools couldn’t go.
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