Topic radar:
Write what is in demand right now
Your readers tell you every day what they want to read - through internal search, their click paths and newsletter opens. A radar bundles these signals into a weekly topic demand map for newsroom planning.
01 The problem - Production follows tradition, reading follows need
Demand and supply can drift far apart in a newsroom
Norwegian publishing group Amedia found in its data analysis that its most-produced section - culture - was read by just 2.6 percent of subscribers; production routines grown over years had decoupled from demand. After the data-driven realignment, flanked by a new live-sports product, the subscription base grew by 5.6 percent within a year.
The same diagnosis is possible in any house without a single external tool: internal search queries reveal unserved demand, conversion data shows which articles trigger subscriptions instead of just collecting clicks, newsletter clicks show topic cycles days ahead of the site statistics.
Not writing more. Writing the right things - and knowing what that is right now.
02 The model - Topic clusters with demand and conversion value
NLP clustering across articles, search queries and newsletter clicks, scored by subscription impact per topic
Unterversorgte Themen-Cluster mit Conversion-Historie: 14
gap_score conv_je100
energie_wohnen 0.52 11.8
schule_kita 0.44 9.6
nahverkehr 0.38 8.1The radar scores topics not by page views but by subscription impact: which clusters sit disproportionately often at the start of a subscription journey, which keep existing customers active? High-reach click topics and high-converting retention topics are rarely the same - only this separation makes topic planning steerable.
03 Business impact - More sales from the same newsroom output
The lever: 8 percent more new subscriptions through demand-led topic selection
No round number: every assumption comes from the sample publisher and is stored centrally. With your real figures only the input changes, not the method.
| Item | Value |
|---|---|
| New digital subscriptions / year today | 13,200 |
| Uplift through demand-led topic selection | 8 % |
| Baseline: Additional subscriptions / year | 1,056 |
| Lever: Additional subscriptions × 7 paid months in year 1 × ARPU | 1,056 × 7 × €10.50 |
| Result: added revenue / year | €77,616 |
Assumptions of a sample publisher - in a real project your data replaces these values.
The radar replaces no editorial judgement - it makes it informed. Journalistically essential coverage stays set; the lever arises where the choice is between topics of equal merit anyway. Mather Economics documented in the US election cycle how the conversion share of a topic cluster can triple within a month - once you see that in your own house, you plan differently.
04 Next steps in your publishing house
From your existing data to a weekly topic map
Internal search, article performance and newsletter clicks flow into one shared topic data base - everything already exists.
The topic model is validated against 12 months of archive: you see which topics triggered subscriptions - and which only clicks.
Weekly demand map into the editorial conference: "These five clusters are underserved and converting - the next investigation pays off here."