New Editorial KPI — Deep.BI’s Content Attribution Scoring for Publishers

Deep.BI
Deep.BI
Aug 23 · 4 min read

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Making the most out of each conversion

Deep.BI’s newest platform functionality called “Deep Content Attribution Score” (DCAS) is designed to give editorial and data teams better insight into what drives user conversion — specifically subscription purchase. With our scoring system, a successful interaction with a user is not simply tracked by a subscription purchase in the moment, our algorithms analyze a user’s website and content interactions up until the moment of subscription and turn them into an analytical tool for more precise decision making. This new functionality offers editors and analysts new data to make strategic decisions about content production and editorial resource allocation from a whole new perspective.

Where did that conversion come from?

Imagine a successful subscription purchase not as a one-off event — picture it rather as the end of a complex interaction of both your website and content with the user. From the initial moment of interaction with your content until final conversion, the user undertakes a journey with precise and measurable interactions. When these interactions are analyzed, they reveal significant insights about user behavior and cast a spotlight on your highest-value content.

The Times of London used a similar strategy to boost their subscriptions by 19% overall and increased their loyal readership by 2 million within a year. Our scoring methodology reduces the workload required — from 3 months by 8 full-time analysts, to a couple hours by our algorithm — once your data structure is determined.

DCAS tracks each user interaction and aggregates them, providing critical data about content that drives conversions. Do you want to know which content converts users to subscribers, or, going even deeper, do you understand which authors drive subscriptions and which topics or pages attract the greatest and most successful interactions? This is what our Deep Content Attribution Score will help you decipher.

The graphs below show individual article-read scores, tracked by day, until subscription, for two users.

Content+Attribution+Scoring+User+Journey+for+Users+A+&+B
Content+Attribution+Scoring+User+Journey+for+Users+A+&+B
User Journey Visualization Using DCAS Along Each Conversion Step

User A took 7 days from the moment of first interaction until subscription, while User B took 40+ days and more articles until the purchase decision. According to our scoring methodology both subscriptions occured when the aggregated DCAS from all the articles read reached 1.00. The number below the graphs show the total number of articles read prior to subscription.

The characteristic hook shapes on all the graphs represent the natural growth of a user’s engagement — from the first ‘hook’ until the final ‘close’. For further analysis, the data can be sorted by author, topic, and/or section according to the needs of various stakeholders.

Find out more about DCAS

Single Article and Author Attribution Scores

Below is a dashboard with two elements: one that shows a Single Article Attribution Score for all users over a 4-month period in the Content Attribution Score column; the second element represents the aggregated Total Article Attribution Score of 106.78 — the number of subscriptions attributed to this specific article through the combined subscription path of all the users who read it and then subsequently subscribed during this time period.

Single+Article+Attribution+Score+-+4+Month+Period
Single+Article+Attribution+Score+-+4+Month+Period
Analysis of a Single Article’s Attribution Score — an aggregate of the Attribution Scores of all the users who read this article and went on to subscribe.

The dashboard above visualizes the performance of a specific article; different dashboards, though, can reveal score analysis of, for example, all articles written by a single author (first dashboard below) or a comparison of all authors writing for the website (second dashboard below):

Single+Author+Weighted+Attribution+Score_+4+Month+Period
Single+Author+Weighted+Attribution+Score_+4+Month+Period
Analysis of a Single Author’s Attribution Score (Absolute and Weighted Scores) — an aggregate of Attribution Scores to this author’s articles that contributed to users’ subscription path during that period of time

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

Content+Category+Weighted+Attribution+Score+-+4+Month+Period
Content+Category+Weighted+Attribution+Score+-+4+Month+Period
Comparison Table of Different Categories’ Attribution Scores (Absolute and Weighted Scores)

Using this approach, it is possible to gain a deeper understanding of how authors, content sections/categories and different sources of traffic even are contributing to the overall financial health of your publication. It also enables you to plan future editorial resourcing based on past content converting users to subscribers; populate your recommendation widgets with greater accuracy; and identify ROI on different types of content and the current profits your content produced weeks or months previously. Editorial teams will benefit by providing coverage users care about and pay for, yielding maximum revenue.

Want to learn more?

Deep.BI is offering a chance for publishers to see how our Deep Content Attribution Scoring engine can help grow your audience and its engagement. With your historical subscription data and user interactions we can provide a one-off analysis to show what content on your website drove users to subscribe — across their whole user journey.

Contact Deep.BI today and let us make your data start working for you.

Published by Jessica Sirotin on August 23, 2019

Deep.BI

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Deep.BI

AI-powered, subscription sales intelligence platform for media brands.

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