Launching a paywall is easy. Pivoting a whole business from an advertising-centric mindset to one focused on reader revenue is not.
Despite 10 years of experimentation and increasing attention, the news industry still needs to develop some of the attitudes, technology and skills needed to sustain exceptional membership and subscription programs.
To succeed with reader revenue, news publishers need to better understand readers and all aspects of the subscription business — data, insights, marketing, loyalty, conversion and retention. That will require fundamental changes in our organizations.
I have spent the last seven years, first at The Boston Globe and most recently at McClatchy, working with teams dedicated to making this transition. This report is intended as a brief guide to the technical and strategic challenges that arise when building a subscription program. There is no “one-size-fits-all” solution, but everyone can be smarter about using data to support reader needs and grow subscriptions.
Among the work to be done:
- Data collection: We must identify, collect and store the relevant reader data and understand its importance.
- Data analysis: We must analyze this data and develop insights to support business decisions and activities.
- Data action: We must eliminate internal business silos and streamline our operations to let data drive our decisions and to reduce the time to market for new products and features.
- Make reader revenue the top objective: Everyone in the business must be aligned around a core vision, goals and incentives that support reader revenue growth.
- Provide a better user experience: We must improve the usability of our websites and apps to increase reader loyalty and engagement.
- Test and measure: We must deploy tools to test, market, and measure the performance of our subscription programs.
- Establish a reader-first culture: We must recognize that technology is only part of the solution and take steps to enable the cultural changes necessary for us to put our readers first.
Every organization is at a different stage in the effort to become data-centric and reader-focused. This report will discuss the steps, decisions and actions needed to transition from a “mostly advertising” revenue strategy to a “reader-focused” approach that balances advertising and subscription revenues while building products that align with those goals.
In each section that follows we will review key questions and tactics, and provide some examples of the discussions to have and the decisions to make, and some of the vendors or technologies involved.
We also must recognize that any effort to improve our understanding and use of data must support a mission to publish meaningful journalism that serves our communities. No data and business strategy can succeed without a readership that finds value in the news coverage we produce.
What it really means to shift to reader revenue
Let’s consider what it means to truly shift your business to prioritize reader revenues. Because to fully commit to that change, you have to commit to a new focus on trust, loyalty, data, and understanding your audiences.
A “pivot” to reader revenue is a pivot to trust
The consumer’s journey to subscribing is simple in the abstract: going from passive awareness of your company to active support of your mission. But news is a unique product — it makes claims about the world, tells you what’s true and sometimes what to believe — so trust is distinctly important to a willingness to subscribe. As such, improving trust in our digital products equally benefits editorial and revenue goals, and must be an active focus.
Publishers are well positioned to benefit from the public’s growing data privacy concerns. The backlash to Facebook’s handling of user data and the new General Data Protection Regulation (GDPR) in the European Union show that people are uneasy. But while data is key to our subscription strategies, news organizations often have a local relationship to their readers and can earn and honor trust to manage the information we collect.
Reorienting ourselves to focus on subscription revenues requires not just adding a paywall, but overcoming organizational reluctance to focus on the needs of readers instead of advertisers.
To do so, we must develop digital products that value readers’ time, support their needs for reliable news and meet other local information needs. Those efforts must be reflections of our internal culture and mission, through our approaches to user experience research and news coverage, the metrics we use to measure success, and the tactics we use to drive engagement and revenue.
A pivot to reader revenue is a pivot to loyalty
News organizations have made significant investments in recent years to grow audience and increase page views. Among other tactics, we have hired SEO experts and social media specialists; written “attention-gap” headlines and tried distributed platforms such as AMP, Apple News and Instant Articles.
While “visitor growth” is valuable, it has lead us to structure our staff and strategy around audience scale at the expense of audience quality. And when newsroom goals are based on page views — instead of loyalty, engagement or subscriptions — we primarily benefit advertising revenues and at best coincidently support the distribution of quality journalism and subscription growth.
This has created a conflict of missions and ultimately favored advertisers over readers. The results are evident when looking at news websites:
- Average page load times are rising, often as a result of ad units added to the page.
- Ad takeovers and abusive redirects cause daily complaints by readers.
- Auto-play audio and video (valued for its pre-roll advertising) is increasingly being blocked by popular Web browsers.
- Google, among others, is leading the charge to punish “obnoxious” advertising experiences.
As a consequence, we are alienating the audience we need most — loyal readers who visit on a daily or weekly basis.
Reorienting ourselves to a focus on subscription revenues requires not just adding a paywall, but rethinking and overcoming a decade of organizational reluctance to focus on the needs of these valuable readers.
A pivot to reader revenue is a pivot to data
Can you answer these questions about your current subscription business:
- What is your annual revenue per user?
- What is your annual revenue per subscriber?
- Do those figures account for the user’s advertising revenue as well as their print and digital subscriptions?
- How many people must be consulted to gather these answers?
- How long will that take?
The last two questions — “who” and “how long” — are the key to a data-focused strategy. The details of revenue performance can be calculated within any organization. But if they are available only on a monthly basis, or they are siloed within their respective departments and not accessible to everyone, then you do not have a data-focused culture that will allow you to reach your goals.
Building a culture around data means:
- Key executives agree on core enterprise objectives and that alignment is communicated across the organization.
- There are trusted sources of key business data that are relied on across departments.
- Updated metrics are available on a daily if not hourly basis.
- Data is “democratized” across the organization, so that anyone who needs it has instant access.
- Decisions are made using data.
A pivot to reader revenue is a pivot to understanding audiences
Consumers discover and read the news on an increasing number of platforms and devices including social, search, email, web and apps. And publishers attempt to target their audience across the same channels, not just for news but also to sell advertising or to market subscriptions.
Unfortunately, we can’t effectively track individual readers across different devices and platforms. And many of our systems (email marketing and website CMS, for instance) do not typically share information. So in any given month one individual reader may look like six or eight “unique visitors” in our analytics reports, and they may receive multiple and potentially conflicting advertising and marketing messages.
This creates a number of challenges:
- We have no full picture of the potential cross-platform business value of individual visitors.
- We are unable to consistently personalize the features and content of our products to encourage engagement and subscription.
- We are wasting time and money through uncoordinated marketing efforts that lead readers to unsubscribe from our email communications.
- We are speaking to readers in a generic and unpersuasive voice that does not respect their time, appeal to their interests, or increase their likelihood to subscribe.
So, our goals to improve our understanding of readers include:
- Providing each visitor with a more relevant experience and increasing their engagement with our journalism.
- Recognizing patterns among individual visitor behaviors that indicate likelihood to subscribe.
- Targeting content, products, services and offers to potential subscribers.
- Reducing conflicting messages (emails, ads, alerts) that do not serve both a business goal and a reader need.
We can summarize this approach as having a “single view” of each reader and speaking in a “single voice” to them.
How to collect and use the right data about your news audience
To improve our websites, boost reader engagement and grow subscriptions, we don’t want “more data.” We want deep insights leading to a better understanding of visitors’ needs and how they interact with our journalism.
The actual data (web metrics, advertising impressions, and newsletter subscriptions to name a few) are just the raw materials. It is not just collecting that raw data, but using it to inform your decisions, that is a foundational requirement of a subscription program.
Depending on the size of your organization, that may not require a fully built-out data warehouse, a staff of data scientists and a million-dollar marketing suite. But it does require an attention to processes, skills and technologies that may be unfamiliar to many traditional news organizations.
It is not just collecting raw data, but using it to inform your decisions, that is a foundational requirement of a subscription program.
Regardless of the size of your organization, it is likely that the systems and skills needed to run a successful subscription program are currently spread across multiple departments and executive stakeholders.
For instance, the responsibility for advertising revenues and audience revenues commonly reside in different departments — a legacy of newspapers’ print-centric organizational structures.
In the digital world those two businesses are tightly intertwined, and success requires shared goals and close collaboration. The first step is to understand the state of your current data sources and processes.
Perform an enterprise-wide data audit
Create a spreadsheet that lists any system in your organization that creates or holds business data. Note your primary data sources and what each contains, what other systems depend on that data, which staff members access and analyze it, and how it is used. Also highlight where personally identifiable information is involved.
The goal is to capture every operational data source, representing every major internal department or business strategy, and inventory both broad and specific types of information you have access to. If there are any data warehouses already operating within the company, that also should be flagged immediately.
For a print media organization some typical categories of data might include:
- Advertising performance
- Digital subscription records
- Digital content analytics
- Financial reports
- Marketing campaigns
- Newsletter subscribers
- Print circulation records
- Registered users (non-paying)
Within each of those categories might be dozens or hundreds of individual data points: subscriber counts and email addresses for newsletters, the number of daily impressions and clicks for advertising campaigns, or a trend of monthly unique web visitors. Write down as much detail as is practical, and understand you will revisit this audit frequently as your plans develop.
Conduct a staff and skills analysis
Create an inventory of the people, roles and skills currently in your organization that handle data. Focus on the data sources identified in the audit above and document who accesses the raw data; who turns it into spreadsheets, reports or dashboards; who reviews the data to make daily or weekly estimates of business performance; and who analyzes and formats it with recommendations for executive decision making.
Here’s a checklist of questions to address in your skills analysis:
- How many people currently work with those data sets — either gathering and analyzing or utilizing it for decisions and actions? (Typically your reader-focused data is shared across multiple departments. Improving the collaboration between these teams is often a first step.)
- What specific data does each person/group use, and do they access the original source of the data or are they only seeing summaries? (As data is shared around the company some of the original context may be lost, leading to a lack of trust in the numbers.)
- What specific reports and analysis does each person use or perform? Who is the intended audience for each report?
- How often is the data processed and provided to stakeholders for review? (Increasing the speed of decision-making requires data to be analyzed and shared with executives at more frequent intervals.)
- What tools are used to access, process and present data?
- What raw data or pre-set reports do decision-makers access directly?
- For each report or dashboard, what decisions are made based on the data? Who owns those decisions?
- How much time is spent monthly on accessing, processing and analyzing data?
- What recommendations for processes and tools improvement are suggested by the staff that work most closely with your data?
- What specific skills are held by your current “data staff” (such as SQL, Excel, statistics, and database administration)?
Review those findings as your planning continues and begin to identify opportunities in both skills and processes. These gaps will identify the areas of focus as you hire new positions and reorganize current roles to improve your data gathering.
Prioritize data needs
What business questions are you currently unable to answer? These could include average revenue per user; the cost of acquiring new subscribers; which digital subscribers also receive an email newsletter; how often the average print user visits the website; and the average number of visits before a reader becomes a subscriber.
How might connecting some of these data sets enable you to make decisions and take action? A broad roadmap for data integration might be:
- Bring in two closely related data sources (for example, print and digital subscriptions) and utilize findings for “offline” targeted marketing (email campaigns, direct mail) and provide early insights.
- Integrate subscriber data, email newsletters and aggregate analytics data to begin simple on-site targeting of messaging, offers and content; build business intelligence dashboards.
- Integrate audience, advertising, analytics and editorial data to build comprehensive reader profiles and support automated marketing and engagement tactics and machine-learning driven insights.
Data and analytics terms to know:
AI (Artificial Intelligence): Technically describes intelligent machines that are “self aware” enough to adapt to their environment in order to achieve defined goals. Often misapplied to systems that use algorithms to examine large data sets and make discrete choices based on analysis — which is better described as “machine learning.”
Algorithm: A set of rules (from simple to complex) followed by a computer to solve a specific problem.
Best of Breed: The acknowledged leader in a specific technology or category. Typically referenced when building a complex system and buying and assembling individual parts instead of sourcing from a single vendor.
BI (Business Intelligence): The technology systems and process of using data to provide insights into business operations to inform decision making.
Database of Record: The canonical source of data for a particular business system. Also see “Operational Database” below.
Data Governance: Structure and policy dictating what data is utilized in the system and how it is transferred and processed to maintain integrity and business value.
Data Lake: Similar to a data warehouse but the data is stored in its native format. This is often a first step in the data collection process, allowing the data to be gathered in one location before it is normalized.
Data Mart: A portion of a data warehouse that will only contain information from a single department.
Data Repository: A generic term to describe any method of storing enterprise data.
Data Warehouse: A collection of company data organized to support business goals and decisions. The format data is adjusted and normalized.
ETL (Extract,Translate, Load): The process of gathering data from disparate systems, normalizing it to align with your other data sources and then uploading it to your central data storage system.
First-Party Data: Information on visitors and customers directly collected and stored by the publisher. (An advertiser could also have first-party data they use to target visitors on your site.)
ML (Machine Learning): In this context, it is the use of algorithms to create predictive models of user behavior. For example, a large volume of data is analyzed and a “propensity to subscribe” value is assigned to individual visitors based on their similarity to people who previously subscribed.
SQL (Structured Query Language): Used to search for specific sets of records in a database.
Third-Party Data: Information on visitors with whom you do not have a prior relationship. This data is often licensed or purchased to support targeting of advertising.
PII (Personally Identifiable Information): Data in your system that is not anonymous and can be connected to an individual visitor. For example: name, email, credit card number, mailing address. PII must be carefully managed within the system.
UID (Unique Identifier): The creation of a serial number used to recognize the same visitor across different platforms. Used to aggregate reader activity into a single record for analysis.
Visualization Tool: Software that allows the analysis of data and the creation of charts, graphs and reports to aid in the understanding of business performance.
Improving your marketing skills
“Marketing” includes a broad range of decisions: what services to offer, what to charge, how to talk to your audience, and where to deliver your services. Marketers describe this as the “4 Ps” of Product, Price, Promotion, Place. To focus on readers, grow engagement and sell subscriptions, you must act upon all four of the “Ps” plus a fifth: people.
Media organizations have typically focused primarily on “promotion,” touting news coverage or subscription offers. Our longer-term goal is to align our understanding of readers with the development of products and services that encourage engagement with our journalism and pave a path to paid subscriptions. Rather than a small marketing team just promoting current products, the whole enterprise has to be listening, measuring and adapting to user needs.
Rather than a small marketing team just promoting current products, the whole enterprise has to be listening, measuring and adapting to user needs.
A full suite of marketing tools is not required to improve your targeting of potential subscribers. But significant efficiencies are gained as your data is centralized.
For example, the systems used during a single website visit might include:
- Web analytics to track and record visitor behavior on your digital properties.
- A data management platform (DMP) to capture anonymous profile information about readers, including details of their current visit, preferences and past interactions.
- A campaign manager to orchestrate cross-channel messaging to consumers utilizing data from your DMP and data warehouse to target and personalize.
- An A/B testing platform to serve personalized content and test variations of design elements, purchase flows, and messaging to understand which perform better.
- Web/email/SMS, the various platforms by which we deliver journalism to our audience.
And a typical website visit might proceed like this:
- A visitor from Facebook lands on a sports story on your website.
- Their browser has a cookie from a previous visit that contains a unique ID (UID).
- Your DMP has a profile associated with that UID, which indicates the reader has visited five times in a month and read four local news stories. The profile also contains an email address and indicates the user is subscribed to a breaking news email newsletter.
- The DMP reports the visitor is now part of an audience segment — “return visitor, potential subscriber.”
- These details are forwarded to the campaign manager, which instructs your content management system to present the user with a sign-up module for your morning email newsletter and a recommended story list, including local news stories. And the meter on the paywall adjusts to allow an additional “free” page view.
- Overnight, the DMP forwards the updated profile information to your data warehouse for storage; and an automated email is triggered offering the user a 20 percent discount on a digital subscription.
- When that visitor subscribes via the email offer, that event updates their profile which now includes details such as: new subscriber; converted from email offer; conversion visit originated from Facebook; last story read before subscription was Sports.
Each of those marketing actions (steps five through seven) are hypothetical, but reflect a range of available options. In practice, your teams would execute a variety of campaigns and techniques and through iterative testing would continuously improve each process in support of business goals.
The value of a data-driven integrated marketing effort like this is in the ability to test, measure and continuously improve performance. Implemented effectively, this approach can provide a better user experience, revenue growth and cost savings.
If you are operating without a full data warehouse or DMP, it is still practical to use some of the same techniques manually. Your normal web analytics provide many of the details needed to identify likely subscribers.
A simple framework you can use to quantify a visitor’s propensity to subscribe is based on just three things: How did they arrive on your site, where are they located and what section did they read?
To create a rudimentary score for a given reader who just landed on your site, add the value from each row below.
|How the user arrived:||Direct
|Where the user is:||Local
|In same state (not local)
|What content the user is reading:||Local news
Based on a user’s score from those simple signals, you could serve up different subscription offers, related story recommendations, newsletter sign-up options and advertising.So — a visitor who came to your site directly (+2), lives in-state (but not in town) (+1) and read a news story (+2) gets a total score of 5. On the other hand, an out-of-state visitor who arrives via search on a wire story is scored at 1. This is far from analytically sophisticated, but it is based on behaviors known across the industry, that local people reading local content by coming directly to the site are most likely to subscribe.
As your targeting grows more sophisticated, requiring tracking behavior across multiple visits, more advanced tools will be needed. But that eventual need should not stop efforts to convert more readers with the tools you already have. And the use and testing of basic propensity scoring will help inform your later technical and staffing decisions.
Marketing terms to know:
Automated Marketing: The algorithmic triggering of pre-set tactics based on an individual visitor’s behavior on your website. Typically designed to drive on-site engagement, or to follow-up via email with potential subscribers.
Audience Segment: A portion of your overall digital visitors that is identified in the data as having similar properties for the purpose of editorial personalization or targeting of marketing campaigns.
A/B Testing: A statistically rigorous experiment where two or more audience segments are served different elements on a web page to understand if one element supports business goals more effectively.
CRM (Customer Relationship Management): A system to track all contact with customers. Often used in advertising and sales departments, but now also used to track reader behaviors, including web visits, subscriptions and customer service calls in order to understand and serve audience needs.
DMP (Data Management Platform): A system to create audience segments for targeting on internal or external platforms. It include profiles of visitors in a segment, but not personally identifiable information. Also used in the creation of segmented audiences for targeting of advertising.
Enterprise Data: Information available and utilized across an organization, as distinct from being confined to a specific department or team.
ESP (Email Marketing Service Provider): Your email vendor. Examples: Adobe Campaign, MailChimp, Cheetah Digital, Constant Contact, Marketo.
Funnel: A description of the purchasing process for a consumer. Typically covers the steps from “awareness” of a product and ends with “taking action” to purchase it.
Propensity to Subscribe: A score calculated using various metrics to predict the likelihood that a specific visitor will pay for access. The score informs the offers presented to that visitor.
SSO (Single Sign-On): An authentication system that allows registered users to access multiple different systems with one account. For example, a social media account may be used to provide paywall access, commenting and email newsletter sign-up.
Aligning departments to focus on reader revenue
As you build reader data and marketing efforts, questions about organizational goals and structure will inevitably arise.
Your IT, audience, marketing, product, or technology groups may control different pieces of the solution. But as new systems and processes are developed and new skills are added, who is in charge? There is no single answer, but the question must be addressed head-on.
A helpful exercise in alignment is to take a whiteboard and sketch out how your organization would execute a few scenarios:
- The purchase of a new source of third-party data to enrich customer records.
- A marketing campaign targeting a specific audience onsite and offsite.
- An optimization effort to improve conversion within a subscription funnel.
- Design of a personalization feature to drive onsite engagement.
- Creation of a C-suite-level dashboard to provide insights into advertising and subscription progress against monthly or quarterly goals.
- A research initiative to understand average revenue per user and create an ad-free experience for your subscribers.
Those initiatives cross multiple departmental boundaries in most organizations. In each scenario, ask which teams and staff members would need to be involved. How are they aligned to make the work possible? Are there duplicative roles, gaps, or conflicting goals or incentives? How could either the process or structure be improved?
Shared goals and incentives
The challenge of digital media’s current business model is felt most acutely in the tension between our advertising, audience and editorial goals.
Advertising growth depends on page views and an increase in unique visitors. Subscriber growth often relies on metered paywalls that potentially limit pages viewed. Editorial success is less tangible but benefits from broad and unhampered access to readers. Those goals are not necessarily in conflict, but the short-term tactics we use to meet them can and do interfere.
In each case, the raw tactics that may benefit one objective (more ads) might conflict with the goals (premium design, engaged readers) of the other two. Even with the best intentions it is difficult for an organization to navigate those challenges.
The ability to fully answer the following questions gives the organization an entirely new and more powerful set of tools. It allows teams to better understand and balance competing priorities, leading to more collaboration and increasingly aligned goals:
- What would be the impact on total revenue if we created an ad-free digital subscription offer?
- How would page views and ad impressions change if page-load time was decreased by 40 percent?
- Is Facebook or Google a better source of loyal readers and potential subscribers?
- What content is most valuable to local, loyal readers?
New skills and titles
While shifting to reader revenue may require hiring new staff with expertise in data and analysis, the focus should be on skills, not headcount.
The new skills you may be hiring for across different roles could include data analysis; business intelligence tools; Python, SAS, R, SQL; data modeling; dashboard design; probability, statistics, machine learning algorithms and system design.
Among the job titles that might encompass those skills:
- Business Analyst
- Data Analyst
- Data Architect
- Database Administrator
- Data Engineer
- Data Scientist
- Machine Learning Engineer
How to apply data-driven insights that will grow reader revenue
The key cycle for audience and revenue growth is “analyze, experiment and iterate.” Your investment in business intelligence and marketing tools will pay off as you understand reader needs and behaviors, and make decisions and create processes that are data-driven, testable, and support organizational learning and staffing decisions.
Many of these tactics are possible even with a small staff. While technology can facilitate the work, the primary requirement is access to the data and the ability to generate insights that drive action in support of business goals.
And, we are only interested in data to the extent that it tells us how the business is performing and provides guidance on what steps to take next. So, it is important to remember that not all data is equal.
- Basic audience metrics are raw data. (For example: page views, unique visitors)
- Key performance indicators (KPI) are metrics or a combination of metrics directly relevant to strategic business success. (For example: subscription conversion rate, average revenue per user)
- Insights are deeply analyzed KPI that inform business decisions and actions. (Weekend visitors are more loyal than average; newsletter subscribers are more like to become paid subscribers.)
Your specific strategies will determine the KPI and the combination of metrics you rely on for insights and decisions. But for any subscription business a few basic metrics are important:
Conversion rate: How many of the readers who are presented with an offer to subscribe actually complete the purchase. This will be tracked in aggregate, but also for each marketing campaign, acquisition channel (email, advertisement, paywall) and audience segment.
Churn rate: Gaining 100 new subscribers in a month is not a success if another 200 cancel. The volume and cause of lost subscribers is something newspaper circulation departments typically understand, but it is still deeply complex for digital. The churn rate should be measured both in aggregate and in as many segments (for example, subscription start date, original marketing campaign, subscription price) as is practical to identify factors driving cancellations.
Average Revenue Per User: In its simplest form this is calculated on a monthly or annual basis (total revenue divided by unique visitors). For more focused measurements, you could divide total revenue by just the number of paying customers, or divide just subscription revenue by the number of paying customers. Combined with churn rates and overall length-of-subscription averages, you can also calculate Customer Lifetime Value (CLV or CTLV). But none of these numbers have meaning in isolation — they must be tracked over time and in comparison to other business costs.
Cost Per Acquisition: CPA is the key number to contrast against ARPU or CTLV. A customer with an annual value of $100 who required marketing expenses of $50 to acquire will take six months to be “profitable” for the business. Understanding this equation is necessary to measure the success of both your subscription business and your marketing efforts:
Profit = ( (subscription revenue per user * length of subscription) – cost to acquire and retain)
Calculating these KPIs is simple, once you have the raw data available. Getting this data is at the heart of the organizational transformation to reader revenue.
We should only be interested in data to the extent that it tells us how the business is performing and provides guidance on what steps to take next.
Think of a marketing campaign promoting a subscription offer as a very long chain of evidence. Each step of the process must be monitored and protected to maintain the integrity of the data. Even one break in the chain results in a loss of data and a breakdown in any effort to improve business performance.
Three typical gaps in that chain of data include:
- The cost per acquisition for individual campaigns and subscribers. Utilizing “average CPA” will not allow you to monitor and improving your marketing efforts.
- Average revenue per user, especially if the subscriber also receives the printed newspaper.
- Campaign attribution that allows churn rates and lifetime value to be assigned to specific email messages or advertising creatives.
As those questions are answered, you can begin to pursue increasingly sophisticated data analysis and marketing tactics. That could involve:
Creating a “propensity to subscribe” score: As noted in the previous section, making a broad guess at a visitor’s likelihood to subscribe is not difficult. A local reader coming directly to your site for the fourth time this week is a potential customer, while an international visitor following a Facebook link is probably not.
Measuring email effectiveness: Email campaigns have a cost, both in real money and in straining the patience of the recipients. You can seek to reduce the volume of emails sent and the cost per subscriber acquisition, increase open rates and click-through rates for subscription campaigns by targeting readers who are most likely to become subscribers and personalizing messages and offers to those targeted audiences.
Improving reader engagement: Increasing loyalty metrics onsite, including pages viewed and return visits; and increasing the frequency and value to readers of contacts in other channels. Some publishers have improved loyalty and engagement metrics using the following tactics:
- Personalizing recommended stories and filtering out already-read stories.
- Providing cross-platform notifications for topics readers have indicated an active interest in.
- On mobile apps, reducing the number of push alerts sent by letting readers opt-in to specific topics.
- Sending email notifications of breaking news updates to readers who have visited an earlier version of the story.
- Highlighting email newsletters of interest to individual readers, and excluding newsletters which they already receive.
- Customizing email newsletters.
Focusing on subscription conversion: Increasing the number of paid subscribers and reducing the cost per acquisition. You could do this by:
- Continuously A/B testing the subscription funnel to remove any usability barriers and to experiment with messaging and pricing offers.
- Using dynamic metering, with each reader receiving a customized number of pages in front of the paywall based on their calculated “propensity to subscribe” score.
- Delivering targeted offers onsite, via email and as retargeted ads on other websites.
- Tracking subscription starts.
Building a culture that’s focused on reader revenue
Becoming a reader-focused organization is not about technology or data. This report provides a framework for building those tools, but they are just tools. Growing engagement and shifting to reader revenue requires actions across the organization that authentically support an audience-focused mission. Truly pivoting to readers is a cultural transformation, not a development project.
A few mile-markers on that journey:
- Corporate goals and incentives are aligned across departments and all work is prioritized appropriately.
- Structural changes have been made to encourage collaboration and increase the visibility of mission-critical data.
- Meetings include questions such as “What does the data show?” and “Have we talked to readers about this?”
- Investments have been made in data systems, business intelligence tools, user research, and design and development teams.
- UX and design improvements are treated as strategic assets. And, those teams work to make digital products easier to use by prioritizing positive interactions (browsing, reading, watching) over negative interactions (dismissing pop-ups, closing windows, muting auto-play widgets).
- Personalization informed by deep insights is used to drive engagement and encourage account creation and log-in.
- Third-party vendors and partners are reduced and promoted clicks off-site are reduced. Internal promotion and recirculation is highlighted.
- Data privacy is valued and protected by the limitation of third-party widgets and trackers on-site.
- Revenue is diversified and data insights and user research are used to generate new business opportunities. Those choices are balanced against reader perceptions of quality and trust in our journalism.
- Content is more local, but also more in-depth. Newsroom goals will prioritize engagement, not clicks.
- Metrics are focused on loyalty and engagement. The number of visits, time spent, and breadth of sections read are tracked.
- Social platforms are utilized for marketing and outreach, and judged on revenue or loyal users generated.