How Hookup Sites Use The Power Of Data and AI To Match Users

If you’ve ever been on a dating app – especially if you have a profile on one of the better-known ones – then there’s a good chance that you’re familiar with the process by now: You go to the app, scroll through dozens or even hundreds of profiles in order to find one that catches your eye. If you like what you see, you send them a message. Maybe they respond; maybe not. And if things go well, you meet in person and take it from there. That’s all fine and dandy, but does it really work?

They can be difficult to use. It takes time to browse through profiles and figure out which ones are worth sending messages to. This can be extremely frustrating when you don’t know where to look first. They aren’t very useful for finding people you actually want to date. In most cases, you won’t have any idea whether someone you’re interested in will be compatible with you until you meet in person.

There’s a better way, though. There are hookup sites doing things differently, and Fuck Buddies is leading the way. They’ve built a platform designed around the needs of both users and site owners alike – and this is what makes them so effective at matching people up. Let’s dive in and take a closer look, shall we?

What Is Fuck Buddies App?

FuckBuddies.app is a fairly new dating app, having only launched a couple years ago. What sets them apart from the competition, however, is that they’re a lot different than anything else out there.

For starters, they’ve got a focus on casual sex. Most people on traditional dating platforms are looking for romantic relationships; for Fuck Buddies, it’s the exact opposite. People on this app are specifically searching for no strings attached hookups. They want to get laid, and they want to do it as quickly as possible.

That’s why Fuck Buddies app has focused on using analytics based on AI models to find people who are likely to be sexually compatible with each other. Their algorithms work on two levels: First, they try to determine whether or not you and another user would be a good match for each other based on things like age, location, sexual preferences, and so forth. Then they check to make sure those matches are actually open to hooking up.

After all, if you’re open to fucking, that doesn’t necessarily mean you’re going to want to fuck whoever happens to come along. Sometimes, people just want to do it without getting emotionally involved. This is probably one of the reasons why traditional dating sites still haven’t figured out how to match people up.

How the AI models work

Using AI, Fuck Buddies App can distinguish between the people who want to meet for sex and the people who want a relationship. At the same time, they also use what they call “sex filters” to help users discover potential partners who share their sexual interests. When you log into the app, you’ll see a list of suggested people to talk to based on your own preferences; you can swipe left or right on them to indicate that you’re interested.

The AI models are sophisticated, too. They’re able to detect whether or not you’re interested in a particular person based on the words you use in your messages. They can also recognize patterns in your behavior that indicate if you’re willing to engage in certain kinds of activities (for example, some people may enjoy anal sex, while others might prefer oral sex). Finally, they can detect whether or not you’re an active user, and whether or not you’re currently online.

It should be noted that Fuck Buddies App isn’t just a hookup app; it’s also a social network. People can post status updates and photos, and they can chat with other users via text, video, and voice calls. As such, this gives people an opportunity to get to know one another before deciding whether or not they want to meet in person.

The power of analytics

One thing I really like about Fuck Buddies is that they’re using advanced analytics to improve their matching algorithm. This means …

How Adult Dating Apps Track Users in a Multi-Device World

Here’s how popular dating apps like Fuckr app are using Netlify analytics and other tools to accurately measure user growth in the multi-device age. In this article you’ll learn exactly why analytics and a tracking a crucial for dating apps and sites alike, and how new technology is driving a boom in the Casual Sex industry. How c-suite executives are getting more actionable data from analytics can show you just how powerful analytic platforms like google analytics and netlify analytics can be.


Adult Dating apps, have been around for years, but they’re experiencing an unprecedented wave of growth. New dating app launches hit the market each year, with dozens of others failing within their first two months. But there is something different about today’s dating apps.


The industry has adopted a “lean startup” approach to product development, which means focusing on quick iterations that can quickly determine whether or not a feature will be useful to users. This kind of methodology requires startups to gather feedback from users as soon as possible, so that features can be revised before they go live. It also helps them avoid launching products without having all the bugs worked out, which often leads to poor reviews and low retention rates.


One way dating apps are gathering information is by using a variety of analytics tools, including Google Analytics and Netlify Analytics. These tools help founders better understand how users are interacting with their product, while also identifying areas where improvements might be needed. The latter helps them work toward increasing engagement and retention rates.


Some companies choose to use both these services in tandem, while others may opt for one over the other. While it’s true that each tool has its strengths and weaknesses, most businesses will benefit from using at least some combination of analytics tools to get a complete picture of their app’s performance. Let’s take a closer look at what these tools do and how they complement each other.



Google Analytics is currently the most popular analytics platform used by software companies. It provides businesses with access to a wide range of metrics, including how many people visit their site, where those visits come from, and which pages they interact with.


Netlify Analytics is a newer offering, but it offers several advantages over Google Analytics. It doesn’t require third-party cookies to work, meaning that it won’t flag your traffic as coming from someone who has already visited your website. Instead, Netlify Analytics uses a technology called “IP geolocation,” which allows you to track visitors based on their IP addresses.


This may not seem like a big deal, but it can be extremely helpful when it comes to measuring the effectiveness of marketing campaigns. For example, if you want to know whether sending a push notification resulted in any app downloads, you’ll need to be able to tell whether or not the push notification was sent to new or existing users. Using Netlify Analytics, you can easily see which users saw your push notifications, even if they weren’t logged into the app at the time.


Another important advantage of Netlify Analytics is its ability to provide real-time metrics. With Google Analytics, you typically have to wait up to 24 hours before you can begin seeing data, which makes it difficult to make decisions on the fly. With Netlify Analytics, you can start receiving data immediately after you set it up.


On top of these benefits, Netlify Analytics provides more detailed breakdowns of your user behavior. For example, Google Analytics only tells you how many users are visiting your site, whereas Netlify Analytics shows you what percentage of your visitors are returning visitors. This level of detail can prove invaluable when it comes to discovering trends in visitor behavior.


In addition to offering new insights, Netlify Analytics also presents data in a way that’s much easier to understand than Google Analytics. While Google Analytics tends to present information in a dry, hard-to-understand format, Netlify Analytics organizes its data into easy-to-read, visual representations. This makes it far simpler to spot patterns and identify opportunities for improvement.


Finally, Netlify Analytics can be used to drive conversions across multiple channels. If you run paid advertising campaigns, …

The Link Between Customer Success and CFO Happiness: It’s All About Gross Margin

The Link Between Customer Success and CFO Happiness: It’s All About Gross Margin

If you’re a CFO of a SaaS business, one of your biggest challenges is gross margin. As we all know, gross margin is essentially the difference between the revenue for a subscription and the cost of fulfilling that subscription—and with the software industry’s seismic shift from an on-premises model to a SaaS model, we’ve watched gross margins go from about 99% to more like 70 to 80%. That’s a big change.

Unlike the old on-premises model, the SaaS model now has operations, customer service, and other functions to include in the gross margin equation. A SaaS company needs to fulfil the terms of subscriptions, and that entails both “assets” (e.g., computers, bandwidth, operations) and “services” (e.g., onboarding, adoption, retention).

The good news? Customer success organizations have a tremendous ability to increase gross margin—and that’s music to any CFO’s ears.

We usually think about success in terms of creating a competitive advantage through the delivery of a differentiated customer experience along with the differentiated value. But success teams can also create a competitive advantage by adding incremental gross margin—which can then be used to drive growth through investments in development, marketing, and sales.

So how does that work? Let’s consider the dynamics of how gross margin can drive competitive advantage, outside of customer success. Consider data centre operations, for example, One operational strategy is to use a service like Amazon Web Services to provide all your data centre needs. If a company takes this approach, the company has set the lower limit on its operational efficiency around compute capacity—after all, Amazon has a set price for their services that the company has to pay. (By the way, the company’s competition can get the same price and have the same operational efficiency.)

On the other hand, the company can use a co-location data centre to leverage capital more efficiently than AWS would, and hence decrease its operational costs. That decrease in operational costs can translate into increased gross margin—and that increased margin means an ability to invest in development, marketing, and sales to drive growth, which creates a competitive advantage over competitors who are using AWS. (Note that the growing trend is a hybrid model, we’re a SaaS company that controls the fixed part of computing demand and uses AWS or another service to provide the elastic component.)

So let’s compare this example to how customer success can create a competitive advantage through increased margin: Unlike efficiency gains in the data centre, efficiency gains in customer experience can also coincide with increased customer retention rates—giving you in effect a “double bump” in the increased margin.

For example, a successful team can use marketing automation to nurture users and make onboarding more efficient. These process improvements don’t just improve customer experience, they increase gross margin and create a competitive advantage.

As the chart to the right shows, a percentage point in increased efficiency combined with a percentage point in increased retention rates creates a two-point margin advantage. For a $50M SaaS business, that equates to $1M of investment in development, marketing, and sales.

The Implication

Customer engagement and success programs can be part of a multi-pronged approach to competitive advantage. Not only can they create differentiated customer experience and value, but they can also create gross margin advantage through increased efficiency and retention rates.

The success teams that understand and leverage these dynamics can accelerate growth—and, of course, growth creates a virtuous cycle of investment and further growth within the successful organization. Customer success teams can use these dynamics around the gross margin to help secure investments, by using the “double bump” to justify technology and automation with their CFO.…

Scout Customer Success

Scout Customer Success

How do you grow retention rates? By ensuring adoption and reacting quickly when a customer gets off track. Scout Customer Success can help—by making sure you’re always running the right play with the right customer at the right time.

Score health by user, subscription, and customer

Grow customer satisfaction by monitoring the details of customer health. Scout Formulas calculates a score for each purchased subscription and provisioned user, then aggregates the results into an overall customer score.

Coordinate your customer interactions

Get your customer success, sales, and marketing teams working together to run the right play with the right customer. Scout Customer and Subscription Playbooks automate task creation and assignment to align your teams, so you can make every interaction timely and effective.

Avoid fire drills with predictive analytics

Intervene quickly before a customer gets off track. Scout Triggers use predictive analytics to provide proactive alerts on onboarding, adoption, and retention risks.

Accelerate adoption with user nurturing

Grow the value that your users receive by engaging them individually with personalized interactions. Scout User Playbooks integrate with your e-mail system to automate the delivery of training, feature discovery, and guidance on best practices.

Prioritize time with task management

Align and validate the priorities of your customer success team. From within Salesforce.com, Scout Task Manager makes the next-best-action triage simple and clear for each customer success manager.

Maximize productivity with a 360-degree view

Increase the efficiency of your customer success team. Within Salesforce.com, the Scout Explorer provides a 360-degree view of each customer, subscription, and user.

Engagement Rate is the Margin Squeeze in Digital Advertising

Engagement Rate is the Margin Squeeze in Digital Advertising

Engagement rate, the percentage of the audiences that consumes a particular piece of editorial content, creates the biggest squeeze on digital advertising profits.  The advertising profit contribution of media is defined by the advertising revenue produced from page views minus the costs to create the content and sell the advertising. Unlike print media that uses circulation and pass-along rate to define a sellable inventory of page views, digital media uses audience size and engagement rate.  While pass-along rate acts as a page view multiplier on circulation, engagement rate is a page view filter on audience size.  The consequence is that the engagement rate puts a squeeze on advertising profit margins.

To illustrate this point, the infographic below benchmarks the advertising profit contribution of ad sales and editorial of print vs. digital.  As in previous comparisons, this example assumes the print product is a 50-page magazine with 30 advertisements which means for every additional page of editorial the sales team needs to sell 0.6 ad placements.  On the other hand, for every page of editorial in digital the sales teams typically needs to sell 4 ad placements which is an increase of 6.7 times more insertion orders vs. print.  On the surface, it appears like the increased cost of sales would result in more revenue (i.e., 4 ads vs. 0.6) but this is where engagement rate impacts the revenue and profits.

Scout® Research consistently finds that the engagement rate for an individual piece of the editorial is rarely above 10%.  Average engagement rates across all editorial within a site are usually below 10%.  So at a 10% average engagement rate, which is very good, a page of digital editorial in the infographic example will generate 50,000 sellable advertising impressions (i.e., 100,000 members X 10% engagement rate X 4 ads per page view + 20% additional from fly-bys).  In contrast, the 0.6 ads per page in print multiplied by 100,000 circulations and the 1.75 pass-along rate results in 105,000 sellable advertising impressions.  To make up for the drop in sellable impressions, the digital editorial team has to create two times more content.

In other words, to get to the same revenue assuming digital and print CPM consistency, the digital team has to sell 6.7 times more advertising and produce 2 times more content.  This is the advertising profit margin squeeze.

Engagement rate has at least two immediate implications.

The first is that given the impact on advertising profits, engagement rate is an important new metric for managing digital.  The engagement rate informs the publisher about the priorities for profits.  With a low engagement rate, a publisher needs to improve profits through audience development.  With a high engagement rate, a publisher needs to improve profits through more editorial.

The second implication is that engagement rate creates a structurally different profit model for digital advertising compared to print.  A structural difference that cannot be overcome except through diversification of the revenue model or a rethinking of advertising.…

How to Calculate the Breakeven Point for Digital Subscriptions

How to Calculate the Breakeven Point for Digital Subscriptions

In subscription-based business models, maximizing customer lifetime value is understood to be a key success factor to a profitable business. But how do you know at what point a customer relationship turns profitable? While there are obvious differences between customers, it turns out you can calculate your average customer lifetime to reach the breakeven point using your existing operating metrics. So how is this done?

Here is the standard operational metrics are known by every online service:

  • Customer Acquisition Ratio (CACR)  – the sales and marketing costs to sign up a new customer as a ratio to revenue acquired
  • Customer Renewal Cost ratio (CRCR) – the sales and marketing costs of closing a renewal as a ratio to revenue renewed
  • Research and Development ratio (RD) – the cost to invest in and make improvements to the service as a ratio to revenue
  • Gross Margins (GM) – revenues minus the costs associated with hosting a service and providing customer support
  • General and Administrative ratio (GA) – the cost associated with finance, management, and other functions as a ratio to revenue
  • Churn Rate (CR) – the percentage of revenue not renewed at the end of a subscription term
  • Profit Margin (PM) – the percentage of revenue that are profits

With these operational numbers, the profitability of a customer relationship can be calculated as the total lifetime subscription revenue minus the total costs. The total subscription revenue would be the number of subscription terms multiplied by the subscription price, minus any churn. The total cost would be the customer acquisition costs, customer retention costs, and prorated G&A, R&D, and operational costs. Expressed as a calculation, it would appear as follows:

As demonstrated by the calculation, the total number of terms (i.e. CL, or customer lifetime) is critical to profitability. The equation can be turned into operational metrics by dividing by subscription price to create ratios of each cost in relation to revenues. The result is the following equation:

So by digging out the old math knowledge, solving the average customer lifetime to the breakeven point can be done using the following equation:

At breakeven, profit margin (PM) equals zero which allows the equation to be solved.

The Implication

Knowing your breakeven point on customer relationships enables you to identify the source of profits. You can segment customers quickly into profitable and unprofitable categories. With that segmentation, you can identify what drives profitability and what are leading indicators of churn. In other words, you can optimize your revenues and profits. Scout Research is developing a customer relationship calculator and benchmarking tool for use on our site, which will perform these calculations for you.  Look for an announcement in the near future on the availability of the calculator.…

Manufacturing Scarcity to Drive Publisher Profits

Manufacturing Scarcity to Drive Publisher Profits

I’ve recently been hearing a lot of chatter about scarcity in the publishing world. If you listen to studies (e.g., Pew), twitterers (e.g., paywall), and bloggers (e.g., Jeff Jarvis), manufacturing scarcity sounds impossible – even unethical. But in reality, publishers have the absolute need to manufacture scarcity to drive profitable revenue.
 
From the business ethics perspective, it is a common business practice to manufacture scarcity for profitability.  Take a look at how the airline industry is finally returning to profitability after a decade of losses; most of this profit is generated because of the reduction in capacity (a.k.a., artificial scarcity).  Auto manufacturers often retain pricing premiums by limiting production (a.k.a., artificial scarcity).  In the entertainment industry, movie releases first go to theatres, then to purchase, then to rental (a.k.a., artificial scarcity).  Remember limited edition iPods?  All kinds of products have limited production/editions to create artificial scarcity.  Simply put, artificial scarcity creates profitable revenue.
 
In terms of the viability of the idea in the publishing world, of course, publishers can manufacture scarcity.  While scarcity based on the distribution (e.g., print) is gone, manufacturing scarcity in the digital world is not impossible, only different.  Kevin Kelly’s blog post, Better Than Free, speaks to eight value-generating qualities for manufacturing scarcity on the commoditized web (good read although he overlooks scarce content). It supports the idea that scarcity must now be based on differentiated value to the audience.
 
Here are a few quick examples of both B2C and B2B publishers that manufacture scarcity.  Consumer Reports has always relied on the limited availability of their content to generate profits.  BabyCenter creates a unique experience through the personalization of content to match the stage of pregnancy and throughout childhood.  Rolling Stone is leveraging its archive to create scarcity and new revenue.   TechCrunch and GigaOM are good examples of building revenue from physical events that complement their content.  The FT’s use of a paywall shows how scarcity can be dialled in for specific audience segments.
 
Benchmarking other publishers, experimenting with user experience, and evaluating paywalls are some options for figuring out how to create differentiated value (i.e., manufacture scarcity) and drive profits. Scarcity is a concept that we all need to get comfortable with.…

Importance of Analyzing Unit Cost of Engagement in Advertising

Importance of Analyzing Unit Cost of Engagement in Advertising

For publishers, analyzing the unit cost of engagement in advertising identifies revenue optimization opportunities.  In the next few blog entries, I’ll explore why and how to analyze the unit cost of engagement for ad orders.  This first entry in the series addresses the following questions:

  • What is engagement?
  • What is the unit cost of engagement, and how is it calculated?
  • Why is calculating unit cost important?

What is engagement? For advertisers and publishers, engagement is the length of time the audience spends with media and ad.  Engagement is one of the few scarce commodities on the Web.  An audience member’s options for news, entertainment, socializing, purchasing, and learning are exploding, and like it or not even with the mobile explosion, any one person has a limited amount of time to provide on any given day.  A publisher’s success is premised on maximizing its share of audience time (i.e., engagement) and the revenue it produces.

Today, the publisher’s standard for engagement mistakenly measures the page views rather than the length of time.  Using today’s standard, there is no difference between impressions that last 1 second, 10 seconds, or 2 minutes which of course doesn’t make sense.  Research has shown that the longer a person is exposed to a web page containing an advertisement the more likely they are to remember the advertisement.  Additionally, engagement enhances direct response advertising as a recent study published by TidalTV showed click-through rates of targeted ads increase as engagement increases.

What is the unit cost of engagement? Using a length of time as the measure for engagement, the unit price of engagement is simply the price paid by an advertiser for each second an impression is delivered to an audience member.

"Demand Map for Advertising"

How do you calculate it? The simple answer is to take the total revenue of an ad order and divide it by the total length of all impressions delivered as part of the order.  The chart to the right illustrates the unit cost of engagement.  In this chart, two advertisers pay the same $100 CPM rate to a media publisher for the same target audience.  The first advertiser’s order resulted in 5,000 impressions with an average length of 10 seconds each or a rate of $0.01/second.  The second advertiser’s order resulted in 5,000 impressions with an average length of 120 seconds each or a rate of $0.00083/second (12X lower rate for engagement).

Why is calculating unit cost important?    With all other factors equal, research has shown that increased engagement improves ad campaign performance in both direct response and branding.  By calculating the unit cost of engagement for ad orders, publishers can identify which orders were overpriced and which were underpriced.

Overpriced orders have lower engagement per dollar and lower conversion/recall rate per dollar.  Advertisers with overpriced orders are less likely to advertise in the future representing revenue risk (e.g., the first advertiser in the example).  Underpriced orders have higher engagement per dollar and higher conversion/recall rate per dollar.  Advertisers with underpriced orders are more likely to accept a price increase to continue targeting the publisher’s audience (e.g., the second advertiser in the example).

By knowing which audience segments have higher engagement and which advertisers are receiving good value, publishers can price discriminate to optimize their revenues.  In the next post, I’ll cover the quantitative method for establishing the unit cost of engagement.…

The New Discipline in the Subscription Economy: Recurring Revenue Management

If you’re a subscription business, the most dramatic effects of trends like cloud computing and mobile won’t be felt in your company’s product line. The real disruption will be to your revenue model. Customers will not pay to own your products. Instead, they expect to pay for the value they receive by using your products. Revenue management is the common approach to solving the challenge of optimizing the revenue model.  Unfortunately, the rules of subscription models render traditional revenue management ineffective.  To manage revenue and profits in this case, companies need a new revenue management process to optimize the revenue model.

What is revenue management?

Revenue management is common practice in the distribution-centric Transaction Economy.  The goal is to maximize revenue and profits by pricing products to match customer demand.  Revenue management is pervasive in such industries as airlines, hotel rooms, surgery, advertising, retail, media and rental cars.  For example, airlines offer a passenger a seat between two cities defined by departure time, legroom, seat width, and associated service.  Because the product, in this case, a seat, is both standardized in terms of customer fulfilment and limited in inventory, the airline can forecast demand at specific prices from different customer segments and manage seat availability to optimize revenue.  The airline can forecast demand for higher-priced seats from business travellers that value last-minute bookings and sell the remaining inventory at a lower price to early purchasing leisure travellers who value cheap travel.  While the business traveller and the leisure traveller sitting next to each other expected the exact same product, each valued the trip differently and consequently paid a different price.  By selling the right standardized, inventory-constrained product to the right customer at the right price, the airline maximizes revenue and profit.

Why can’t traditional revenue management be used in the Subscription Economy?

Unfortunately, distribution-centric revenue management doesn’t work for the consumption-centric subscription business model.  For example, imagine if your cellular provider informed you that all the minutes of data transfer were sold out for the day and you could not buy anymore regardless of the price?!  Or imagine if you wanted to sign up for a subscription and the provider said they were sold out?!  These are principles of the distribution-centric revenue management process.

The revenue management process is different from subscription business models for two reasons as shown in the figure.  The first difference is that customer fulfilment is variable.  While each customer receives a standard subscription agreement, each of them will use the product differently.  Unlike the airline example where the airline defined customer fulfilment (i.e., a seat), in the Subscription Economy, customers define fulfilment based on their individual usage (e.g., amount of texting consumed in a cellular plan).  Customer demand can no longer be determined from purchase data alone.  Customer demand must be measured by usage data and purchase data together.

Second, for subscription business models, there is no limitation in inventory.  In other words, the differentiated value between customer segments cannot be derived simply from product availability (i.e., inventory management).  Unlike airlines that create differentiated value and revenue based on managing inventory of a particular product package (i.e., a seat), in the Subscription Economy, differentiated value and revenue opportunities have to be created by providing differentiated product packaging (e.g., different combinations of minutes, text, and data in a cellular plan).  Rate plan management replaces inventory management for revenue optimization.

These differences highlight why revenue management for subscription business models requires a new approach which the “use it or lose it” dynamic highlights the most. The “use it or lose it” dynamics states if the customer does not use your product at a level that matches the subscription agreement, the customer will cancel the subscription, and you’ll lose the revenue (A complete description of “use it or lose it” can be found here ).  So in the Subscription Economy, managing recurring revenue (i.e., the ability to proactively manage the subscription revenue model) boils down to matching the right rate plan to the right customer usage at the right price.

Why is recurring revenue management required?

Just as revenue management is a well-chronicled competitive advantage in Transaction Economy industries such as airlines, revenue management will be a requirement for …

Measurement is Key for Paywall Success

Measurement is Key for Paywall Success

From following the paywall hashtag on Twitter over the last 18 months, there has been a steady increase in the debate about paywalls, both pro and con, but mostly without any data or revenue models.  Our previous research (http://blog.scoutanalytics.com/advertising/the-digital-drop-off/) showed that the move from print to digital significantly reduces the revenue capacity of a publisher.  The reality is this: Without a fundamental change in digital ad units and their revenue production, publishers have no choice but to pursue alternate revenue streams.  So where do publishers go when advertising revenue becomes unsustainable? Higher-margin marketing services and subscription revenues are quickly becoming the answer, also known as a metered paywall.

Not all publishers will be able to implement a full subscription model.  For example, existing controlled circulation can gain revenue from higher-margin marketing services, but those publishers are not likely to have existing non-paying users begin paying.  In the case of paid print circulation, transitioning existing subscriptions and pay-per-use revenue through a metered paywall is critical to success online.  For those publishers that can migrate existing subscription revenue, just how important is a metered paywall?

In the post on calculating the monetary value of an audience member, (http://blog.scoutanalytics.com/advertising/profit-is-more-important-than-monthly-unique-visitors/) the monetary value of an annual audience of 17.6 million unique users was calculated at roughly $12M, and an average revenue per user (ARPU) of approximately $0.68. Increasing the audience size and page views through audience development and editorial will increase costs, but will not dramatically impact the revenue because of a sell-through rate near 70% overall.  The net may be an increase in revenue, but also an associated decrease in margins that are already squeezed.

To make larger jumps in revenue and profits, the publisher has to leverage the existing audience and content for untapped revenue streams.  Marketing services now represent the path for new revenue by monetizing the audience, and subscriptions are the path for new revenues from content.  This scenario assumes that the publisher implements a metered paywall model requiring a user to register in order to read more than three articles per month and subscribe to read more than 10 articles per month.

In addition to the existing ad revenue, anonymous users that convert to registered users will receive daily e-mails of the most relevant articles based on their reading habits, along with a daily deal.  The expectation is that the daily deal business will generate $20 per year per e-mail recipient or a $20 ARPU increase.

Those users that convert to being a subscriber will pay $10 per month or $120 per year, allowing them access to unlimited content.

The following infographic illustrates the impact.  Each row represents a stream of revenue with the ARPU that is multiplied by the audience size to calculate revenue contribution.  Because conversion rates to registration and subscription vary based on loyalty, the audience composition calculates the audience size at each level of revenue contribution.

With over $10M in revenue from daily deals and over $13M in revenue from subscriptions, the overall revenue is increased to $35.4M — an increase of 195%.


The Implication

The bottom line is this: Higher-margin marketing services will enable a publisher to monetize the existing audience, while a subscription model will monetize the existing content.  Optimizing conversion is dependent on establishing the meter on the right content and at the right levels of engagement.  Just implementing a metered paywall will miss significant revenue opportunities. Implementation of a paywall requires deeper optics into the audience to establish paywall parameters and in turn multiply revenue from the audience and content.…