Articles by: "Michael D. Silberman, SVP Strategy—Piano"

Retiring third-party cookies: Stop panicking and start planning

March 17, 2021 Michael D. Silberman, SVP Strategy—Piano

Last January, Google announced the end of third-party cookies by 2022. The industry went into a frenzy.

All the while, Google forged ahead quietly on its own. Occasionally, it would pop up to say, yes, we really are going to stop supporting third-party cookies and no, the pandemic hasn’t changed our plan.

It wasn’t until last this past January that Google fully reappeared announcing its plan to start publicly testing Privacy Sandbox. This included some associated privacy controls, and early performance results for FLoC, or “Federated Learning of Cohorts,” which allows ad targeting at large groups of users based on common interests. And just a few weeks ago, they affirmed that they would not adopt new tracking tech after phasing out third-cookies. Their claim: It’s no longer necessary to “track individual consumers across the web to get the performance benefits of digital advertising.”

The end of ad tech?

Another frenzy ensued. First reactions predicted the end of non-platform ad tech. And many companies saw their stocks drop. But then, by the second day after the announcement, some observers started pointing out that Google hadn’t actually done much besides make it clear (yet again) that support for third-party cookies will end this year. They also stated that they aren’t going to adopt a solution like Unified ID 2.0, which The Trade Desk, Prebid.org, and IAB are lining up behind.

That wasn’t the only announcement of note in the past few weeks. IAB Tech Lab released draft guidelines for advertising identifiers like Unified ID 2.0, LiveRamp, ID5, etc. The Trade Desk announced the start of beta testing for Unified ID 2.0, with advertisers being able to transact on impressions using the tech. And just a week after Google’s identity announcement, the company published another blog post with initial details of a plan to extend publishers’ first-party “Publisher Provided Identifiers” (PPIDs) from private marketplace deals to open auction.

Pointing out that the ad landscape is confusing and in flux for publishers is a bit of an understatement. And that’s without mentioning Apple’s moves on tracking consent and IDFA. Let’s try to clarify things a bit.

Prepare to have options

Between Google, Apple, and the “open web” ad tech players, there seem to be at least three different approaches publishers will need to test and adapt to:

Google’s FLoC, which might (or might not) become an industry standard;
Apple’s strict app tracking rules and Safari ITP limits; and
Unified ID 2.0 or whatever other standard emerges from the IAB TechLab working group, assuming that either regulators or the browsers don’t block those cookie replacement solutions.

Publishers and advertisers alike have fought an increasingly brutal battle against the walled gardens. Chatter has abounded that the cookieless future could tip things in their favor. Google’s latest announcement actually runs counter to that theory. Their publicly confirmed focus on Privacy Sandbox means, in the words of Magnite CTO Tom Kershaw, that Google will “actually use the same tools as the rest of the industry.” The end result could be a peaceful coexistence of the Chrome solution, user logins and publisher first-party segments. And that would mean choice for publishers, advertisers, and consumers alike.

Focus on first-party data and identity

While there’s an element of self-interest, it does seem clear that Google is developing solutions to make it possible for publishers to activate first-party data. These include non-PII user IDs in the Google ad ecosystem. Of course Google relies heavily on first-party data and known user identities in YouTube, Search, Gmail, and its other properties. So, limiting publishers’ ability would be hypocritical. It would also be risky at a moment when the company is under antitrust investigations in more than one country. However, from a publisher perspective, this is further reinforcement of the need to focus on first-party data and identity.

Start your transition now

In some ways, we’ve been our own worst enemies in the run-up to third-party cookie deprecation. Many publishers and advertisers have awaited Google’s next move as if we were pawns in a drawn-out chess game instead of coming up with a plan and starting to execute on it. Let’s not so quickly forget that where third-party data offered efficiency, it often lacked effectiveness, due to uncertainty of where it comes from and, therefore, its accuracy.

Six steps for transitioning away from third-party cookies

There are six areas you need to cover as you begin your shift away from third-party data:

1. A minimum amount of zero-party data

It’s not just about first-party data. Zero-party data — the data that users volunteer to you via other explicit data-capture tactics like registration forms — will be just as important. You need a certain amount of data to generate effective lookalike segments. To do that, it’s necessary to start targeting some segment of your audience and guiding them to register and provide their data with incentives such as the ability to access locked content.

2. Tools for storing and managing consent

Whether it’s a data-management platform, a customer-data platform or a consent-management platform, figure out now where you will store all of this data and consent.

3. Audience segmentation tools

You need audience segmentation tools to create subsets of your users. This can be based on like characteristics extracted from zero- and first-party data. They can also leverage lookalike modeling to produce larger audience pools.

4. Collect data at the right moment

Collecting more data over time requires thinking through the right moment in the customer journey to ask for it. At each stage, aim to understand how to move users deeper into the funnel with the right offers that will net you more data to round out your customer profile.

5. Vet identity partners

You need one of these to replace cookie-matching and activate all that zero- and first-party data you will collect. Be sure that you vet their offerings to see which identifier could work best for your business.

6. Start testing solutions now

As Travis Clinger, SVP, Addressability and Ecosystem at LiveRamp, said in our recent Piano Academy event: you don’t want to get caught trying to apply new technologies and strategies in the middle of the Q4 holiday season. It’s worth testing different solutions, including identifiers and different data- or consent-management platforms, now.

We’ll be sure to hear more from Google publicly throughout the year. It will remain important to read successive announcements with a critical eye. We’ll need to remain focused on the steps required to prepare for third-party cookie deprecation now (as opposed to needless panicking). And remember you have a whole industry navigating this transition with you as we move towards a new era of privacy-first personalization.

Advertising | Perspectives | Revenue

Why zero-party data is publishers’ secret weapon in a post-cookie world

March 2, 2020 Michael D. Silberman, SVP Strategy—Piano

The clock is ticking on third-party data.

The death of third-party cookies — courtesy of Google, Apple, GDPR and CCPA — also means the accelerated fade of third-party data. And from ad tech companies to marketers and media companies, the shift has left many wondering how they’ll proceed. Many fear the effect the changes could have on their business.

At Piano, though, we see opportunity where others see threat. And we think the shift away from third-party data is an opportunity to thrive. It puts new emphasis on first-party data. It also enables those companies to draw on something even more powerful: zero-party data.

Every day, users provide media companies with the type of explicit, accurate, data other businesses covet. Consumers willingly volunteer this data as they register or subscribe in order to unlock more content or build a deeper relationship with their site of choice. And it’s exactly this type of information that, if used right, can push those companies ahead in this new data landscape.

The problem with third-party data

To understand their audience and find new users, companies in and outside of media have long been relying on third-party data. This type of data, sold by data aggregators, is available in huge (if shrinking) volumes and can potentially shed light on many different consumer types. However, there are three primary problems with it:

The rising consumer concern around online privacy. Consumers are becoming increasingly aware of tracking and privacy. And that’s driven government privacy and consent regulations like GDPR, as well as the changes in browser cookie policies. This has reduced the reliability of third-party data.

It can be out of date and unreliable. This is why consumers are often targeted for a product they’ve already purchased or decided against, only to have ads “follow” them around the internet for weeks. If you’re building a strategy around data that’s two months (or even two weeks) old, it’s likely already outdated. And the trend towards limiting third-party cookies only means third-party data will become even less reliable in the future.

Sourcing is often hazy. How third-party data is collected, aggregated and then sold is generally a black box. User consent isn’t always known or verifiable. As the industry moves to tighten rules around data transparency, the value and utility of third-party data will continue to decline.

Unlike third-party cookies, third-party data probably won’t necessarily vanish completely — though it will certainly diminish in value. Even so, there are better alternatives available, especially to media companies.

If third-party data is finished, what now?

Some important questions still remain unanswered in this post third-party cookie world. However, what’s not unclear is that the more reliable the data, the higher the value for publishers’ marketing partners and the more they’re willing to pay for targeted ads. And for that, third-party data falls short next to the other options:

Zero-party data is intentionally shared with a business by its customers. This could range from customer relationship management (CRM) data, such as the email address or gender filled out in a registration form, to preferences selected during website customization. As data explicitly shared as part of a value exchange or to improve the customer experience, it’s the most valuable data a company can collect. Critically, it belongs to the customer, not the business. It can’t be used without consent and must be carefully protected. However, it is highly accurate, reliable, and transparent in sourcing.

First-party data is often thought to encompass zero-party data. However, it also includes behavioral data collected by observing customers as they browse your website or app, or visit your store. This type of data is implicit, rather than explicit, but is still considered premium for many reasons. It’s collected by you so is more likely to be accurate. And any insights drawn from your own audience are likely to truly reflect their preferences and behaviors. This means that campaigns and strategies are more likely to succeed. Crucially, there are fewer concerns over data misuse and GDPR non-compliance as you control the conditions in which data is collected and stored.

Second-party data is when first-party data passes through a second set of hands. In other words, second-party data is someone else’s first-party data. It occurs in like-minded publisher alliances, when suppliers exchange data with their retailers for mutual benefit. It also happens when agencies strike exclusive deals with major publishers to empower programmatic buying. Gaining explicit consent to use and share second-party data isn’t always an easy feat, but can be handled reliably with the right technology setup.

Media companies are in a unique position to take advantage of these data sources . This is particularly true for first-party data and the gold standard of all user data: zero-party data.

Maximizing your zero-party data

In the media landscape, companies can use zero-party data to both benefit their advertising partners and personalize the experience they offer users themselves. All while finally aligning the interests of their subscription and advertising teams — something many media companies have been striving to do for years.

Zero-party data is already available through registration and subscription sign-ups. But the savviest companies will maximize their store using many of the same techniques subscription companies have already implemented to optimize their business. That starts with quality content that puts users at the center of the audience experience. It continues in the form of exclusive content, personalized recommendations and customized offers that engage the user and personalize the experience. Only when a true value exchange is in place will users be incentivized to provide their data to improve their experience even further.

A data management platform, or DMP, can then help companies use that data to understand and segment their users. Lookalikes will also allow them to extrapolate the data to cover their entire audience. In doing so, they’ll be able to target ads to relevant users and engage more effectively with their chosen demographic.

Take Piano customer Mediahuis, for example. One of the largest publishers in Europe, they built a substantial base of 4.4 million registered users. They then used the zero- and first-party data collected to create a significant, audience-based advertising business across Belgium and the Netherlands. Audience campaigns grew to 27% of total ad revenue, with clickthrough rates 26% higher than non-targeted campaigns.

That’s the power of zero- and first-party data. Combining your advertising and subscription businesses through a focus on known users leads to a stronger and profitable monetization model, drawing from multiple revenue streams.

And that, as far as we’re concerned, is hardly something to be afraid of.

About the author

Michael Silberman, SVP Strategy, leads Piano’s Strategic Services team. He helps clients develop reader revenue strategies and drive success and revenue on the Piano platform. He joined Piano in 2018 after 10 years building the digital media business at New York magazine, and earlier, as one of the top editors launching and growing MSNBC.com in the early days of the consumer Internet. Visit piano.io to find out more about Piano Zero, an end-to-end solution that empowers zero- and first-party data.

Advertising | Perspectives

How publishers are rethinking audience in a user-first world

October 15, 2019 Michael D. Silberman, SVP Strategy—Piano

On September 25, Vox Media acquired New York Media — the company behind my former employer, New York Magazine. Vice Media acquired Refinery29 on October 2. And five days after that, news broke that digital media company Group Nine had reached an agreement to acquire publisher PopSugar.

The long-expected digital consolidation is finally upon us. Media companies, it’s clear, are looking to survive by getting bigger in an effort to increase revenue and better compete by way of sheer scale. By expanding their audiences, they hope they’ll be able to build a more sustainable revenue model.

But is that necessarily true?

There’s nothing wrong with chasing scale or growing your business through intelligent mergers. (Piano recently took that route too, acquiring Cxense.) However, for media companies, growing overall audience doesn’t necessarily mean increased engagement or enhanced revenue. Not unless a critical percentage of that audience is made up of loyal users.

Identifying (and keeping) loyal users

At Piano, we often define “core audience” as those users who view at least 10 pages a month. If 5% of your audience or higher visits that often, that’s solid performance. However, if that number falls below 2%, there’s work to be done to drive deeper engagement and grow that share.

Building your core audience — and their engagement — is key to reaching your overall audience and revenue goals. And that starts with putting users at the center of your site experience. The media companies that Piano works with — companies that have made reader revenue the centerpiece of their businesses — understand that better than most. After all, they’ve realized that they need to build relationships if they expect users to pay.

So, what lessons can digital media companies learn from leaders in subscriptions? And what other opportunity does a user-first digital landscape present?

Understanding what your audience values

What do your most loyal users read or watch on your site? Which topics resonate with them most? Are their preferences different than those of mass, one-off users?

A successful user-first strategy starts with knowing how your site’s core audience behaves and understanding what users are looking for when they get there. Both are key factors that contribute to subscription conversion, and audience data like this plays a large part in driving strategy. However, while data explains a lot, our customers find that talking to users is just as important.

Asking users what they value about your content, website and brand, either through surveys or one-on-one interviews (ideally both) can give you an idea of unmet needs and what you could be doing that you aren’t yet. It helps develop an understanding on how they see you compared to a competitor and recognize the opportunity in those differences. And as you work to understand your users, you may begin to identify different segments of your core audience — along with the different content habits, demographics and benefits they gain from your brand.

And that’s the kind of information that’s critical to the next step of building a user-first strategy: defining the customer journey.

Defining the customer journey

At Piano, we’ve developed a model for the customer journey that can be applied to both media and other types of business. It helps define the milestones of increasing user engagement and key conversions you want to drive along the way. This is what it looks like:

Of course, these generalized milestones are applied differently from brand to brand. Business Insider and The Daily Beast, for example, both use registration as a sampling tool. They offer temporary access to premium articles in exchange for registration. In that case the Known stage of the customer journey becomes an important milestone, with a set of specific tactics employed to target active users who might not be ready to subscribe, encouraging them to register and experience paid features.

That highlights another essential element of this approach: considering the “next best action” you want your users to take to move them from one step of the customer journey to the next, then using small nudges, “micro-experiences,” to move them along that path.

In a user-first world, building deeper user engagement means considering the value exchange between the user and business. What action do you want the user to take? What’s the benefit to them? What are you asking in return? If you ask for permission to track them, for example, does that enable you to improve their reading experience by only showing articles they haven’t read yet? Are you making that benefit clear?

GDPR and the coming California privacy rules require sites to ask permission for tracking, but few sites are doing more than disclosing what’s being tracked. There’s a missed opportunity to use human language instead of legalese and take it as a moment to build a relationship.

Lastly, the specific tactics also define the metrics to focus on. In the case of the two sites I mentioned above, the exposure rate (the proportion of users who see a registration offer) and the conversion rate (of those exposed, how many register) are both key metrics to measure and understand.

In the Piano database, there’s a pretty wide range of exposure rates, but registration conversion rates are fairly stable — 2.2% for the bottom quartile and 3.4% for the top quartile. If you have enough audience, even a 1% conversion rate can bring in a lot of registered users. If subscriptions are a goal, then exposure rate, conversion rate, revenue per user, retention and customer lifetime value are all crucial metrics to consider.

Building user-first revenue

If you get the user journey and value exchange right, you open up a world of opportunities to drive revenue. For subscriptions, donations and memberships, certainly. But less obviously, direct, trusted reader relationships become more valuable for advertising too.

That value hinges on publishers collecting first-party data and capturing user consent to track and display targeted advertising. Insider and Mediahuis in Belgium have each developed in-house solutions to create rich first-party profiles of users. Their approach primarily uses behavioral data on content consumption and extending their reach via lookalike modeling. They also allow advertisers to bring and match their first-party data to refine that targeting.

Diving a little deeper into the current ad landscape, increased privacy regulation and consumer awareness, plus changes in web browser cookie policies have made publisher-user relationships both valuable and essential. Valuable because publishers can build trust and encourage users to log in, allow tracking and even volunteer data in the right circumstances. Essential because without it, programmatic ad revenue drops, as browsers squeeze out third-party tracking.

Focusing on user relationships also opens up other product possibilities. Some other innovative examples we’ve seen: Austrian publisher Russmedia developed a gamified “Landlepunkte” points system to encourage user engagement, with points redeemed for swag, digital coupons and local event tickets. It dramatically increased user registrations, logged-in users and articles per user. Amedia in Norway turned 73 local newspapers and a national obsession with local professional soccer teams into a national live streaming sports network.

That’s the type of value a user-first strategy can bring. That and the loyalty obtained from users who intuitively know they live at the center of the experience you offer. No matter how big (or small) your company is.

About the author

Michael Silberman, SVP Strategy, leads Piano’s Strategic Services team, helping clients develop reader revenue strategies and drive success and revenue on the Piano platform. He joined Piano in 2018 after 10 years building the digital media business at New York magazine, and earlier, as one of the top editors launching and growing MSNBC.com in the early days of the consumer Internet.

Revenue

Predicting conversion: Mastering the complex variables of user behavior

August 14, 2019 Michael D. Silberman, SVP Strategy—Piano

When a visitor comes to your site: Who’s most likely to take the action you want and who’s not? And what would you do differently if you knew the answer to that question? Because the answers are critical for subscription success.

In my former role at New York magazine, where I spent a decade building the digital media business, I often found myself facing these questions. While we weren’t selling digital subscriptions yet, we knew the value of loyal users. That is, the five to ten percent of users who came back four or five times a month and consumed 40% to 50% of total pageviews. We wanted to entice more people to come back more often.

I knew there were users who converted from new to returning to loyal along a fairly straightforward path, and others who took longer, or a more convoluted journey, to reach that point. And I also knew there were quite often those that never got there at all. If only we could understand those paths and predict audience behavior, I figured, maybe we could do a better job of nurturing users towards loyalty.

Unfortunately, we didn’t have the tools back then to make those predictions — which meant we had to try to urge everyone down the same path. And since each of those users had different needs and affinities towards the brand, that inevitably meant we lost users along the way that we might have been able to entice if we’d had a better idea of what personalized path was most likely to lead them to come back again and again.

Finding the right formula

Back then, like media executives everywhere, I wished for something that would help my team better understand our audience and their future actions. Was there a magic formula we could apply?

Fast forward to today and my current role as SVP Strategy at Piano. Piano has spent years trying to find answers to the same questions I had when I was at New York magazine. And what we came up with turned out to be much more complex than anything I ever could have envisioned.

In fact, it’s not a straightforward formula at all. And it’s not one set of behaviors but dozens. Not only that, but the exact combination of behaviors that matters is different on every website.

Predicting future actions

When Piano launched LT[x] — a machine learning framework that predicts likelihood to act — it opened up a window into the behaviors that matter for conversion, beginning with subscribing. Rather than the simple formula I might have envisioned back at New York magazine, though, the subscription propensity model that emerged from LT[x] (called LTs) tracks 76 different metrics, using machine learning to pull them together and predict conversion actions.

Before launching, we beta-tested LTs with two customers, and saw exactly the types of results it could achieve. One customer experienced a 20% increase in its paid conversion rate, while the second saw an increase in conversion rate of over 40%. The metrics that mattered — those that predicted who was most likely to subscribe — included the share of visits on desktop, pageviews per visit and the days of the week a user visited. Equally interesting were the metrics that predicted low likelihood to subscribe. If visitors arrived from social media even 20% of the time it drove low subscription likelihood, and if 95% of pageviews were on articles, with little homepage traffic, that also indicated low likelihood. For both of those initial sites, though, the actual weight of each metric varied.

It’s only been since we rolled the model out further, though — across five customers, and eight sites and counting — that we saw just how much difference exists from site to site. The way each metric contributes to likelihood to subscribe is unique to every site we’ve worked on. In some cases, in fact, the same metric can have the opposite effect from one site to the next.

Users are different and so are the metrics

Take one site we’re working with, as an example. There, where visitors are looking at the site plays a central role in their likelihood to subscribe. Since it’s a regional newspaper, that’s not surprising — the most active users are local. For other sites, a particular metro area may have little significance, but national boundaries still tend to matter — with users in a site’s home country generally having higher subscription likelihood.

There are some behaviors that pop up again and again. For the same newspaper client, the more unique sections and authors users read also contribute to that same site’s high likelihood to subscribe rates — also easy to understand for a newspaper with a diverse range of writers and content types. The more a user is engaged across the board, the more likely they are to become loyal and subscribe. This breadth of engagement is almost always a strong positive signal.

Meanwhile, for other customers, it’s engagement metrics like number of active days or number of paid offers viewed or the share of pageviews from recirculation that are top predictors. For clients with very high value content where visitors are quick to convert, it may be whether a visitor is coming from desktop or mobile that has the biggest impact, with the typical high engagement metrics having a smaller impact.

All of that means there is no one magic metric for the industry to focus on across the board. The good news is that, with the spread of AI-enabled technologies, understanding and harnessing this information is becoming possible for the first time.

Customizing the experience

So, what does this mean for the media providers trying to use those predictions to customize their user experiences?

Once the metrics that matter are determined for each site, companies can start to use the predictive powers of the algorithm to personalize the cues they put in front of a particular user and the path they take to lead them to convert. That could mean putting a subscription offer in front of those most likely to subscribe, for instance, but a registration offer in front of those less likely to. Or it might mean changing the meter height based on whether a user is more or less likely to subscribe.

And for users, that personalization matters just as much as it does for the companies that provide it. Customers expect one-to-one personal experiences. It’s what they wanted when I was still in media, and even more so it’s what they’re looking for today.

About the author

Advertising | Perspectives

The end of attention means the start of something better

February 26, 2018 Michael D. Silberman, SVP Strategy—Piano

The digital media business is finally making the shift from attention to user engagement. We see users as individuals rather than sets of eyeballs and focus on winning hearts and minds. This is a huge, ultimately positive change that will produce a much healthier media ecosystem. But it’s not going to be easy. It requires new technology, new marketing and product skills, and most importantly a change in mindset from content-first to customer-first. That means moving away from some very entrenched habits.

The first 20 years of the consumer internet, especially in media, have been almost entirely about aggregating audience. Sites seek to attract millions, often tens of millions, occasionally hundreds of millions of people, with all those eyeballs “looking” at billions of banner ads. That focus on big, unidentified, often undifferentiated audiences made it possible for media companies to take the existing ad models—based mostly on audience size—and adapt them pretty easily to digital. Yes, there were significant creative and technical challenges in making that shift—learning to create digital stories and to sell and serve digital ads. But fundamentally the model itself didn’t change much.

The relationship with the audience was still largely a one-way, anonymous relationship, despite the new ability to engage directly, to measure behavior and to learn more about that audience. Most media companies were shortsighted, opting to avoid friction-inducing roadblocks like registration in order to maximize unique visitors, pageviews and ad impressions, missing a chance to develop a direct relationship with readers.

The end result: massive harvesting of user data, ad-cluttered sites powered by the ad tech Lumascape, “recommended for you” widgets, ad fraud, and ultimately unhappy, ad blocking users.

Signs of the attention model’s collapse were everywhere in 2017—from Google and Facebook gobbling up most of the ad revenue and virtually all of the growth to Mashable’s fire sale to big ad revenue misses at Buzzfeed and Vice. It became clear that advertising alone is not enough to support quality media.

Finally, media companies are taking action. In November and December alone, The Atlantic, Business Insider, and Wired launched paid products and the New York Times reduced its free story count. More is certainly coming in 2018, especially following Facebook’s pivot away from publishers, and the likely change in publisher audience strategy.

But simply launching a paywall, adding affiliate links or announcing an event series isn’t enough (hello, Buzzfeed). That’s just throwing new revenue streams up against the digital wall like spaghetti. There are four essential elements required for success in the new user-engagement era of digital media: customer knowledge, product strategy, enabling technology and marketing skill.

Let’s dig into each in turn.

Customer knowledge

In the attention era, media companies didn’t need to change their fundamental model. We could still follow an editor-first content strategy—writing about what editors thought was important or interesting. And the ad tech revenue stream didn’t require any understanding of who was reading beyond some basic demographics. Yes, there were audience analytics, paying attention to SEO trends and later social traffic. But the starting point was always what WE thought was interesting. We didn’t truly know our customers. In the user engagement era, understanding the reader (or viewer) has to come first. Whom are we serving? What can we learn about them? What do they need to live their lives, do their jobs or be entertained? Then we can apply editorial and product creativity to serve, surprise and delight them with great products and stories they didn’t know they wanted.

Product strategy

Once you know your customer, developing the right product to serve them takes more than creativity. It also requires focus, experimentation and iteration. In product management terms, it’s “finding product-market fit.” Focus means keeping your eye on the customer you’ve identified when deciding what product ideas to pursue and rejecting ideas that aren’t a fit for those customers. Experimentation and iteration go hand-in-hand. Buildenough of the product to test it with your customers (or at least a few of them), see what works and what doesn’t, and iterate to make changes and improve the product. This method will apply across multiple dimensions of product and business decisions—from editorial and product focus, to features, to pricing. It’s also an ongoing process, continuing even after achieving success.

Technology

While there has been a massive investment over the past 20 years in ad tech, there’s been relatively little investment in software and services to understand and engage with users as individuals, to measure behaviors like loyalty and conversion to repeat usage. Driving user engagement and powering consumer-paid content requires a robust technology platform that provides measurement and reporting, customer messaging, content gating rules, entitlements, and payment processing. Moving forward, machine learning will be a powerful tool for anticipating which users are most likely to become loyal and ultimately willing to pay.

Marketing skill

It’s become conventional wisdom among media business observers that—through his often disparaging tweets—President Donald Trump deserves a lot of credit for the recent success driving subscriptions at The New York Times, The Washington Post, and other media companies. The follow-up question is often “What happens when the ‘Trump bump’ fades?” Piano’s CEO, Trevor Kaufman, points out that’s a pretty limiting way to look at it. No one asks, “Will consumers pay for Nike shoes?” the way media pundits ask “Will people pay for journalism?”

The problem is that most media companies don’t know how to think like product marketers. They generally don’t have the skills in house, haven’t got the tools available and aren’t building marketing into their business plans and P&Ls. So, once you understand your customer, create a compelling product they’re willing to pay for and have the technology support. The last element to put in place is the ongoing marketing plan to drive customers through the engagement funnel. Then Google and Facebook transform from behemoths with the power to slash your audience and destroy your business into just another channel for marketing your product.

The ultimate vision is a healthy media market based on true relationships with known customers. For publishers, creating products that meaningfully connect with a loyal audience will unlock multiple revenue opportunities—whether consumer-paid products, events, merchandise sales or even advertising based on that real customer connection.