Last month, Facebook announced additional metrics to give advertisers more insight into consumer behavior, including how many visitors come to their websites after clicking on ads and whether those individuals are new or returning visitors…

And as the balance of power between Facebook and advertisers shifts more in favor of the latter, here are the four demands marketers should make in order to glean as much insight as possible:

More Third Party Verification

According to Mike Kisseberth, chief revenue officer at digital publishing and marketplace platform Purch, the key phrase is third party verification. In other words, it’s not that these new metrics aren’t interesting measures marketers will like, but these marketers are still stuck having to simply trust Facebook. Third party verification, on the other hand, would enable marketers to take the data more seriously.

“Third party verification is important because it allows you to trust the numbers there,” Kisseberth said. “As marketers, the responsibility is ultimately on you to know if [ad dollars] are contributing to the revenue of the business…I think there’s a balance point – third party verification is something marketers want, but they also…[need] to figure out if the money they’re spending is actually contributing to sales. You have a third party measuring whether it’s viewable, but did it actually drive performance at a level that is justified given the spend?”

Read the full article by Lisa Lacy here:



By Andre Baden Semper

Consider something radical: it’s about time marketers stopped looking at consumers in terms of audiences. Yes, it’s true. Marketers need to look beyond audiences and delve a little deeper.

With the sophisticated digital marketing tools that allow marketers to securely collect user data available today, marketers can – and should – aim to achieve a single view of each consumer.

After all, consumers are individuals so delivering the right offer at the right time comes from using data correctly to build an understanding of that individual and what will make him or her click.

But why is it important? Well, for starters, taking the ‘single customer view’ builds long-term relationships with  consumers. It ensures that a potential customer is understood and marketed to thus appropriately.

In taking the single customer view, a marketer’s data set should include every interaction point that they’ve had with a customer website, including advertising data.

It’s not enough to look at short-term revenue from advertising or commerce; what is important is to look at longer-term gains from membership and increase the focus to understand the lifetime value of a user.

Of course, privacy is of tantamount importance here. The way in which marketers collect this single customer view data should be anonymised and encrypted, so that it is used to benefit a consumer’s purchase journey only.

It is very possible (and, indeed, crucial) for marketers and publishers to balance data privacy with developing a single view of each customer to ensure that each customer gets the most tailored, optimised ads and offers.

Building a single customer view is all about putting your data to work in the most efficient way possible — by focussing on the customer. Here, we break down the how and why of taking steps towards a single customer view.

These are best practices compiled through our considered approach to looking at consumers. At Purch, we believe that when in doubt, a marketer should always put the user first.

Play the long game

The single customer view requires marketers to look at audience members individually, in terms of each consumer.

Rather than trying to monetise quickly on a consumer’s first visit to a website, it’s better to look at what you can do to facilitate loyalty over time.

Revenue from a single impression isn’t nearly as valuable as a user’s long-term loyalty.

Read the full article here:


By Ross Benes

Publishers have gotten data religion.

A few years ago, publishers began enlisting data scientists to help with audience building and monetization. But back in 2014, publisher data teams usually consisted of only a person or two. Since then, several publishers have expanded their number of full-time data experts. And their roles have grown too. Media data scientists are now developing apps based on machine learning, shaping content-management systems, teaming up with first-party data providers and testing augmented reality features. Here are a handful of large publishers that have increased their emphasis on data analysis.

In 2013, Mashable brought on Haile Owusu, who has a doctorate in theoretical and mathematical physics, as its chief data scientist to work on the site’s analytics tool that predicts which articles will go viral. Since then, Mashable has hired two additional full-time data analysts and added an intern. In the past year, the data team led by Owusu has helped shape Mashable’s new CMS and its Knowledge Graph tool, which tracks how branded content on Mashable is shared through social platforms, email and text messages. The team was not affected by the round of 30 layoffs Mashable did in April. “There was a pent-up demand for insights around the performance of our content,” Owusu said.

The tech network, which publishes Top Ten Reviews and Live Science, is a different type of publisher in its data focus and commerce-heavy strategy. Purch launched its own ad tech platform, Ramp, in 2014. Since then, its number of data scientists grew from one to five. Their focus is mostly on creating recommendation models that pair content with related consumer products. “We realized how much data we had and that we needed to analyze it to know whether we were charging the right price for advertising,” said Purch CTO John Potter. With Microsoft’s HoloLens, Snapchat’s Spectacles and Google Glass in the news, Purch’s data team has been testing augmented reality features in Purch-owned shopping app ShopSavvy.

Read the full article here:


By Mike Kisseberth, CRO, Purch

Today, everyone in publishing talks about data.

Data has become the key to competitive advantage in our industry. But how? Why are publishers investing in data capture and analytics? What are the benefits? In what ways are publishers using proprietary as well as third-party data to deliver better experiences/content for audiences?

1. Identifying trends

According to Chris Wiggins, Chief Data Scientist at The New York Times, “Statistical analysis leads to a story, and many newsrooms are making a big investment in newsroom analytics.” For legacy news media, the ability to identify and break of-interest stories that drive traffic and scale is critical. Data teams can analyze Web usage patterns to determine and even predict stories that might have been overlooked by a team of editors.

Beyond just breaking news, data can also be gleaned to get a better understanding of consumer intent. This is especially imperative for reviews-based content and product analysis where resonating with highly motivated buyers in just the right way is key.

2. Content That Performs

Thanks to the flood of reader data publishers can access, and the incorporation of more advanced technology on the back-end to harvest and analyze that data, performance metrics have evolved considerably. And though the transition from page views to more insightful metrics like “attention minutes,”  “click rate,” bounce rate,” “time on page,” “user satisfaction” or “viewability” has been a slow one, the rewards have been well worth the wait. These newer methods allow us to better evaluate content and advertising engagement and interest levels.

Further, enhanced data capabilities also provide publishers with an invaluable tool to assess which content areas are in fact meeting consumers’ needs and which areas they should broaden to meet reader demand. More often than not, low performance metrics indicate low-quality content. With better metrics, publishers can more fruitfully create and deliver whatever their audience is actually interested in and boost performance simultaneously.

3. A True User-First Approach

Publishers have more data on their readers than ever before, whether it be through proprietary means or third parties. As a result, it’s now possible to bracket readers by interests or intent to make ultra-refined content recommendations, pushing media the way that Amazon might push products, or Netflix does with video. BuzzFeed’s former Director of Data Science, now at Condé Nast, recently touted this capability and the way it lets his editorial team deliver a better and more user-centric experience.

4. Layering Commerce

In addition to ultra-refined content recommendations, because of their wealth of data, digital publishers are in a great position to facilitate e-commerce opportunities on-site. Through product reviews and comparisons, as well as integrated storefronts, publishers can actually streamline purchase decisions and point in-market shoppers in the best direction. This is a huge benefit for readers who already trust the publisher, while broadening the publisher’s revenue potential. Years ago, publishers didn’t have these capabilities. Now, thanks to data that can tell us exactly what audiences are interested in buying and browsing, commerce is more of a reality.

As we enter 2015, expect data to become an even greater asset for traditional and digital-first publishers. As competition for consumer attention continues to heat up, figuring out how to make incremental, user-first changes and build better, holistic site experiences through hyper-targeted content, e-commerce functionality, etc., will be the key to being successful over the long-term.


Five Strategies to Integrate Data and Editorial at Publishing Companies

By Antoine Boulin, President of Media at Purch

For publishers today, data has become the key to competitive advantage. But executing against data has traditionally been a challenge. Only in recent years has the publishing industry invested in the technology required to make sense of data’s complexity and volume. It speaks to a culture shift, with publishers realizing the need to be more than just content players. We must be technologists in order to survive. We’re seeing that pay off now, through data-first publishers like Vocativ and BuzzFeed.

Data is ushering in a new wave of benefits for the publishing industry, including the ability to help publishers identify relevant content, create better content and develop a true user-first approach. For legacy news media, the ability to identify and break stories that drive traffic and scale is critical. Data teams can analyze web patterns and other online activity to determine and even predict stories in their infancy that might have been overlooked by a team of editors.

Publishers have more data on their readers than ever before, whether it be through proprietary means or third parties. As a result, it’s now possible to bracket readers by interests to make ultra-refined content recommendations, pushing media the way that Amazon might push products or Netflix promote videos.

But as more publishers take advantage of data to gain a competitve lead, new challenges are emerging, especially for legacy players.

With technology adoption increasing and in-house data teams becoming common, ensuring that these new resources operate well among long-standing editorial teams is the key to making a data strategy work.

I’ve been helping to lead our own data-editorial integration at Purch for the past year. Here are some of the more important lessons learned during that process:

1. Understand data’s role. Data informs editorial decisions. It shouldn’t define them. A content strategy needs to be shepherded by content creators — those with expertise in creating high-quality content that traffic drivers such as Google or Facebook reward. If you let data lead editorial, you might see some short-term gains in scale, but, long-term, you’re more likely to be punished. Take Panda, Google’s search algorithm. Panda weeds out sites that might be optimized for SEO but feature thin, low-quality content. The publishers most affected put data first and don’t feature strong editorial.

2. Put editorial at ease. For legacy publishers with established editorial teams, integrating with data can seem like an affront to their livelihood. That’s why publishers need to be clear from the beginning — that data is a tool to support editorial and make it stronger. By communicating this well, you give your teams peace of mind and clarity on their roles. You can also avoid silo-building and encourage collaboration.

3. Communicate the benefits. It’s important to highlight just how the data team will benefit the editorial arm to get buy-in. You have to sell the potential, both at a general level — identifying stories, better content, user-first approach, etc. — but also in detail, by looking at actual, high-quality editorial content that could have performed better with data analysis supporting it (e.g., “If this piece was published in May instead of June, its reach would have tripled”).

4. Remove barriers to collaboration. At Purch, we have weekly meetings with our editorial and data teams to discuss our successes and failures. We’re big on video conferencing to remove the physical barriers between team members. We also brainstorm together. These two teams have their own languages and identities, which can lead to silos rather quickly. Eliminate any potential barriers — physical, cultural, etc. — to build mutually beneficial relationships.

5. Writers are not data scientists. Many data-minded publishers are eager to highlight when they’ve made their analytics tools available to editorial. This helps editorial better understand the data team’s role while also giving the writers first-hand insight into what content works or fails. To a point. Editorial members aren’t data scientists. Giving them access to data without greater context provided through experience can cause them to waste time chasing the wrong metrics when creating stories or content. You have a data team for a reason, so use them.

In the end, data is the go-to asset for competitive advantage in modern publishing. The benefits are great, but as adoption grows, marrying new data teams with old guard editorial requires a thoughtful approach. Adding data to the mix needs to be carefully sold and implemented internally or else the idea can stumble and lose traction before it even begins.

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