Goldilocks Criteria: Customer Data Platforms

This is the second in a series of posts designed to help managers think about business requirements for selecting enterprise vendors and software.  Please also check out my first post on Business Intelligence platforms.

Customer Data Platforms (CDPs) inspire a lot of confusion.  Best to begin with what they are and what they are not.

CDPs are:

  • A centralized platform for storing all of the user data about all of your users
  • A platform that can be used by non technical employees to activate / action upon user data
  • An safe-haven for secure user data management, compliant with the latest regulations and best practices
  • A bridge to combine your user data with external data sets
  • A rules engine for user segment management.  Want to build cohorts of users who opened an email and clicked on a Facebook ad – no problem
  • A platform for collaboration, breaking down individual business unit data silos

CDPs are not:

  • CRM solutions designed for sales or support teams to manage intricate customer interactions and workflows
  • DMP solutions focused only on anonymous cookied / IDed users (though they are coming close to covering this feature set)
  • Tag Management solutions designed to wire up various vendor libraries and SDKs.  Many CDPs were Tag Managers, but I think the historic focus on tag management is a disadvantage to be a best of breed CDP.  Just because you were a horse, it doesn’t make you a better car

And why do people integrate Customer Data Platforms?  Centralizing user data, strengthening the intelligence around it, and democratizing access to use it should impact business goals across the board from decreases systems costs to improved conversion rates.

The basic ins and outs of a CDP.

Given all of this, let’s review my Goldilocks (“just right”) criteria for picking a Customer Data Platform:

Connectivity and I/O

Customer Data Platforms are only as good as the pipes that bring data in and out of them.  You want many different roads into the platform from plug and play SDKs / libraries to full read / write APIs.  You also want pre built connectors into the most popular data sources (CRM, event ticketing platforms, etc) and data activation endpoints (ad networks, social media channels, email service providers, etc).

Security and Compliance

As we’ve learned over and over recently, user data security and governance is no easy tasks.  Outsourcing this to a vendor may be a hard decision to make, but it’s often much harder managing and maintaining secure and compliant user data solutions internally.  You want a partner with a tract record of secure data management, comparable customers that you trust and no fear of security audits from your team or others. You also want a partner that is quick to update to changing industry rules and regulations (ex. GDPR).  Internally, you want robust rules, roles and permission settings to partition off sensitive data for specific users and use cases.

Administrative Usability

CDPs are designed to democratize data-driven activities for non-technical users.  As such, you should require a modern, usable UX for non-engineers to get busy with the data.  Some providers require light scripting for segment creation or segment activation. No good. Best to trail the administrative user experience with some of your least technical colleagues before pulling the trigger on a vendor solution.

Identity Management and Identity Resolution

There are a number of features in this functionality bucket, but in short, you want your CDP to consolidate literally all of your available user data into a singular user profile.  This might mean partnering with a device or identity-graph provider to stitch emails to cookies.

This also means flexible data storage limits so that you don’t have to discard potentially valuable user data limits.  At Viacom, a certain % of the US population visits our sites / websites or volunteers their email addresses. That said, our TV signals reach the homes and mobile devices of a much larger user base.  We need systems to allow us to pull all of our data together without worry about a vendor’s storage costs or historic architectural limits.

Real Time Segmentation Updates

You user’s profiles and segments should update in real time as they take actions on and offline.  Many CDPs update segments hourly – which is no bueno. If a user views / interacts with your website or an online ad, their profile should update immediately so they can activate to the next event in your funnel.  Many of the CDPs who came from legacy industries (again, Tag Management) are just not architectured to support real time updates. This is of growing importance.

Integrated and Automated Machine Learning

The next generation CDPs go further than data storage and segment storage.  The best support unstructured data and use machine learning to automatically create useful user segments.  Some even crawl and categorize your content (pages, emails, posts) to find interesting patterns and apply those as dynamic segments to your users.  This is the type of thinking you want to see from your Customer Data Platform partners.

The platform should also support custom data science models – whether run internally within the CDP or through easy and performant read / write APIs.

ML fanboy alert – this is one of my very top considerations when reviewing partners.

Smart Orchestration

Getting your users through a funnel from start to conversion is never easy.  Your CDP should monitor and track your progress and where possible add dynamic intelligence to usher users through funnel events and towards your target goal.  The alternative is intricate manual workflow creation and management, which is hard to set up and even harder to manage against other initiatives.

This dynamic orchestration allows for truly personalized, omni-channel user journeys – experiences and messages that change based on the individual user’s profile properties and the best likelihood of conversion.

Industry Momentum

There is a ton of investment in the CDP space right now.  You’ll want to pick a horse with recent major funding from venture capital or a strategic investors.  Many of these companies will not be in business in two year’s time.


Goldilocks Criteria: Selecting Modern Business Intelligence (BI) Platforms

This is the first of a new series of posts dedicated to helping people select data tools and infrastructure. I’ve listed out the ‘perfect’ feature set for a dream product. Of course, these features rarely exist in a single solution, but if they did, I’d use it! First up: business intelligence platforms.

For the purposes of this discussion, let’s define Business Intelligence (BI) platforms as data platforms that non-technical business users to explore, prepare, and present data germane to their work. BI tools are crucial for making informed decisions.

Turns out this is ancient stuff. BI platforms we first utilized back in the ’60s – the 1860s – when Richard Devens coined the term in his Cyclopædia of commercial and business anecdotes when Devens used the term to describe how Sir Henry Furnese, a banker, gained an advantage over his competitors by using and acting upon the information surrounding him.

We’ve also come a long way since the 1960s, evolving from cumbersome on-premise solutions to nimble, cloud-based platforms. Today’s BI tools are designed for accessibility, allowing professionals without a technical background to glean insights easily.

BI tools serve a specific purpose: analyzing and reporting on data consolidated from various sources. Some BI platforms sit on top of separate data warehouses and some modern platforms serve as the data aggregator/data store as well. BI tools pack a ton of functionality but are typically narrow-scoped. They don’t execute tasks but rather provide insights that inform actions taken on other platforms.

You will also see BI in the form of Embedded Analytics within various tools – like your CRM system or your Web Analytics platform. Generally, Embedded Analytics helps inform micro insights like, which email subject performed best, as opposed to providing a holistic data view of data across multiple use cases. 

So how does this work in practice? A great use case for BI platforms is to create easy-to-digest OKR dashboards for your company, teams, and individuals. Your BI platform should allow teammates from different business units to pull up live views of their progress towards their outcomes/goals… anytime/anywhere… on their phones… without support from business analysis or IT.

OK, enough preamble. Here are the goldilocks (aka “just right”) criteria I look for in BI platforms:

Integrated data warehouse

Traditionally, BI tools sit on top of separate data platforms managed by engineering teams. More recently, a new class of products has emerged that allows you to upload/connect to your data without engineering support. I find this to be a huge advantage as it allows moderately technical users to get up and running without distracting/relying on external resources. (Self-service also leads to challenges with data governance but that’s another story.)

As an example, imagine easily joining together all of the spreadsheets you store in Google Drive / Dropbox with live data connections to Google Analytics / Meta Analytics / financial data / more and then exploring and visualizing this data as you choose. That’s what these new platforms do all without the help of data engineering resources.

Data engineering for dummies

Some of the best data scientists I’ve worked with estimate that they spend 80-90% of their time on data hygiene before they can begin analysis and exploration.

What does that mean for BI tools? Any functionality that supports easy data manipulation for the sake of improved clarity is awesome. That means – joining data together via drag and drop, changing data types with a click, and deduplicating rows without writing SQL is all a huge value add, extending the range of users who can go deep with the data without external assistance.

Live data! From the cloud! On your phone!

Data that arrives attached to an email is DOA. This is one of my absolute pet peeves. Further, once people begin offline discussion and editing of the data, the risk of multiple inaccurate versions/views of the same data set is commonplace. 

BI tools need to pull from a live backend at all times. When I pull up a link to view a dashboard the data should be (pseudo) real-time, up-to-date, and time stamped clearly with the data last run.

This also means the platform should be mobile-centric. Old-timers still want their landscape printouts, but there is nothing more powerful than conversing with colleagues and pulling up live data views on your phone à la minute. 

AI / ML ready

I don’t want to overstate this one as we’re in the very earliest of innings, but your platform should have the foundation of supporting automated machine-learning-driven insights. You may not find these immediately valuable (they rarely are out of the box) but in a few years, you be getting voice alerts when your data spikes unpredictably in ways you may not have imagined. There is no sense in investing in a platform that is not actively working on automated data insights.

As a start, I’d like to see my platform present basic statistics around the data that I’ve onboarded. This means simple distribution and correlation reports. As you play with these statistics you’ll be able to more easily wrap your arms around the data at hand, steering deeper analysis and insights. Simple predictive analytics is another good baby step before full-blown AI.

This all said, you separately need to invest in training your teams to take advantage of these statistical insights. Leveling up the data fluency of your team is always more valuable than standing up a wiz-bang technology solution.

Narrative & collaboration focused

A perfect platform would support metrics-backed storytelling – and not just the sharing of pie charts. That means as a product owner, I can use a BI platform to explore a set of data and then build a coherent, sharable narrative around it. That could manifest itself as an online presentation with live data at different altitudes, supported by text, images, video, and other added insights. It also means that I should be able to draw / pin annotations within the data itself.

Further, the presentation should support active conversation around what’s being presented. Unlimited named user accounts, threaded comments, open annotations, creating next-step action item, @ mentions and more are a natural fit here.

Governance gone wild

Sad to say, this is critical. Like supercritical. Like, as soon as you create your second dashboard you need extreme governance otherwise you’ll never find it again or know if the data set that powers it is up to date, approved, and official. 

I’ve seen smart approaches here and they center around clear labeling of the data, its origins, similar/duplicative data, and more. Having an easy way to validate data as “best” or “official” helps too. Ultimately, ML/AI will be a huge help in this arena.

An integrated, dynamic “data catalog” that shows you the breadth of your data, its lineage, stamps of approval, and error reporting is also a must-have.

User-level data FTW

BI tools typically play in the aggregated, anonymous altitude. You can see how all your site visitors behave, customer acquisition by location, sales by campaign, etc. Data is viewed on the content, page, campaign, and location level – rarely at a user level. In a perfect world, a graph model would be deployed at the atomic data layer allowing pivots by the above altitudes but also on the user level.

A new breed of system called Customer Data Platforms is jumping into the fray here, promising a single view of the user. These CDPs are being leveraged today by Marketing and Sales team but the application of this granular view to more typical BI use cases is immense. Perhaps CDPs are the topic of the next post in this series…

In the Press: Get to Know a Few of Viacom’s Data Scientists

Here’s a great profile on a few of my Data Science colleagues here at Viacom. So excited to see a few of my hires (Matthew and Preeti) profiled!

Viacom has a strong track record of hiring data scientists with deep academic backgrounds who have also completed business training boot camps.  Matt and Preeti were graduates of the Insight Data Science Fellows Program – an intensive 7 week post-doctoral training fellowship bridging the gap between academia & data science.

With any new hire, there is a learning curve.  The transition can go smoothly if there are mentorship opportunities and other senior data scientists in place before the new cadets arrive.

Introducing Data Science at Viacom: Where to Start?

In my new role as Corporate Vice President of Data Strategy at Viacom, I have joined a small group of data experts affectionately known as “data mercenaries.” Our primary focus is to explore opportunities for scaling data science and data platforms throughout the organization. With many areas ripe for data superpowers, a key question is what to work on and why?

This transition marks an exciting departure from my previous experiences leading and building consumer-focused teams. The potential for growth and impact is immense. In addition to taking on specific data projects and products, we are entrusted with the responsibility of optimizing Viacom’s overall data efforts and assets. In this blog post, I will provide insights into our initial steps toward achieving this goal and share the process we have implemented.

Defining Work with Precision and Consistency: Our first priority is establishing a robust process for defining the work undertaken by each data group. This pre-work phase offers numerous benefits that are both extensive and significant. By adopting a shared methodology, we can document the goals, expectations, and return on investment (ROI) for each project. This levels the playing field for all teams, ensuring a common understanding, garnering buy-in, and securing the necessary support. Now, let’s delve into the details of our process:

  1. Problem Statement: We begin by crafting a concise, S.M.A.R.T. sentence that effectively defines the scope of the project, expected outcome metrics, and delivery timeframe. Achieving consensus on this statement can be a time-consuming process, often taking several days.
  2. Context: Next, we provide an overview of the project’s landscape and rationale. This context is crucial for understanding why we have undertaken the project.
  3. Success Criteria: To validate the project’s value, we establish measurable key performance indicators (KPIs). These criteria serve as benchmarks and ensure that the project moves forward with clear objectives.
  4. Scope: We outline the project’s initial starting point and identify components that may be deferred for future consideration. This delineation helps in defining project boundaries and prioritizing tasks.
  5. Decision Makers: Identifying the individuals with decision-making authority is essential for effective project management and implementation.
  6. Stakeholders: We adopt the RACI (Responsible, Accountable, Consulted, Informed) approach to clearly define roles and responsibilities. This identifies the project owner, support personnel, and those who need to be consulted or kept informed.
  7. Constraints: We identify both movable and immovable roadblocks or challenges that may impede project progress.

Condensing the Information for Widespread Understanding: We consolidate the above information into a concise, one-page document. This document is then shared widely across the organization, ensuring that it is accessible to both senior managers and junior data engineers. By doing so, we create a clear vision of our objectives and provide a measurable framework for assessing progress. This practice also enables us to analyze various projects and allocate resources efficiently.

Continuous Improvement: Although we are in the early stages of implementing this process, we are continually learning and refining our estimation methods to enhance the accuracy and quality of our work. The impact of this alignment initiative has already generated excitement within the organization, as the newfound clarity fuels enthusiasm and a shared sense of purpose.

At Viacom, our commitment to data strategy is unwavering. By implementing a comprehensive process to define project work, we are fostering alignment, clarity, and accountability across teams. Through collaborative efforts, we aim to optimize our resources and unlock the full potential of our data-driven initiatives. As we navigate this transformative journey, we remain dedicated to continuous improvement and embracing new opportunities for growth and innovation.

Utilizing Data Science to Identify Social Media Influencers: Viacom’s Success Story

In its inaugural year, Viacom’s Social Data Strategy team has achieved remarkable success by employing data science to discover and collaborate with social media influencers, resulting in substantial revenue growth from their massive social following of approximately one billion fans.

A pivotal achievement during this journey was the development and implementation of the Social Talent Platform (STP), an innovative and data-driven tool that has empowered Viacom’s social casting teams to identify the most suitable influencers for specific campaigns. This blog post delves into the development process of the STP, highlighting the key steps taken to ensure its outstanding results.

Data Acquisition

To build the STP, Viacom strategically partnered with leading social influencer data companies, social listening data firms, and cutting-edge machine learning toolsets. These collaborations facilitated the creation of bespoke deals that incorporated custom features, showcasing Viacom’s expertise in business development and strategic product planning. Additionally, the team harnessed unique data sets not commonly used in social talent searches, setting the STP apart as an exceptional platform.

Data Management

Viacom seamlessly consolidated data from various sources by utilizing licensed data aggregation platforms and tailor-made data environments. Alongside building a custom database for social media talent, Viacom also curated comprehensive social profiles for content and advertisers. This process presented challenges in mapping entities from diverse data sets, a common hurdle in data-driven endeavors.

Data Modeling:

Two Ph.D. data scientists on the team crafted proprietary algorithms for the STP, enabling precise comparisons between influencers, content, and advertisers. These advanced algorithms analyzed multiple dimensions, including audience demographics, topic relevance, post-emotionality, and more. Significantly, Viacom’s models integrated non-social data and harnessed time series data to predict future engagement growth.

Data Storytelling

Viacom took data visualization to the next level by developing a bespoke front-end JavaScript application. This interactive tool allows talent, content creators, and advertisers to gain meaningful insights from the results of the custom search tool. Understanding the importance of creativity at Viacom, this application has been instrumental in effectively conveying outcomes that surpass traditional spreadsheets.

Patented algorithms help Viacom’s content teams select the best social media influences.

The culmination of Viacom’s efforts is the Social Talent Platform (STP), a patent-pending data platform revolutionizing the process of identifying social media influencers and generating engaging social content. The exceptional success of the STP is a testament to the unwavering dedication and expertise of the entire team at Viacom, who have harnessed data science to drive the company’s influencer marketing to new heights.

First data, then vibe: How Viacom casts influencers in 90 percent of its campaigns.

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At first glance, there’s little that sets Shaun McBride—a charismatic former skateboarder who goes by the handle “Shonduras”—apart from the millions of other social media influencers enjoying the spoils of Internet fame. But in March, Viacom’s brand studio, Velocity, anointed McBride as a creative consultant, a move based largely on his Snapchat success.

McBride is among the many influencers the unit works with, mainly on campaigns, in a given year. His prominence shows how seriously Viacom takes its digital talent strategy: Velocity uses social media influencers in 90 percent of its campaigns, an approach that has evolved over years.

“A few years ago—the good old days—we could’ve put up a social post on certain platforms on behalf of an advertiser in the hopes of getting perhaps over 50 percent of those people to actually see it organically,” said Lydia Daly, SVP of Social Media and Branded Content Strategy for Viacom Velocity. “Now it could be as little as less than five percent meaning our distribution tactics have had to evolve.”

Influencers have become a cornerstone of the unit’s distribution strategy. And while McBride puts up big numbers on Snapchat and YouTube, Daly said much more goes into the casting of influencer partners than a sizeable following. To select the perfect influencer partner, Daly deploys a five-person team that combines old-school Hollywood casting techniques with new-fangled data science.

Part one: The reach

Of course, the numbers come first. “You’re looking at the numbers,” said Daly “and that helps you to whittle down from hundreds of thousands of potential influencers in the world to the 20 or so that might make sense for the campaign and make it onto our final talent proposal list.”

Those numbers go beyond cumulative subscriber counts to include average video views, breakdown of sponsored video views versus non-sponsored video views, growth trajectory over time, audience demographics, even the engagement metrics that indicate an influencer’s active subscriber base. Viacom guarantee campaign performance, so the Velocity brand studio is just as invested in accurately calculating a social influencer’s real reach as the brands that question the tactic’s value.

“You want active fans who are likely to remain active for a particular campaign,” said David Berzin, VP of data strategy, who leads a team of data scientists, including one doctor of mathematics and neuroscience, that collects and interprets influencers’ value. “The follower count is a lifetime number which is not necessarily relevant for a campaign you’re planning for next month.”

Then there’s the audience itself. “We have ways of looking deeply at the talent’s audience to find if it’s a good fit,” said Berzin. In some cases, Viacom looks for a perfect reflection of an audience they already have, say MTV’s core viewership. For other campaigns, they’re looking for a way to extend their reach into a new niche audience.

But in all cases, the numbers are just the beginning. “We don’t try to be prescriptive with the data.”

Part two: The vibe

Once the numbers are tallied, it’s up to Daly’s social talent casting and management team to work the talent. Here, casting relies—as it always has—on keeping up with trends in the marketplace and close relationships with talent agencies and managers. The team keeps a few different wish lists of talent: “interesting influencers in certain categories, those that fit well with Viacom’s brands and ones that are on our radar… that [are] kind of at a weird tipping point.”

And, of course, there’s chemistry. Daly’s team takes the lead, looking for charisma and personal spark while disqualifying influencers based on a client’s red flags: “There are certain clients that are extremely conservative—they would not want an influencer who has ever sworn in a video,” Daly said. Although that hasn’t stopped her talent team from passionately making the case to clients for creators they have faith in.

But even in evaluating personal chemistry, the data team plays a role. “We have a patent pending social data analysis tool that examines the fit between an advertiser, a content property and social talent,” Brezin said. “Consider it a set of customized set of ranked Venn diagrams that we create for each of our campaigns.” The data team will examine what kind of content the advertiser’s preferred audience watches along with traditional data like demographics.

Then there’s emotionality. Using natural language processing, the team can extract words and phrases and “bucket them into certain emotions. That basic fingerprint of emotionality gives you a good sense of how that audience typically reacts, and might react,” to a particular content strategy.

Part three: The relevance

The perfect influencer, according to Berzin and Daly, isn’t necessarily someone big, but someone who’s about to be big. “You really want to look for ebbs and flows, and talent that’s about to peak as opposed to an inflated follower count,” Berzin said. But Viacom is also looking for someone who’s relevant.

For Trojan’s campaign at last year’s MTV awards—designed to get millennials to wear condoms—the team wanted to propose Shannon Boodram, a YouTube sexologist at the top of their wish list. Though she didn’t have the tremendous follower count that advertisers crave, her sex-positive social presence was a perfect match for the campaign. And she seemed to be at a tipping point.

To help bolster her reach, they paired her with a comedic heartthrob,Josh Levya, said Daly. His 2 million strong subscriber base ensured Boodram’s on-brand message for Trojan cut through the social noise. The campaign for Trojan culminated in an appearance by Boodram and Leyva on the red carpet at Viacom’s MTV Video Music Awards–with Boodram wearing a dress of her own design made from Trojan condoms, naturally. The pairing resulted in an avalanche of positive press for Trojan and Boodram.

“It just speaks to how Viacom can elevate the brand of the talent.” said Berzin, “It’s a two-way street.”

The Ultimate Influencer?

Velocity has garnered some ink for its deal with influencer-turned-consultant McBride, aka Shonduras. While it might seem like Viacom’s just hedging its bets by going with the Snapchat flavor of the month, it instead found in him a kind of ideal influencer: one with the right reach, vibe, authenticity, adaptability  and business savvy to be more than another distribution channel.

“There are some influencers who post content to social media that goes viral, then accidentally become famous and start doing branded content deals,” said Daly. “Shaun is not one of those.”

He’s a motivated businessman, interested not just in how to build his own platform success, but in the interplay of content between platforms. His experiments with content formats, actively tracks trending content and is tireless when it comes to engaging directly with his fans, all of which pays off handsomely in brainstorms.

“In Shaun, and other creators, we are always looking for people who can craft stories across platforms,” said Dr. Thomas De Napoli, Velocity’s senior director of content and platform strategy. “That’s what our studio aims to do, and Shaun makes sure we’re doing it in a way that means something to fans.”

Shaun has already made major contributions to creative strategy sessions both on the brand and channel side of Viacom and is increasingly becoming an in-demand contributor for such meetings.

And, according to Berzin, McBride is, in addition to a content creator, a born metrics geek. “We have data teams that crunch numbers all day, but he just observes the numbers and his insights are often directly in line with our prioritized metrics.  And, he has a unique point of view as a social talent that…is just invaluable to us.”

But, as Viacom has found time and time again, the ultimate influencers may not be the folks with the biggest Snapchat following, the most liked Facebook posts, or the most fire Tweets. The ultimate influencer is situational, empowered as much by timing and authenticity as by the brute force of numeric popularity.

Social media & measuring where fans will go next.

David Berzin, Vice President, Social Data Strategy at Viacom Talks About Staying in Step with the Social Media Landscape

Reposted from

David Berzin HeadshotV by Viacom: As marketers, how should we be thinking of social media right now?

David Berzin: We’re solving for two variables, really. We’re not just building out branded campaigns and monitoring fan response in the present – we’re also using the data we collect now to help us predict fan response in the future.

V: But social media can be extremely fickle. Is that a good or a bad thing for brands?

DB: I think it’s a good thing because it pushes us to work harder. With the evolving media landscape and its increasing audience and platform fragmentation, changing content consumption habits and new technologies have created challenges for the entire industry.  But that evolution has also created a host of new opportunities that continue to help us break new ground.

V: So what does that evolution look like in terms of social platforms and shifting behaviors in audiences?

DB: Well, considering we have the youngest demos of any major television company, we need to be nimble. Then add in the fact that 20 percent of all Millennials are now mobile-only, the growth of live social video and the impact Snapchat is having on the entertainment industry, and you begin to see all these layered nuances.

We know marketers want to reach their fans beyond linear with a scale and breadth of touch points. Our Echo campaigns do just that. To compliment that, we recently launched 3.0 version of the Echo Social Graph (ESG) – our proprietary cross-social measurement tool that helps marketers capture the true reach of our social-by-design campaigns.

The goal is to capture more social platforms than ever before with new insights on emotion, audience and social talent.  ESG data also feeds in to our campaign design and projection toolsets, giving our creative teams data-driven insights to really help inform their artistic decisions.

V: What are you trying to capture beyond page views and interactions?

DB: (laughs) Pretty much as wide and deep as you can get. The Echo Social Graph is a constantly evolving platform that, at its core, reaches outside of the traditional television footprint.  It’s a measurement of social that more accurately reflects how our fans interact with our content.  Think about it – traditionally, marketers have been limited to transacting on sampled Nielsen ratings that measure TV campaigns in isolation, without really seeing their extended reach and impact across emerging platforms.  And when you’re working with social data, where platforms emerge and evolve at lightning speed, flexibility is key. The ESG is basically able to capture all of the new interactions so that all of our fans’ snaps, loops, dubs and lip sync videos are measured.

V: That’s a lot of data – how do you derive real meaning from that?

DB:  Well, here’s a perfect example. We just did a campaign with Trojan for the 2016 MTV VMA Awards that focused on normalizing the idea of condom use. Snapchat proved to be a huge win for us – we ended up more than doubling the impressions we anticipated delivering. That’s a great key learning about where our most engaged audiences are and how Snapchat is an integral partner for live programming and branded content.

V: Do you have a sense of what marketers are asking for next?

DB: More and more, they’re really interested in identifying social influencers. It’s all about finding a good fit between talent, our advertisers and our brands.  That’s why we developed a Social Talent Search platform that we use to leverage the data on top-tier and long-tail social influencers, as well as calculated metrics that allow our casting teams at Viacom to find the perfect talent for each campaign.

V: Is there one thing you’d like to measure that nobody’s attempted yet?

DB: I’d love to dig a lot deeper into the measurement of intra-network referrals within social media networks.  For example, what actions drove users to follow, share or otherwise engage with our content on social media.  We have some visibility into this for paid campaigns, but not enough for organic activities. It’s these kind of challenges that make us constantly think about “what’s next” vs. “what’s been done before.”

Viacom’s social media “echo-system.”

At Viacom we’re hard at work at new tools to create, predict and measure our social media campaigns.

“Viacom’s data-driven ad sales unit Vantage is upping the volume on its Echo social media product.

Now, with version 3.0 of Echo, the company is offering marketing clients what it is calling an entire “Echosystem” of tools. Those tools will help create social media campaigns, predict how many people they will reach, optimize them while they are in the market, and provide a thorough analysis when they’re over…”

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