This week our motorcycle-riding data scientist, Wils Corrigan, Ph.D., returns to share what marketers need to know about data science. While the Anametrix platform automates tasks assigned to data scientists – allowing more time to actually analyze the data – it’s up to Wils and his team to answer clients’ really specific marketing questions. Give Wils and his team a marketing question and he will roll up his sleeves, dive deep into the data and use his predictive analytics modeling and statistical power to deliver the answer. We caught up with Wils to find out what he thinks every marketer should know about what he does.

I am a statistician and a geographer, which are well-defined terms; however, at work, I’m called a data scientist, which is still a bit ambiguous. It’s highly likely that if you ask a dozen different “data scientists” to provide a definition for data science you will get twelve different answers. Most of the answers will include math, statistics, computing with data, machine learning and data visualization. Some will mention big data, computer programming, messy data, creation of data products and communication skills. Then a few will have some odd items on the list, claiming that to be a data scientist one must know six sigma, have advanced spreadsheet skills, know rules-of-thumb but not theory, think models are bad or have a penchant for storytelling. The term data science has become one of those nebulous buzzwords that make statistics or data analysis seem interesting and sexy but gets added to the list of confusing industry jargon (e.g. data mining, big data, predictive analytics, etc.).

Since even the definition of the term is so unclear, you, as a marketer, might be wondering what data scientists do, how we provide value to a marketing department and why data scientists are in such high demand.

Let’s start with what data scientists do. My working definition of a data scientist is someone who engages in a multi-disciplinary approach to data analysis intended to assist clients with making decisions in the face of uncertainty. I’ve often heard questions similar to, “does the data analysis just look in the rear-view mirror?” Usually in these situations the “rear-view mirror” referred to previous experience with analyses or applications that only used simple graphs and descriptive statistics, which barely scratched the surface of what was possible to learn from the data. Now, while I do like driving, and especially riding, it truly is a poor analogy for data analysis. So instead of Mario Andretti, let’s use the example of Sherlock Holmes.

1. Sherlock first consults with the police (consultation with client)
2. Then he investigates the crime (data gathering)
3. He sifts through the information to determine what is reliable and what is unreliable (data sublimation)
4. Sherlock deduces what happened (exploratory data analysis and inferential statistics)
5. He decides upon where and how to catch the criminal, hopefully before another crime is committed (inferential statistics and predictive modeling)
6. Finally, at least ideally, based upon his experience in numerous cases, Sherlock assists the authorities in figuring out how to reduce the chance that similar crimes happen again (optimization)

These are just some of the steps a data scientist can take when solving a problem, and note that each step can be every bit as important as any other to the overall outcome. For example, data of unknown quality leads to results of unknown quality. As I mentioned in my blog interview with Anametrix CEO Pelin Thorogood, the only thing worse than having no data is having bad data.

Now, let’s dive into some of the terms used above in the Sherlock Holmes example:

• Exploratory data analysis is examining the data to improve understanding and enhance subsequent analyses and modeling. Data visualization is a good example of this technique. Through exploratory data analysis, data scientists can identify outliers, patterns and trends, as well as indicate a need for additional data.
• Inferential statistics tests hypotheses and make inferences about populations (potential customers) based upon samples (current customers). Techniques include regression trees, ANOVA and linear and nonlinear models, to name a few. This process allows data scientists to draw conclusions about the importance and effectiveness of different strategies and tactics.
• Predictive analytics refers to the application of statistical models and machine-learning algorithms to create predictions or forecasts. A predictive model uses a set of observed values and its relationship with multiple predictors to estimate which outcome is the most likely to occur given a particular set of circumstances. Data scientists can then tell marketers the most likely results of proposed plans and increase the amount of information available for decision-making.
• Optimization takes predictive analytics a step further by providing recommendations to improve performance or revenue, based upon the expected outcomes from the predictive models. Techniques include linear, quadratic and integer programming and global optimization heuristics.

Using the processes and techniques listed above paired with good quality data, data scientists can provide value to a marketing department and answer questions such as:  “Which campaigns are working?” “What factors affect churn?” “What campaigns will perform best during the holiday season?” Or even “How should I allocate my marketing spend for next year?” I’m guessing the data scientist’s ability to answer these difficult questions is why the field is in such high demand.

So what does every marketer need to know about data science? While it is a new field with an ambiguous definition, data science builds upon decades of research in several fields. Data science allows marketers to get great value from data by transforming data into information and removing the guesswork in the answers to those difficult marketing questions. But start early. Data scientists can be more effective if they are involved in the beginning stages and can assist with determining which data to collect and use, the design of data collection methodologies, the quality of existing data assets and the scope of the marketing questions.

Thanks Wils for sharing what marketers need to know about your field. To find out more about how the Anametrix platform automates data scientist tasks, click here to schedule a demo. If you’d like to work with Wils and his team to answer a specific marketing question, please outreach sales@anametrix.com.

Some companies have become groundbreaking innovators in using big data to drive business results. One such company is U-T San Diego (Union-Tribune), which uses the Anametrix marketing analytics platform to optimize readership, monetize content and drive revenue. The Anametrix insights have helped the media outlet realize double-digit profits at a time when its media peers are struggling to survive.

U-T San Diego publishes more than a newspaper. It distributes news via its website, Twitter, Facebook, a mobile platform and a local cable TV station. With the data produced across all these media, the publisher tracks whom is engaging on these platforms, what stories are being read and what is trending. But that’s just the top layer.

U-T San Diego also tracks results for each of the news teams, as well as individual author contributors. News teams and writers get real-time dashboard reports, enabling them to optimize article content. And the metrics have real depth – article traffic by date, top headlines (those getting the most attention), duration (how long an article generates audience views), the number of pages readers follow a story, page movements and domain referrals, to name a few

From the ClickZ article, Profitable Newspaper: Hold the Presses!

I shouldn’t have been surprised. I mean, transparency, data, and customer centricity have always been the pillars upon which I’ve built my philosophy of better products, better marketing, better business. But you could have snapped a photo of me with my mouth hanging open as I listened to Joseph Gordon, director of research at [U-T San Diego], present at the eMetrics Summit in San Francisco …

– Jim Sterne, new Anametrix Advisory Board Member

So what about the results? News traffic for authors is up by more than 24 percent using the Anametrix platform. Even more impressive, this has taken place as the publisher transitioned to a paid-content model. Most publishers lose as many as 40 percent of readers when they institute pay walls. The Anametrix platform has helped the publisher use data to increase revenue 15 percent year-over-year during the transition to the new model.

Joseph’s Emetrics Presentation from Anametrix on Vimeo.

“We’re finding a way to preserve our journalism by maintaining revenue,” UT San Diego’s Director of Research Joseph Gordon said. “Not surprisingly, these dashboard reports have become very popular with editors. We now know what we are doing right and wrong. The platform has proven its worth to the company.

Joseph also gave his take on the next evolution in TV analytics. People mostly use Nielsen Company to tracked TV viewership, but U-T San Diego is now combining these ratings with audience data from live streaming. The Anametrix analytic insights delivered a holistic picture of broadcast performance, which showed Joseph the outlet’s TV station was missing out on substantial weekly revenue by not including the ratings from online streams in marketing campaigns. By adding Nielsen ratings to data about viewers watching live streams on laptops, tablets and smartphones, the publisher was able to show advertisers a clearer picture of results and to price the spots accordingly. To bolster the viewership results, U-T San Diego is also able to simultaneously tracking social media platforms on the same programming and share a picture of how paid media is affecting earned media.

If you have stories or comments on marketing analytics, or managing big data for business results, post a story or comment below or connect with us on the Web, Twitter, LinkedIn, Facebook or Google+.

Last week we announced Convince & Convert founder Jay Baer had joined the Anametrix Advisory Board alongside Huw Griffiths, Jim Sterne and Eric Otterson. Beyond his work at social media and content consultancy Convince & Convert, Jay is a popular marketing keynote speaker and author. His next book “Youtility: Why Smart Marketing is About Help Not Hype” will be released on June 27.

Today Anametrix CEO Pelin Thorogood talks with the marketing and social business visionary.

You’ve advised companies in every industry about using social media to support their businesses. What are the most common misconceptions about using social media in marketing?  What are businesses doing right when they use social media successfully?

Perhaps the most common misconception is that social media is a particularly good customer acquisition vehicle. Indeed, it is possible to find new customers via social, but the mechanics of social media are such that it’s a far better loyalty and retention opportunity, overall. The people who interact with your company in social media directly are almost invariably already connected to the brand. A study from DDB found that 84 percent of people who “like” brand pages on Facebook are current or former customers. Of course they are! Why would you “like” something on Facebook that you don’t actually like in the real world? Consequently, Facebook (and many other social media sites) are a trailing indicator of consumer preference, not a leading indicator of it. As such, most companies would be better off thinking strategically about social media the way they think about email, as a way to deepen and perpetuate bonds that already exist.

How has what you describe as the Power of Now changed business relationships with customers given their power to review, recommend and rate online? How should businesses respond in terms of speed of response and quality of offerings?

Reactive social media has changed business forever. The expectations among customers have been ratcheted way up, and businesses must keep pace—which is difficult and costly. We expect a response to a letter in a week or two. We expect a response to an email in a day or two. We expect a response to a voice mail in a few hours. We expect a response to a tweet in 45 minutes. That squeezing of the response time horizon is a huge challenge, and coupled with the fact that every customer is a reporter—with many carrying a mobile photo and video studio with them at all times via smartphone—and you have a tough environment for companies. But, those that can nimbly respond in this instantaneous environment can actually win customer hearts and minds through customer service and customer experience like never before. It’s both a curse and an opportunity.

Marketing teams don’t typically know how to measure the impact of social media on their marketing success. What do you tell your clients about measurement across digital media and specifically in social media? 

The challenge isn’t that social media isn’t measurable. The challenge is that there are too many specific data points in social media, and figuring out which ones matter can be confusing. Remember that the goal is not to be good at social media. The goal is to be good at business because of social media, and you have to measure accordingly. Measure things that drive business value such as leads, sales, propensity to promote and the Net Promoter Score among people connected to your company in social versus those that are not. The other problem is that many marketers want to calculate “the ROI of social media” when what they really need to calculate is the ROI of very specific social media tactics, and then roll all of those together into an umbrella ROI calculation. Lastly, too many marketers are trying to rely on a software solution to find ROI. There is no magic button to press. That’s why Anametrix is so important, and so valuable. It combines all the disparate data sources and enables marketers to figure out what matters by examining KPIs side-by-side.

What’s your view on the challenge for marketing teams to come up with the best marketing mix across paid, owned and earned media with social media radically changing the traditional marketing funnel? 

The biggest challenge in determining the role of social in the marketing mix, and its corresponding budget, is that social doesn’t do the same things for the brand that search marketing does, for example. Search is a direct response tactic. So is print, in many cases. Social is an additive part of the funnel, not a causative driver of direct commerce. Studies from Google and Monetate bear this out and show that social is almost never the cause of a direct conversion, but rather a top of the funnel awareness and loyalty builder, like email.

What can you share with us about your new book, “Youtility?”

Companies are competing for attention pixel for pixel against not just other companies, against our actual friends and family members. Your Facebook feed, Twitter feed and email inbox are a confluence of personal and commercial relationships. This is unprecedented and is a huge obstacle for business. To win in that hyper-competitive environment, companies must create marketing that is truly, inherently useful. Marketing of this type—marketing that is so useful, people would pay for it if you asked them to—I call Youtility. In my new book (available June 27, pre-order now at YoutilityBook.com) I profile more than 20 companies that are using Youtility to create long-term customer relationships. We look at the three types of Youtility and the six blueprints for creating it in your own company. Youtility is the first marketing framework for the age of information overload.

Jay, thank you for your valuable insights into social media and marketing. We look forward to reading your new book, Youtility, when it comes out June 27. 

The Anametrix marketing analytics platform exists to find pictures of insight within your Big Data. So it’s probably no surprise that we love pictures that illustrate new truths about Big Data. Here are some of our favorite infographics exploring many aspects of  data for marketers.

In this first infographic, Search Engine Journal worked with Dr. Riza Berkan to define the “Big” in Big Data. It shows we can no longer measure Big Data in terms of terabytes, petabytes or even  exabytes; we now have zettabytes of information. A zettabyte is 1,000,000,000,000,000,000,000 bytes, and you can see its equivalent in books, weight, distance, surface area and trees. We’d need, for example, more than three times the number of trees on earth today to make one zettabyte of books.

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Now that we’ve arrived at a metric for “Big,” let’s explore the data’s origins. How much Big Data exists? Where is it generated? What kinds of items comprise Big Data? Wired Magazine shows even more detail in its interactive chart of “the world’s largest and most interesting data sets,” sourced from CERN, Google, Facebook, Kaiser Permanente, Nasdaq, National Climactic Data Center, Radicati Group, Twitter, US Census Bureau, Library of Congress and Wired Research. It’s not surprising that business emails dwarf the competition with nearly 3 million terabytes produced each year. How fast does Big Data proliferate? What vertical sectors derive the most value from Big Data? Wipro combined its datasets with others from McKinsey Global Institute, ComScore, Radicati Group, University of Texas at Austin and U.S. Dept. of Labor to generate this infographic:

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The Wipro data illustrates that retail garnered the highest value from Big Data, boasting a 49 percent increase in productivity and a $9.6 billion increase in sales. Monetate used data from AP, Centurylink Business, Cisco Engineering, Columbia Business School/NYAMA, Edgell Knowledge Network, IBM, IDC, Kevin Conley, Mobithinking, Twitter and Winterberry Group for its “Retailer’s Guide to Big Data.” The research shows that retailers plan to focus their Big Data initiatives on merchandising and marketing (62 percent and 60 percent of respondents, respectively) with ecommerce/multichannel campaigns as the third highest-ranking priority (44 percent) and supply chain, store operations and operations trailing behind. On the other hand, nearly 30 percent of retailers expect their first Big Data deployment to be in marketing while merchandising and ecommerce/multichannel tied a deployment priority for 20.4 percent of respondents.

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So, how will marketers – including and beyond the retail category – tackle Big Data this year? Infogroup Targeting Solutions and YesMail Interactive surveyed 700 attendees at DMA2012 and a Forrester Research eBusiness Forum to find 68 percent plan to increase data spending; 56 percent plan on hiring new employees for data positions; 45 percent expect analyzing or applying data will be their biggest challenge; and 83 percent plan to start considering using real-time data.

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Finally, how will marketers use Big Data to measure the effectiveness of their campaigns? A survey by Winsper’s Black Ink division reveals 73 percent of respondents believe measuring ROI is “very important,” but only 44 percent are satisfied with their company’s capability to measure ROI and 29 percent admitted they do not currently measure marketing ROI. Top challenges to calculating return on marketing investment (ROMI) were lack of marketing data integration (60 percent) and data accuracy (53 percent).

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What does Big Data mean to you? How much Big Data do you manage? How do you leverage Big Data to measure ROMI? What are your favorite Big Data infographics? We’d love your feedback – please post a comment below or on LinkedIn, Facebook or Google+ or tweet @anametrix with #BigData #infog.

We had a great time at the eMetrics Summit in San Francisco this week where we announced Anametrix Collaboration and demoed it to wide interest at our booth.  As Anametrix CEO Pelin Thorogood said at the launch of the new feature, “Analyzing the right data, collaborating with the right people and acting at the right time facilitates a world of connected and informed decisions that enable meaningful actionability – and can revolutionize how business is conducted.”

Available immediately to clients, Anametrix Collaboration enables teams of users to deliver right-time marketing, which improves campaign effectiveness, lowers customer acquisition costs, expands customer lifetime value and drives business revenue. Converging data, people and processes into one robust platform creates a truly social business, empowering employees with capabilities that allow them to connect, communicate and share information in a more productive and collaborative fashion.

Social business was a hot topic – both at the show and during meetings Pelin Thorogood had with industry luminaries like Constellation Group’s Ray Wang and Convince & Convert’s Jay Baer. Ray sees internal team-collaboration tools as fundamentally shifting the way businesses operate. From what’s he’s seen, companies tend to have more success adopting these platforms when they’re built directly into the software applications that teams are using. Anametrix is delivering this capability natively in its marketing analytics platform. Stay tuned for more thought leadership from Ray – we’ll be announcing a webinar with him soon on our events page!

Jay had this advice for marketers to reap the benefits of social business:  “The problem with most data collection and KPIs isn’t the numbers, it’s that marketers tend to hide and protect them like nuclear launch codes. If you want your company to improve, metrics need to be socialized and spread like a numerical dandelion. Anametrix Collaboration makes this process simple, and the results are compelling.”

Anametrix Collaboration enables clients to:

• Share Anametrix report and dashboard findings and insights among teams and enable feedback and action.

 


• Define and control collaboration circles before broader distribution.
• Keep all interactions on findings or questions entirely within the analytics platform environment.
• Present teammates with the most helpful insights by voting on comment relevancy.
• Elicit new report recommendations within the native analytics environment.
• Receive proactive notifications on reports and insights that your teammates believe will be relevant for you.

Among the many clients considering using Anametrix Collaboration are HP Snapfish and U-T San Diego, both of which delivered presentations at the eMetrics Summit. Mario Pacini, General Manager and CMO, Snapfish Americas, shared how the company leverages chat to minimize email overhead, close sales and – by using the customer chat data within the Anametrix platform – increase customer retention and loyalty. Joseph Gordon, Director of Research at Top 25 news outlet U-T San Diego, shared how he has used Anametrix to connect all the data he can get his hands on to help grow readership, inform advertisers and support the editorial and sales teams. Leveraging circulation analytics, niche product development, CRM systems, survey analysis, GIS and spatial analytics, database profiling and audience segmentation, Joseph has enabled the news outlet to thrive in today’s challenging publishing climate. Look for the video footage on our SlideShare channel soon.

Special thanks to all of you who visited us at eMetrics! If you’re near Chicago, be sure to see us at the Social Media Strategies Summit Apr. 23-24. Or contact us to see a demo of Anametrix Collaboration.

We are excited to announce Wils Corrigan, Ph.D., has joined Anametrix as director of data science. Most recently, Wils worked as a senior data scientist at Geoscape International, where he created demographic datasets by enhancing U.S. government data and consumer surveys. Before Geoscape, he analyzed data at Nielsen Claritas as a core member of the research and development team. He holds bachelor’s, master’s and doctoral degrees from the University of California, Santa Barbara.

Today, Anametrix CEO Pelin Thorogood talks with Anametrix’s motorcycle-riding director of data science about how he’s driving value for clients.

Wils, welcome to Anametrix. The data-scientist career is considered one of the fast-growing fields, and the Harvard Business Review even went so far as to label the data scientist the sexiest job of the 21^st century. What led you into this field?

I hesitate to use the term “field” for data science, if only because an online search produces dozens of different definitions for both data science and data scientists. No one can agree on what it is. In its current use, it is actually a relatively new term. Personally, I see it as a combination of understanding data, statistics, machine learning and – more or less – programming. From my perspective, the field of data science sort of coalesced around me. It was as if suddenly, and rather recently, there was a new term to use for what I was doing.

Growing up, I loved sports, particularly football, basketball and baseball. Beyond watching the games, I always looked forward to the months before the seasons started when the sports magazines came out with all of the statistics from the previous season. I poured over all the numbers, and I knew that there must be something else that could be done with them.  But, at ten years old, I certainly didn’t know what that was.  After getting my master’s degree in statistics, I sort of had an epiphany where I realized I had become exactly what I wanted to be as a child.

What do you see as the future for this industry?

These days, people and companies have realized that there are vast amounts of data available and have started gathering it more systematically. There is no longer the same lack of knowledge about what is going on because you can actually find out through data. There is an old advertising maxim: half of the money spent on advertising is wasted, but no one knows which half. Now it’s possible to start figuring out which half it is. With the abundance of online and offline data available, marketing departments can get information out of that data and plan accordingly.

The “field” of data science is going to grow and change. There will be continued growth in the amount of data available. As more people and companies realize that there is useful information in all this data, they will find people who can analyze the data and provide insights.

The world is beginning to see the power of predictive analytics. Tell us more about how marketers can use predictive analytics in their campaigns.

First, predictive analytics is just one aspect of analyzing data and is enhanced by combining it with other methodologies.  For example, the best predictive models are often terrible at explaining what is going on in a particular situation. Conversely, complicated models might help with an in-depth understanding of a phenomenon but provide only mediocre prediction. Using both, along with data graphics, optimization and other techniques, we can reveal a much more interesting and useful picture.

To answer your question, let’s use an online ad campaign as an example. We would first create a model that would help us understand which ads bring in the most business, are the most profitable, etc. We would fit a predictive model to the historical data and then forecast the most likely outcome. If the prediction is for a bad outcome, the marketers might decide to change their plans to improve this outcome. This would lead us back to our original model to see what works and what doesn’t work. It is all tied together, and it is an ongoing process of model fitting, seeing what is likely to happen and evaluating each scenario.

Tell us about what you’ll be doing at Anametrix as the new director of data science.

I will be helping clients gain information and insights from their data. Once we determine business objectives with the client, I will show them what we should be looking at analytically.

Like any statistician, I will spend time a great deal of time cleaning or scrubbing data, which I call data “sublimation,” using an archaic definition. Almost all data is messy, and all big datasets have problems. One of my tasks will be to evaluate new data and scrub the datasets to make them useful for reporting and analysis.

I will also be creating models for clients, devising efficient and automated processes within statistical software and the Anametrix platform and finding unique ways to help current and new clients.

If marketers have time to do only ONE thing with data science, what would be something they could easily do that would have a high impact on their business?

Pay attention to data quality! An analysis or data graphic can only be as good as the quality of the input data. I like to say that the only thing worse than having no data is having bad data. For a real-world analogy, let’s say you’re on a road trip; you’re driving, and your friend is navigating. Unbeknownst to you, your friend doesn’t know how to get to the destination. Would you rather your friend honestly say, “I don’t know how to get there” or start offering guessed directions? In the same way, results gained from bad data, or even good data with the wrong analysis, is a little better than guessing.

What else should our readers know about you?

When not analyzing or cleaning datasets, I’m often riding my motorcycle, a Triumph Sprint ST, or reading comic books. My favorite comic is Usagi Yojimbo, a story of a rabbit samurai living in late 16^th century Japan. As a comic book fan, I do attend San Diego Comic-Con, unless it happens to conflict with the MotoGP or World Superbike championships. Motorcycles take precedence over comic books!

Wils, thank you for talking with us today about data science, predictive analytics and motorcycles. We look forward to hearing more about your work with Anametrix clients.

In Part II of this blog, Anametrix CEO Pelin Thorogood talks with Ryan Swan, director of business analytics at Symantec, one of the world’s largest software companies. Here they explore the future of business analytics to drive multichannel marketing and where the field of data science is going.

We’re seeing the convergence of marketing channels across traditional, online, social and mobile – along with advances in the underlying analytics technology. Where do you see these trends going in the next few years?

Looking not too far down the road, I believe companies will invest less and less in traditional marketing channels. Online, social and mobile offer ways not only to reach customers, but also to gain tangible intelligence about them. Using this data, the marketing team can quickly make changes in campaigns and offers, test out different hypotheses, different programs, determine what is most effective with customers. It’s not just a matter of where buyers “hang out.” As a marketer or analyst, you can add so much more value when you have the data generated by these channels. There’s a great deal to be learned, which can help marketers change their paths, adjust campaigns for more value.

What does your crystal ball tell you about the future of business analytics in marketing and other business functions?

The data scientist can have a tremendous impact on an organization. That’s where the idea of “sexy industry, sexy field” comes into play. Everyone recognizes how much impact you can really have in helping an organization be successful. I’m seeing that people in this field don’t have to do a slow climb up the career ladder. They can make career advances very quickly because there’s so much value to be added if you have the right people.

But it’s no longer enough just to manage complex data sets or provide data. Insights analysts must justify that data, understand what it means to the organization and apply it to make business decisions. It’s not easy to find people who can handle these large data sets, synthesize the information and apply it to the business. People with these skill sets are in high demand. They are the analysts who can manage the data and turn it around quickly to support business decisions.

Marketing teams and data scientists typically don’t speak the same language. How do you solve this problem at Symantec?

That’s one of the biggest challenges we face as an organization. Analysts and marketers don’t always speak the same language. We’ve done some interesting tests in creating dual-track presentations. One presentation was oriented to analysts, the other to the marketers. The presentations said roughly the same thing, but they were constructed completely differently. This test is one example we use to help show our analysts how to communicate more like a marketer.

We continually work at training our analysts to think and communicate as marketers. That’s not necessarily easy to do after years of training. But it’s ultimately where the value is. It doesn’t matter if you have a lot of valuable knowledge. You must be able to help convert that knowledge into actionable programs. We are focusing on finding and nurturing the talent to tackle this problem.

Are there any other valuable lessons you’ve learned that might help others with regard to BI and Big Data that you’d like to share?

Don’t be constrained by how things have worked in the past. The world of business and technology is moving quickly. We are having many interactions with our customers every day. We now have the ability to hear what they are telling us. I recommend that you show just how valuable this knowledge can be to the business, and the organization will absolutely respond. That’s how you break down barriers. Synthesize the data for the business user and provide the insight quickly. That’s the biggest lesson I’ve learned.

Ryan, thanks for your views and insights on the world of business analytics. We wish you well as you continue to add value to the Symantec business with your team of analysts.

Ryan Swan is director of business analytics at Symantec, a Fortune 500 software company and long-time bellwether in security. In this two-part Data Alchemy blog, Ryan talks with Anametrix CEO Pelin Thorogood about the data-science field and the use of analytics in Symantec’s business. The software maker has 175 million active users of its Norton products, and 100 percent of Fortune 500 companies use Symantec products. Not surprisingly, given the scale of the business, those relationships and the data they generate provide a goldmine of information.

Ryan, there’s a spotlight on data scientists and analytics lately as a “sexy” field. Tell us about how you got into business analytics. Where did it all start?

I always had an inclination for numbers, for analysis. A girlfriend in high school once told me, “We’re going to be at a 10-year reunion, and you’re going to spend your time rattling off baseball stats.” In actual fact I ended up with a degree in economics and went into finance before joining Symantec where I supported sales teams by looking at deal structures, pricing, understanding the important levers in how decisions were made. But as Symantec was ramping up online capabilities, the timing was perfect for me to join the e-commerce organization as it built this customer channel. My team covers business analytics to help marketing build programs, messages and offers for customers.

How does Symantec use business analytics to understand customers and drive value in the organization?

When you look at the Symantec business model, we are a 95-percent subscription-based business. That’s the fundamentals. As a subscription company, we’re all about asking: What’s the true value we deliver to our customers? How do we engage with them? And how do we keep customers as long as possible? The data helps us understand customer intent. We’ve done a lot of work to discover the levers of customer loyalty, specifically to define what customer segments are likely to be loyal and those less so. And, of course, we want to use that knowledge to match our marketing dollars to where the most good can be done.

As an analyst leading a business analytics team at Symantec, what are your biggest challenges in using what we’ve been calling “Big Data”?

My interest has always been how to put real information behind decisions. But our biggest challenge is a cultural mind-set. The answer to managing data doesn’t lie in building more structured datamarts. Companies now have opportunities to use data in much more efficient ways by leveraging some of the new tools and technologies. We need to be an early adopter of those technologies. At the same time, it’s important to combine IT and the world of business users. You cannot simply be a data developer anymore. The data scientist, the analyst, needs to understand the business application to be relevant in this new world.

Where do you most rely on analytics in your Symantec marketing programs, and where do you want to go?

Our focus is on finding ways to delight our customers. If we have happy customers, I believe the rest will fall into place. We are constantly looking at customer segments, identifying profiles, the people we know we can delight. Who are those customers, and how do we target them with relevant messages? With the number of interactions and data we have with customers on a daily basis, there’s so much we can understand about what they want, what their intent is. Our goal is to leverage that data and knowledge to deliver a relevant experience.

What is your approach to segmenting customers and creating profiles with the data you gather?

Our customer segmentation model has eight core segments, which break down into 70 micro-segments, but it’s not always easy to align the data with all of them. Our most important focus is learning how our loyal customers interact with us. What do they do on our websites? How do they interact with our products? What features do they want? What are they saying on social platforms? What kinds of devices are they using to browse and buy? We do “look-alike” modeling, which enables us to ask who else behaves this way and might similarly become a valuable customer.

Ryan, thank you for this view into the world of business analytics at Symantec. Look for Part 2 of this blog in the next few days.

Noran El-Shinnawy, senior product marketing manager at Anametrix, is blogging this week from SES New York, as well as moderating sessions at the event as an expert in digital media. She led two Meet the Expert: Roundtable Forums, and moderated panels on Paid Search Analytics and Multi Touch Attribution Analysis and the ClickZ Express Clinic on Conversation Optimization: Live Website Evaluations. Here she shares highlights from a presentation made by Andrew Beckman, CEO of Denver-based Location3 Media, a digital marketing company, on one of the core questions in marketing today: Bringing Together Paid, Owned and Earned Media.

Marketers know that new and often complex relationships among paid, earned and owned media can support or play havoc with marketing campaigns and budget allocations. The marketing team needs to align content marketing and campaigns (owned) with media “buys” (paid) while also leveraging and measuring earned media. That’s why the session at the SES conference this week with Andrew Beckman was so on target as he explored ways to “intertwine paid, owned and earned media strategies to create an integrated digital marketing approach.” Here are some highlights from what he told the SES audience today:

• Strategy Segmentation is a big issue, he points out. A company may have a unified brand, but the strategy often comes from different teams or agencies responsible for paid search, SEO, PR/social media and creative design. That makes developing a cohesive or integrated strategy a challenge.

• When it comes to owned media, he recommended that the marketing team build out enhanced profiles for owned assets, Google+ and social media. The consumer path to purchase begins with intent, research and reaching out to friends for their recommendations. At that point your owned, earned and paid strategies may come into play.  Andrew specifically recommended the following steps: feed the top of the funnel with non-branded paid and organic search terms; use paid search to drive direct response, SEO best practices to drive non-branded organic traffic audience retargeting to maximizing previous engagement with paid media; and establish long-term engagements with customer and develop more brand advocates.

With all this, search is the catalyst, Andrew told the audience. He shared some fascinating facts that put a sharp light on both opportunities and challenges:

• Google has 34,000 searches per second (2 million per minute; 121 million per hour; 3 billion per day; 88 billion per month).
• 20% of daily queries have never been seen on Google.
• 70% of queries in the United States have no exact matched keywords.
• 54% of queries in the United States have three or more words and the number is rising.
• The importance of page 1 position is key: 75% of searchers never scroll past the first page of results.
• Listings in the top position on page one for search results receive 42% of all click -through traffic.

Analytics, Andrew maintains, are the foundation of every program.  Most people, he said, double count sales, have missing scripts, etc. The first thing to do is to check that your analytics are working and implemented properly. Marketers need to identify high performing keywords; analyze conversion percentages; decide which keywords can be used in organic search efforts; and optimize for clicks and conversions.

To sum up his presentation, Andrew made these recommendations:

• Design paid campaigns and site architecture to complement each other, not compete with each other;
• Use PPC search query data to uncover top performing terms that don’t work well organically;
• Create an influencer outreach target list and strategy to gain high quality links back to your own site;
• Incorporate content such as videos and images tagger with proper keywords that rank well;
• Use social and display to help bolster your search marketing efforts; and,
• Track earned, owned and paid media through a centralized strategy.

If you have insights or stories to share about your strategies and programs involving paid, owned and earned media, let us hear about them. Post a story or comment below or connect on the Web, Twitter, LinkedIn, Facebook or Google+.

Noran El-Shinnawy, senior product marketing manager at Anametrix, is in the Big Apple this week attending SES New York, one of the premier events for online professionals seeking new skills and time to network with others in digital marketing. Noran is well-known for her digital marketing savvy, regularly writing for Search Engine Watch and contributing to SES Magazine and ClickZ. Here’s her report about one of the top SES panels of the week – the Dawn of Convergence Analytics and release of the study sponsored by Anametrix of the same name.

SES New York saw a full-packed session for the Dawn of Convergence Analytics, a panel presented by Andrew Edwards and Rand Schulman, Managing Partners at Efectyv Marketing, and moderated by Adam Singer, Product Marketing Manager at Google Analytics. No one was surprised at the intense interest. We’ve been waiting expectantly for release of the  Dawn of Convergence Analytics study, now available for download. Marketing analytics is evolving rapidly, and that’s why Anametrix sponsored the study. Here’s what the panelist addressed in the presentation today at SES and in the newly released study:

What is Convergence Analytics?

Rand and Andrew kicked off their presentation by defining Convergence Analytics. They made an analogy to the Wright brothers. When the brothers built their first plane, they used existing technologies, but combined them in a way that was new and innovative. Convergence Analytics is not much different from that. It, too, is a cluster of existing technologies, which are brought together in an innovative manner, delivering something new to marketers.

In short, Convergence Analytics can be defined as the confluence of:

·      Digital Marketing

·      Big Data

·      Cloud Computing

·      APIs

·      Sophisticated Presentation-Layer Visuals

Because of this confluence, there is an opportunity to provide the best single-view into all marketing data, and that’s what the big rush is about.

The problem with Convergence Analytics, though, is that everybody  (referring to vendors, agencies, marketing companies and consultants) claims to be measuring everything within a single platform, as though it is the, “one ring to rule them all.” They do so by connecting different data from multiple sources, thus providing marketers with a 360-degree view of their customers.

Convergence Analytics Vendors

Few capabilities appear to be common among most Convergence Analytics vendors. These vendors offer some sort of a capability stack composed of extracting, transforming, loading, analyzing, displaying and predicting incoming data from multiple sources and channels, including social mobile, email, ERP, call center, etc. They then “spit out” this data into actionable insight for the end-user or practitioner.

What Drives Convergence Analytics?

Convergence analytics is driven by many reasons, most notable, the growing demand by marketers for more information to be able to own, understand and act on their data.

Other factors include the maturation of web analytics as a whole, “free and superior” tools such as Google Analytics. The result – stakes are raised in terms of competition and confluences of enablers, including powerful algorithms, as well as a growing blended offering of both products and services.

In essence, the three main drivers behind convergence analytics are:

1.     Data

2.     Demand

3.     Technology

There are 5 important facts about Convergence Analytics:

1.     Convergence Analytics will change EVERYTHING about the way digital marketing is being measured. Anything short of measuring multiple data streams is pretty much obsolete. Moving forward, Convergence Analytics tools will put marketers at the center crossroads of providing value to both their organization and customers.

2.     Differentiation is going to be difficult. Most vendors in the space are claiming to do three things: 1) measure everything; 2) create pretty visuals; and 3) provide data that improves the decision making process.

3.     Customers may not fully comprehend the power of Convergence Analytics or how to use it. It’s as though marketers are running as fast as they can, but that may not necessarily be fast enough. There’s a need for expertise, a process that’s better optimized, as well as internal expertise to understand and make use of the incredible amount of data being tracked and measured.

4.     Organizations need experts. Now more than ever, and with multi-channel data getting increasingly complex, marketers have a growing need for domain expertise that can handle the mash-up of multiple data sources and make sense of them. New and emerging roles such as data scientists and analysts will be needed to ensure that things like proper data collection, tool deployment and data analysis are handled smoothly.

5.     Convergence Analytics is not just a marketplace, but an approach as well. The saying, “The CMO is dead,” is simply not true. In fact, this role has become increasingly important, as marketers are experiencing a paradigm shift toward a true multi-channel and multi-dimensional understanding of both their customers and the data these customers are providing them.

Key Takeaways from the Session

Convergence Analytics seems to be evolving naturally and is heavily driven by data, demand and technology. Vendors look to be measuring everything, making multi-channel the new “norm.” These tools and technologies have the ability to extract, transform and load, coupling these capabilities with functionality like robust algorithms and visualization tools. There is an increasing need for expertise in the field, as marketers need to make sense of the massive amount of data being gathered, stored and analyzed. Vendors will continue to face differentiation challenges, but the future lies within tools that include predictive modeling and self-learning apps.