MAD Perspectives Blog

Making Data Matter

Peggy Dau - Monday, April 06, 2015


It really is the overarching conversation across all business sectors. Data. It's always been there, but we seem to have fallen in love with data all over again. We've been using it since we were in elementary school. We've collected it at every point in our life. We absorbed data about which friend liked the same sports team or what color shirt they wore or what time they ate dinner. We align ourselves with those of similar interests and seek out those with opposing opinions, if only to debate or educate.

Completion of our education did not stop the data deluge, it only changed its form. In fact, some of use decided to become analysts or scientists that steeped themselves in using data in new and fascinating ways. We are no longer memorizing multiplication tables or historic dates, nor are we organizing arguments to support theses. Now we are absorbing data about our industry and its people, processes, products, technologies, tools and communication methods. We manage a LOT of it instinctively. However, the rise of BIG data reinforces the relevance of data and the technology industry for all sectors, whether they are financial services, healthcare, consumer products, hospitality, travel, manufacturing, environmental - ok, you get it. Big data is applicable and pertinent for all industries.

But, how do we make all that data matter? I collect data points every day as I work with clients to develop strategies and content that reflects their value to their target customers. This means understanding their customers' needs, communicating in a manner that resonates with the customer, while still reflecting the key attributes of my client's technology. It's not always easy finding and collecting the right data. All that data doesn't always makes sense. It can be confusing.

This is why we are now seeing the focus grow beyond data collection and analytics. We're going to learn a lot about predictive and prescriptive analytics in the coming months. These are the magic 8 balls of the big data industry, thus far. They are fortune telling at their current best. If we can anticipate the needs and behavior of our customers, we can improve how we address and fulfill them. We can adapt business processes, modify go-to-market models, refine marketing and ultimately, improve revenue and profit margins.

That's the holy grail - make the data matter. But it's just not that easy. I have technology clients managing data, analyzing data, manipulating data and making recommendations borne from data. In the media sector, Nielsen reports data about what we watch. Ooyala shares data about how we watch. Cisco reports about how many bits move across networks. Bay area start-up Guavas provides data on the health of those networks. These are all indicators that should be acted upon. We are all awash in data and I, for one, am eager to see how we make this data really matter for business customers and consumers.

What's your perspective?



Big Data - Is It Worth The Money?

Peggy Dau - Monday, March 30, 2015


Every organization seems to be prioritizing their need for data. Small or large businesses, finance, accounting, sales, R&D and marketing - they're all seeking data. But, are they willing to pay for it. We could argue yes when  IDC predicted Big Data would drive 16.1B in revenue for severs, storage, software and services, in 2014. But that's just the infrastructure. What about the analytics to understand the data? 

Let's consider the use of data as it relates to marketing. Of course, it could be used for R&D, manufacturing, sales and other aspects of business. However, there is an interesting shift in marketing, with CMOs increasingly investing in technology to better under understand their customers and to manage their marketing programs.

Marketing executives all prioritize their need for data analytics to drive smarter campaigns and improve personalization. Yet, when it comes to prioritizing budget, e-Marketer says analytics falls behind digital commerce and customer experience. However, according to the CMO Survey, this is about to change. While marketing analytics currently accounts for 6.4% of the overall marketing budget, this is expected to increase to 11.75 over the next three years. 

It's not unusual for the budget to lag behind the defined need. After all, why spend the money if it's not aligned with your business goals. The interesting thing is that the data analytics could drive more effective digital commerce and/or customer experience investments. The global marketing analytics market is expected to grow to $2.1B by 2019 according to marketsandmarketsThe big question is if that investment is worth it. It comes down to more than analytics, as there must be a human element that capitalizes upon the finding. But, the reason analytics is hot, is the promise that data analytics can improve marketing ROI. 

Here are just a few examples:

  •   - understanding customer use and engagement of different mediums allows marketing to optimize use of channels such as email marketing, social media, webinars
  •   - identifying customer needs allows marketer to develop relevant content at every stage of the buying cycle
  •   - uncovering audience personas and motivations allows marketers to align messaging, agenda and/or sponsorships at business conferences 
  •   - align human effort more effectively 

Marketing budgets represent approximately 10% of the overall corporate budget. Isn't it worth the investment in analytics to uncover the data that will can lead to a tangible increase in marketing ROI? 

What's your perspective?




A Data Driven Christmas?

Peggy Dau - Wednesday, December 10, 2014


As we enjoy the holiday season and prepare to enjoy time with friends and family, we also struggle to find the perfect gift. We seek suggestions, rack our brains from creative ideas, and sometimes default to the ubiquitous gift card. What we are seeking is the data to support our decisions.

Whether we are shopping in-store or online, we want validation that we are selecting a gift that will please the recipient. Our intent is to create joy. However, sometimes we mis-fire and our seemingly perfect gift falls flat. Could a data driven gift selection process improve our "win ratio"?

E-commerce sites are investing in big data to improve the consumer experience, to expedite your purchase decision by providing recommendations, and to drive revenue. In-store promotion seek to attract buyers with heavy discounts, but recommendations are absent, unless shopping with a friend. Many of us seek to avoid the physical shopping experience due to lack of time or distaste for crowds. More of us are shopping online as noted by the lackluster Black Friday figures. Yet overall spending both online and in-store declined by 32% from 2013, per the National Retail Federation. There are a variety of reasons for the decline, but perhaps better marketing driven by an understanding of customer need would result in a smaller decline.

Amazon has long been noted for its recommendation engine. Not that it's perfect, but it does gather a lot of information about consumers and it has the ability to recommend products that are similar to those we have viewed, purchased or queried. But, what if we are seeking something completely different than any prior purchase? Amazon, or any other e-commerce site, would struggle to recommend an interesting item. Without incorporating data that reveals our goal, recommendations fall flat.

Big data is the combination of internal data, such as Amazon has collected in it internal databases, plus the combination of external data collected from the internet. It includes data from online searches, social networks and other publicly available sites. It is this unstructured data that does not reside in an existing database that begins to reveal the intent or motivation of the buyer. With an understanding of intent, recommendations can become more relevant and actually influence consumers to take the desired action of buying a gift.

Businesses, B2C or B2B, must define their own intention for using big data. Then, they can create the strategy to collect and analyze the right data to help them achieve their goals. Then, they can provide the context that will motive at customer to take the desired action.

Each industry's buyers and sellers have unique influences driving their behavior. Acknowledging and identifying these drives, helps big data become more relevant and allows big data to lead to measurable actions. Perhaps next year will be a data driven holiday season. For this year, the data is informative but not inspiring or influential.

Will you have a data driven Christmas ( or Hanukkah or Kwanza)? Has data streamlined your gift buying and giving?

What's your perspective?



The Marriage of Data and Storytelling

Peggy Dau - Tuesday, July 08, 2014

A few weeks ago I wrote about finding the story in the data. This relationship between data and storytelling continues to evolve as increasing amounts of data are available to us. Domo released an enlightening info graphic that exclaims that Twitter users tweet 277,00 times and that Apple users download 48,000 app severy minute of every day. These are just few examples shared in the infographic, which reflects that data never sleeps. With its chronic insomnia, data provides an unending source of stories to entice, educate, elucidate, engage or enrage readers. 

Even as big data is on the cusp of entering the trough of disillusionment phase of Gartner's Hype Cycle, data will continue to be the source of validation for all levels of business strategy and the stories we tell to explain those strategies. Our stories take the form of quarterly earnings, product announcements, R&D proposals, go-to-market programs and customer experience initiatives. The data, that we collect from internal and external sources, structure and unstructured, serves to support those stories. Data and story are intrinsically bound until death do they part.

Of course in any marriage there are supporting cast members. At this wedding, the maid of honor is social media. She provides context in the form of voluntary updates. She can be emotional, repetitive, succinct, and pragmatic. She adds color to the story and sometimes is the instigator of the story. On the other side of the aisle is mobility. He is the enabler of location based data, subscriber data and usage data. He provides a different kind of context to big data, delivering the insight that allows big data and storytelling to target their efforts even more specifically. By bringing these players together and consolidating the value each of them provides, we move closer to the using data prescriptively. Understanding the context of the data is, for now, the secret sauce. This allows our stories to not only share what and when something is happening, but why. We will be able to suggest better solutions for our customers because we will more fully understand the issues that are enablers versus those that are inhibitors to healthy relationships.

Stories have been a key element of all business, from those that introduce a new norm (Ford), found a business segment (HP, IBM), challenge the norm (Apple, Google) or provide new ways to connect (Bell Labs, Facebook). All stories have a common foundation, data - about the market, the product and the opportunity. Data can exist without story, but its value would not be appreciated. What's your story?

What's your perspective?




Big Data Making the Connection

Peggy Dau - Monday, June 16, 2014

With all the buzz about big data, the primary assumption is that it will help companies better understand their customers. This is not wrong, but is is just one aspect of what big data can do. In his recent conversation at HP Discover, Brian Kraznich CEO of Intel, spoke about how big data can help us uncover "known unknowns and the unknown unknowns". For example, we know we can uncover data that will help us understand consumer preferences. It's just a matter of aggregating and analyzing the data from multiple sources. But, what about making the connection between various data points that reveals something we never imagined?

Industries from financial services to oil & gas to telecommunications & media are all using big data to improve their businesses. How big is Big Data?  It's big enough that there is now a data visualization award at this week's Cannes Lions event - the "Oscars" of the advertising industry.  It's important enough that data scientists are paid more than business analysts at financial services firms.

We've all read about Netflix's use of its subscriber data to influence its production of the hit series "House of Cards". However, Netflix is also using data to identify the impact of Quality of Experience (QoE) on the subscriber behavior. For example, what is the rebuffer rate? What is the bitrate? What is the network capacity? One benefit in correlating this data is that it allows Netflix to make smarter decisions about where and when to cache content, usually near the "edge" of the network, to better server their customers.

Netflix is not alone in its focus on the network. Cable operators and telecommunications providers have long been monitoring and measuring network performance. They capture data from across their networks in order to provide a better a subscriber experience, but also to reduce their operational costs. They have adopted big data analytics solutions to address concerns such as extracting data from call data records and comparing it to network alerts with the goal of  improving customer service. The analysis may reveal that a small number of network nodes are responsible for the majority of customer issues. The service provider can then pursue options such as providing online self-service tips, performing proactive network maintenance or performing network equipment upgrades. The results include reductions in the volume of calls to the call center as well as reduced on-site visits, improved customer service margins and happier customers.

A benefit for all service providers is the ability of big data analytics to unify systems for network monitoring, management and troubleshooting. With a variety of hardware and software in the network and at the subscriber premises,  aggregating disparate data is a challenge. Big data solutions enable capture, aggregation and analysis to:

     - measure network usage

     - reduce network equipment costs

     - perform fraud analysis

     - uncover bandwidth issues

Getting ahead of the curve on these issues will allow cable operators, telecommunications providers, wireless carriers and OTT Players to manage their networks more efficiently, which ultimately allows them to serve their subscribers more effectively. Big data provides the insight to prepare them for the increasing demands on the network to provide connectivity, deliver high bandwidth video and enable interactivity.

What's your perspective?



Finding the Story in the Data

Peggy Dau - Monday, June 09, 2014

Batting averages. Market share. Global warming. Presidential front-runner. What do all of these statements have in common? They are statements based on data. They are the beginning of a story. Whether it is a journalist reporting or an analyst writing or a brand positioning, the basis of the story is in the data. It's little wonder that big data analytics has become the catchphrase for every marketer (myself included!).

We've always been data driven. The only difference now, is that we have MORE data. It has always been able to find the data to support any type of debate. However, now individuals are voluntarily sharing their thoughts and opinions on the internet and social networks. It is the power of this unstructured data, especially when combined with existing structured data from existing systems, that is attractive to brands. They have the opportunity to tailor a story to meet specific, self-defined customer needs. But the challenge lies in how to sift through all that data.

Enter - big data analytics. Analytics is now big business. Every IT company has jumped on the bandwagon. IBM and its Watson supercomputer are positioned to provide personalized advice to doctors, financial analysts or online shippers. While HP Vertica is sifting subscriber data at telecommunications companies around the globe and analyzing social data feeds for NASCAR.  Not to be outdone, Teradata is providing greater customer insight to the hospitality industry and food suppliers.  Each of these vendors is providing the 'secret sauce' to help their customers connect with their customers by telling the most relevant story. And, it all comes from the data.

A report from Columbia's TOW Center for Digital Journalism, speaks to data-driven journalism. But, hasn't journalism always been data driven? Yes, but instead of having staff researchers manually scour files and reports, or spend hours online searching for the right data, there are an increasing number of tools to help them uncover the data to create or support the story.  They are not alone. The term data scientist has gained great cache in the past few years. Whether it is for advertising firms or for financial services, the value of data has never been higher. 

One only needs to look at the history of US presidential elections.  Remember the predictions Dewey defeating Truman in the 1948 US Presidential election? Newspapers had determined that they had their story and went to print with "Dewey Defeats Truman" on the front page. Perhaps access to more data would have prevented that now famous error.  Today's pollsters have many more tools available to them today as proven by Nate Silver's eerily accurate predictions in the 2012 US Presidential race.

Brands are learning how to tell their stories with a deeper understanding of their customer base. Dove has hit home runs with their ads reflecting real women rather than models. Telecommunications vendors are modifying their marketing outreach to reflect the knowledge they have about subscriber consumption. Advertising conglomerates, perhaps the kings of storytelling, have invested in analytics to improve ROI for their clients. Big Data Analytics is not a passing fad, it is a logical step on the journey for meaningful, measurable communication between individuals and businesses. Have you found your story in the data?

What's your perspective?




Big Data Uncovering Audience

Peggy Dau - Monday, March 24, 2014

When thinking about big data, it's easy to get lost in the statistics and forget that this data actually represents people. It is an amalgamation of demographics, sentiment, issues, and interests. Big data is the combination of structured data captured by systems and unstructured data found online. When all this data is aggregated and analyzed, the goal is to gain a better understanding of customers, collectively or individually. Is it any wonder that the media & entertainment industry is hungry for data?

The symbiotic relationship that exists across the media & entertainment industry reflects the interdependencies each segment has on the other. Broadcasting cannot exist with advertising. Advertising cannot exist with broadcast or publishing. Studios and their theme parks reinforce key brands. All of them are focused on audience, which means they need data - and lots of it. Using TV as the example to prove the point, the underlying value of the TV audience is to enhance the value of ad inventory. In fact, the advertising industry's investment in big data is significant.

GroupM, part of WPP, announced the creation of a new unit to focus on data driven audience buying. They want to achieve sophisticated audience targeting by analyzing the highly granular data that is now available about what the TV audience is doing on and off TV. audience behavior on screens other than the TV now provides greater insight into audience sentiment and needs.  In addition, WPP has a pan-company practice called the Data Alliance that is responsible for helping brands and their agency partners connect diverse information sources.

However, big data is not just about audience as related to TV and advertising. Shazam, the mobile app that identifies the music and media playing around you, is using the data about what songs or media users upload, correlating that data with critics reviews (positive or negative) to make predictions about which artists are up and coming or what styles of music are appealing to consumers. The music industry is acknowledging the power of big data with three recent announcements that reflect the desire of the music labels to identify future artists - and subsequently new revenue.

Other participants in the media industry such as cable networks, satellite operators, communication service providers, publishing firms collect and analyze data to:

  • Improve media business insight into subscriber/viewer behaviors and preferences
  • Increase subscriber/viewer acquisition and loyalty
  • Exploit revenue opportunities across multiple distribution channels
  • Improve management of royalties, copyrights and content security
The bottom line is that big data, for media, is all about uncovering the audience. Investing in ongoing, deeper insight into audience behavior is critical to defining new revenue models and continued success. Without these revenue streams, the industry cannot continue to invest to meet the demands of its audience. It's a bit of a catch-22, but there is obvious commitment, from all segments across the industry, in collecting big data, applying relevant analytics and acting upon resulting business intelligence to influence content development, marketing, scheduling and distribution. And it all starts with the audience.

What's your perspective?



Twitter Success Tied to Data and TV

Peggy Dau - Tuesday, November 12, 2013

In advance of the Twitter IPO last week there was a lot of buzz aligning Twitter to the television advertising spend. As many of us know, television represents the lions share of advertising spend with the percentage of ad dollars allocated to TV actually increasing in 2013. Twitter's ability to capture even 1% of those ad dollars would certainly turn it into a revenue and profit generating enterprise. EMarketer already predicts that Twitter's ad revenues will reach ~$1B  by 2014. However, by positioning itself as the social network for TV viewers, Twitter hopes to catapult itself into the lead position for online/mobile advertising. Of course, that means they would need to disrupt Google, Facebook, Bing and others.

The secret sauce, of course, lies in the data that provides brands with insight about their target audience. And, it supports Twitter's goals for advertising revenue from display ads and native ads.  Data is also a source of revenue in and of itself. Companies, like Datasift and Gnip, pay for access to the Twitter Data Firehose. It provides them with data from and about customers. Datasift and Gnip "resell" this data to app developers and solution providers who require real-time access to Twitter data.

It is also dependent on the continuation of the trend of consumers spending time on the second screen while watching TV, the primary screen. Twitters acquisitions of BlueFin Labs and Trendrr, reflect its commitment to and acknowledgement of TV as central part of their strategy. Both acquisitions solidified their analytical and presentation capabilities of data specific to television engagement and content consumption. Nielsen's launch of Twitter TV Ratings, solidifies Twitter's position as a meaningful provider of relevant data. Nielsen correlates TV-specific tweets to audience reach and impressions - all valuable data points for advertisers.

As Twitter continues to create tighter bonds to TV (check out its recent announcement with Comcast) they will continue to identify more uses for its data. And, through partnerships create incremental data - all of which provides value to advertisers and business partners. So keep it coming Twitter. You've taught us how to communicate concisely. You've made the term #hashtag part of our daily vernacular. You may also show us how to harness power of data relevant to the largest global advertising audience - TV viewers. 

What's your perspective?



No Cure for Data Addicts

Peggy Dau - Monday, August 19, 2013

Addiction. It has a negative connotation, yet every industry has the same addiction. We live in a data driven society where every action must be justified by numbers that support investment, change, penalty, promotion, success, failure - you get the picture. When was the last time you your day did not rationalize a business activity  without dependent numbers? It's no wonder that big data is enjoying such growth. With a mentality reinforced by friends, colleagues, management, wall street and even the federal government, we seek numbers to support every decision we make. But, do these numbers really provide the "fix" we crave?

It's Monday, so that means we are measuring box office success of the latest movies. Just in case you missed it, "The Butler" was considered a success, coming out of its opening weekend with revenue of $25M, against a cost of $30M.  On the other hand, "Jobs" is considered weak on opening weekend earnings of $6.7M against a budget of $12.7M.  At the same time Trendrr, the television engagement tracking service, shares that NBC Sports investment in Premier League Soccer is a success - at least for this week - taking the number one and two spots as related to social volume. What do these numbers say to you? Do they influence your desire to see these movies or programs?  Probably not, but, they validate investment in actual production, acquisition of rights or advertising.

Why do we collect the data and analyze the numbers? Because they are an attainable metric that shows progress against stated or unstated goals. There is a lot of attention being paid to social media measurement or the ROI of social media. The original measures of success - numbers of followers, tweets or likes, do not provide a tangible return on investment. But they do provide an indication of consumer interest. The thing to remember about numbers, is that they are only accurate in retrospect. You cannot reveal a number until an action or many actions have occurred. The bigger question is if these numbers can be a predictor of future success or failure.

The financial services industry has been using sophisticated data-based models for years in its attempt to improve investor return. Technologists are using and modifying equally sophisticated algorithms to monitor and measure social media activity - also with an eye toward predicting the best channels through which businesses can engage their customers and ultimately increase revenue. In all cases, the next wave of investment is to bring context to all of these numbers. I've written about the importance of context in social media monitoring, and filtering of "dirty" data. Natural language processing continues to advance with an understanding that prescriptive analytics is the next phase of big data investment.

For all the attention to customer data, there is also an increasing number of internal data available to businesses. Whether this is related to supply chain, sales, research & development, content management or financials, this is the data upon which the business relies for ongoing performance. Social technologies do exist behind the firewall and changing the ways corporations connect and collaborate across geographies, business units and other corporate silos. The McKinsey Global Institute has already stated that there is $1 Trillion in value that corporations can create through the use of social technologies. The question is how businesses define what that value looks like and how they create it.

Context will be increasingly important as big data, cloud, mobile and social all come together to provide data and numbers like never before. What does this mean for business? It means smarter, yet more complex content management systems, technologies to capture, integrate and analyze data from an increasing volume of sources and devices (think big data + social media monitoring + machine-to-machine) and the emergence of  consultants to help business make sense of all the data. Rather than seek rehab to overcome this addiction to numbers, we will continue to feed our addiction through creation of tools and processes to attain even more data.

What's your perspective?






Hey Big Data, Don't Forget We're Human!

Peggy Dau - Tuesday, August 06, 2013

Every where you turn, there is more data. Social networks provide data revealing consumer likes and dislikes (and it would be nice if Facebook created a thumbs down button!). Analytics firms promise to crunch this data and provide insight to drive sales, marketing and product strategies. Netflix produced "House of Cards" based on its analysis of their subscriber data. That success led to them to produce new episodes of "Arrested Development" and "Orange is the New Black" - neither of which has had the same impact as House of Cards. So, is that data really as revealing as we think?

As always the devil is in the details. While the data itself can highlight trends it also includes a lot of "noise". By this I mean that is hard to sift through the incredible volume of data to find the meaningful insights that can influence meaningful actions. Companies, large and small, are looking to data to help them improve their business. Whether it is to reduce the cost of doing business or to drive an effective product launch, companies must rationalize their decisions. Data is the key. It is tangible. It cannot be disputed. Or, can it?

The interpretation of data requires more than analytical tools. It requires an understanding of context. How many times have we read quotes or seen video snippets that infer a meaning different than what the speaker intended? It is the same with analyzing data. Structured data is easier as it is typically machine driven data, derived from content stored in databases. Unstructured data, such as that found in social networks, blogs, audio or video files presents new challenges. Often this data reflects thoughts that are a reaction to other content. Understanding the relationship between these different types of data is critical to gleaning the most relevant insight. As Colin Powell once said "Experts often possess more data than judgement."

So how do we apply judgement to analysis of structured and unstructured data to guide strategies, tactics, actions? We don't leave it solely to data collection tools or data analysis programs. We remember to use our common sense and intelligence when looking at the data. Yes, filtering that data is hugely helpful and the tools that can help with that sifting are big time savers. But individual or group knowledge cannot and should not be neglected. It is this insight born from experience that drives innovation, creativity as well as pragmatic action. As your business incorporates social data, subscriber data and other big data into its planning and decision making processes, don't forget to be human and remember there are humans on the receiving end of those decisions who can make or break your business.

What's your perspective?