The Smarter Data Manifesto

  |  June 14, 2013   |  Comments

You need to look beyond the ways data used to be collected to how technology can help you use your data far more effectively, because you have important goals.

"An organization's ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage." - Jack Welch

Fifteen years of working with many organizations (it is an impressive list) has taught us most organizations have not yet achieved a point where marketing analytics is like financial reporting: simplified; fairly universal; with a clear line of sight to business financial statements and tied to specific goals and objectives. If everyone had a simple marketing analytics framework that made reporting simple, then everyone in the organization could help steer the organization in the right direction at maximum speed.

Data drives (or should drive) marketing efforts, and now we are able to use data in ways, in quantities, and at a scale previously not possible. However, over 90 percent of the data we collect is noise, not signal, in terms of understanding our customers. This is why since the earliest days of digital analytics people fell in love with vanity metrics like HITS (a.k.a. how idiots track success). Social media and mobile technologies gave birth to even more vanity metrics.

Signs of a Data-Driven Organization

The point of being data-driven is not the collection of data and not the distribution of reports, even if they contain brilliant insights. The true value of data comes from how we use it to drive action (keep in mind that data, by itself, has no intrinsic value).

The main benefit of collecting data is to use the data to:

  • Improve the experience of your customers.
  • Understand and optimize how your internal business processes are performing.
  • Determine how you can outperform your competitors.

At the heart of using data effectively is the need to understand and define the evolving, complementary roles of analysts and marketers with the complete support of the organization's infrastructure.

Of Auto Mechanics and Data Scientists

John Lovett, senior partner of Web Analytics Demystified, and I debated the role of analysts (increasingly, the need is for data scientists), what the terms "data" and "analytics" mean, and how to help organizations become data-driven. John and I debated from different perspectives - John is a statistician/analyst, and I am a marketer. Please take a few moments to watch this interview conducted by Bryan Kramer, CEO of PureMatter.

I suggested how, in order for an organization to become data-driven, a few key things must happen:

  • First, the organization must agree on a few key line-of-sights metrics that can be used to drive a business, much like a driver can look at a couple of key dials on the dashboard.
  • Second, the technology dimensions of using data must be communicated in a way that not-mathematically-inclined people can understand so they can apply them to answering business questions. I also explained how we need to make data more accessible within the organization to ask questions from our data sets - like having a "Google Now"-type voice command interface.

John responded that, while he agrees business people need simplified dashboards like an automobile, when you have car trouble, you go and see the auto mechanic.

I then agreed that, while businesses need technicians to ensure data is being collected and processed properly - as an auto mechanic would do for a wounded car - their roles are changing. Today, few mechanics need to repair the things they used to in the last decade.

To diagnose the problem, auto mechanics plug the car into a computer and then use the results to guide replacing parts. No one rebuilds carburetors like they used to when I was younger. These technicians are necessary, but the scope of their involvement has become limited, and as digital analytics become more standardized, their roles will continue to change.

What Is Holding Your Organization Back From Being Data-Driven?

A division of labor has always had its advantages, and in one way, technological changes require an even greater degree of transparency among all parties and require all to become cross-skilled and cross-functional. However, the expertise of each party is changing enormously, and that will change how an organization evolves in using data to drive marketing decisions.

Many organizations are unwilling to adopt or even acknowledge the importance of these changes. The failure in organizations to become data-driven is self-imposed, and rarely does the resistance come from departments within. No matter the training of individuals within the company, it is the C-suite's responsibility to promote and support the expanding technologies, making it possible to refine the use of data. This is not the obligation of the data folks.

Making non-technical people work with reports in the form of charts and graphs is not going to magically transform them into data people. This is why we're seeing some technologies today humanize the people-data interface with visualization tools like Tableau and narrative tools like Narrative Science ( which just got additional funding from the investment arm of the CIA) and Automated Insights. Some technologies in development will offer a voice command interface to the data just like Google Now.

The Data - Information - Insight - Action Cycle

"The world cares very little about what a man or woman knows; it is what a man or woman is able to do that counts." - Booker T. Washington

The best way to explain the possibilities that live in the changing role of data analysts in emerging technologies and the continued importance of marketers is to look at an example.

A friend recently told me that an automated report from one of his narrative tools identified a new referral for people searching on the terms "Shaq 2 year old." No one had to sit back and wait for someone else to analyze this and decide what to do about it. The tool itself took data, processed it to create information, and then related that information to achieve insight: recommendations for action. My friend received this report:

Data: new referrals coming from "Shaq 2 year old"

Information: identify a viral video featuring Shaq O'Neal competing in a basketball challenge against 2 year old Titus (Go Titus!) is driving these new searches, driving new traffic that may or may not be relevant to this toy business.

Insight: the business has a few options:
  1. The traffic is relevant and you might attempt to drive it to a landing page that features the basketball-oriented products with some messaging around customers' kids being the next Titus, then possibly embed the video on the page.
  2. The traffic is irrelevant and, if you are paying for those clicks through PPC, add a negative keyword for "Shaq" or drop the bids on those terms until the viral surge slows down.
Action: let's start designing that landing page.

What most analysts do today, if you're lucky, is identify this new keyword (it will probably take some time) and then get the report to you a week later, when it is too late to capitalize on it. This isn't the fault of the analyst nor the marketers; it's the fault of the current ways the organization uses data.

What Do Your Current Reports Look Like?

Are they Excel spreadsheets with numbers and graphs, or more like a to-do list with research and data behind it? Very few analyst reports would include the fact that there was a viral video making the rounds, and virtually none of them would contain an insight so you could develop an action plan to create a new landing page and organize the resources in near real time.

An organization becomes data-driven when it supports using data in near real time to make lots of small changes that allow you to improve the customer experience (bring visitors to a more relevant landing page), outperform your competitors because they were neither data-centric nor agile enough to create a dedicated landing page, or smart enough to reduce the costs associated with bidding on this irrelevant query that is surging and may eat up the budget on unqualified visitors.

Even without the exciting new technology becoming available, existing technology is good enough to provide intelligent data points to your analysts and business teams. Still, the organization needs to be structured so that small, agile teams can gather insights from the data, empowered by technology and processes to act in ways that make a difference.

The Illusive Data Scientist

Image via http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

I have a ton of respect for those talented data scientists, but in reality, even if they have the title of data scientist, they do not possess the same business skills as marketers. These are not just people who have the coding and hacking skills to make sure the data is collected properly with tags, etc., or those who can perform the proper SQL or R queries. These are professionals who have the business chops to drive revenue with their technological skills.

Vincent Granville said, "Talented data scientists leverage data that everybody sees, visionary data scientists leverage data that nobody sees." The problem is, as I said in the video, we need to find somewhere between 150-180,000 new data scientists in the next few years, and this isn't going to magically happen.

Driving a Data-Driven Organization

As an organization, what you need most is the equivalent of great auto mechanics who know plugging your car into a computer isn't going to offer a perspective beyond basic service. You want a skilled core team with an ace auto mechanic who can offer insight and respects that you know how to race your car.

You need to look beyond the ways data used to be collected to how technology can help you use your data far more effectively, because you have important goals. You have customers you want to satisfy. You want to know how to structure a good experience so it becomes fabulous.

The ability to accomplish these goals exists. You have to decide today if you are going to keep up with the world by learning and adapting and becoming a truly data-driven organization. Then again, you could keep paying lip service to the future, throwing away resources in the process and having your reports...ignored.

Image on home page via Shutterstock.


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Bryan Eisenberg

Bryan Eisenberg is co-founder and chief marketing officer (CMO) of IdealSpot. He is co-author of the Wall Street Journal, Amazon, BusinessWeek, and New York Times best-selling books Call to Action, Waiting For Your Cat to Bark?, and Always Be Testing, and Buyer Legends. Bryan is a keynote speaker and has keynoted conferences globally such as Gultaggen, Shop.org, Direct Marketing Association, MarketingSherpa, Econsultancy, Webcom, the Canadian Marketing Association, and others for the past 10 years. Bryan was named a winner of the Marketing Edge's Rising Stars Awards, recognized by eConsultancy members as one of the top 10 User Experience Gurus, selected as one of the inaugural iMedia Top 25 Marketers, and has been recognized as most influential in PPC, Social Selling, OmniChannel Retail. Bryan serves as an advisory board member of several venture capital backed companies such as Sightly, UserTesting, Monetate, ChatID, Nomi, and BazaarVoice. He works with his co-author and brother Jeffrey Eisenberg. You can find them at BryanEisenberg.com.

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