Wear These 6 Hats to Boost the Impact of Your Data-Driven Marketing

Wear These 6 Hats to Boost the Impact of Your Data-Driven Marketing

By Karen TaylorAug 10 /2015

data-driven-marketingIf you’re like most technology companies, you’re swimming in a sea of Big Data. Within that ocean are thousands of rich, valuable insights you need to share with multiple audiences. But your challenge is corralling all of that data into meaningful messages and memorable take-aways.

There is a general assumption data-driven marketing is easy. After all, you just slap some numbers into a chart or graph, right?

Not so fast. It’s that kind of thinking that gave us the word “junkchart.” These are charts and graphs that distract viewers from the intended message with extraneous information. While they may look interesting, they either confuse the message or misinterpret the data. Or both.

Author Steven Few, in Show Me the Numbers, said the skills required for more effectively displaying information are not intuitive and rely largely on principles that must be learned.

While you probably don’t have time to get a degree in data visualization, you can still employ the principles of clear, impactful, data-driven communication. Just think in terms of wearing six hats throughout your process to pinpoint exactly what you want to say, identify your target audience, find the story behind the data, prepare the data, determine how your audience will process the data, and bring the data to life in ways your target audience can understand.

Visualize This: 6 Hats for Communicating Data with Impact

To communicate amazing insights gathered from your technology company’s data, don these six hats: architect, marketer, journalist, data scientist, cognitive scientist and graphic designer. 

Architect Hat — Determine Your Messages

As the architect of the project, you’ll take a leadership role. You’ll choose the data you want to communicate, create the analytical direction, set the tone, establish the parameters and oversee the process.

Marketer Hat — Identify Your Target Audience

Wear a marketing hat to pinpoint exactly whom you are communicating to and what insight the target audience should gain from your data-driven communication.

Is your audience industry analysts, customers, prospects, the media, partners or your employees? Every audience will have a different need for your data.

There are four basic types of audiences to consider for data-driven messages:

  • Casual Observer. These are people who have little data or domain knowledge, but are looking for an overview understanding and major themes.
  • Data Actors. These are individuals who have significant domain knowledge, but limited time. They want to view data quickly and take away an in-depth, actionable understanding of the intricacies and interrelationships. They tend to act on and leverage meaningful data to drive change. Other data actors may also be looking for interesting anecdotes or data to back up a story.
  • Analysts. These are individuals with deep domain and extensive data knowledge. Analysts use data to create richer understanding, often to inform data actors and casual observers. They often discover, explore and condense large amounts of data for a given topic into reports, presentations, apps and more.
  • Executives. These individuals only have time to glean the significance of the data, so they can draw conclusions regarding business challenges.

Journalist Hat — Establish the Data-Driven Narrative

Audiences are more receptive to communication if it’s framed in a narrative or story. Put on a journalist’s hat to find a story to communicate your data

Senior Advisor to Deloitte Analytics, Thomas H. Davenport, identified four key dimensions of stories to tell with data:

Analytical stories can be about the past, present, or future.

  • Past story. These stories report what happened last week, month, quarter or year, such as quarterly sales or weekly product orders.
  • Present story. These stories tell about the present, such as technology companies’ business results.
  • Future story. These stories are predictions, taking data from the past to predict the future, such as how likely it is for an event to happen and future economic projections.

Does your data tell a what, why or how to address the issue story?

  • What story. They simply tell what happened.
  • Why story. These dive into the underlying factors that caused the outcome.
  • How to address the issue story. These explore various ways to improve the situation identified in the what and the why stories.

There is a depth dimension to analytical stories.

  • CSI story. These are relatively small, ad hoc investigations into why something is happening.
  • Eureka story. These involve long, analytically driven searches for a solution to complex problems. These stories are often long, important and expensive.
These are stories based on the analytical method used.
  • Correlation story. These stories show the relationships among variables that rose or fell at the same time.
  • Causation story. These stories show how one variable caused the other. Note: People frequently confuse causation with correlation.

Data Scientist Hat — Prepare the Data

A central challenge in visualization is creating an effective layout. Put on a data scientist hat to choose your data’s ideal presentation style. There are the four basic options: pie charts, bar graphs, line graphs and maps. But there are other sophisticated choices, too.

In 2010, three scientists at Stanford University created a list of techniques for visualizing and interacting with diverse sets of data, which they called the “visualization zoo.” They organized data-driven visuals into four categories: time-series, statistical distribution, maps and hierarchies.

  • Time-series. These graphics visualize sets of values changing over time. Time-varying phenomena are central to many sectors, such as finance (stock prices, exchange rates), science (temperatures, pollution levels, electric potentials) and public policy (crime rates). Examples of time-series visuals include index charts, stacked graphs, multiple time series and horizon graphs.
  • Statistical Distributions. These visualizations are used to reveal how a set of numbers is distributed to better understand the statistical properties of the data. Some distribution techniques include histograms, box-and-whisker plots, stem-and-leaf plot, quantile-quantile (or Q-Q) plots, scatter plot matrix (or SPLOM) and parallel coordinates.
  • Maps. Naturally, maps are a great way to visualize geographical data. They can, for example, depict the movement of a quantity in space and time, or the geographic distribution of data. Some types of maps are flow maps, choropleth maps graduated symbol maps, and cartograms.
  • Hierarchies. While some data is simply a flat collection of numbers, most data can be organized into natural hierarchies, for example: spatial entities, such as counties, states and countries; command structures for businesses and governments; and software packages. Hierarchal visualization techniques include node-link, adjacency and enclosure diagrams.
  • Networks. These visuals help communicate relationships by using nodes. Each node has exactly one link to its parent, while the root node has no links. Layout techniques typically seek to position closely related nodes close together and unrelated nodes far enough apart to differentiate relationships. Some techniques include force-directed layouts, arc diagrams and matrix views.

Cognitive Scientist Hat — Apply the Laws of Visual Perception

Cognitive scientists understand the principles of perceptual organization. These mental shortcuts help minimize visual overload and maximize information impact.

Wear a cognitive scientist’s hat to present your data in ways that exploit our visual perception abilities in order to amplify cognition.

Gestalt psychologists have developed a set of principles to explain people’s innate perceptual organization. These principles are often referred to as the “laws of perceptual organization.” They include:

  • Law of Similarity. Items that are similar tend to be grouped together.
  • Law of Pragnanz. Reality is organized or reduced to the simplest form possible.
  • Law of Proximity. Objects near each other tend to be grouped together.
  • Law of Continuity. Lines are seen as following the smoothest path.
  • Law of Closure. Objects viewed together are seen as a whole.

Graphic Designer Hat — Select a Presentation Style that Fits

Graphic designers think in terms of shapes, patterns, white space and colors. Wear this hat to ensure your data-driven visuals balance the form and function of your data, while highlighting your key message and achieving your intended communication purpose.

To think like a designer, consider these elements of great design:

  • Color. Our brains intuitively recognize differences in color, hue, size and shape, and attach specific meanings to each element. These visual properties are called “pre-attentive variables” because the process of perceiving them is subconscious, immediate and automatic. Use this knowledge to intentionally highlight the most critical information within your data using color.
  • Fonts. The fonts you choose to communicate your data can help or hurt the power of your message. Here are a few tips:
    • Use serif fonts, like Garamond or Times, for printed documents
    • Use sans serif fonts, like Arial or Calibri, for on-screen presentations
    • Avoid specialty fonts that are primarily decorative
    • Right align numbers and column headers
    • Left align dates and text
  • Simplify. Experts agree: simpler data graphics are preferable. In other words, less is often more. Try to bring down big data numbers into to manageable, bite-size, memorable chunks.

An Example of Impactful Data-Driven Communication

This infographic, created by Under Armour, an athletic apparel and digital fitness company, uses data from its popular MapMyFitness app that beautifully illustrates the power of these six steps.


Click to see full infographic

Clearly an architect (or architect team) decided what data-driven message the company wanted to communicate. The target audience was the company’s own sales team — giving them valuable consumer facts to use in the field. The many data points tell the story of MapMyFitness users’ active lifestyles. At the data scientist phase, Under Armour decided to display the data as primarily percentage comparisons. Using cognitive science, the technology company applied several Gestalt principles, such as proximity and similarity. And, finally, the company rendered the data into an elegantly designed graphic that communicates a lot of data clearly without clutter.

Walking through these six steps to communicate your high-impact data may seem daunting, but it’s worth the effort. Making sure your targeted audiences understand the full depth of your messages will have long-term benefits. You’ll be better able to share important information, impress customers and partners, win business, and make smarter data-driven business decisions.

Karen Taylor is a professional freelance content marketing writer with experience writing for over 100 companies and publications. Her experience includes the full range of content marketing projects — from blogs, to white papers, to ebooks. She has a particular knack for creating content that clarifies and strengthens a company’s marketing message, and delivers optimum impact and maximum results. Learn more at KarenTaylorWrits.com

Karen Taylor
The Author

Karen Taylor

Karen Taylor is a professional content marketing writer with experience writing for over 100 companies and publications. Her experience includes the full range of content marketing projects — from blogs, to white papers, to ebooks. She has a particular knack for creating content that clarifies and strengthens a company’s marketing message, and delivers optimum impact and maximum results. Learn more at karentaylorwrites.com.