Google Data

This article was originally posted on Google Think Insights.

Most organizations recognize that being a successful, data-driven company requires skilled developers and analysts. Fewer grasp how to use data to tell a meaningful story that resonates both intellectually and emotionally with an audience. Marketers are responsible for this story; as such, they’re often the bridge between the data and those who need to learn something from it, or make decisions based on its analysis. As marketers, we can tailor the story to the audience and effectively use data visualization to complement our narrative. We know that data is powerful. But with a good story, it’s unforgettable.

Rudyard Kipling once wrote, “If history were taught in the form of stories, it would never be forgotten.” The same applies to data. Companies must understand that data will be remembered only if presented in the right way. And often a slide, spreadsheet or graph is not the right way; a story is.

Executives and managers are being bombarded with dashboards brimming with analytics. They struggle with data-driven decision making because they don’t know the story behind the data. In this article, I explain how marketers can make that data more meaningful through the use of storytelling.

The power of a meaningful story

In her “Persuasion and the Power of Story” video, Stanford University Professor of Marketing Jennifer L. Aaker explains that stories are meaningful when they are memorable, impactful and personal. Through the use of interesting visuals and examples, she details the way people respond to messaging when it’s delivered either with statistics or through story. Although she says engagement is quite different from messaging, she does not suggest one over the other. Instead, Aaker surmises that the future of storytelling incorporates both, stating, “When data and stories are used together, they resonate with audiences on both an intellectual and emotional level.”

 In his book Facts Are Sacred, Simon Rogers discusses the foundations of data journalism and how The Guardian is using data to tell stories. He identifies ten lessons he’s learned from building and managing The Guardian’s Datablog, a pioneering website in the field. I found three of the lessons particularly insightful:

1. Data journalism (and analytics in a broader sense) is a form of curation. There is so much data and so many data types that only experienced analysts can separate the wheat from the chaff. Finding the right information and the right way to display it is like curating an art collection. 
2. Analysis doesn’t have to be long and complex. The data collection and analysis process can often be rigorous and time consuming. That said, there are instances when it should be quick, such as when it’s in response to a timely event that requires clarification. 
3. Data analysis isn’t about graphics and visualizations; it’s about telling a story. Look at data the way a detective examines a crime scene. Try to understand what happened and what evidence needs to be collected. The visualization—it can be a chart, map or single number—will come naturally once the mystery is solved. The focus is the story. 

Stories, particularly those that are meaningful, are an effective way to convey data. Now let’s look at how we can customize them for our audiences.

Identify the audience

Most captivating storytellers grasp the importance of understanding the audience. They might tell the same story to a child and adult, but the intonation and delivery will be different. In the same way, a data-based story should be adjusted based on the listener. For example, when speaking to an executive, statistics are likely key to the conversation, but a business intelligence manager would likely find methods and techniques just as important to the story.

In a Harvard Business Review article titled “How to Tell a Story with Data,” Dell Executive Strategist Jim Stikeleather segments listeners into five main audiences: novice, generalist, management, expert and executive. The novice is new to a subject but doesn’t want oversimplification. The generalist is aware of a topic but looks for an overview and the story’s major themes. The management seeks in-depth, actionable understanding of a story’s intricacies and interrelationships with access to detail. The expert wants more exploration and discovery and less storytelling. And the executive needs to know the significance and conclusions of weighted probabilities.

Discerning an audience’s level of understanding and objectives will help the storyteller to create a narrative. But how should we tell the story? The answer to this question is crucial because it will define whether the story will be heard or not.

Using data visualization to complement the narrative

Analytics tools are now ubiquitous, and with them come a laundry list of visualizations—bar and pie charts, tables and line graphs, for example—that can be incorporated into reports and articles. With these tools, however, the focus is on data exploration, not on aiding a narrative. While there are examples of visualizations that do help tell stories, they’re rare and not often used in meetings and conferences. Why? Because finding the story is significantly harder than crunching numbers.

In their “Narrative Visualization: Telling Stories with Data” paper, Stanford researchers discuss author versus reader-driven storytelling. An author-driven narrative doesn’t allow the reader to interact with the charts. The data and visualizations are chosen by the author and presented to the reader as a finished product, similar to a printed magazine article. Conversely, the reader-driven narrative provides ways for the reader to play with data.

With the advent of data journalism, we’re now seeing these two approaches used together. According to the Stanford researchers, “These two visual narrative genres, together with interaction and messaging, must balance a narrative intended by the author with story discovery on the part of the reader.”

A good example of a hybrid author-reader approach is the presentation of The Customer Journey to Online Purchase tool. A few short paragraphs explain why the tool was created and how it works, and an interactive chart allows marketers to break down the information by industry and country. Additional interactive data visualizations provide even more context.

Another extremely efficient and visual way to tell a story is by using maps. In a tutorial on visualization, I show how a large data set can be transformed and incorporated into a story. It’s an example of how to take charts and graphs to the next level in order to add value to the story. In this case, I use Google Fusion Tables and some publicly available data to illustrate analytics data with colorful, interactive maps. The visualization provides more content for those interested in diving deeper into the data.

A good data visualization does a few things. It stands on its own; if taken out of context, the reader should still be able to understand what a chart is saying because the visualization tells the story. It should also be easy to understand. And while too much interaction can distract, the visualization should incorporate some layered data so the curious can explore.

Marketers are responsible for messaging; as such, they’re often the bridge between the data and those who need to learn something from it, or make decisions based on its analysis. By rethinking the way we use data and understanding our audience, we can create meaningful stories that influence and engage the audience on both an emotional and logical level.

Posted by Daniel Waisberg, Analytics Advocate

click to enlarge

Announcement #2: New Content Experiments with Google Tag Manager

Google Tag Manager has been built to be very intuitive, even for people not familiar with coding. Businesses can now let their marketers or business analysts run experiments without requiring a developer to be involved. App experiments are now accessible to everyone.

1. Sign up for an account at www.google.com/tagmanager and create a mobile container
2. Download the SDK for either Android or iOS. 
3. Start programming! Use the SDK to instrument configuration and events you care about in your app.
4. When you’re ready to dynamically change your app, use the Google Tag Manager interface to start configuring. Remember to press the “Publish” button to push your rules and configurations to your users.

1. Create one or more remarketing lists using Google Analytics
2. Update your tags to track Product ID, Cart Value, and Page Type as custom variables (or dimensions)
3. Enable the Dynamic Link in Admin > Property > Dynamic Attributes. This will let Google Analytics send attributes to your AdWords account. [more below]
4. Create a Dynamic Remarketing Display campaign in AdWords

Step 2: Make sure the attribute names match your custom variables

Step 3: Click ‘Save’ and finish by creating your first Dynamic Remarketing campaign in AdWords

• the four components of the Analytics platform: collection, processing, configuration and reporting 
• how Analytics collects the data you need across different devices
• how your data is transformed before you see it in your reports
• key concepts for customizing your Analytics data in useful ways

• Check the Campaigns report to better understand users being driven into your app, and see how they use your app. 
• Find out from the Day Parts report when users are interacting with your campaigns.
• Use Search reports to find out which keywords and search queries are acquiring the most new users.

Click image for full-sized version

1. You find that one (or several) of the pages on your site is missing Google Analytics, or some pages had Google Analytics deployed twice causing duplicate pageviews and inflating traffic.
2. Google Analytics custom variables were inconsistent or missing on some portions of the site, leading to data quality issues.
3. An unauthorized marketing tag was piggybacking off of another tag.
4. One of the tags on an international site you managed did not follow the new EU Cookie Laws related to privacy.

1. All the tags on your site, split up by specific pages’ tags, and even pages they are missing from.
2. Cookies set by various tags and what pages they are set on.
3. How the tag is deployed – through a tag management system or directly from a page source.
4. Instances of tag piggybacking – one tag being loaded by another tag.

1. Tag name and functionality
2. Pages or the category pages the tag needs to be on
3. Information collected through the tag about visitors (cookies set)
4. Firing rules