We live in the age of data. From global warming stats to customer behavior patterns, new technologies have made it easier to collect, store, access and analyze information. But our use of these technologies has also eroded our attention spans and fueled post-truth misunderstandings. To combat these trends, the question becomes: How do we present evidence in ways that the human mind can rapidly and accurately absorb it?

Data visualization techniques frequently lead to ingenious solutions by telling stories driven by data. Numbers have an important story to tell, but visual analytics is needed to give them a clear and incontrovertible voice. Breaking this down further:

• A narrative coupled with data helps explain to audiences what’s happening in the data and why a particular insight is important.
• When visuals are applied to data, they can enlighten audiences to insights that they wouldn’t see without charts or graphs.
• Finally, when narrative and visuals are merged, they can engage an audience. When analysts combine visuals and narrative with the right data, the extracted stories of insight can influence and drive change.

Let's transition to a bit of history. It's not representative of a modern data visualization, but many academics, information scientists and industry professionals consider this example to be a significant achievement in the field of information graphics. Drawn by Charles Joseph Minard, it tells the story of Napoleon’s ill-fated 1812 invasion of Russia.

Minard was a French civil engineer recognized for his representation of numerical data on geographic maps. The illustration depicts Napoleon's army departing the Polish-Russian border. A thick band illustrates the size of his army at specific geographic points during their advance and retreat. It displays six types of data in two dimensions:

1. The number of Napoleon's troops.
2. The distance traveled.
3. Temperature.
4. Latitude and longitude.
5. Direction of travel.
6. Location relative to specific dates.

A modern redrawing of Minard's figurative map translated in English is available here. This type of graph representing illustrations of flows was later called a Sankey diagram, named after Matthew Henry Phineas Riall Sankey. He first used it to show the energy efficiency of a steam engine in a publication in 1898.

Sankey diagrams today

Fast forward to today, and sankey diagrams provide the illustration of different kinds of flows like energy, material, money and customer experience interactions. In other words, it provides the summary of all the paths involved in a process. Path analysis (an analytical application that leverages sankey diagrams) is an exploration of a chain of consecutive events that a given customer or cohort performs during a set period while using a website, online game or mobile app (although other use cases can apply outside of digital analytics). As a subset of behavioral analytics, path analysis is a way to understand customer behavior in order to gain actionable insights into the data.

While it's possible to track a user’s path, and even show that path as a visual representation, the real question is: How do you gain actionable insights? If path analysis simply outputs a pretty graph, it may look nice, but not provide anything concrete to act upon. There's a mindset that, just like in the offline world of purchasing and marketing, there's only one way to get to a goal. However, the digital world is different.

As I have met with clients over the years, I've frequently heard (or witnessed debates) that pathing analysis is a waste of time. The reasoning behind this is that the increasing fragmentation of visitor pathing presents an incredible analysis challenge. There are exceptions. Structured experiences represent subsets of a digital experience that are a good fit for path analysis – examples like online application forms and product purchase check out processes commonly achieve successful results.

But think about the business objective of every online form or sales funnel that you have experienced. The brand’s mission is for the customer to complete the task. What drives an organization mad? It’s when you start, but don’t complete the process. The sales opportunity transforms from a cold to hot lead in meeting your macro-conversion goal, but suddenly disengages. What happened? We were getting along so well. And then poof! The revenue was at your fingertips, and now it's disappeared like a ghost in the night.

Senior management obsesses about the missed opportunity of large percentage drop-offs related to conversion goals, and analysts fret because they struggle to answer why. In actuality, a better name for path analysis might be “abandonment analysis”.

The solution

The term "marketing data management" describes how SAS Customer Intelligence 360 can enhance and extend customer data activation, while allowing users to move beyond a traditional customer data platform with our hybrid architecture. Leveraging data collection mechanisms, SAS 360 Discover captures first-party behavioral information across the entire digital customer experience with a brand’s websites and mobile apps. As visitor journey data flows in, SAS transforms that valuable information into structured views, allowing an analyst to perform path analysis.

When someone interacts with your brand digitally, they leave a sequential data trail. Attributes like traffic sources, pages viewed, goals achieved and other events of interest are available. Analysts can adjust their scope to focus on sessions, anonymous visitors or identifiable traffic. Segmentation of journeys is enabled within a single click based on any available dimension, and when analyzing identifiable traffic, both online and offline variables can be filtered on. To enable one’s ability to segue from research to driving results, SAS Visual Analytics is the provided analysis tool for SAS 360 Discover. Sharing discovered segments into SAS 360 Engage for targeting and personalization closes the loop.

With that said, I invite you to view a video that will address the following topics on how SAS:

1. Delivers customer journey analytics​.
2. Approaches pathing analysis (software demonstration).

Learn more about how the SAS platform can be applied for marketing data management and see our customer use cases.