Visual Analytics: New Maps For Data-Driven Marketing

Published: April 2, 2013

Data analytics has never lacked for coordinates. Filling a spreadsheet with lead scoring algorithms, customer retention costs or predictive conversion metrics is second nature for most B2B marketers.

While the coordinates have provided an embarrassment of riches, the accompanying cartography has been barren. However, analytics is about to get a new set of maps.

{loadposition C2C13IAA}Visual analytics, or visualizing Big Data, is fast becoming a necessary capability for any company that wants to make Big Data accessible and navigable for their sales and marketing teams. It is a roadmap for customer data changes, demand generation data sets and lead management that feeds sales and marketing automation systems. It is data without the intimidation factor.

“The changing audience for consumer insights within corporations requires customer intelligence functions to adapt the way they package and communicate insights,” said
Heiko Schäfer, a Customer Insight Consultant. “Rather than relying on traditional research reports, communication that uses storytelling, visual, and interactive communication, as well as consumer and customer immersion, is often more effective.”

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Many leaders in the marketing automation industry such as SAS, SAP, Adobe, and Tableau have pushed visual analytics quickly to the forefront. Some of the solutions are as traditional and simple as bar charts. Others are brand new. Because the speed and volume of data have accelerated so quickly, traditional architectures and software might require companies to compromise the details they want to visualize.

Even the most common descriptive statistics calculations can become complicated when users are dealing with Big Data and don’t want to be restricted by column limits, storage constraints and limited support for different data types. One SAS solution to these issues is an in-memory engine that speeds the task of data exploration and a visual interface that clearly displays the results in a simple visualization. For example, what if you have a billion rows in a data set and want to create a scatter plot on two measures? The user trying to view a billion points in a scatter plot will have a hard time seeing so many data points.

Show The Results

“Everything we do as a company is based on analytics, and more specifically predictive analytics,” according to Wilson Raj, Global Customer Intelligence Director at SAS. “Visual reporting enables us to take some of the things we do currently and show the executional aspects of them. Instead of talking through the consequences of an inbound marketing campaign, we can use the tremendous amount of data available, apply visual analytics and show the results. It’s easy to apply predictive analytics. It’s more meaningful to show them in action.”

Raj points to visualization as the most aggressive move the company has made toward taking Big Data to actionable data. The most important element, as he indicated, is the dynamic nature of visualization. If SAS is working with a client on a demand generation campaign, for example, it can use the dynamic graphics to make variables in spending, offers and media translate into results (or lack thereof). If a retention campaign costs $3.50 per customer for an automotive customer, the visualization capability can analyze current data segments, predict cost and response rate and then update the graphic in real time. It’s predictive analytics that make sense at every level of the company and in any geographic area.

At the end of 2012, SAS put visual analytics into action with Taiwan’s 50-branch Cosmos Bank. The company needed to make data more accessible and actionable at the local branch level as well as the corporate C-suite. SAS integrated corporate and consumer data into real-time dashboards and visualizations for risk management, customer development, product marketing and financial management.

By clicking or drilling into items of interest, Cosmos board members and risk managers can generate and share reports, dashboards and visualizations. For example, senior managers use SAS Visual Analytics to break out cash-card issuance by branch or sales representative. The bank reduced the time analysts spend accessing data for others, shifting their efforts to projects that create value for the bank. Also, processing and analyzing all transactions throughout the month provides a more accurate view of customer behavior.

“This is radically altering marketing as we know it,” Raj said. “Not how we think about marketing, but how we execute marketing.”

Seeing Is Believing

Tableau CMO Elissa Fink has seen visual analytics move from an IT focused capability to the province of the CMO. In fact, she sees visual analytics as the confirmation that marketing can and must do more with predictive data

“We know data is useful, we know it is more accessible, but it’s up to marketing to make it more useful,” Fink said. “We’ve all seen Excel sheets with 1,600 cells. If you can see it better, you can make sense of it.”

Fink has seen many “aha” moments over the past few months that show her that visual analytics works on a B2B level. One UK software client was struggling with an unexpected lack of revenue during a launch trial period. The Excel sheet for that period of time had all the data, but only after plotting it in new data formats did it become evident that the company had coded their invoices improperly. Too many invoices were applied to “free trial.” Another client, this one in the health care business, found that a pattern of claim rejections were due to diagnosis coding errors. After that pattern became evident visually, it recovered $2.5 million in billings.

“When you can see the patterns,” she said, “you can ask better questions about the data.”


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