Customer Insight Visualizations to Drive Business Decisions

By Paul Santilli
WW OEM Business Intelligence & Customer Insights
Hewlett Packard Enterprise





In today’s internet-based world, the massive growth of data collection and storage requirements is putting major stress on the capacity of the current infrastructure for data warehousing and analytics development. More and more customer-focused systems are being deployed in networked end-points solution states which generates massive volumes of data that must be gathered and processed in a highly efficient, reliable and secure manner.  The ongoing management of this is going to require systems, tools, and data visualization techniques that will allow organizations to quickly act upon this information and make clear and fast decisions.

It is common knowledge that customer- generated information provides valuable insights into how an organization is addressing customer requirements. It also offers an assessment of how well the company provides the products and services customers need, as well as how competitive they are within the industry. Good Data Visualization Techniques allow these Customer Insights to be quickly and easily acted upon by multiple levels within an organization.
 
Why are tools like Data Visualizations (also commonly known as “InfoGraphics”) valuable for management usage? Because it is often more effective for visualizations to reflect systemic customer behaviors and perspectives without  requiring investment in pursuing resource-intensive data deep-dive analyses.

Data Visualizations to represent Customer Insights
There are a number of reasons why Data Visualization techniques are effective ways for representing Customer Insights information, with some key areas highlighted below:

1. The rate of growth of customer data will require quick and efficient ways of interpreting data for a company to remain nimble enough to react.
2. Customer Insights contain both quantitative and qualitative content.
3. Good Visualizations will reduce analysis overload – traditional data analytics provide too many options for analytics and resultant actions that may or may not be aligned to the business objectives.
4. Storylines can be customized to reflect a priority-based assessment of the issues for customer resolution.
5. Data Visualizations are easily communicable and are effective for multi-level management engagement, fitting for customer satisfaction concerns.

Key elements of effective Data Visualizations:
There are many guidelines published that  highlight good elements of an Infographic Data Visualization. What is most appropriate for Customer insights processing are the following points:

1. Storyline – the visualization has to tell a story. What is it that was measured, what results were obtained, how does it relate to industry benchmarks / competition and why is it important?
2. Data facts – facts you want to convey that reflect the content of your analysis. Information like metrics, perspectives, data facts, preferences, etc. Clever Data Visualizations will have the ability to incorporate multiple data streams within one graphic, so the reader can see, for example, how customer sentiment is viewed against a deliverable, measured over time, and also compared with industry and competitive benchmarks., Important Data facts can be:

1. Time based – providing a time horizon on data collection
2. Volume/frequency / quantity based – the volume of information
3. Comparisons to established baselines / benchmarking – why is this data important and what is the relevance
4. Reflect Inputs vs Outputs – good indications as part of a storyline
5. Combination of some/all of the above

1. Structure – the structure of the visualization should be in line with the content that is being reflected. The graphics should be part of the story and not detract the reader from the purpose of what is being addressed.
2. Simplicity – Most importantly – the content must be easily recognized, on topic and readily communicable.

The key is to make sure the reader understands what the storyline entails, what the key metrics being communicated are, and why the user would be interested in this content. It should take no more than a few seconds to quickly understand the general content and theme without having to study the graphic in detail to arrive at high-level conclusions. If it exceeds this amount of time, then the Visualization is either too complex with the metrics provided or the storyline is not clear in conveying an appropriate narrative.
 
Visualization types for Customer Insights
Customer Insights Data Visualizations are effective if they provide content that includes:

1. Raw Data populations (quantity of responses via inquiry) – information that can be reflected in Pareto charts to determine priority, the size of the issue, the impact to the organization,, and the priority of the concern for Management to address.
2. Customer sentiment mapping (heat maps) – where are customers most concerned about the products and services your organization provides to them.
3. Customer prioritizations (radar charts) – this can reflect the urgency of the issue and the relative impact it has to a customer’s satisfaction and growth objectives.
4. Performance to goals (line / bar charts) – this can be either how your organization performs over time to a customer metrics, or how you are compared against industry or competitors in the field.
5. Unstructured feedback – very important, as it allows for the customer to articulate content outside of the surveying parameters, and is also very important to include in an effective Data Visualization.

Data Visualizations and well-conceived Infographics can communicate valuable information to an organization without requiring a significant amount of detailed output compilation. Visualizations and Infographics can spotlight key areas where organizations can most impact customer issues and provide easily communicated perspectives throughout the organization to help build continuity and momentum in driving to improve customer satisfaction. Sometimes more of an art than a science, the ability to generate Data Visualizations and Infographics are excellent skills for Customer Insights practitioners to embrace.

Paul Santilli leads the WW Business Intelligence and Customer Insights Organization for Hewlett Packard Enterprise’s OEM Business, and has been with HP for over 20 years. He is responsible for Business and Competitive Intelligence Modeling and Customer Insights analytics, where he is the Chairman of HPE Executive Customer Advisory Boards worldwide. Additionally, Paul heads up the WW OEM Marketing & Evangelism Team, focusing on Marketing & Sales Enablement, WW Communications and Social Media platforms.

Paul also is on the Strategic Competitive Intelligence for Professionals (SCIP) Board of Directors and has presented worldwide on various topics related to Intelligence and Insights in both keynote and workshop forums. Paul has a Bachelor’s degree in Engineering from the University of Michigan, and earned a Master’s degree in Engineering and Business at Stanford University.