Consumers aren’t always the best at telling us what they need. As Henry Ford famously said when developing the Model T, “If I had asked people what they wanted, they would have said faster horses.” In other words, what consumers say they need and what they actually need don’t always align. The introduction of the Need-Gap Analysis has enabled researchers to quantitatively measure the unmet needs of consumers in order to identify where there is an opportunity for a brand or product to fill that gap.
There is no one-size-fits-all when it comes to a Need-Gap analysis. At Cue Insights, we take the extra step to ensure that we are effectively communicating these complex data relationships to our clients in a way that is easy to understand and visually engaging. Using a toolkit of standardized data visualization techniques to choose from as the foundation for your research approach, you can customize those foundational formats to fit your specific goals and objectives.
Here are 3 techniques for visualizing Need-Gap Data and the reasons for choosing one over another.
1. Quad Maps. If you are looking to identify gaps in a particular brand’s offerings, consider a Quad Map to illustrate your Need-Gap data. The benefit of the Quad Map approach is being able to plot a single brand’s performance across multiple attributes and pinpoint that brand’s strengths and weaknesses.
As an example, a brand of vitamins has noticed a decline in its Net Promoter Score and is trying to identify specific aspects of the brand that could be causing this. A quad map plots a series of brand-specific attributes (e.g., the variety of vitamin formats available, how innovative the products are, if the brand is trustworthy, if the brand cares about its customers, etc.) based on how important each attribute is to consumers versus how satisfied (or dissatisfied) consumers are with that attribute. With a quad map, clear divisions can be drawn between those attributes most in need of improvement and those that do not need as much attention.
2. Indexed Data in a Line Chart. If you are looking to identify gaps between a competitive set of brands, consider a line chart that maps indexed data. Plotting data against a benchmark (or, Parity Zone as in the example below), is an effective way to compare brands against each other and identify meaningful differences. Indexing data in a line chart is ideal when showing 1 metric (e.g., brand satisfaction) for multiple attributes and multiple brands in a single view.
As an example, let’s assume this same brand of vitamins wants to understand how it stacks up against other competitors in the vitamin category. By plotting each brand’s indexed satisfaction data across a series of attributes in a line chart, it is easy to spot which attributes are stronger or weaker for one brand compared to another, or which attributes fall within the “parity zone” and can be deemed more comparable to the competition. Another benefit of the Indexed line chart is the ability to order those attributes plotted on the x-axis based on a 2nd metric, such as how much they impact overall satisfaction (as derived from a separate drivers analysis), by showing the strongest drivers on the left.
3. Charting Discrete Metrics in a Bar Chart: If you are looking to better understand gaps across audiences or subgroups, consider a front-to-back stacked bar chart. Bar charts offer a simple way of representing data that everyone can easily understand, but sometimes adding another audience (or 3rd axis) can be harder to interpret. With a front-to-back stacked bar chart like the example shown here, you can compare multiple brand attributes across multiple audiences.
As an example, if our same vitamin brand is looking to understand how different groups of consumers rate the brand across different attributes, a front-to-back stacked bar chart can be a helpful visualization technique. In the example below, satisfaction data is plotted for both men and women and further breaks these audiences down by age range. In this view, it’s easy to spot where there are notable gaps from one audience or subgroup to another and where there may be greater targeting opportunities.
Cue Insights’ dedicated team of experts in data visualization and design approaches every research study with the client’s unique goals and objectives in mind in order to generate the highest level of value and insights from your data.
Have questions about data visualization or interested in exploring a Need-Gap Analysis? Email denise.burns@cueinsights.com to schedule a consultation.
