This excel is divided between a UX audit calculator and a design debt calculator.
The UX audit is based on the 10 Nielson's Heuristics because is the most complete set of 265 guidelines that cover both pragmatic and hedonic properties necessary to correctly assess an end-to-end user journey. You can also explore other data sets like the 247 Usability Guidelines from UserFocus. You just have to switch the database in the 'lookup heuristics tab.

What makes this calculator special, is that serves as a diagnostic method to help designs and product managers measure both pragmatic and hedonic qualities for calculating the design investment opportunities through the prioritization of design debt efforts. This translates into the most effective way to measure design debt because it does not only look at the user interface properties (usability), like most of the design debt calculators available in the market do. But also measures the attitudes like the perception of ease of use, usefulness, and satisfaction. This brings a unique analysis to measure the effectiveness, efficiency, and satisfaction of the digital product.

In order to know 'where to play and how to win', we need to establish what we need to manage.

Use this spreadsheet to audit your digital solution to discover and calculate where your gaps lie in terms of user data, user experience, and customer satisfaction. Visualizing your design debt in a product orientation enables you to maximize value. From this vantage point, you can help product teams move the product/service forward in its UX maturity, as well as lower its long-standing design debt.

There are 4 types of design debt:
1 - UX Design Debt;
2 - Operational Design Debt;
3 - Visual Design Debt;
4 - Testing and Research Debt.

1) This type of debt has a direct implication for the business goals because it impacts negatively adoption rates, learnability, discoverability, and integrity of the user flow which will directly affect general customer satisfaction.

2) Operational design debt occurs when there is a poor underlying structure and/or inefficient process or governmental models to support good ways of working for better design and development teams' work. To reduce operational debt the first step is understanding the shortcomings of underlying structures and processes. This requires listening to your team carefully, gathering feedback about design organization within your company, and, if you are a manager, noticing clues from your reports during 1on1s. Things that can contribute to operational design debt are lack of a design system; Lack of accessible components; Outdated design files or inconsistent with the implementation; Messy design file organization that is difficult to work with (no styles and components, flat structure, etc., which force designers to rebuild elements from scratch or spend time cleaning up existing files before starting a project); And last but not least, weak designer-developer workflow and communication.

3) This type of debt takes into account the UI inconsistencies of the product interface like colors, typography, grids, etc. The extreme is when system parts have such an incongruous look and feel they seem to come from another product. This debt undermines the product's credibility by making it look less trustworthy and professional. Analyzing visual inconsistencies in the product requires a cross-team effort to get complete and credible results.

4) Failing to prioritize and support UX research to inform and drive the product decision will just open the door to high volumes of testing and research debt. The absence of user testing will result in a bad UX, leading to lower engagement and product adoption, customer churn, and a higher bounce rate. A product is doomed to failure if there has been no user research.

This excel is composed of a few tabs:

1. How to Use
This is the first tab and its purpose is to give you guidance on how to use it and what are the purposes and goals of this tool.

2. Template
Here is where the magic happens. The "Template" tab is the main point of entry for data for the UX audit and its UX debt calculator. Fulling this tab will generate the results and graphs that you need to help your team(s) to make better decisions.

3. UX Audit Results
Use this tab to uncover where your experience is failing your users. As soon as you start filling out the 'Template' tab, this session will automatically start generating visual graphs to illustrate where your experience is been weaker. You will be able to analyze the experience not only by heuristics but also breaking it down by sub-heuristics.

4. Design Debt Results
This tab does exactly the same as the previous one, but instead of covering the heuristics been audited, it will provide you feedback regarding your volume of design debt. Here, you will uncover where your symptoms. As soon as you start filling out the 'Template' tab, this session will automatically start generating visual graphs to illustrate where your design needs the most payout. You will be able to analyze the debt not only by heuristics but also by UX, visual, and operational design debt.

5. Impact-effort Matrix
Do you have lots of work that waiting to prioritize?
It is much compeller to prioritize when you have a great number of works to do. It is essential to standardize your decision-making method. This excel will help you do that in a very simple method. By using an Impact-Effort Matrix. In this tab, you will know which work is not worth the effort, which work you should address first, and the work you maybe can consider for later. In this way, it will be much easier to eliminate the works which not contribute to your business value.

6. Issues Distribution
Visual reference of the distribution of issues per Severity and Heuristic.

7. Lookup Heuristics
All of these metrics were retrieved from the scientific contribution of Nielsen and Norman.
- Nielsen, J. (1995). 10 Usability Heuristics for User Interface Design.
- Nielsen, J. (1994). Enhancing the explanatory power of usability heuristics. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '94). Association for Computing Machinery, New York, NY, USA, 152–158.