How do you deal with incomplete or unreliable data in your competitive analysis?

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Competitive analysis is a vital skill for any business that wants to understand its market position, strengths, weaknesses, opportunities, and threats. However, gathering and analyzing data from your competitors is not always easy or straightforward. You may encounter situations where the data is incomplete, unreliable, outdated, or inconsistent. How do you deal with these challenges and still produce a meaningful and actionable competitive analysis?

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1 Assess the data quality

In order to assess the quality of data you have or can access, you can use various criteria such as relevance, accuracy, timeliness, completeness, consistency, and validity. Ask yourself if the data is relevant to your research question and objectives, if it is accurate and free from errors or biases, if it is current and updated regularly, if it is complete and covers all the aspects you need to analyze, if it is consistent and comparable across different sources and methods, and if it is valid and based on reliable sources and methodologies. By assessing the data quality, you can identify any gaps, limitations, or risks in your data collection and analysis process.

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? Mayur Palta

GTM | Field Engineering | Competitive Strategist | Board Member | Author
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In order to assess the quality of data, one must validate and verify the sources of where the data is gathered from. In case the source of data is credible, the second logical step should be to qualify the soundness of information. How accurate is the information? Is there a way to test this hypothesis? Is there supporting evidence or contracting evidence that questions the soundness of information? Easier to quantify the soundness of intelligence gathered by assigning it a score and comparing again what other intelligence is readily available or verified.

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2 Fill in the gaps

The next step is to fill in the gaps in your data as much as possible. You can use secondary research to look for alternative sources of data, such as industry reports, market surveys, customer reviews, or academic studies. Additionally, you can conduct primary research with tools like SurveyMonkey or Qualtrics to collect data directly from your target market. Estimation techniques, such as benchmarks, ratios, averages, or projections, can also be used to infer data from available information. By filling in the gaps, you can increase the completeness and accuracy of your data and reduce the uncertainty in your analysis.

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Monica Gauba

[email protected] The FIRST and ONLY SAAS Platform that defines GTM strategy, aligns execution & teams
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Reach out to experts in your industry or vertical and create a focus group. Tap into their knowledge and experience with the products in question to fill the gaps in your knowledge and data. Do a webinar with your target market and do brief surveys during the webinar to ask questions that will help you assess the gaps.

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3 Validate the data

The third step is to validate the data you have or have collected. You can use various methods to check the reliability and consistency of your data, such as triangulation, corroboration, and sensitivity analysis. Triangulation involves comparing and contrasting data from different sources, methods, or perspectives. Corroboration looks for evidence or confirmation that supports or contradicts your data. Sensitivity analysis tests how your data or results change when you vary certain assumptions, parameters, or scenarios. By validating the data, you can increase its credibility and reduce errors or biases in your analysis.

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Meenakshi S

AVP, Insurance products, Domain practice leader, D&I lead, POSH Committee Head
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When we derive competitive data what we may miss out, is the correlation to the parameters.. Each one individually may seem right, but when seen in conjunction, it may seem out of place.. For example let's say a product is being sold in 100 regions. And that statement is accurate.. But 80/100 belong to North America, and the rest across Canada and Mexico.. Then we cannot expect these insights to help us drive a product launch in China or Japan.. It matters a whole lot to understand which are the 2 apples we may want to compare, instead of comparing an apple and a pear

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4 Communicate the data

The final step is to communicate the data you have analyzed in a clear and concise way. You can use tables to organize and display your data in rows and columns, making it easy to compare and contrast different values or categories. Charts can illustrate and highlight your data in graphical forms, making it easy to see patterns, trends, or relationships. Frameworks can also be used to structure and simplify your data in logical or strategic ways, making it easy to understand and apply. For example, SWOT, Porter's Five Forces, or Value Proposition Canvas can be used to analyze strengths, weaknesses, opportunities, threats, competitive forces, or value propositions. By communicating the data effectively, you can increase its clarity and impact for your audience.

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Susan Coelius Keplinger

CEO at Force of Nature | Performance Marketing at Scale
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When presenting your findings, be transparent about the data’s limitations. Clearly indicate any assumptions made during the analysis to fill data gaps. This honesty will build trust with your audience and can lead to more informed discussions.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you deal with incomplete or unreliable data in your competitive analysis?

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3

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1 Assess the data quality

2 Fill in the gaps

3 Validate the data

4 Communicate the data

5 Here’s what else to consider

Competitive Analysis

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