What are some common challenges when conducting a competitive analysis, and how can you overcome them?

1 Data availability

One of the main challenges of competitive analysis is finding reliable and relevant data on your competitors and the industry. Depending on the level of disclosure, transparency, and regulation, you may face difficulties in accessing accurate and timely information on your competitors' financial performance, strategy, products, customers, and market share. To overcome this challenge, you can use various sources of data, such as public filings, industry reports, media articles, customer reviews, social media, surveys, and interviews. You can also use tools like web scraping, data mining, and analytics to extract and process data from online sources. However, you should always verify the quality and validity of the data you use and avoid relying on outdated or biased information.

2 Data analysis

Another challenge of competitive analysis is analyzing the data you have collected and deriving meaningful insights from it. Depending on the size and complexity of the data set, you may need to use advanced techniques and tools to process, visualize, and interpret the data. You may also need to apply various frameworks and models to compare and contrast your competitors' performance, such as SWOT analysis, Porter's five forces, value chain analysis, and benchmarking. To overcome this challenge, you can use software and platforms that can help you automate and simplify data analysis, such as Excel, Tableau, Power BI, and R. You can also use best practices and guidelines to structure and present your data analysis, such as defining your objectives, selecting your criteria, choosing your metrics, and using charts and graphs.

3 Data interpretation

A third challenge of competitive analysis is interpreting the data analysis and translating it into actionable recommendations. Depending on the scope and purpose of your competitive analysis, you may need to consider various factors and scenarios that can affect your business decisions, such as market trends, customer preferences, regulatory changes, and competitive reactions. You may also need to balance between quantitative and qualitative aspects of your data analysis, such as numbers and narratives. To overcome this challenge, you can use frameworks and tools that can help you synthesize and communicate your data interpretation, such as SWOT matrix, gap analysis, strategic map, and scorecard. You can also use storytelling and visualization techniques to convey your key findings and suggestions to your stakeholders.

4 Data implementation

A final challenge of competitive analysis is implementing the data interpretation and recommendations into your business strategy and operations. Depending on the level of alignment and support from your organization, you may face resistance or obstacles in executing your competitive analysis results, such as budget constraints, resource limitations, organizational culture, and change management. You may also need to monitor and evaluate the impact and effectiveness of your competitive analysis implementation, such as ROI, KPIs, and feedback. To overcome this challenge, you can use frameworks and tools that can help you plan and manage your data implementation, such as SMART goals, project management, change management, and risk management. You can also use feedback and learning loops to track and improve your data implementation outcomes and processes.

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?