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Competitor Research

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Digital Marketing - Study Notes:

Businesses can use AI tools to make their competitor research more efficient. They can do this using:

  • Competitive analysis
  • Sentiment analysis
  • Trend analysis
  • Comparative analysis
  • Pricing analysis

Competitor analysis

Digital marketers can use AI to streamline the competitive analysis process. AI can automate data collection from various sources such as competitor websites, social media platforms, and online reviews.

This automation allows for the rapid gathering of vast amounts of data. And this data can then be analyzed to uncover insights into competitor strategies, pricing models, and customer feedback. For example, digital marketers could use AI to analyze competitor social media activity to identify successful content strategies.

AI tools can help you with a range of competitor analysis tasks. For example, you can use AI tools to analyze competitors’ content on social media channels such as TikTok, YouTube, and Instagram. And you can use AI tools to compare how competitor social media accounts are performing. In addition, you can even research competitors’ websites and marketing channels using Similarweb. Similarly, SEMrush provides insights into competitor website traffic, keyword strategies, and backlink profiles. This helps businesses to uncover competitor strategies, pricing models, and customer feedback efficiently.

Sentiment analysis

Sentiment analysis uses AI to assess the public opinions and feelings expressed in text data from social media, reviews, and forums. This analysis helps businesses understand how competitors are perceived in the market.

They can then identify strengths and weaknesses from the consumer perspective. For example, digital marketers could use sentiment analysis on competitor reviews to gauge customer satisfaction. Tools like MonkeyLearn can perform sentiment analysis on competitor reviews to gauge customer satisfaction, and identify strengths and weaknesses from the consumer perspective. For instance, a business can use MonkeyLearn to analyze reviews on Amazon or social media comments to understand the sentiment towards a competitor’s product launch.

Trend analysis

Machine learning models can analyze historical data to identify patterns and trends. This enables businesses to predict future competitor actions.

Businesses can then use this predictive capability to anticipate competitor moves and adjust their strategies proactively. For example, digital marketers can use predictive analytics to anticipate competitors' product launches.

Comparative analysis

AI tools can conduct a comparative analysis between your products and those of your competitors. This enables you to identify unmet customer needs and potential market opportunities.

This process involves examining various attributes of products, customer feedback, and market trends. You can then highlight gaps that your business could exploit. For example, you could use a market gap analysis using AI to find unmet customer needs.

Rival IQ can compare social media performance metrics between your business and your competitors, highlighting engagement rates and content effectiveness. Additionally, you can use Clearbit to compare market segmentation and audience demographics, helping to uncover market gaps and opportunities that your business could exploit.

Pricing analysis

Real-time pricing analysis through AI enables businesses to continuously monitor competitor prices. They can then adjust their own pricing strategies dynamically.

This helps to ensure that their business remains competitive by reacting promptly to market changes. For example, digital marketers can use real-time pricing analysis with AI to stay competitive in the market.

Tools like Prisync provide real-time competitor price tracking and dynamic pricing suggestions, ensuring that businesses remain competitive by reacting promptly to market changes. For example, digital marketers can use Prisync to adjust their pricing strategies in response to competitors’ discount campaigns or price changes.

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Clark Boyd

Clark Boyd is CEO and founder of marketing simulations company Novela. He is also a digital strategy consultant, author, and trainer. Over the last 12 years, he has devised and implemented international marketing strategies for brands including American Express, Adidas, and General Motors.

Today, Clark works with business schools at the University of Cambridge, Imperial College London, and Columbia University to design and deliver their executive-education courses on data analytics and digital marketing. 

Clark is a certified Google trainer and runs Google workshops across Europe and the Middle East. This year, he has delivered keynote speeches at leadership events in Latin America, Europe, and the US. You can find him on X (formerly Twitter), LinkedIn, and Slideshare. He writes regularly on Medium and you can subscribe to his email newsletter, hi, tech.

Clark Boyd
Neil Patel

Neil Patel is the co-founder of NP Digital. The Wall Street Journal calls him a top influencer on the web, Forbes says he is one of the top 10 marketers, and Entrepreneur Magazine says he created one of the 100 most brilliant companies. Neil is a New York Times bestselling author and was recognized as a top 100 entrepreneur under the age of 30 by President Obama and a top 100 entrepreneur under the age of 35 by the United Nations.

Neil Patel

ABOUT THIS DIGITAL MARKETING MODULE

Search Strategy Research and Planning
Clark Boyd Clark Boyd
Presenter
Neil Patel Neil Patel
Presenter

The Search Strategy Research and Planning module begins by discussing the factors to consider when conducting a search audit. It covers how to calculate the size of a marketing opportunity, how to plan a budget for a search campaign, and how to determine the resources required to deliver a search marketing plan. You’ll learn how to use situation analysis and audience profiles with industry and competitor research when developing a search marketing strategy, and how to select tactics and best practices for tracking and measuring search channel performance in a search strategy. The module also covers factors to consider when building audience personas and the metrics used to assess different objectives in a search marketing plan.