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How can artificial intelligence automate competitive analysis for business innovation?

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Competitive analysis is a vital process for business innovation, as it helps you identify your strengths, weaknesses, opportunities, and threats in relation to your competitors. However, conducting a thorough and timely competitive analysis can be challenging, especially in fast-changing and complex markets. That's where artificial intelligence (AI) can help. AI can automate and enhance various aspects of competitive analysis, such as data collection, analysis, visualization, and reporting. In this article, we'll explore how AI can automate competitive analysis for business innovation and what benefits it can bring.

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David Sweenor

B2B Marketing Leader, Founder TinyTechGuides, DataIQ 100, Top 25 AI and Analytics Thought Leader, Master Gardener

1 Data collection

One of the most time-consuming and tedious tasks in competitive analysis is data collection. You need to gather relevant and reliable information about your competitors' products, services, prices, strategies, customers, reviews, feedback, and more. This can involve multiple sources, such as websites, social media, blogs, forums, news, reports, and databases. AI can automate data collection by using web scraping, natural language processing, and computer vision techniques. These techniques can extract, process, and classify data from various online sources, and filter out the noise and irrelevant data. AI can also monitor and update the data regularly, ensuring that you have the latest and most accurate information.

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Ranganath Venkataraman

Digital Transformation through AI and ML | Decarbonization and Oil&Gas | Project Management and Consulting
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As important as data collection is data validation. As AI helps to collect data, it also supports customizable validations that ensure your competitive analysis isn't influenced by misleading data e.g. one-off outliers. These validations also remain unaffected by personnel shifts within organizations, a known source of lost expertise in data checking.

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2 Data analysis

Once you have the data, you need to analyze it and derive insights that can inform your business decisions and innovation efforts. This can involve comparing and contrasting different aspects of your competitors, such as their value proposition, market share, customer segments, pricing models, strengths, weaknesses, opportunities, and threats. AI can automate data analysis by using machine learning, deep learning, and statistical methods. These methods can identify patterns, trends, correlations, anomalies, and gaps in the data, and generate insights that can reveal your competitive advantages and disadvantages, as well as potential areas for improvement and innovation.

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David Sweenor

B2B Marketing Leader, Founder TinyTechGuides, DataIQ 100, Top 25 AI and Analytics Thought Leader, Master Gardener
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A great example on how generative AI could help with this process is that it can analyze various scenarios for the competitive team. It can quickly use a variety of frameworks (SWOT, Five Forces, etc.) to present the information in a way that is most consumable for the end-user.

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Ranganath Venkataraman

Digital Transformation through AI and ML | Decarbonization and Oil&Gas | Project Management and Consulting
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Data analysis can broadly be characterized as descriptive, predictive, and prescriptive. These are ideally in order of level of sophistication. Descriptive analysis gives the current state i.e. competitor A has 25% of the market share for product A and their price point is 10% lower than ours, but with fewer features that customers want. Predictive analysis suggests what could happen e.g., if you were to lower your prices. The final stage is prescriptive analysis: with a robust data model and comprehensive models and analytics, the output of the model is a recommendation geared to your needs e.g., "lower price by 5% and remove feature x to achieve an extra 3% market share".

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3 Data visualization

Another important task in competitive analysis is data visualization. You need to present the data and insights in a clear and compelling way, using charts, graphs, tables, maps, dashboards, and other visual tools. Data visualization can help you communicate your findings and recommendations to your stakeholders, such as your team, managers, investors, and customers. AI can automate data visualization by using data science and design tools. These tools can create and customize visualizations that suit your objectives, audience, and data type, and highlight the key points and messages. AI can also interact with the visualizations, such as by zooming, filtering, or drilling down, to explore the data further.

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4 Data reporting

The final task in competitive analysis is data reporting. You need to document and share the data and insights with your stakeholders, and provide actionable suggestions for your business innovation. Data reporting can involve writing summaries, reports, presentations, or proposals that explain your competitive analysis process, results, and implications. AI can automate data reporting by using natural language generation and summarization techniques. These techniques can create and edit written content that summarizes the data and insights, and provides clear and concise recommendations for your business innovation. AI can also adapt the content to different formats, styles, and tones, depending on your purpose and audience.

AI can automate competitive analysis for business innovation by performing various tasks that would otherwise require a lot of human effort, time, and resources. By using AI, you can collect, analyze, visualize, and report data more efficiently, effectively, and accurately, and gain deeper and richer insights into your competitive landscape and your innovation opportunities. AI can also help you stay ahead of the curve and respond to changing market conditions and customer needs faster and better.

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David Sweenor

B2B Marketing Leader, Founder TinyTechGuides, DataIQ 100, Top 25 AI and Analytics Thought Leader, Master Gardener
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Historically, competitive intelligence required people to create reports and competitive battle cards--and there are a handful of companies that help automate this process. But with generative AI, this model is now flipped on its head. One can imagine that a sales professional or product manager can simply ask (via a prompt) how to compete against a certain vendor or product. Enabled by a template, the system would format the prompt correctly and then create personalized advice and guidance for the AE or product manager.

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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 can artificial intelligence automate competitive analysis for business innovation?

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1 Data collection

2 Data analysis

3 Data visualization

4 Data reporting

5 Here’s what else to consider

Business Innovation

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How can artificial intelligence automate competitive analysis for business innovation?

1

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1 Data collection

2 Data analysis

3 Data visualization

4 Data reporting

5 Here’s what else to consider

Business Innovation

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