Competitor analysis is a non-negotiable.
If you know what your competitors are doing, you can respond appropriately. Without this information, your competitors might overtake you and capture some of your market share.
Competitor analysis isn’t as heavy as it used to be. Thanks to artificial intelligence (AI), it’s faster and more insightful and can be used to predict their next move!
In this blog, we’ll cover:
- What’s AI-powered competitor prediction?
- The benefits of AI-powered competitor prediction.
- Five ways to use AI for competitor analysis.
- Four AI tools to help you predict your competitor’s next move.
What is AI-powered competitor prediction?
AI-powered competitor prediction uses AI to analyze competitor data to predict what they’ll do next.
Machine learning models process vast amounts of competitor and related data, such as:
- Publicly available customer sentiment (Trustpilot, Amazon, Google My Business, etc.)
- Publicly available financial reports
- Their social media activities
- Competitor press releases
- Market trends
AI-powered machine learning is smart. It doesn’t analyze each data piece in a silo. It identifies patterns and connections between data sets to provide a comprehensive competitor report with accurate predictions.
This allows your company to:
- Better prepare for market shifts, adjusting your product offerings and marketing strategies to maintain a competitive edge.
- Adjust your pricing based on demand or seasonality.
- Understand potential competitor product launches to reinvent or innovate your products.
- Plan and tailor your marketing strategies.
Now, let’s take a look at an example in action.
When it comes to the travel and leisure industry, setting the right pricing at the right moment can make all the difference. Fortunately, AI algorithms now offer a powerful solution by analyzing historical pricing data from your company and competitors.
For instance, you’re in the RV rentals business, a sector known for its seasonal fluctuations and competitive pricing strategies. By tapping into AI-driven analytics, you gain insights into past pricing trends and patterns, enabling you to anticipate potential moves by competitors for upcoming seasons or holiday periods. Incorporating an AI Market Research Tool can further enhance the process by automating data gathering and analysis, providing timely competitive insights.
Regardless of your industry, understanding consumer shifts empowers you to fine-tune your pricing strategy, ensuring competitiveness while maximizing profitability.
What are the benefits of AI prediction in competitor analysis?
Traditional competitor analysis is:
- Restrictive in data analysis (there’s only so much data humans can process)
- Lacks personalization
- Time-consuming
- Not as insightful
- Laborious
- Manual
- Boring
AI does the heavy lifting for you.
Its algorithms can process and analyze large datasets from multiple sources much faster than human analysts, thus eliminating data silos.
For example, an AI system can solve 10 math problems in one minute, whereas a human can only solve one every five minutes.
AI’s speed reduces the time required to gather and interpret competitive intelligence, allowing businesses to react quickly to competitor activities.
AI uncovers patterns and correlations in data that might not be obvious through manual analysis. These insights can include:
- Shifts in consumer preferences
- Predicting future market trends
- Competitor moves
Unlike manual processes, AI systems can easily scale to track as many competitors as you need without a proportional increase in effort or resources.
AI models improve and become smarter as they process more data, resulting in more refined and accurate insights.
Even if the upfront investment in AI is sizable, its cost-effectiveness is realized in the long run, especially when compared to traditional methods.
How to use AI for competitive analysis
Now that we’ve covered the benefits let’s peel back the curtain on how to actually use AI to conduct your own competitive analysis.
Data collection
The algorithm needs as much competitor data as you can find.
Some sources of data include the following:
- Any keynote speeches or presentations from their CEOs and other business leaders.
- Historical data (previous launches, pivots, changes in strategy, etc.)
- Any offline marketing material like newsletters, leaflets, and flyers.
- Their website and social media channels.
- Media press releases.
- Consumer feedback.
- Financial reports.
The next step is extracting the sources. You can:
- Download audio and visual content and extract the text.
- Download online text-based data.
- Scan and digitize print data.
The data is stored in a warehouse or any suitable data management system as long as it’s easy to access and analyze.
That said, here are a few important data collection considerations to keep in mind.
Don’t be lazy or half-hearted. Data collection isn’t the most stimulating and enjoyable activity, but the quality of AI’s predictions depends on it.
Have a deep think about all competitor data sources. There are obvious ones, like their website, and less obvious ones, such as email newsletters or podcasts.
For example, if a travel nursing agency wants to learn more about what their competitors are doing, they can ask their AI to focus both on public and internal historical data.
For public data, the AI can find news articles, blogs, social media posts, and more information related to competitors, including job postings, which can help understand whether other staffing companies are focusing on location or specialties and provide information on their marketing campaigns.
For internal data, AI can help analyze nurses’ preferences, specialties and locations, performance, and more and compare this information with that of other companies.
Different data sources provide information that contributes to a more comprehensive view of your competitors.
Relying on a single source could result in a biased or incomplete analysis. And if one source is weak or corrupted, you must have other sources to fall back on.
And be sure to practice good data hygiene. Bad data = bad decisions.
Predictive analytics
Predictive analytics is a statistical and data-mining technique that analyzes historical data to predict future events.
Predictive analytics uses various statistical algorithms and modeling techniques to identify the likelihood of future outcomes based on historical data.
Analysts can feed the data to AI to build prediction models once the data is collected and prepared.
The models are validated and tested using a subset of data not used during the model-building phase to assess the model’s accuracy.
It’s deployed to make real-time predictions about competitors if it passes validation.
Note: Changes in underlying data patterns or real-world events might necessitate model adjustments to maintain accuracy.
Benchmarking
Benchmarking is a strategic approach where a company compares its processes and performance metrics to best practices from competitors.
Benchmarking involves the following steps:
- Defining what you want to achieve through benchmarking.
- Select specific aspects of your business that are directly comparable to your competitors, such as sales processes, product quality, or customer service reviews.
- Choose metrics that reflect the areas you want to benchmark. For example, suppose you want to reduce your cost per lead. In that case, you might analyze purchase intent data to devise personalized B2B marketing and sales strategies that target only leads with high purchase intent.
- Select benchmarking partners who are direct competitors with a reputation for excellence in the areas you’re evaluating.
- Gather data from within your organization and competitor data and let AI identify any performance gaps.
- Based on the benchmarking results, develop recommendations, including detailed action plans and timelines.
- Regularly revisit the benchmarking process and refine it (if needed) to ensure it remains relevant to current market conditions and your strategic goals.
Sentiment analysis
Sentiment analysis determines the emotional tone behind words. It helps you understand customers’ sentiments toward products, services, or brands as expressed through comments, reviews, or social media.
Here’s how sentiment analysis can be leveraged for strategic decisions:
- Analyze customer feedback to gauge overall satisfaction with a competitor’s product or service. Sentiment analysis helps identify whether the feedback is generally positive, negative, or neutral.
- Check how customers feel about various brands or products. This analysis can reveal emerging trends and shifts in consumer preferences.
- Continuously monitor public sentiment about the competitor to identify ways you can turn their weaknesses into your strengths.
The benefits of using AI for sentiment analysis:
- As long as the data is reliable, the sentiments will be reliable, too.
- It can handle large data volumes from different sources.
- The analysis is performed in real time.
Risk management
If you can predict risk with AI, you can mitigate risk.
Here’s how:
- Predictive models can analyze patterns from past business cycles and external market dynamics to identify potential risks before they become visible.
- By simulating different scenarios (e.g., economic downturns and supply chain disruptions), you can understand possible impacts and prepare accordingly (and in advance of your competitors) to reduce the impact of risk.
- Predictive analysis helps estimate future product demand based on historical sales data, market trends, seasonal fluctuations, and consumer behavior.
- With better demand forecasts, you can maintain optimal inventory levels — enough to meet customer demands without overstocking, which can tie up capital and increase holding costs.
Effective risk management involves various strategies and tools that help businesses predict, assess, and mitigate risks. These include:
- Customer risk assessment frameworks such as ISO 31000 and the COSO ERM Framework.
- Tools that simulate financial scenarios help in understanding potential profit outcomes under various risk conditions.
Best tools for using AI to predict your competitor’s next move
Are you looking for the best marketing analysis software or business intelligence tools to monitor key indicators in real time? Here are the top tools to help you do so.
Unkover
Unkover is an AI-driven competitive intelligence platform tailored for SaaS companies. It automates the collection and analysis of competitor information, such as website changes, customer reviews, press mentions, and funding rounds.
Unkover’s tools include tracking for real-time updates on:
- Strategic market changes aimed at enhancing strategic decision-making and maintaining competitive advantage
- Competitor’s activities
- Email insights
The platform integrates with common business tools like Slack and Salesforce to streamline workflows.
Similarweb
Similarweb offers web analytics services for businesses. It helps companies understand, track, and grow their digital market share by providing insights into their competitors’ traffic and marketing strategies.
Similarweb offers tools for analyzing:
- App performance and engagement
- Benchmarking against competitors
- Website traffic
It’s used by marketers, analysts, and industry leaders for market research.
Sprout Social
Sprout Social is designed to help businesses enhance their online presence and check audience sentiment on social media.
Use it to track your competitors’ social conversations to grasp overall sentiment. Sprout Social supports various social platforms, including:
- X (formerly Twitter)
It’s a versatile tool for comprehensive social media research.
Semrush
Semrush is an all-in-one online visibility management and content marketing SaaS platform.
It provides tools and insights for:
- Search engine optimization (SEO)
- Pay-per-click (PPC) advertising
- Social media management
- Competitive research
- Content marketing
Semrush helps businesses improve their online presence, optimize their digital marketing strategies, and stay ahead of competitors in the online marketplace.
Wrapping up
The advantages of competitive analysis are clear:
- The ability to analyze copious amounts of data from varied sources.
- The ability to make better decisions at a fraction of the time.
- Real-time data process.
There’s no reason not to use AI for competitive analysis.
Incorporating Unkover into your competitive analysis is a good place to start.
By leveraging its AI-driven insights, you can observe competitors more effectively and adapt your strategies swiftly to market changes.