Know Your Competition Better Using Codemonk’s CleverCompete

Karan Kariappa -

Recently at Codemonk HQ, we received a bare-bones request from a client to automate the Competitor Analysis function. How we should do this was the first step in defining the problem statement, essentially building the solution from the ground up, thereby providing this very client with the power to simply ask the right questions and obtain the appropriate answers about their competition.

At the beginning of the collaboration, we had to answer a few fundamental questions.

CleverCompete - Product Ideation

Upon consecutive brainstorming sessions with the client, we obtained established the below corollaries:

Q: What was the client truly in need of?

A: A solution through which clients are readily presented with competitor information, market developments, best practices to adopt, pitfalls to avoid and avenues for growth and collaboration, among many more crucial information pertain to their brand, products/services, positioning and market forecasts.

Q: Any specific value add that the client was looking for?

A: Automation of information aggregation, presentability of insights, and capability to comprehend large volumes of unstructured data, turning them into meaningful, actionable insights.

Q: How do we ensure that the developed solution is precisely applicable to the industry within which the client operates, while aggregating data pertaining to the above requirements?

A: Bringing standardisation and intelligence at the data extraction level, such that a machine learning model could be developed, upon which a solution can be stitched to provide the automated insights. This was, standardisation of information is guaranteed while staying true to the norms and regulations of the industry within which the client operates.

Q: Once we train a machine learning model to obtain these insights, how do we provide them to the client in the most efficient, simplistic, and engaging format?

A: In accordance with the client’s domain and expertise, develop a chat bot that understands the intricacies of this industry, while taking into consideration the above mentioned large unstructured data set. This chat bot would act as the communications interface between the client and the LLM trained to achieve this automation.

Q: How do we ensure that the developed solution fits the client’s bill?

A: Deploy it for a test group, observe the traction, obtain feedback from the client and develop an agile pipeline to progressively improve the product.

With these questions were answered and aligned with the client’s needs, Codemonk began its product development journey to create CleverCompete - A Competitor Analysis solution built using machine learning algorithms that studies industry specific markets and players.

And, the first step in doing so, as mentioned above, was the standardisation data sources that was fed into CleverCompete machine learning model (LLM). And these were the data sources we looked at:

The standardisation data sources

The below mentioned checkpoints were used as definite channels of data aggregation:

  • private and public APIs, industry reports, news articles, social media, patent databases, and market research tools.
  • financial statements, customer reviews, competitor press releases, job postings (to analyze hiring trends), and interviews/discussions from podcasts/webinars/seminars and press conferences.

The next invariable step included the indexing, categorisation, and processing of the extracted information. Team Codemonk had to ensure that the obtained output from CleverCompete, in the form of insights, are classified into easy digestible buckets of information that the client could consume with easy and implement even more effortlessly.

Refining Information Categorization

  • We segmented competitor information into customer demographics, market positioning, product development, pricing strategies, partnerships, and supply chain dynamics.
  • We applied deep learning for sentiment analysis on customer feedback, enabling insights into competitor strengths/weaknesses.

This helped the engineers understand how the end consumers perceived all products and services within this category, and more specifically, how our client’s competition performed across the above areas. We now had to enrich this data with external factors that influences that intelligence we provide to the client.

For that, Codemonk’s team of engineers established a few boundary conditions and process parameters according to which the CleverCompete LLM was fined tuned.

Automating Competitive Benchmarks (LLM fine-tuning) by:

  • introducing AI-driven benchmarks that dynamically adjust according to industry trends and competitor growth trajectories.
  • automating analysis of competitors’ financials, KPIs, and compare them to industry averages, allowing your client to track quarterly and annual changes.
  • Utilising Natural Language Processing (NLP) to identify competitor strategies in new markets, product innovation, or regulatory changes.
  • Automatically summarising long-form content like research reports, patents, and SEC filings to highlight relevant insights.
  • Mapping sentiment trends across competitor brands, using social listening across platforms.
  • Identifying key influencers and stakeholders within competitors’ networks to understand potential partnerships, or risks of poaching talent.
  • Mapping sentiment trends across competitor brands, using social listening across platforms.
  • Identifying key influencers and stakeholders within competitors’ networks to understand potential partnerships, or risks of poaching talent.
  • Applying AI to track patent filings, R&D spending, and technology roadmaps to predict competitors' future product or service innovations.

Now, Codemonk’s CleverCompete machine learning model (LLM) was armed with all the essential capabilities, data sources, content curation capabilities, and reporting functionalities desired by the client. Next, we had to present these actionable insights to the client using a chatbot that delivered on the below mentioned requirements:

Insights and Presentability

Although the CleverCompete engaged with users primarily through a chat interface, Codemonk had to include additional modalities to present valuable insights to the users. And, we did that across these categories:

  1. Enhance Data Visualization:
    • Present complex competitor data with dashboards, graphs, and heatmaps for easy interpretation.
    • Include predictive models to forecast competitor moves based on past trends and external economic indicators.
  2. Market Trends Analysis:
    • The GenAI model could analyse industry-specific reports, trend forecasts, and news to predict market shifts.
    • Help understand how competitors might capitalise on these shifts.
  3. Customer Feedback Analysis:
    • Automatically analyse reviews, social media mentions, and forums to understand how competitor products are received by customers.
    • summarise vast data into key themes, such as pricing complaints, product satisfaction, and unmet needs.
  4. Competitor Product Feature Comparison:
    • Perform in-depth product/service comparisons, analysing feature sets, pricing, and customer reception.
    • AI-driven summaries provide recommendations on how to differentiate your client's products.
  5. Competitive Strategy Mapping:
    • identify strategic moves such as mergers, acquisitions, or partnerships.
    • Monitor competitor hiring trends to predict expansion into new markets or services.
  6. Financial Health and Risk Assessment:
    • generate detailed reports based on financial statements, press releases, and investor updates to assess a competitor's financial health.
    • This includes risk indicators like debt levels, revenue growth, and spending patterns.
  7. Sales and Marketing Strategy Insights:
    • Analyze advertising spends, influencer partnerships, and campaigns to gauge where competitors are focusing their marketing.
    • AI can detect shifts in ad spend or strategy, offering insights into potential new product launches or target markets.

CleverCompete was developed and deployed at client’s premise with the objective of providing real-time competitive intelligence, predicting marketing position, feedback-drive product development, informed and substantiated decision making, all the while empowering them to increase their presence in the market.

The client could continuously monitoring and alert systems provide up-to-date intelligence on competitors' actions, allowing quick responses. With AI-based predictive analytics, our clients could anticipate competitors' market positioning changes and adapt accordingly, while understanding competitor strengths and weaknesses in product offerings enables targeted innovations to outperform competition. The cherry on top of this magnificent cake was the data-driven insights on financial performance, market reception, and strategic directions, which collectively provided business leaders with the most optimal route to take for their business progression with as less risk as possible.

As we observe the adopt of CleverCompete in the market place, we invite fellow tech enthusiasts to let us know, what we could have done differently or better or that matter…or what variations in competition analysis would they like to see come out of Codemonk.