Making ‘Market & Competition Sensing’ Smarter with AI

• Artificial Intelligence
• Product Focus

In the age of digital transformation, one question business leaders are often asked is: How will AI reinvent your industry? The same is true for the Market and Competitive intelligence industry. In the past few years, the explosion of information sources via internet platforms and the rise of data-driven Machine Learning (ML) models have thrown up multiple, alternative data sources as well as technologies to harness them. Yet, who amongst us has not experienced information overload from multiple sources all the time–and hoped that AI may help cut through the clutter?

Course5’s AI-driven Market & Competition Sensing (MCS) platform provides personalized and curated insights for business decision-makers. The platform uses AI technologies like Machine Learning (ML) and Natural Language Processing (NLP) to transform real-time data into insights that are relevant to an individual’s role and domain—a key need of business decision-makers today.

Turning the Data Deluge into Ingestible Insights

Traditionally large data dumps from a plethora of sources have been made available to end-users for consumption. A large amount of time is required to consume and make sense of this data. Traditional methods also need manual labeling of this data for further analysis. This further consumes valuable employee time. In the age of agile and time-constrained decision-making, every moment counts.

Using machine learning models for competitive landscape analysis, the MCS platform synthesizes large amounts of data and transforms it into refined information that is easy to make sense of and act upon. Here’s how.

The MCS platform collates large amounts of input data from multiple online sources such as news articles, blogs, Twitter feeds, and others. Next, the data is moved through a series of data processes that turn the data into concise, intelligible insights.

One of the first processes is cleaning data to remove stop words, web links, punctuations, quotations, and other non-essential text elements. The cleaned data is further processed via model training & classification for topic detection. Finally, the information is showcased to the user via AI-driven content relevancy and prioritization models.

The Natural Language Processing (NLP) engine classifies and segregates content into multiple themes via automated and user-generated tags. The NLP engine automatically generates tags using topic modeling and classification algorithms. User tags are used to re-train the engine to create newer and better tags.

A heuristic approach is used to provide a priority score for content relevancy and prioritization. A quantitative score for each action is empirically determined using category, brand, competition, source, recency, and other parameters. The platform also uses user input-based personalization to deliver personalized feeds based on the likes/dislikes of the user, content relevancy and priority for that particular user, and source credibility.

Curated insights that eliminate duplicate content are another key pillar of the MCS platform. News items from multiple sources often contain repetitive content which wastes the user’s time. The MCS platform uses AI algorithms to help the user view a distilled content feed with relevant content items while avoiding repeated content. The platform achieves this through similar content clustering. The NLP engine consumes raw data from input sources and applies vectorization and unsupervised clustering techniques to group similar content. The MCS platform uses NLP frameworks to pre-train models to create vectors. Once the content is converted into vectors, a density-based clustering approach is used to group similar content.

The MCS platform also enables viewers to view a short auto-generated summary of articles/reports so they do not have to go through the entire content to know if it’s relevant. An extractive text summarization-based approach is used to select the most critical sentences from an article and summarize them using Natural Language Processing (NLP) and Natural Language Generation (NLG).

Driven by evolving client needs and research at Course5’s AI Labs and product teams, we can see that the use of AI and advanced analytics will continue to transform insight generation and the way we access and use insights. While technology will invariably play a key role in this evolution, it is a combination of human ingenuity and artificial intelligence that will propel the market & competitive intelligence industry forward in the most productive and impactful ways.

Course5 Market & Competition Sensing (MCS) is a part of the AI-powered Competitive Intelligence platform, Course5 Compete. The Compete platform also includes ‘Digital Shelf’ and ‘Brand Experience’ capabilities.