Advanced Competitive Intelligence
Automate the competitive analysis process. Accurate and up-to-date insights enriched by AI and machine learning models for better strategic decision-making.
COMPETITIVE INTELLIGENCE
What is Competitive Intelligence?
Competitive Intelligence is a strategic process that involves the collection and analysis of information about competitors, customers, products, and market trends to support business decisions. It is a fundamental tool for keeping companies competitive in today’s dynamic and highly competitive market environment.
OUR FRAMEWORK
Imetrics Framework for Competitive Intelligence
Breakdown
How the Imetrics Method Works
Extraction, Transformation, Load
The Imetrics framework is based on the ETL (Extraction, Transformation, Load) process, which enables the collection of competitor data and resolves the complexities of data storage and analysis management. In sectors where data is updated more frequently, the limitations of a non-automated process become evident. The use of digital data collection tools overcomes the limitations of manual research, significantly reducing the risk of errors, attributing value and quality to the data itself, and overcoming the limitations of a manual and non-digitized process related to:
- Quantity of available data
- Time/frequency required for data collection
- Error rates
- Overall cost-effectiveness compared to manual methods
Process Phases
Extraction – Obtain “raw data” from competitors’ publicly available data regarding the structure of their product-service offerings.
Transformation – In the ETL process, the “raw data” is formatted and standardized based on specific rules dictated by the data source and type. The data is then sorted and grouped into categories. This phase shapes the collected but otherwise unusable data into relevant information for the client and the business sector. It prepares the data for the Data Load process.
Load – Refers to the loading and storage mechanism of data within continuously updated databases. The subsequent historicization of data fuels AI technologies to extract insights for business purposes.
The process provides accurate and systematic intelligence on the competitive offering structures of Clients’ target competitors. It is designed to meet the frequency of data collection and the quantity of collectible information required by different business sectors.
Employing the ETL process facilitates Competitive Intelligence strategies, simplifies and automates data collection to integrate targeted strategic decisions for the business.
Database & Benchmark
At the core of our CI process is a robust structure aimed at the systematic organization of collected data. The continuous flow of CI data is of crucial importance to Imetrics. These data are collected and organized within our vertical databases, where different types of information converge: structured data (such as numerical values, dates, and percentages) and unstructured data (such as text and images) generated by the ETL process (Extract, Transform, Load). Through this process, data is homogenized and structured to be ready for analysis.
Clients can access the database’s data that is relevant to them and use it in their internal systems to develop customized analyses.
Benchmarks, obtained by aggregating data into reports, provide an initial static view of the database. They gather and structure a defined flow of competitor data, allowing the utilization of standardized data and historical data. Benchmarks provide a view of competitors’ product-service offering structures compared to the industry, helping to better understand the market they operate in.
Moreover, the continuous flow of data contributes to building a historical dataset that can be used for further in-depth analysis.
Data Analysis & Reporting/Sharing
To maximize the value of competitor data representing each company’s competitive sector, it is necessary to contextualize and analyze them comprehensively. This means that competitor data can be used to create targeted analyses that aid in making both operational and strategic decisions. By utilizing a combination of technologies and functions like data mining, together with industry-specific domain knowledge, raw data can be transformed into useful business information for our clients.
Competitor data is valuable to different areas of the company, each with its specific interests. Therefore, the CIdeck platform enables direct access to Imetrics databases for data extraction and report generation, providing access to large amounts of constantly updated data. It offers a unified view of the data present in the client’s database, which can be customized to their needs. The continuous flow of competitor data allows for immediate information retrieval and the ability to filter data using defined parameters to create personalized reports and obtain specific and relevant information.
The ETL process feeds competitor data into Imetrics databases with a frequency typically determined by the sector and data source.
The data update frequency allows the platform to serve as a “checkpoint” for the monitored data’s status, enabling clients to detect data variations that are particularly important in rapidly evolving competitive dynamics.
Through analysis activities facilitated by the platform, it is possible to:
- Identify early-stage competitive trends
- Reduce uncertainty in decision-making processes
- Visualize and receive alerts for observed data variations
XAnalytics
Monitoring the collected data is a critical activity for creating data trend histories. These data serve various levels of data analysis, allowing the identification of patterns, trends, relationships, and other insights crucial for making strategic decisions.
To achieve this, functions like AI can be used to extract insights from the collection of competitor data, searching for non-superficial and immediately evident correlations that can be key factors in decision-making.
The use of AI platforms, machine learning (ML), and other advanced systems enables the generation of probabilistic and non-probabilistic projections as well as predictive models. This allows for the processing of essential insights for strategic business decisions, utilizing historical data and correlations to project future and probabilistic scenarios.
Furthermore, conducting second-level analysis and extracting value from the collected data is crucial to gaining a deep understanding of the business context and identifying new growth opportunities. Processing essential insights for strategic business decisions provides a comprehensive and accurate view of the competitive landscape and market dynamics, enabling informed and targeted decision-making.
- Second-level analysis and value extraction from collected data
- Processing essential insights for strategic business decisions
- Utilizing historical data and correlations to project future and probabilistic scenarios
Make Better Strategic Decisions
Enhance the reliability and speed of competitive analysis processes. Contact our experts for more information.