For decades competitive intelligence initiatives at corporations around the world have fallen victim to an all too familiar cycle of surprise, reaction, overload and, ultimately, death. Our research shows that up to 70% of typical competitive intelligence activities add no value to the business it serves.
Corporate competitive intelligence tends to be implemented reactively, rapidly becomes unmanageable, rarely delivers meaningful insights, and dies until that next surprise market event, merger or acquisition has executives calling for more intelligence and better insights.
And then the cycle begins again.
One way to break the "CI cycle of Death" is by leveraging technology, specifically Artificial Intelligence in the form of Natural Language Processing (NLP) and Machine Learning (ML). The correct competitive intelligence software tool allows researchers and analysts to focus on developing insights, the value CI has to offers. The right tool serves as a force-multiplier for the modern competitive and market intelligence team, freeing the analyst from low-value tasks like collecting and organizing information from multiple sources.
For the purpose of this article, I will explore how a fictitious company, BullsEye, is able to deliver valuable insights by investing in a competitive intelligence software tool.
Our story begins with BullsEye, a global retailer that has a wide and diverse set of competitors, only a few of which are global brick-and-mortar companies like itself. BullsEye also competes with specialists in each of its many product categories, including general merchandisers like Walmart, online home furnishing outlets like Wayfair, and online mass marketplaces like Amazon and Alibaba. To further complicate matters, BullsEye faces a different combination of competition in each geography.
Effective competitive monitoring in a complex environment across multiple geographies around the world requires BullsEye to monitor a very large number of companies. Because BullsEye competes with only certain segments of these businesses, especially the larger general merchandisers and online platforms, its important that they filter out any information about these competitors that is not directly relevant to BullsEye.
Further complicating this already highly complex competitive environment is the increasing pace of change and disruption. New, streamlined, tech-enabled business models like online mattress sales and other new direct-to-consumer entrants pose yet another concern for BullsEye. The team must be able to “see around the corner” to identify early signals of disruption, whether from a new entrant, a key innovation, or new technologies.
BullsEye has decided to invest in a competitive intelligence software tool to help its team consolidate all the company’s intelligence information in one system.
They believe the investment in a CI software tool will improve the company’s market position by decreasing their “time to action” while also allowing them to pick up weak signals to provide early warning of disruption.
BullsEye has identified a number of requirements for its CI software solution:
To achieve the efficiencies that BullsEye is seeking, all this content, both internal and external, should then be processed through a set of algorithms that use natural language processing (NLP) technology to identify entities that are mentioned within that content, tagging each entity automatically.
Machine learning models must then process the content and apply a set of algorithms called classifiers, which separate content into different categories and classifications.
This combination of automatic metadata tagging and machine-learning classification permits robust filtering and search capabilities, empowering BullsEye analysts to discover relevant content that they didn’t even know they were looking for.
This model is fundamentally different than a traditional keyword- or “taxonomy”-based tagging system. In these traditional systems, the user must already know what they are looking for because they must tell the system to search for particular words or phrases. If the user does not specify a particular term or phrase, the machine will not know it is relevant.
With an NLP-based system, the technology actually reads the language in each piece of content and identifies entities based on the context in which they are mentioned.
With the power of NLP technology, BullsEye’s users can apply an extremely rich set of filters to identify content that is most relevant to them.
For instance, when searching for news, users can filter by:
In addition, with this robust pre-processing of data each individual BullsEye user can create an unlimited number of searches using an infinite combination of filters to define relevant content.
The company's requirements necessitate that it purchase a solution specifically designed to enable better collaboration around competitive and market intelligence activities. Built-in knowledge management and collaboration capabilities will allow BullsEye users to better interact between regions and functions, providing improved market intelligence, deeper competitive landscape analysis, and increased information sharing capabilities - all of which will serve to maximize the overall value for the business.
BullsEye will see its greatest value when collaboration capabilities are pre-built the system and include functionalities such as liking, favoriting, or commenting on content, as well as when users can create projects within the system to enable collaboration around a particular topic or effort within a smaller group.
Integrations with other key systems such as the company’s CRM or Slack instance will allow employees to share meeting notes, documents or news directly to and from the company’s various software platforms increasing employee engagement and adoption of their intelligence program.
The creation and continual updating of competitor profiles has long been one of the most time-consuming and least valuable activities competitive intelligence practitioners are required to perform. With user-customizable dashboards, the need for a manual process of creating and updating competitor profiles can be completely eliminated.
BullsEye’s chosen competitive intelligence software tool automatically aggregates content from a host of sources and creates dynamically updated dashboards, including recent news, financial data, job postings, press releases, and social media, which allows for efficient and effective competitor monitoring.
Because advanced NLP tagging and machine-learning models are applied to this information, the result is a robust competitor analysis tool. Since these dashboards serve as templates, users can easily switch between competitors’ profiles and make “apples-to-apples” comparisons.
CI software containing dynamic analysis builders allows BullsEye users to conduct data interrogation/analytics to view trends and produce basic reports from several external sources, including customer and competitor financials. The system’s widgets make it easy for users to analyze trending topics and spikes in news coverage of a competitor.
Users can view, edit, create and share dynamic analysis all within the software tool. The chart builder enables users to determine and create the best visualization for data and information by adjusting the chart types in just one click.
Additionally, the built-in advanced report and newsletter builder enables users to automate and simplify more of the formatting, production, and distribution process that goes into sharing CI, getting key insights to their team more effectively and efficiently.
A tool alone is not the answer. Even the most powerful, advanced technologies like NLP are not yet comparable to a well-trained human analyst when it comes to delivering high-level strategic impact. Therefore, BullsEye would be wise to complement its competitive intelligence software tool with a highly-trained outside analyst.
Human intervention is especially helpful during the initial phases of implementing a new CI software tool, while the members of the BullsEye team are still getting up-to-speed with the platform.
The analyst should work as an integrated part of the BullsEye team, attend regular team meetings, and be considered a key member of the team. This allows the analyst to become fully enmeshed in the rhythms of the business and enables them to quickly understand key concerns and topics of interest. They can then immediately use this knowledge to better and more rapidly tune the platform to ensure maximum value.
As markets become more competitive they will also become more complex and the volume of data will also increase. Under these conditions, CI practitioners will continue to experience an increase in research and analysis requests and these requests will come from a greater number of departments than in the past.
Each request received will carry with it the expectation of insights curated specifically for the intended reader. It is the combination of all these forces that require CI professionals break from traditional CI practices. Purchasing a tool built to support competitive intelligence activities is not the only way of ending the too common cycle of corporate CI boom and bust, but it is an investment that will aid CI practitioners to meet the changing needs of how they gather, analyze and share insights.
As CEO, Peter Grimm brings a rich blend of national security and commercial strategy consulting experience to Cipher. Following service in the US Navy and as a government counterterrorism analyst, he joined the strategy practice of Deloitte Consulting where he led teams delivering strategic planning and business transformation services for clients in both the Federal and commercial spaces. Peter is passionate about helping clients make smarter, faster decisions.