An important element to understand is how trend posts are distributed/plotted on a Project visualization graph.
Very simply, every human-written Insight report and human-curated Signal link published on the Quantumrun Foresight Platform is scored by Quantumrun foresight professionals using three scoring variables:
- Year/Time: What year the forecast or trend will begin affecting industry, or the world.
- Likelihood: The likelihood that the forecast or trend will become a reality.
- Impact: The impact this trend/innovation might have on industry or the world.
The average score from Quantumrun professionals determines the position of each given trend post on the project page graph.
Important: Quantumrun professionals score all content from the perspective of the general market or the general industry. (Optionally: Quantumrun also works with market research companies so that we can integrate external scoring data as well.)
However, the average scores applied by Quantumrun professionals may not align with the perspectives or realities of professionals from your internal team or your specific organization. In this scenario, Quantumrun recommends deactivating the toggle found at the top of your project’s left sidebar.
By deactivating this toggle, the project page will switch from presenting the posts using the Quantumrun team’s average score to only presenting the posts using your team’s averaged scores.
Initially, this will mean that your team’s project page will appear empty. To fill it up again, your team needs to start opening each of the posts listed in the left sidebar.
Rating left sidebar content
Once you open an Insight or Signal post from the left sidebar, it will appear as an overlay overtop the graph.
As you scroll down, you will find a ranking box where members of your team (who you invited into this project) can take turns scoring trend content according to the following categories:
- What year they think the forecast or trend will begin affecting industry, or the world, or your specific organization.
- The likelihood that the forecast or trend will become a reality, or will affect your specific organization.
- And its impact on industry or its relevance to your organization.
Each team member will be able to view the team’s averaged score after they press the ‘apply ratings’ button.
The second column will display the averaged score collected from members of the Quantumrun Foresight team, but only on those posts created by Quantumrun.
Many organizations find this scoring interface useful because it allows them to conduct anonymous team scoring on various topics.
It also allows individual organizations to see the difference in perspective in how a trend is scored by foresight professionals, for the general market, compared to how an organization’s team scored that same research in a way that is specific to the organization.
Finally, below the ‘apply ratings’ button, will be a list of team members whose scoring are still pending.
Important note: After each team member applies their scoring, the average score will change, meaning that the trend post being scored on will appear to change position on the graph after each person adds their scoring. However, each post will settle on a final position after all team members contribute their scoring.
SWOT and VUCA graphs
For project types that feature graph types that are overly subjective, the graphs will always appear empty when you first load them. The reason being—using SWOT as an example—what might be a Strength for one company, might be a Weakness for another.
In these types of project types, the scoring block detailed above may have one or more extra dropdowns that are not scored by Quantumrun professionals.
As a result, the scoring from Quantumrun professionals will only become visible AFTER your team enters your own scoring FIRST to one or more of the posts listed in the project’s left sidebar.
Assessing the results
Once your team rates most-to-all of the content on the left sidebar, you will find the graph visualization filled with trend insights that are positioned on the graph in a manner that reflects your team’s averaged perspective (score) of each trend insight.
At this point, depending on the Project type you selected, you can begin the exercise of interpreting the graphing results in a manner that generates unique business insights specific to your organization.
How your team interprets the results of this visualization will be unique to how your organization interprets the project’s insights and data. It will also be influenced by who leads/facilitates the meetings or workshops around this platform.
Scoring refresh
Every six months, the Quantumrun Foresight team applies fresh scoring to our library of Insight reports. The goal of this activity is to update the scoring applied to our internally written reports to better align with present-day market realities.
Our goal over the years ahead is to eventually refresh our scoring data quarterly as the size of our team grows.
Scoring AI-curated signals
The Quantumrun Foresight team is committed to scoring insights and signals written or curated by Quantumrun team members.
However, Quantumrun does not allow scoring on client-uploaded content without a client’s permission.
Quantumrun also does not allow human scoring on signals set to private.
Algorithmic scoring
As of July 3, 2023, Quantumrun Foresight introduced algorithmic scoring to AI-curated Signal posts.
The reasoning behind this decision was to apply a base level of scoring to the thousands of Signals the platform collects each weekday that is beyond the (current) ability of Quantumrun’s human research team to score manually.
The algorithmic scoring was engineered around a proprietary scoring formula. While this algorithm is not a perfect replacement to human Signal scoring, it will provide a foundation of scoring data to support easy graph visualizations. Additionally, Quantumrun plans to refine the algorithm quarterly to continuously improve its scoring output.
Market research collective scoring
As of July 3, 2023, Quantumrun Foresight has entered into an agreement with one of its market research agency partners to introduce human Signal scoring to a percentage of the platform’s AI-curated Signal posts.
The human participants stem from a variety of professional backgrounds; they are cycled monthly; and they contribute scoring data to Signals on a part-time basis.
The goal of this partnership is to introduce human scoring to a percentage of AI-curated Signals with the objective of expanding this program to the majority of AI-curated Signals at a future date.
Legal considerations
All scoring applied to internal and external content found on the Quantumrun Foresight Platform strictly represent the beliefs and points of view held by Quantumrun Foresight and are not endorsements of any company or technology or political or cultural movement. The scoring represents the collective points of view of Quantumrun Foresight staff and the machine learning systems the team has engineered. The scoring does not represent a definite prediction about the future or future events. By visiting and/or otherwise using the Quantumrun Foresight Platform in any way, you indicate that you understand and accept these statements and the terms of use and content policy as set forth on the website and agree to be bound by them. If you do not agree to the terms of use of the website, please do not access the website or any pages thereof. Any descriptions of, references to, or links to other products, publications, or services does not constitute an endorsement, authorization, sponsorship by or affiliation with Quantumrun Foresight with respect to any linked site or its sponsor, unless expressly stated by Quantumrun Foresight. Any such information, products, or sites have not necessarily been reviewed by Quantumrun Foresight and are provided or maintained by third parties over whom ARK exercises no control. Quantumrun Foresight expressly disclaims any responsibility for the content, the accuracy of the information on the Quantumrun Foresight Platform, and/or quality of products or services provided by or advertised on these third-party sites.
