RESEARCH MANAGEMENT + STATISTICAL MODELING

The BPN Platform makes it practical to integrate “research management” and “statistical modeling” by organizing data, research, and qualitative insight on key drivers, connecting that evidence to related issues, and producing probabilistic scenarios based on logic rather than random Monte Carlo simulation. 

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We drive everything to integrated financial statements and produce interactive reporting so you can see clearly the upsides and downsides you are modelling.

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LOGICAL GRAPHS + PROBABILISTIC GRAPHS

Thanks to 10 years of R&D funded by National Science Foundation grants and private investment, BPN produced a patented architecture that integrates two kinds of graphical models from separate fields of artificial intelligence.

Logical Graphical Models infer some qualitative relationships (the dotted line) from others.

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Probabilistic Graphical Models infer odds of scenarios for some Issues (dotted green and orange) from scenarios for other Issues (yellow) and the conditionality between them (blue and red).

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Our patented architecture makes it practical to use a Logical Graphical Model to organize stories about Issues more scalably than with folders and tags and to translate them into a Probabilistic Graphical Model that produces many scenarios without writing stories for all of them. With this insight, leaders can make better decisions.

RUNS IN THE CLOUD

Our innovative integration of both Logical and Probabilistic Graphical Models runs in the cloud on a standards-compliant technology stack, with or without integration with the existing models in your Excel files, and integrates with visualization and reporting software like Tableau.

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PLATFORM IN ACTION

The Bullet Point Network Platform makes it practical to see quantified, probabilistic estimates of the impact of fundamental drivers based on your team’s research into those drivers. 

Leveraging our platform, our team has analyzed many public and private companies across industries. Below is one simplified example in the technology sector, based on five key drivers of expectations for the long-term profitability and value of an internet ad-driven business:

  • Daily active users in 2030

  • Average hours spent per user per day in 2030

  • Average ads per user per hour in 2030

  • Average ad CPM (the cost an advertiser pays for one thousand views or clicks) in 2030

  • Weighted average cost of capital ("WACC")

Using the Logical Graphical Model in the BPN platform, we attach research directly to key drivers of scenarios instead of losing the research in a "roach motel" of folders and tags:

The Probabilistic Graphical Model helps us model the influences one issue can have on another in order to produce realistic combinations for their impact on a strategic decision. Below, you can see us exploring a potential tradeoff between the two key drivers of ads viewed per minute of use vs. minutes of use per day, dragging blue bars to change odds of scenarios for each driver and watching odds for the other change in response:

The BPN Platform also allows network users to drill, in either direction, from scenarios for one Issue to scenarios for other Issues that it influences or is influenced by:

By producing 100 realistic combinations of these key drivers, we produce 100 scenarios for future expectations for financial results and valuation of the stock. Below is 1 scenario for expectations and valuation 2 years from now. Drag the dot at the bottom to explore any of the other 99 scenarios.

The Platform’s multi-dimensional architecture makes it practical to explore multiple horizons without duplicating stories and models.  Below, we summarize percentiles for this stock price not only 2 years from now, but also at the earlier horizons of this year-end and next year-end.

The Bullet Point Network Platform makes it practical for leaders to connect stories to statistics, driving strategic decisions with data science about not only the past, but also the future.