In an insightful interview with Yoni Rubin on
Website Planet, we delve into the revolutionary features of MRGN’s AI-powered business intelligence platform tailored for small businesses.
Yoni explains how their platform simplifies the creation of a comprehensive operating plan, which usually demands considerable financial expertise and time, by enabling one-click onboarding through cloud-based systems or alternative methods. He also highlights the platform’s capacity to manage budgets and simulate multiple financial scenarios, providing small businesses with a powerful tool for strategic decision-making.
With continuous enhancements, including AI-driven real-world data integration and industry benchmarking, MRGN aims to transform into an all-encompassing solution that not only interprets data but significantly augments it for insightful business planning.
Can you describe how MRGN’s AI-powered business intelligence works and its main benefits for small businesses?
Absolutely. Small businesses start by entering our platform and creating their operating plan, which is foundational to their personalized business intelligence dashboard. If they use a cloud-based accounting system, they can onboard with just one click. If not, we provide other quick onboarding methods. Within minutes, they have a comprehensive operating plan ready—something that typically requires extensive finance expertise and many hours of work.
Once the operating plan is set up, businesses can manage according to their new budget and simulate various scenarios with ease. For example, they can explore the financial impacts of taking a loan, expanding physical locations, or changing team size. Our platform currently supports about 15 different scenarios, with more being added continually.
Regarding our use of AI, about a year ago, we filed a provisional patent, now under review by the USPTO, that describes how our platform can enhance your operating plan with real-world data. Our machine learning engine integrates information from various news sources to predict upcoming events and their potential impacts on your business, helping you prepare effectively.
Additionally, we’re working on KPI and salary benchmarking to eliminate the need for tedious internet searches. Our platform will soon provide comparative insights into standard costs and salaries in your industry, directly alongside your data.
Ultimately, our goal is to evolve into an all-in-one business intelligence platform that not only interprets your data but also enhances it with real-world information. We aim to include marketing ROI analysis, sales funnel optimization, and more, making it the comprehensive solution for small businesses looking to leverage their data for strategic decision-making.
How does MRGN assist businesses in lowering their expenses and increasing their revenue?
MRGN is currently a budgeting platform, as of April 2024, that enables users to simulate various scenarios. In about a month, we’re launching our unit economics simulation which will offer a comprehensive dashboard to help users achieve their sales targets. This feature, prior to the integration of AI, will guide businesses in areas like average revenue per user (ARPU), average revenue per account (ARPA), and necessary sales personnel to meet financial goals.
Once we incorporate machine learning, the platform will become even more powerful. It will analyze data from internal systems, such as CRM, to provide specific recommendations. For example, if your revenue target is $4 million but your current projections show only $3 million, the system might suggest hiring additional sales personnel to bridge the gap.
On the competitive intelligence front, MRGN will automatically provide notifications on how competitors are performing and how you compare, offering insights for revenue optimization.
Regarding expense reduction, our platform will compare your spending with similar companies to highlight areas of potential savings. For instance, if you’re spending more on IT services or raw materials than peer companies, MRGN will suggest strategies to reduce costs. This approach ensures that businesses can optimize both their revenue growth and expense management effectively.
What makes MRGN’s approach to financial simulations and operating plans unique?
MRGN distinctly sets itself apart in the realm of financial simulations and operating plans, particularly for traditional small businesses. Unlike the seventeen other competitors in our space, such as Fluxo, Giraffe, Dryrun, and Clockwork, most target only early-stage tech companies. This focus leaves a significant gap in services tailored for traditional small businesses, which is where MRGN excels.
Our competitors, if they offer scenario planning at all, tend to provide what we call ‘vanilla scenarios’—basic models like waterfalls, and low-mid-high scenarios, commonly used by CFOs. These are often not understandable to businesses without a CFO. In contrast, MRGN offers robust, real-world scenarios that are intuitive and applicable to everyday business decisions.
For example, we enable small business owners to simulate the impact of various significant changes, such as taking a bank loan, selling a portion of equity, or expanding from one to multiple locations. We also provide simulations for HR-related changes like adjusting headcount during peak seasons, which directly shows the financial impact of hiring or letting go of staff.
This approach not only makes our platform unique but also highly relevant and accessible to small business owners who might not be familiar with more complex financial terminologies and models. By focusing on practical, real-world scenarios, MRGN demystifies financial planning and helps small businesses make informed, strategic decisions without needing a CFO-level understanding of finance.
Could you elaborate on the machine learning insights provided by mrgn and how they are generated?
Absolutely. Our approach involves two primary streams of development. The first involves using off-the-shelf large language models (LLMs) for three distinct purposes:
- Users can upload an existing spreadsheet-based budget into our system, which the LLM will parse and integrate into our database, facilitating a one-click onboarding process.
- Users can interact with a chatbot to ask questions about their business data in relation to real-world standards—such as comparing customer acquisition costs to industry averages—and receive instant responses.
- The platform can proactively provide insights without the need for user queries, leveraging the predictive capabilities of our LLMs.
The second stream is focused on developing our proprietary machine learning technology, currently under patent review, which allows us to discuss it openly due to provisional protection. This system develops APIs to integrate with external big data sources. For example, our first API was for salary benchmarking, comparing our users’ salary structures against market standards, helping them identify if they are overpaying or underpaying their staff.
Following this, the machine learning engine synthesizes these insights to suggest actionable strategies. For instance, it might analyze your hiring plan and payroll data to make recommendations such as adjusting salaries or modifying staffing levels—like suggesting a reduction to 75% of salary for certain employees or identifying personnel changes.
We plan to extend these APIs to sources like Forrester and Gartner for accessing comprehensive KPI data. Moreover, we aim to integrate news sources and stock market data, enhancing our predictive capabilities. Our machine learning engine parses and correlates this data, taking into account variables such as geographical location, industry, revenue, and headcount. This enables us to provide tailored advice on potential impacts—like economic downturns or market shifts—specific to the user’s business context.
We are building these APIs one at a time to ensure each one delivers value independently while contributing to the broader capabilities of our machine-learning engine. This iterative development strategy enhances the robustness and efficacy of our platform.
How does MRGN compare a business’s metrics with industry benchmarks to guide critical decisions?
MRGN leverages an API with Gartner to access comprehensive KPI data, essential for benchmarking. Gartner, having expanded into small business services through its acquisition of Kaptara, provides a rich database of KPIs across various segments of the small business sector, including retail, professional services, and SaaS, among others.
We run periodic API calls—weekly or monthly—to perform a full data dump of relevant small business data from Gartner. This data is then segmented by our machine learning engine based on specific business sub-segments such as PropTech, FinTech, and InsurTech, as well as by company stages like pre-seed or Series A.
The machine learning engine categorizes these data points and aligns them with the corresponding businesses on our platform. For example, it might identify and notify a client if their customer acquisition cost is 20% higher than the industry norm. This notification includes detailed insights on how we arrived at this conclusion using Gartner’s data, ensuring transparency.
Further, we provide actionable suggestions to improve key performance indicators. For example, if a business needs to reduce its customer acquisition cost, we offer guidance on adjusting marketing expenses or enhancing revenue strategies.
In essence, MRGN simplifies complex data comparisons to provide straightforward, actionable insights, making critical business decision processes more accessible and informed.
Can you share a success story or a significant impact MRGN has had on a small business owner?
Certainly. I’d like to share the experience of a Consumer Packaged Goods (CPG) company called Singular Sound, which produces a unique hardware device for guitar players. This device allows guitarists to practice as if they have a partner playing with them, without actually needing one.
Initially, Singular Sound was uncertain about its financing options. They were considering a bank loan but were unsure about how the loan terms, particularly the separation of principal and interest payments, would affect their cash flow. Lacking a clear operating budget, they turned to MRGN.
Using our platform, they quickly onboarded and created an operating plan. They then ran a simulation to understand the financial impact of accepting the bank loan. Just a couple of clicks later, they discovered that the loan terms were unfavorable and would not benefit their business financially. Instead of taking on expensive bank debt, they decided to launch a Kickstarter campaign. This alternative funding route was a success—they raised $2 million, which adequately supported their business needs.
This example highlights how MRGN helps small businesses make informed, strategic decisions by simulating various financial scenarios and their outcomes.
Find out more at: mrgn.ai
