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Fast Company
3 days ago
- Business
- Fast Company
Stop flying blind: Build a data-driven revenue model with bottom-up forecasting
One of the biggest challenges early-stage startup founders face is predicting and managing revenue growth. In most organizations, this looks like top-down forecasting and starts with determining the target revenue goal for that period. This goal is often based on ambition, investor expectations, or competitive pressure. But this method is flawed; you can't set revenue goals based on a finger held to the wind. What should be an exercise rooted in science lacks real-world input, and thus prevents leaders from obtaining the performance-based insights necessary to make data-driven decisions. The solution? A reality-based assumption model, known as a 'bottom-up' go-to-market (GTM) forecast. Here's what to know: WHAT IS A BOTTOM-UP FORECAST? In short, it's a more reliable way to determine how various functions and tactics are expected to contribute to revenue. This methodology allows businesses to draw a straight line to their revenue goals based on assumptions or known variables. This is very similar to a 'revenue bridge,' a tool designed to outline how portfolio companies intend to achieve their ultimate revenue goals. HOW A BOTTOM-UP REVENUE MODEL WORKS This approach to forecasting first involves segmenting the business by its core functions, which include sales, marketing, and customer success. Then, each business unit should outline the activities it performs that contribute to revenue acquisition or expansion. This data can be pulled from historical campaigns and updated for current market conditions. However, if this historical data isn't available, a little creativity is necessary. There are a plethora of benchmark data sources available (full disclosure: We provide custom benchmarks based on industry verticals and business models to our portfolio companies). Ultimately, with a bit of legwork, organizations can develop a set of assumptions to manage, with the goal of getting more and more confident over time. Of course, budget is a major factor in this exercise, which is a common constraint. However, there is no more effective way to justify budgets than with supporting bottom-up assumptions. This differs greatly from the top-down approach, which works backward from a target and effectively tells everyone to 'run' at it. That's flying blind. WHY IS BOTTOM-UP FORECASTING IMPORTANT? Top-down revenue models tend to be more pie-in-the-sky than rooted in reality. When a company forecasts this way, it's more likely to overestimate its growth and underestimate the costs and challenges of scaling. It can also miss critical allowances for changes in the process. Markets and audiences don't remain static, organizations need to account for change that comes with penetration, competition, and market headwinds or tailwinds. On the flip side, bottom-up forces business leaders to take a good, hard look at their teams' actual performance or assumptions of performance. Even if the topline isn't sexy initially, leaders will know where they're heading if their employees continue to perform at past levels. This can potentially define what the chasm of 'go-get' revenue looks like versus the expectation. Optimistic but grounded in data is far better than optimistic and hopeful. Additionally, a bottom-up approach is not only a financial plan—it's also an operational plan. This method covers the goals, costs, and resources needed to achieve the company plan. As a result, this framework becomes a living, breathing tool to be updated and discussed on a regular basis. THE MAP TO PERFORMANCE-BASED BUDGETING A bottom-up pro forma isn't just essential for forecasting; it's also the foundation for budgeting decisions. Before you know what funds you have to allocate, you need to know where growth is expected and how each department and resource contributes to it. For example, how many sales reps do you need to hire to meet your revenue targets? If you don't have an understanding of what those resources should yield in terms of performance, your budget—and their compensation—will be a total shot in the dark. But once you have a pro forma, you can use it to inform budgets and have confidence in your decision-making as results begin to filter in. Based on the early-stage companies we evaluate and invest in, underperforming sales reps stay in a role three to six months longer on average than they should. Human nature wants to see our investments produce, but consider the opportunity cost of making key decisions too late. Time is the one variable that remains constant. ADJUSTING YOUR MODEL AFTER A FUNDING ROUND After a funding round, the pressure to scale quickly is intense. Without a reliable GTM model, businesses risk over-promising, burning out, and missing key opportunities. That's why it's critical to revisit and update your bottom-up pro forma based on current realities. Ask yourself: How many hires do we need, and how fast? Does our 'quota-in-field' align with our revenue goals? Which marketing channels are most effective, and how will increased spend impact leads? What revenue can we expect from existing customers versus new sales? This discipline helps to build internal clarity and inspire confidence with investors. KPIS AND BENCHMARKS KPIs and benchmarks are foundational to a bottom-up pro forma. While they vary by company, industry, and goals, key benchmarks include sales rep efficiency, customer acquisition cost (CAC), and revenue per employee. By consistently tracking these metrics, you can fine-tune your go-to-market model and drive better performance. Just as important are cultural KPIs. These metrics define how your team thinks and operates. The best CEOs use a select few KPIs as both their north star and operational compass, aligning strategy and execution across the business. What numbers should become the shared language of your company? THE BOTTOM LINE IN BOTTOM-UP Early-stage startups looking to scale should build reliable revenue acquisition through both top-down and bottom-up forecasting. This will help create a science of growth for your organization. By understanding your growth levers and required resources, you can avoid the trap of overpromising and underdelivering. The goal of a well-constructed forecast model isn't perfect accuracy, but to build confidence in a framework that evolves with real results. That framework becomes more accurate and predictive over time. Before you know it, you have that coveted map, and while others are flying into mountains, you're cruising safely toward performance.
Fast Company
14-05-2025
- Business
- Fast Company
How tech companies can use AI to strengthen customer experience
It's no secret that many technology companies have changed the way they approach customer experience and success. With the end of ZIRP (zero interest rate policy) in 2022 and 2023, budgets are much tighter today than they were in the recent past. This has led some organizations to cut corners, often at the expense of customer experience and success. In fact, Forrester's Customer Experience (CX) Index reached an all-time low in 2024—a sign of how serious the situation has become. Even as leaders grapple with these pressures, the vast majority of tech companies continue to see the value in customer experience. ZenDesk, for example, has found that 90% of companies view customer experience as their company's main priority. But, as leaders scramble to re-optimize customer success, one tool is frequently overlooked: AI. In this article, I'll share a few practical ways that technology companies should (and shouldn't) use AI tools to build a better customer experience that can help drive revenue and build deeper, more sustainable relationships with customers. The end of ZIRP led some to claim that customer success in technology, especially SaaS, would drastically change, but these predictions have turned out to be seriously exaggerated. While some budgets have been cut, customer success remains vital for all technology companies, and many are now looking for cost-efficient ways to beef up customer success. In some cases, this can mean transferring low-risk, redundant work to digital tools like AI, which allows staff to focus on higher-impact tasks. At SaaS companies, for example, customer success managers and specialists can sometimes spend hours a week combing through spreadsheets, querying technical or subject matter experts, or reading or summarizing customer communications. These are all areas where AI can have a clear and immediate impact with minimal risk. Instead of spending substantial time perfecting customer presentations, customer success professionals can now use AI tools like Microsoft 365 Copilot or Google Gemini to quickly and easily elevate the look and content of their slide decks, which frees up time for higher-level tasks. Similarly, team members can use AI to summarize email threads and meetings, execute sentiment analysis on customer interactions, and use specialized AI agents to answer SME-level questions that might otherwise be difficult to answer. These are all low-risk, low-effort, high-impact applications for AI in customer success. They're also cost-efficient, and allow people to focus on support tasks that are more likely to have a tangible impact on customer satisfaction and retention. Instead of spending time staring at a spreadsheet or sifting through a deck, your people can spend their time providing direct support to customers. This is something that we've had success with at AvePoint, as recently highlighted by Microsoft. MANAGE RISKS TO MAXIMIZE BENEFITS While AI can be transformative for customer success, it can also create new vulnerabilities and trigger unintended consequences when deployed without the proper guardrails or in the wrong scenarios. Remember the chatbot that tried to sell a Chevy Tahoe for $1 (a transaction that the chatbot referred to as 'legally-binding')? While AI tools have applications in many different customer experience scenarios, this gaffe shows what can happen when AI is hastily deployed without proper controls. This technology has the potential to completely change and improve the customer experience, but it also has the potential to cause harm if implemented in the wrong scenarios. Beyond that, AI also presents new data security issues that are specific to customer success. Consider, for example, what might happen if confidential communication with a customer is ingested by an LLM. This could lead to confidential information being repeated by the LLM, opening up your organization to significant legal and financial risks. That's why tech companies should use software that can identify sensitive information and categorize it based on its sensitivity, keeping it out of circulation while also ensuring that AI tools like chatbots provide more personalized, relevant recommendations for users. It's also important to understand that customer-facing employees need guidance and training to help them use AI securely and efficiently, something I've written about in the past. This means that leaders need to set up robust training programs and implement software to secure and optimize AI data. Once they've done that, they can expect productivity boosts to follow. THE CUSTOMER COMES FIRST—NO MATTER WHAT Today, a world-class customer experience demands careful strategic investments. By deploying AI in the right workflows, technology companies can maintain strong customer relationships, ensuring both satisfaction and success in a post-ZIRP environment. At the end of the day, your customers are the lifeblood of your organization, so it's critical to get it right.
Forbes
12-05-2025
- Business
- Forbes
Boosting ARR In B2B SaaS: A Founder's Journey To Sustainable Growth
Terry Chen, CIO / COO / VP, Global Relations at Modulate. getty I still remember the evening I stared at our startup's dashboard, heart sinking as our growth plateaued. As a software as a service (SaaS) founder, I had poured every ounce of energy into acquiring new customers, yet our annual recurring revenue (ARR) was stubbornly flat. It felt like trying to fill a leaky bucket—new deals came in, but revenue leaked out through downgrades and churn. One night, after losing a hard-won client due to avoidable issues, I realized growth isn't just about getting more customers; it is about delivering more value to the ones you already have. Thus began my journey to rethink everything: pricing, customer success, upsells, retention and how we acquired users. What follows are the hard-won strategies that transformed our ARR trajectory. These are not silver bullets or flashy hacks but proven tactics any SaaS founder or revenue leader can execute to steadily and sustainably boost ARR. Pricing is one of the most powerful—and underutilized—levers in SaaS. A 1% improvement in price can improve operating profit by up to 11%. And yet, many startups set their pricing by gut instinct instead of strategy. Adopt value-based pricing: We moved from cost-plus to value-based pricing, aligning prices with customer outcomes, not just features. This helped us avoid underpricing while better reflecting the return on investment (ROI) we delivered. Optimize packaging and tiers: We restructured our plans to match customer segments. Tiered pricing created natural upgrade paths and allowed us to better serve both startups and enterprises. Experiment and iterate: We ran controlled A/B tests and periodic price reviews. Even minor tweaks—bundling, discount ladders, trial durations—had a significant impact on conversion and average contract value. Takeaway: Treat pricing like a product: Test, evolve and ensure it reflects the value you're delivering. Retention is the bedrock of ARR growth. A 5% increase in retention can boost profits by 25% to 95%. For us, the shift came when we built out customer success (CS) not as a support function but as a strategic pillar. Build a proactive CS team: Instead of waiting for issues, our CS managers anticipated needs, trained customers and aligned on their success metrics. This shifted the conversation from troubleshooting to value delivery. Strengthen onboarding: We invested in user onboarding and guided setup, one of the biggest indicators of long-term retention. Users who saw success early rarely churned. Drive continuous value: With usage-based health scores and feature engagement tracking, we could detect when accounts were slipping and intervene. This not only reduced churn but surfaced upsell opportunities. Takeaway: Don't just solve problems. Enable success. Happy customers don't just stay—they grow. Once retention stabilized, expansion revenue became the next growth frontier. In my experience in tech and corporate strategy, I've noted that many top SaaS firms earn the majority of new ARR from existing customers. We aimed to follow that path. Identify upgrade candidates: By analyzing product usage, we found customers approaching tier limits and proactively proposed higher plans, often with tailored offers. Introduce add-ons: We built modular features (analytics, integrations, compliance tools) that could be added à la carte. This provided upsell paths without forcing users to jump tiers. Make it easy to upgrade: In-app upgrade flows, trials of premium features and CS-led quarterly business reviews (QBRs) created a frictionless path to increased spend. Takeaway: Upsells work best when tied to real value. If your customer is growing, your relationship should too. No SaaS grows on retention alone. We needed to feed the top of the funnel—but with limited budget, we focused on scalable, repeatable acquisition tactics. Product-Led Growth: We leaned into a freemium model that let users self-educate. Our best leads often came from free users who converted after real engagement. Referral Programs: Inspired by viral loops like Dropbox's, we rewarded customers for inviting others. Referral-origin customers were often the stickiest. Content-Driven Inbound: We built a content engine—guides, benchmarks, case studies—designed to rank and attract our ideal customer profile (ICP). Over time, it became our top source of qualified leads. Takeaway: Acquisition doesn't have to be expensive—if your product, content and customers do the talking for you. ARR doesn't grow linearly—it accelerates when the right systems reinforce each other. • Strategic pricing makes every deal worth more. • Customer success protects that revenue and deepens relationships. • Expansion strategies grow the lifetime value (LTV) of your existing base. • Scalable acquisition fills the funnel with high-quality leads. The turning point for us wasn't any single tactic. It was realizing that ARR is the output of an engine, and that engine needs to be optimized at every stage. Once we did, revenue climbed faster, churn fell, and the business felt infinitely more resilient. If you're a B2B SaaS founder wondering where to focus next, start with value—price for it, deliver it, protect it, and let your best customers carry the story forward. Here's to building something worth staying for—and paying for. The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation. Forbes Finance Council is an invitation-only organization for executives in successful accounting, financial planning and wealth management firms. Do I qualify?
