The heart of decision making

Oct 28, 2024
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0 MIN READ
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Scott Woody
Co-Founder and CEO
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tl;dr

  • Customer spend data is the heart of every critical decision
  • Software businesses make these decisions every day, therefore they need realtime revenue views to move at max possible pace
  • In usage-based pricing (UBP) every team can affect revenue, so every team needs this data

Revenue rules everything around me (RREAM)

When it comes to your business, the most important data point is how much your customers are spending on your product. The catch? In most companies accessing that data is very difficult. Customer spend is typically computed once a month, or worse, once a quarter, which means your company is often flying blind between those reporting periods.

Worse yet, that data is siloed away. In a typical SaaS business, revenue gets calculated and verified monthly, but by the time you’ve got it locked down, you're already a quarter behind. And even when you finally have it, the data is tucked away in spreadsheets, closely guarded by the FP&A team. This setup makes it incredibly hard to react to changes in real-time—and in today’s fast-paced business environment, that delay can be costly.

When I worked at Dropbox, I could tell you exactly how many customers moved through our checkout flows, every single day. BUT, because of the siloed nature of our billing process, it would sometimes take months to understand the financial impact of our work.

Software needs realtime data

The monthly or quarterly cadence of revenue reporting makes sense for accounting - where you prioritize exact accuracy and auditability. This cadence also made sense in the industrial age, where businesses sold products like cars—things that take time to manufacture, ship, and sell. But in the modern, continuous, PLG-driven software world, the game is different.

You can make decisions that impact revenue today, not weeks or months from now. Especially when you have usage-based pricing (UBP). So why are we setting our business’s decision-making clock speed to our revenue accounting systems? It's like using a sundial when everyone else is running on atomic clocks.

In growth at Dropbox, we could move the needle by millions of dollars with just a week’s worth of focused effort. We could spin up another ad campaign or streamline our payment funnel. But if we didn’t know in real time that we needed to do that, we’d miss our window—and The Street doesn’t take kindly to missed opportunities. Without a continuous view into spend performance you end up trying to read tea leaves on purchase page click-through rates to infer revenue trends. Trust me, it’s a nightmare.

We all have quotas now

In a world driven by UBP, almost every team in your company can affect revenue in some way. Finance can adjust pricing and packaging, engineering can improve margin structure by optimizing queries, product can drive incremental adoption, and sales can push revenue higher through end-of-quarter deals and time-based incentives.

If all these teams have the power to influence revenue, and if revenue data is essential for making informed decisions, then waiting for lagging, backward-looking reports from finance is no longer an option. You need that data in real time. And now it’s not just finance, but everyone across the organization who needs access to it.

Real-time spend visibility allows teams to act fast, make decisions that drive immediate impact, and stay ahead of market expectations. The faster you can access and act on this data, the more agile and competitive your business becomes.

Company Industry Outcome-Based Pricing Model Key Metrics for Pricing Notable Features
Salesforce (Agentforce) CRM / AI Customer Service

$2 per conversation handled by Agentforce (AI agent)

A conversation is defined as when a customer sends at least one message or selects at least one menu option or choice other than the End Chat button within a 24-hour period.

Number of support conversations handled by the AI agent

First major CRM to adopt a "semi"outcome-based pricing for AI; aligns cost with actual support volumes (clear ROI)

Addresses inefficiencies of idle licenses by charging only when value (a handled conversation) is delivered

Intercom (Fin AI) Customer Support Software

$0.99 per successful resolution by "Fin" AI chatbot - clients pay only when the bot successfully resolves a customer query

Fees accrue based on AI-solved issues

Count of support conversations resolved by the AI agent

Early adopter of AI outcome-based pricing in 2023

Lowers adoption risk by charging for resolved queries instead of a flat rate; combines usage- and value-based pricing to tie cost directly to support effectiveness.

Zendesk (AI Answer Bot) Customer Support

Per successful AI chatbot-handled resolution

No charge if the bot fails and a human must step in

Number of customer issues or tickets auto-resolved by the bot

Aimed at cost-conscious customers wary of paying for unproven AI

Aligns price with realized automation benefit; part of a broader industry shift from per-agent pricing to value-delivered pricing in support

Chargeflow Fintech (Chargeback Management)

Charges a fraction of recovered funds on chargebacks

Example: ~25% fee per successful chargeback recovery

No fees for chargebacks lost

Alert service charges $39 per prevented chargeback

Value/count of chargebacks recovered (disputes won) and chargebacks prevented (for prevention alerts)

4Ă— ROI guarantee on recoveries

No contracts or monthly fees

Revenue comes only from successful outcomes; pricing directly aligns with merchant's regained revenue, meaning Chargeflow only profits when the client does (win-win model)

Riskified*

(source: https://www.chargeflow.io/blog/riskified-vs-forter)

E-commerce Fraud Prevention

remain fraud-free

PAYGO, 0.4% per transaction

Only charges for transactions it approves that

Number or value of approved transactions without fraud (i.e. successfully processed legitimate sales).

Provider shares financial risk of fraud with clients; pricing tied to outcome of increased safe sales

Incentivizes vendor to maintain high accuracy (they only profit when fraud is stopped)

Foster continuous improvement in their fraud-detection algorithms

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