Private equity firms don’t get second chances at a first look. That’s why our private equity analytics tools matter. Whether evaluating a financial services platform, a call center-based business, or a SaaS company, the window to uncover meaningful patterns is short—and the data rarely arrives clean. Oh, and you have five other deals on your plate.

Dark Sky Data helps deal teams move faster and dig deeper. Upload raw files, pick the columns in Excel that you want to filter on, and instantly generate the insights that usually take 20+ hours to build. You do not need to build a model from scratch.

• Cancellation Curves show when events happen—like cancellations, churn, or claims
• Balance Curves track how loans paydown or receivables increase —by cohort, by product, over time
• Response Curves reveal how fast leads respond to outreach or buy, if not a first-call close
• Claim Frequency Curves show when claims occur—by month-in-force, by product, or by customer segment.

These tools were built by an investment banker, designed specifically to give deal teams clarity under pressure—no code, no complexity, no delay.

And your data stays secure—we never store, retain, or reuse it.

Balance Curves are part of our private equity analytics tools, helping deal teams transform raw financial data into intuitive, time-aligned visuals that reveal how money moves across a cohort. Whether you’re analyzing loan paydowns, deposit growth, or client AUM accumulation, this tool aligns every record to a shared starting point—making patterns easy to see and compare.

For private equity firms, Balance Curves offer a fast, flexible way to validate performance claims during diligence or monitor cash flow dynamics post-close. From equipment leases to installment contracts, wealth accounts to payment plans, the curve shows how value is created—or depleted—month by month.

Use it to spot inconsistent trajectories, uncover revenue momentum, or compare cohort behavior across time—without building a model from scratch.

No code. No rework. Just speed and clarity.

Minimum required columns:

• Fund Date
• Original Balance ($)
• Periodic Balances
• Term (in months)
• Date of Data Download

Have more fields? Great. Filter by anything: Vintage, Agent, Industry, Channel, State—and more.

From static spreadsheets to side-by-side insight—in seconds.

• Are balances actually building the way the target claims?
• How quickly does cash come in—and when does it slow down?
• Which cohorts are paying down faster—or lagging behind?
• Is AUM growing consistently across client vintages?
• Do some geographies, products, or agents show stronger momentum?
• Are there early warning signs in revenue or repayment trends?

If it’s in the data room, it’s on your screen—instantly.

Validate cancellations. Evaluate the frequency of claims. Uncover timing risks. In seconds. Cancellation Curves are part of our private equity analytics tools, letting deal teams create Cancellation Curves and other event timelines directly from raw contract or customer data—aligned by start date and normalized across cohorts. Instead of relying on anecdotes or static snapshots, you can measure exactly when cancellations, defaults, or prepayments occur—and filter by agent, geography, channel, or product.
In diligence, this means fast answers to critical questions: Are Month 6 cancellations spiking? Do certain vendors drive higher early churn? Is behavior shifting over time? Cancellation Curves also work for churn, prepayments, delinquencies, and usage trends—revealing hidden patterns that influence risk, retention, and performance across your target.

Built for acquisition diligence. Designed by an investment banker.

Minimum required columns:

Have more fields? Perfect. Filter by anything: Vendor, Product Type, Channel, Geography, Agent—and more.

From timelines to truths—in seconds.

• What is the cancellation rate?
• How have recent vintages performed?
• Are cancellations actually improving—or just offset by recent growth?
• Do certain vendors, agents, or terms show early signs of churn?
• When do key events—defaults, prepayments, cancellations—typically occur?
• Are longer-term contracts truly stable—or just delaying risk?
• Which cohorts are underperforming—and how early can we tell?
• Are event timelines consistent across product lines and geographies?

If it happened in the data, we’ll help you see when—and why—it matters.

USE CANCELLATION CURVES FOR

• Are response patterns consistent with what the Target claims?
• How quickly do prospects respond after a campaign launches?
• Do different mailers or creatives generate faster inbound volume?
• Which list sources, regions, or channels produce higher engagement?
• Are A/B test results statistically meaningful—or noise?
• Can the business reliably staff reps based on predicted response curves?

If it’s in the file, we’ll help you see it—clearly and instantly.

USE MARKETING RESPONSE CURVES FOR

• How can I prove that one of my dealerships is putting through a high volume of early claims?
• Should the earnings curve for loss ratios change based on claim frequency or timing?
• When do most claims occur—early in the contract, or later?
• Which products or plans are generating the most frequent claims?
• How do claims frequency patterns differ across states or vehicle types?

If the data’s in your file, the answers are just a few clicks away.

USE CLAIM FREQUENCY CURVES FOR