BADIR™ in Action: A Real Revenue Drop Analysis (Case Study)

Nothing focuses a leadership team like a sudden, unexplained drop in revenue. Every day without a clear diagnosis means continued losses, mounting pressure, and decisions made on guesswork. The BADIR™ framework was built for exactly this situation — bringing structure, speed, and rigor to what would otherwise be a panicked, disorganized response.

This case study walks through how BADIR™ was applied to diagnose an 18% revenue drop — from the moment the alarm sounded to a concrete, data-backed recommendation delivered in under 24 hours.

The Situation

A mid-market SaaS company noticed that monthly recurring revenue (MRR) for October came in 18% below the September figure and 22% below plan. The CFO flagged it on November 1st. The CEO wanted answers before the board call on November 3rd.

The data team had two days. Without a structured framework, this kind of crunch typically produces one of two outcomes: a rushed, shallow analysis that misses the root cause, or an exhaustive data dump that overwhelms decision-makers without giving them a clear answer. BADIR™ avoids both.

Step-by-Step BADIR™ Revenue Analysis

Step 1 — Business Question (B)

The first step was to frame the question precisely. The team defined it as:

“MRR dropped 18% MoM in October. Is this driven by new business shortfall, contraction in existing accounts, or elevated churn — and which specific customer segments, product lines, or sales channels are responsible?”

This framing is critical. It immediately scopes the analysis to three possible root causes (acquisition, contraction, churn) and three dimensions to investigate (segments, products, channels). This prevents the team from chasing every possible explanation.

Step 2 — Analyze (A)

Before pulling data, the team mapped the analytical approach:

• MRR decomposition: Break the 18% drop into new MRR, expansion MRR, contraction MRR, and churned MRR — a standard “MRR waterfall” — to isolate which component(s) drove the decline.
• Cohort comparison: Compare the October cohort behavior against September and August to identify if churn timing is anomalous.
• Segment breakdown: Within whichever MRR component was the primary driver, break down by customer segment (SMB vs mid-market vs enterprise), product tier, and sales channel (direct vs partner vs self-serve).
• Timeline analysis: Was the drop concentrated at the start, middle, or end of October? This often points to a specific event or policy change.

Step 3 — Data (D)

With the analytical plan set, the data needed was specific and manageable:

• CRM subscription data: start dates, end dates, plan tier, ARR/MRR value by account — August through October
• Billing system data: payment events, downgrades, cancellations by date
• Customer segment data: company size, industry, acquisition channel
• Support/CX ticket data: volume and categories for September–October (to spot service-related churn signals)

What the Data Revealed

The MRR waterfall analysis revealed an important pattern: new MRR and expansion MRR were actually in line with the prior two months. The entire shortfall came from two sources:

1. Churned MRR was 3.1x higher than the August–September average, concentrated in mid-market accounts (50–500 employee companies) on the legacy “Professional” plan.
2. The churn was front-loaded: 78% of the churned accounts cancelled in the first 12 days of October.

Cross-referencing with support tickets revealed that a pricing change that took effect October 1st — a 15% price increase for legacy Professional plan customers — had not been preceded by adequate advance notice or a migration path. Many of these customers chose to cancel rather than absorb the increase on short notice.

The root cause was clear: the revenue drop was a pricing transition failure, concentrated in a specific plan and customer segment, triggered by a specific event on a specific date.

The Recommendation

Based on the analysis, the recommendation had three components:

1. Immediate rescue campaign (November 1–15): Proactively reach out to the 34 mid-market Professional-plan accounts that cancelled in October. Offer a 90-day price lock at their previous rate in exchange for re-subscribing. Many teams report significant win-back rates when the underlying issue was pricing surprise rather than product dissatisfaction.
2. Prevent further churn: For remaining legacy Professional-plan customers who had not yet churned, send a personal email from the account team offering a grandfather pricing option or a migration path to a new plan at a comparable price point. Give them 30 days to decide.
3. Process fix: Establish a minimum 60-day advance notice standard for all pricing changes, with a dedicated migration guide and account team outreach for accounts above a defined ARR threshold.

Results

By applying BADIR™ and delivering a structured recommendation within 24 hours, the team avoided the more common outcome: spending two weeks debating hypotheses while churn continued. The board presentation on November 3rd included not just a diagnosis but a specific recovery plan with named accounts and expected MRR recovery.

This is the power of BADIR™ under pressure: structure prevents panic, and precision prevents wasted effort.

How AskEnola Replicates This at Scale

This analysis required an experienced analyst who understood both BADIR™ and the company’s data architecture. Most organizations don’t have that analyst available on a two-day turnaround — or at all. AskEnola changes that equation entirely.

A business leader can type: “Why did our MRR drop in October?” — and AskEnola connects directly to the data warehouse, runs the BADIR™ framework automatically (MRR decomposition, segment breakdown, timeline analysis), surfaces the root cause, and delivers a structured recommendation — in minutes, not days.

No SQL. No analyst required. No two-day wait. Just answers.

FAQ: BADIR™ in Revenue Analysis

What is BADIR™ used for in revenue analysis?

BADIR™ is used to diagnose revenue changes — drops, spikes, or deviations from plan — in a structured, repeatable way. It’s especially effective for MRR/ARR analysis in SaaS, where the root cause can be new business, expansion, contraction, or churn, and the investigation needs to move quickly.

How fast can BADIR™ diagnose a revenue problem?

An experienced analyst using BADIR™ manually can typically deliver a structured revenue diagnosis in one to three days, depending on data complexity. With AskEnola automating the BADIR™ framework, the same analysis takes minutes.

Can AskEnola run a BADIR™ revenue analysis automatically?

Yes. AskEnola connects to your data warehouse and applies the BADIR™ framework automatically when you ask a revenue question in plain English. You don’t need to structure the analysis yourself or write SQL — AskEnola handles the decomposition, segmentation, and insight generation, then delivers a recommendation.

What data sources are needed for revenue analysis?

For a subscription revenue analysis, you typically need subscription/billing data (MRR, plan tier, dates), CRM data (account segments, acquisition channel), and — for churn analysis — support or usage data. AskEnola connects directly to your data warehouse so these sources don’t need to be manually assembled.