How to Apply BADIR™ to a Marketing Analytics Question (Worked Example)

Analytics frameworks are easiest to understand when you can see them in action. The BADIR™ framework — Business question, Analyze, Data, Insights, Recommendations — becomes immediately intuitive when you walk through a real marketing scenario step by step. In this post, we’ll apply BADIR™ to one of the most common and consequential marketing analytics questions: Why did customer acquisition cost spike?

This worked example will show you exactly how each step functions in practice — and how AskEnola can automate the entire process.

The Marketing Question

Imagine you’re the VP of Marketing at a SaaS company. You just closed Q3 and your Customer Acquisition Cost (CAC) jumped 28% compared to Q2. Your CFO is asking questions. Your board meeting is in three weeks.

You need to know: Why did CAC spike, and what should we do about it?

This is exactly the kind of question BADIR™ was designed to answer. Let’s walk through each step.

Step 1 — Business Question (B)

The raw question — “why did CAC spike?” — isn’t precise enough to guide an analysis. A good BADIR™ business question has three elements: a measurable metric, a timeframe, and a decision attached to it.

Refined business question: “Our blended CAC increased 28% from Q2 to Q3 FY2024. Which channels, campaigns, or audience segments drove this increase, and what specific changes to our media mix or targeting would restore CAC to Q2 levels by Q4?”

Now we know exactly what we’re solving for — and why the analysis matters.

Step 2 — Analyze (A)

Before pulling a single row of data, we map out the analytical approach. For a CAC spike analysis, the logical structure is:

• Decompose CAC by channel: Break total spend and total acquisitions by channel to see which channels have the highest and fastest-growing CAC.
• Trend analysis: Compare Q2 vs Q3 performance by channel — which channels’ CAC increased, stayed flat, or improved?
• Hypothesis testing: For the channels with the biggest CAC increase, investigate spend changes, conversion rate changes, and CPM/CPC changes separately. The root cause could be higher media costs, lower conversion rates, or both.
• Audience/campaign breakdown: Within problem channels, did specific campaigns, audiences, or creatives drive the spike?

This plan prevents us from drowning in data. We know exactly what to look for before we start.

Step 3 — Data (D)

With the analytical plan in place, we identify the data sources needed:

• Paid media data: Spend, impressions, clicks, CPM/CPC by channel and campaign — from Google Ads, Meta, LinkedIn
• CRM/conversion data: New customer acquisitions by source, by week, by campaign
• Landing page analytics: Conversion rates by landing page and traffic source
• Date range: Q2 (Apr–Jun) and Q3 (Jul–Sep) for comparison, with weekly granularity to spot when the spike started

Notice that we’re not pulling everything available — just what’s needed to test our specific hypotheses. This keeps the analysis fast and focused.

Step 4 — Insights (I)

After running the analysis, a pattern emerges. Here’s what the data might show in a scenario like this:

• Paid social (Meta) CAC increased 47% — the largest contributor to the overall 28% blended CAC increase
• Meta CPMs increased approximately 35% in Q3 (consistent with industry seasonality heading into Q4)
• However, Meta conversion rates also dropped 18% independently of CPM — suggesting a creative or audience issue, not just market costs
• Drilling into campaigns: three retargeting campaigns that performed well in Q2 were refreshed with new creative in July — and all three saw conversion rate drops of 20–25% immediately after the refresh
• Paid search CAC was flat; organic and email channels were unaffected

The insight: The CAC spike is driven primarily by Meta, with roughly half attributable to seasonal CPM increases and half attributable to creative underperformance on three refreshed retargeting campaigns.

Step 5 — Recommendations (R)

With a clear insight, the recommendation writes itself:

1. Immediate action (this week): Pause or revert to Q2 creative on the three underperforming retargeting campaigns. Expected impact: recover approximately 10–12 percentage points of the CAC increase within 2–3 weeks.
2. Short-term (this quarter): Reallocate 15–20% of Meta spend to paid search, which maintained CAC stability and has room for volume increases. This partially offsets the remaining CPM-driven cost increase.
3. Medium-term: Build a creative testing cadence so future creative refreshes are A/B tested against controls before full deployment — preventing a recurrence.

This is a decision-ready recommendation that the VP of Marketing can take into the board meeting with confidence.

How AskEnola Automates This Entire Process

Manually, this BADIR™ analysis might take an experienced analyst two to three days — pulling data from multiple sources, running the channel decomposition, doing the creative-level breakdown, and writing up findings.

With AskEnola, the same analysis happens in minutes. A marketing director types: “Why did our CAC increase in Q3 and what should we change?” — and AskEnola connects to the underlying data warehouse, runs the BADIR™ framework automatically, and returns a structured insight and recommendation.

No SQL required. No dashboard configuration. No data analyst in the critical path.

FAQ: BADIR™ in Marketing Analytics

What is a good example of BADIR™ in marketing?

A CAC spike analysis is a classic BADIR™ marketing example: define the question precisely, map the analytical approach (channel decomposition, trend analysis, hypothesis testing), pull the right data, surface the insight (which channel and what root cause), and recommend a specific media mix or creative change. The worked example above walks through this in detail.

How long does a BADIR™ analysis take?

Manually, a thorough BADIR™ analysis on a marketing question typically takes one to three days for an experienced analyst. With AskEnola automating the process, the same analysis returns in minutes.

Can AskEnola automate BADIR™ for marketing?

Yes. AskEnola is purpose-built on the BADIR™ framework. Marketing users can ask questions in plain English — about CAC, conversion rates, channel performance, campaign ROI — and receive structured, decision-ready BADIR™ outputs automatically.

What data do you need for a BADIR™ marketing analysis?

For a typical marketing analytics question, you need paid media data (spend, CPM/CPC by channel and campaign), conversion/acquisition data from your CRM or attribution tool, and enough historical data to establish a comparison baseline. AskEnola connects directly to your data warehouse so you don’t need to manually export or combine these sources.