Question

A business analyst has recently started using ChatGPT, for assistance in evaluating market trends for an upcoming product. In one instance, the analyst is curious about the possible impacts of a new social media campaign on product sales. How can the analyst interact with ChatGPT to get valuable insights?

Answer 1

Short answer: treat ChatGPT as a quick analyst, modeler and coach — give it clear context, data and a specific question, then iterate. It can help you design experiments, estimate incremental sales under assumptions, suggest metrics and attribution approaches, create SQL/Python code for analysis, and explain statistical methods to measure causality and ROI.

How to interact (step-by-step)
1. Start with clear context and goals
- Product, price, current sales baseline, channels, campaign duration and budget, target audience, creative types, launch date, KPI (sales, revenue, sign-ups, LTV).
2. Provide data or summary statistics
- Time series of sales (daily/weekly), historical advertising spend & impressions, clicks, CTR, conversion rates, traffic sources, seasonality/holidays, promo periods, competitor activity.
3. Ask a specific question
- Examples: “Estimate incremental weekly sales if this campaign generates X impressions and Y CTR,” or “Recommend an experiment to measure sales uplift,” or “Write Python to run a difference-in-differences test on sales.”
4. Tell ChatGPT what output you want
- E.g., forecast ranges and assumptions, code, step-by-step experiment plan, KPI dashboard template, SQL queries, or an executive summary.
5. Iterate and refine
- Provide more data or ask for sensitivity analysis, confidence intervals, or alternative models.

What ChatGPT can produce for you
- KPI list to track (impressions, reach, clicks, CTR, CPC, CVR, CPA, CAC, ROAS, incremental sales, LTV, retention, churn).
- Baseline vs scenario forecasting (with clear assumptions).
- Sensitivity analysis (showing how outcomes change by CTR, CVR, budget).
- Experiment design and sample-size calculations (A/B or geo test, holdout groups).
- Statistical methods and code: time-series forecasting (ARIMA/Prophet), difference-in-differences, interrupted time series, regression with controls, uplift models, propensity score matching.
- SQL to extract the necessary data and Python/R scripts for analysis and visualization.
- Interpretation guidance and recommendations on measurement windows, attribution, seasonality, lag effects and confounders.

Practical prompt templates you can copy
- Quick estimate:
“Context: baseline daily sales = 200 units, average order value = $60. Campaign: 1 month, budget $15,000, expected impressions 1,000,000, estimated CTR 0.5%, landing page CVR 2%. Goal: estimate incremental sales, revenue, and ROAS. Show calculations and sensitivity for CTR +/- 0.25% and CVR +/- 1%.”
- Experiment design:
“Design an A/B test or geo holdout to measure incremental sales for a social campaign with 10k daily users in target region. Provide sample size calc, recommended holdout percentage, measurement period, and analysis method.”
- Data analysis request:
“I can provide CSV with daily sales and daily ad spend by channel for 12 months. Write Python (pandas/statsmodels) to run a difference-in-differences test comparing sales before/after campaign across treated and control regions, and output effect size and p-value.”
- Attribution & reporting:
“Recommend a suite of dashboards/metrics and SQL queries to track campaign impact on acquisition, conversion and revenue. Include suggested breakdowns (by creative, device, cohort).”

Short worked example (illustrative)
- Given: baseline daily sales 100 units; campaign month impressions = 500,000; CTR = 0.6%; landing CVR = 2%; avg revenue per sale = $40; budget = $10,000.
- Steps:
- clicks = 500,000 * 0.006 = 3,000
- attributed sales = 3,000 * 0.02 = 60
- incremental revenue ≈ 60 * $40 = $2,400
- ROAS = revenue / ad spend = 2,400 / 10,000 = 0.24 (i.e., negative return)
- Use sensitivity analysis: change CTR or CVR to see when campaign becomes profitable; or plan A/B test to measure real uplift instead of assuming conversion path.

Important measurement & analysis methods to consider
- Controlled experiments (A/B tests, geo holdouts) — best for causal inference.
- Difference-in-differences or interrupted time series — when randomization isn’t possible.
- Media-mix modeling — to apportion sales across channels over longer time frames.
- Uplift modeling / incremental response modeling — to predict which users are most likely to be incrementally influenced.
- Propensity-score matching — to create comparable control groups from observational data.

Common pitfalls to watch for
- Attribution leakage (over-assigning conversions to last click).
- Ignoring seasonality, promotions, product launches or competitor moves.
- Short measurement windows missing lagged effects (brand campaigns often have delayed impact).
- Small sample sizes that lead to inconclusive tests.
- Confounding variables (price changes, out-of-stock events).

Offer
If you like, paste a summary or a small sample of your data and the specific question (forecast, experiment design, SQL or Python code), and I’ll:
- run a quick calculation or sensitivity analysis,
- propose an experiment and sample-size estimate,
- or generate code/queries to run on your data.

Which would you like to do now?