Gartner, Inc.: possible revenue paths
Latest verified revenue: $1.505B for the quarter ended 2026-09-30. Reported source.
Forecasts are conditional on the listed data cutoff. Sources were refreshed for this model run on 2026-11-04; the cutoff above identifies the latest verified revenue observation included in the fit. Some forecast quarters may already have ended by the computation date. Read each target date before treating an estimate as a future-quarter outlook.
PyMC seasonal AR(1) quarterly log-revenue growth with Student-t shocks; posterior predictive paths include parameter uncertainty and new shocks at every horizon.
Each line is one possible future revenue path. The 80% and 95% intervals are pointwise model probabilities, not simultaneous coverage for the whole path. The median is not an expected dollar mean. 2 sampled paths extend beyond the displayed chart range; the plotted intervals use the full simulations. Open full-size chart.
| Quarter ending | Predictive median | 80% predictive interval | 95% predictive interval |
|---|---|---|---|
| 2026-12-31 | $1.850B | $1.747B–$1.955B | $1.673B–$2.042B |
| 2027-03-31 | $1.632B | $1.527B–$1.740B | $1.455B–$1.826B |
| 2027-06-30 | $1.795B | $1.651B–$1.943B | $1.572B–$2.052B |
| 2027-09-30 | $1.671B | $1.530B–$1.831B | $1.446B–$1.955B |
Future period-end dates are approximate: month-end issuers retain month-end dates; weekly issuers advance by 13 weeks. Future 53-week adjustments are not known.
Historical forecast checks
Each historical cutoff refits the model using only earlier observations. Across the available outcomes, the 80% intervals covered 10/16 and the 95% intervals covered 16/16. Small samples and overlapping outcomes do not establish nominal coverage; covering every outcome can indicate overly wide intervals.
| Horizon | Cases | Bayesian median MAPE | Last reported revenue MAPE | Same quarter last year MAPE | 80% covered | 95% covered |
|---|---|---|---|---|---|---|
| 1 quarter | 4 | 7.33% | 12.29% | 6.99% | 1/4 | 4/4 |
| 2 quarters | 4 | 5.38% | 3.48% | 5.06% | 3/4 | 4/4 |
| 3 quarters | 4 | 5.93% | 11.03% | 5.06% | 3/4 | 4/4 |
| 4 quarters | 4 | 8.84% | 3.61% | 3.61% | 3/4 | 4/4 |
All point-forecast errors use the same historical cases. A simple baseline may have lower error than the Bayesian median; this rollout does not establish that the Bayesian model is superior for every company.
Up to four historical cutoffs spaced four quarters apart, with at least 16 training quarters; full four-quarter outcomes required. Fixed specification, no winner selection.
These are retrospective checks using latest-restated data, not forecasts archived before their outcomes. Results from DDOG or another company are not transferred to this company.
Sampling diagnostics and limitations
Maximum R-hat 1.002 · minimum bulk ESS 3123 · minimum tail ESS 3744 · divergent transitions 0. The required sampling checks passed. These diagnose computation, not predictive calibration.
Fixed priors
mu is long-run quarterly log growth; phi is growth persistence; sigma is the shock scale; season gives quarterly effects; shock_df controls tail thickness.
| Parameter | Prior / fixed assumption |
|---|---|
| mu | Normal(0,0.10) |
| phi | Normal(0,0.5) truncated to [-0.98,0.98] |
| sigma | HalfNormal(0.10) |
| season | 4 Normal(0,0.15), centered to sum zero |
| shock_df | 5 |
Future regime changes and uncertainty over alternative model structures remain outside these intervals. The model should be scored on subsequent releases, and its forecast vintage preserved.
- Revenue-history baseline; does not identify customer-level discounts, acquisitions or future structural breaks.
- Intervals are pointwise, conditional on this model and priors, not guaranteed coverage or simultaneous path bounds.
- Latest-restated historical data; retrospective tests are not archived real-time forecasts. Small test samples cannot validate nominal coverage.
- Student-t log shocks can make dollar means undefined; point forecasts are medians.
- Sources were freshly acquired for this run. Each issuer has its own latest reported period; preliminary ranges are not treated as exact actual revenue.
Management guidance versus reported revenue
Latest SEC earnings candidate sources were archived where available, but an exact numerical quarterly consolidated-revenue range and target were not fully verified. This is not evidence that the company never guides.
No verified numerical quarterly consolidated-revenue guidance is plotted for this research sample. This does not establish that the company never provides guidance; annual, segment-only and qualitative outlooks are separate.
Preserved Bayesian forecast vintage
Computed 2026-11-04 using revenue through 2026-06-30. These original numbers are retained unchanged to allow subsequent scoring; they were not refitted with the new results. Forecasts computed after a target quarter ended are retrospective estimates, not pre-release predictions.
Original predictive medians and intervals
| Quarter ending | Original median | Original 80% interval | Original 95% interval |
|---|---|---|---|
| 2026-09-30 | $1.565B | $1.482B–$1.655B | $1.423B–$1.728B |
| 2026-12-31 | $1.892B | $1.770B–$2.019B | $1.692B–$2.118B |
| 2027-03-31 | $1.683B | $1.552B–$1.826B | $1.473B–$1.929B |
| 2027-06-30 | $1.837B | $1.682B–$2.016B | $1.586B–$2.136B |
The report below retains its original forecasts, validation and company-specific discussion. Those revenue estimates use a different model or information cutoff; they are separate from the Bayesian outlook above. Any EPS model below has not been recalculated using the new Bayesian revenue paths.
Gartner, Inc. IT
Revenue Intelligence Report • 69 quarters of SEC filing data • Updated 2026-10-14 • Data through 2026-06-30
Earlier summary (2026-10-14): Gartner, Inc.'s latest quarterly revenue is $1.7B for the period ending 2026-06-30. The model projects $6.9B across the next four quarters (+6.4% versus the preceding four quarters). The forecast uses SG&A data; model selection and estimation exclude the latest quarter, whose retrospective holdout error is 6.7%. These are statistical associations, not estimates of the causal effect of spending.
Investment Thesis
The ARDL forecast is based on reported revenue and SG&A data through 2026-06-30. Training MAPE measures fit to historical data; the separately reported holdout error measures a withheld quarter. Spending coefficients describe associations and do not establish causation.
Retrospective one-quarter holdout: model fitting and selection exclude this quarter. Historical inputs use the latest available restatements, so this is not a preserved real-time forecast.
Revenue Forecast
Quarterly Detail
| Quarter | Model Forecast | Actual | 95% Range | YoY Growth | Status |
|---|---|---|---|---|---|
| Q2 FY2026 Period end 2026-06-30 |
$1.79B | $1.68B | $1.67B–$1.92B | +6.1% | ✓ In range |
| Q3 FY2026 Period end 2026-09-30 |
$1.60B | $1.45B–$1.77B | +5.2% | ||
| Q4 FY2026 Period end 2026-12-31 |
$1.82B | $1.62B–$2.05B | +4.0% | ||
| Q1 FY2027 Period end 2027-03-31 |
$1.57B | $1.37B–$1.81B | +4.2% | ||
| Q2 FY2027 Period end 2027-06-30 |
$1.88B | $1.61B–$2.19B | +12.0% |
Seasonal Factors
| Fiscal Quarter | Seasonal Factor | vs Trend | Interpretation | Obs. |
|---|---|---|---|---|
| FQ1 (Jan–Mar) | 0.9974 | -0.3% | In line with trend | 16 |
| FQ2 (Apr–Jun) | 0.9885 | -1.2% | In line with trend | 16 |
| FQ3 (Jul–Sep) | 1.0049 | +0.5% | In line with trend | 16 |
| FQ4 (Oct–Dec) | 1.0148 | +1.5% | In line with trend | 16 |
Spending and Revenue
Time-Varying Spending Associations
Current SG&A elasticity: 0.5326x
The model-term chart shows relative absolute term magnitudes. Its percentages are not shares of revenue or causal contributions to growth.
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