Root-cause attribution for multi-location KPI drops
When a location underperforms, get an honest sketch of the cause — campaign, channel, cohort, season, local market, competitor, weather, or an ops event — instead of a 6-week investigation.
The problem
A multi-location operator finds out at the end of the quarter that 30 stores are down 12% year-over-year. Dashboards show the drop. They do not show the cause. Is it that paid spend got pulled? Is it channel mix shifting? Is it an aging customer cohort? Is it normal seasonality? Did a competitor open three miles away? Was there a heat wave or a cold snap? Did one of the locations go through an ops disruption? The investigation costs weeks of analyst time and usually ends inconclusive. Attribution platforms (Branch, AppsFlyer, Adjust, Kochava, Singular, Northbeam, Triple Whale, Rockerbox) attribute paid-media install and purchase events — narrow scope. Marketing mix modeling specialists (Marketing Evolution, Nielsen MMM, Analytic Partners, Ipsos MMM, Meta Robyn, Google Lightweight MMM) are enterprise priced and consulting heavy. BI platforms (Tableau, Looker, Power BI, Sigma, Hex, Mode) give you the primitives. RCA tooling (Anodot, Falkon, Anomalo, Outlier, Sisu) explains anomalies in data, but not tied to operator-specific factors like local competition and weather. None of them produce a fast, honest root-cause sketch for a multi-location operator that covers the eight factors that actually explain most KPI drops.
What success looks like
When a KPI moves at any location, a root-cause sketch is produced within hours. The sketch attributes contribution across eight factors: campaign changes, channel mix shifts, cohort aging, seasonality, local market context, local competitor activity, weather, and operations events. Each factor gets a contribution estimate and a confidence range. The sketch acknowledges what it cannot explain. Diagnostic time on 'we do not know why' drops from the majority of ops review hours to a small fraction. Decisions get made on evidence, not guesses.
How most operators solve this today
Several categories already touch root-cause analysis. None of them produce a multi-location operator sketch across the eight factors that actually matter:
Attribution platforms (Branch, AppsFlyer, Adjust, Kochava, Singular, Northbeam, Triple Whale, Rockerbox)
Free to $25,000+/month
Strong at attributing paid-media events. Narrow scope. No coverage of competitor, weather, or ops factors.
Marketing mix modeling (Marketing Evolution, Nielsen MMM, Analytic Partners, Ipsos MMM, Meta Robyn, Google Lightweight MMM)
Free to $500,000+/year, consulting heavy
Powerful but enterprise priced. Lead time is months. Not for fast operational diagnostics.
BI with attribution (Tableau, Looker, Power BI, Sigma Computing, Hex, Mode/ThoughtSpot)
$10 to $3,000+/user/month
Primitives for analysis. Building an 8-factor sketch is an analyst project per location.
RCA tooling (Anodot, Falkon, Anomalo, Outlier.ai, Sisu)
$30,000 to $300,000+/year
Explains anomalies inside the data warehouse. Does not attribute to operator-specific factors like local competition and weather.
Build it in-house
Senior data engineer ($140-240k) + analytics manager ($100-160k) + ongoing maintenance
Custom causal-inference plus weather feeds plus competitor data plus shapley logic. Works for one KPI at one location. Scaling is the maintenance burden.
What changes when this is an agent skill
When a location's KPI moves, the sketch runs against eight factors: campaign changes (spend, creative, audience), channel mix shifts, cohort aging, seasonality patterns from prior years, local market context (foot traffic, regional events), local competitor activity (new openings, closures, promotions), weather (heat waves, cold snaps, severe storms), and operations events (staffing gaps, equipment issues, hours changes). Each factor gets a contribution estimate using shapley-value approximation, marginal contribution, and counterfactual comparison against peer-cohort locations. The sketch explicitly flags what it cannot explain. Ops review meetings start with the sketch and spend time on decisions, not diagnostics.
How this compares
branch.io
Strong at attributing paid-media events. Narrow scope. No coverage of competitor, weather, or ops factors.
appsflyer.com
Strong at attributing paid-media events. Narrow scope. No coverage of competitor, weather, or ops factors.
adjust.com
Strong at attributing paid-media events. Narrow scope. No coverage of competitor, weather, or ops factors.
kochava.com
Strong at attributing paid-media events. Narrow scope. No coverage of competitor, weather, or ops factors.
singular.net
Strong at attributing paid-media events. Narrow scope. No coverage of competitor, weather, or ops factors.
northbeam.io
Strong at attributing paid-media events. Narrow scope. No coverage of competitor, weather, or ops factors.
triplewhale.com
Strong at attributing paid-media events. Narrow scope. No coverage of competitor, weather, or ops factors.
rockerbox.com
Strong at attributing paid-media events. Narrow scope. No coverage of competitor, weather, or ops factors.
nielsen.com
Powerful but enterprise priced. Lead time is months. Not for fast operational diagnostics.
tableau.com
Primitives for analysis. Building an 8-factor sketch is an analyst project per location.
looker.com
Primitives for analysis. Building an 8-factor sketch is an analyst project per location.
anodot.com
Explains anomalies inside the data warehouse. Does not attribute to operator-specific factors like local competition and weather.
anomalo.com
Explains anomalies inside the data warehouse. Does not attribute to operator-specific factors like local competition and weather.
outlier.ai
Explains anomalies inside the data warehouse. Does not attribute to operator-specific factors like local competition and weather.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Per-Location Performance Benchmarking Agent
Compares every location to its peer cohort on the metrics that move budget — at 2σ thresholds with root-cause attribution.
Related skills
Peer-cohort benchmarking for every location
Compare each location to its real peers — by revenue, traffic, market type, service mix, seasonality, and age — not to a misleading network average.
Per-location reporting that does not wait for month-end
Every marketing source — paid media, GBP, POS, email, calls, foot traffic — feeds one record per location, refreshed daily, ready to roll up.
Outlier alerts that compare each location to its real peers
Flag the locations that are genuinely out of pattern — judged against their peers, not the network average — and route every flag to a root-cause sketch.
Benchmark each location against the peers it should actually be compared to
Per-location performance framed against the peer cohort that actually matches — tenure, vertical, market tier, insurance mix, service mix, LTV cohort.
Per-location forecasting that does not wait for month-end
Sales, leads, conversion, CAC, LTV, churn, ROAS, GBP impressions — forecasted per location, per month, refreshed weekly.
Customer cohorts every AI agent reads at runtime, not weekly
Cohorts like "high-LTV", "at-risk", and "lapsed" computed on your unified customer record and exposed as a real-time signal — so your save-flow, your email, your ads, and your loyalty program all act on the same up-to-date answer.
One master record per location — reconciled across every source, propagated everywhere automatically
One trusted record per location, reconciled across POS, Google Business Profile, Yext, HR, and your CMS — and when anything changes, every marketing surface updates automatically.
FAQ
- How is this different from an attribution platform like Northbeam or Triple Whale?
- Those attribute paid-media events. We attribute KPI movement across eight factors including competitor activity, weather, and operations events — far beyond paid media.
- How is this different from a marketing mix model?
- MMMs are powerful but slow and expensive. We produce a fast sketch with explicit confidence ranges, designed for operational diagnostics, not annual planning.
- Which factors does the sketch consider?
- Campaign changes, channel mix shifts, cohort aging, seasonality, local market context, local competitor activity, weather, and operations events. Custom factors can be added.
- How quickly does it produce a sketch?
- Within hours of a KPI movement. The intent is operational diagnostic speed, not quarterly modeling.
- How does it handle confidence?
- Each factor gets a contribution estimate with a confidence range. The sketch explicitly says what it cannot explain rather than guessing.
- Where does the data come from?
- Your campaign platforms, your POS or revenue system, your competitor data feed, public weather data, your ops tracking, and the peer-cohort layer. We integrate with what you have.
- Does it work alongside our existing BI?
- Yes. The sketch can be surfaced inside Tableau, Looker, Power BI, or your own dashboards.
- Does this work for operators with fewer than 10 locations?
- Yes. There is no minimum location count.