What is marketing attribution and why is it so hard?
Marketing attribution is the practice of assigning revenue credit to the touchpoints that influenced a buyer, from an ad impression to a demo request. It is hard because buyers cross devices, cookies decay, dark social is invisible, and most purchases involve a committee rather than a single click. No model is correct in an absolute sense; every model is a lens that trades off coverage, bias, and operational cost. The right question is not "which model is true" but "which model drives better decisions in our business."
Read the full attribution definition →
What is UTM tracking and how should we standardize it?
UTM parameters are the five tags appended to a URL that let analytics tools identify the source, medium, campaign, term, and content of a click. Standardize them with a written taxonomy, a required-fields rule in your campaign builder, and lowercase-only values to avoid "Google" and "google" splitting a report. Reserve utm_source for the platform, utm_medium for the channel class, and utm_campaign for the initiative. Audit the live set quarterly, because every ad hoc launch leaves a trail of one-off tags that corrupt your rollups.
Read the full UTM tracking guide →
What is multi-touch attribution and when does it make sense?
Multi-touch attribution assigns fractional credit to several touchpoints across a buying journey rather than crowning one winner. Linear splits credit evenly, time-decay rewards recency, position-based weights first and last, and data-driven uses a model to learn weights from your own conversion history. It makes sense when sales cycles are long, deal sizes justify the instrumentation cost, and you have enough converted journeys to train on. For short-cycle, self-serve products, first-touch and last-touch usually produce the same decisions with a tenth of the overhead.
Read the full multi-touch guide →
First-touch vs last-touch attribution: which should I pick?
First-touch rewards the channel that originated the buyer and biases investment toward brand and demand creation. Last-touch rewards the channel that closed the click and biases investment toward paid search and retargeting. Both are directionally useful, both are wrong in isolation, and both are easier to implement than any multi-touch model. Most operators report both side by side, treat the delta as a signal about pipeline shape, and reserve a data-driven model for the handful of decisions where fractional credit actually changes a budget call.
What is CAC and how does attribution change the number?
Customer Acquisition Cost is fully loaded sales and marketing spend divided by new customers in the same window. Attribution changes the per-channel view, not the blended number: last-touch will overweight paid, first-touch will overweight content and events, and a data-driven model will land somewhere in between. For board reporting, keep CAC blended and segment-level. For budget allocation, use whichever channel-level attribution has produced the cleanest post-hoc payback story in your business, and revisit the choice annually.
Read the full CAC definition →
What is cohort analysis and how does it fit with attribution?
Cohort analysis groups customers by a shared starting event, usually signup month, and tracks a behavior like retention, expansion, or payback across the group over time. Attribution tells you where customers came from; cohort analysis tells you what happened after they arrived. Together they close the loop: a channel that acquires customers who churn at month three is not a growth channel, no matter what the acquisition dashboard says. Always report cohort LTV and retention alongside channel-level attribution, or you will optimize for the wrong buyers.
Read the full cohort analysis guide →
What is A/B testing in a sales and marketing context?
A/B testing is a controlled experiment that routes traffic or outreach to two or more variants and measures the lift of one against a baseline. In marketing it covers landing pages, ads, and email subject lines; in sales it covers outbound sequences, scripts, and routing rules. Statistical significance requires enough volume and a pre-registered success metric, which is why most sales A/B tests at the rep level are directional at best. Keep tests simple, change one variable at a time, and codify the winner in your playbook rather than running the test forever.
Read the full A/B testing explainer →
What is customer segmentation and why does it matter for analytics?
Customer segmentation is the practice of grouping accounts or users by attributes like industry, size, geography, product plan, or behavior so you can analyze and act on them differently. Blended metrics almost always hide the real story: a flat conversion rate can mask one segment doubling and another collapsing. Pick three to five durable segments, apply them consistently across acquisition, retention, and expansion reporting, and make sure your CRM enforces the taxonomy at write time rather than trying to clean it in the dashboard.
Read the full segmentation primer →
What is sales forecasting and how do analytics support it?
Sales forecasting is the practice of predicting bookings for a future period based on current pipeline, historical conversion rates, and rep judgment. Analytics support it with stage-to-stage conversion math, aging reports, and win-rate curves by segment and source. The strongest forecasts combine a bottom-up roll-up with a top-down trend model, then compare the two and investigate the gap. Attribution feeds forecasting indirectly: knowing which sources produce faster-closing, higher-retention pipeline lets you weight your coverage model instead of treating every dollar of pipeline the same.
Read the full sales forecasting guide →
How do we handle attribution now that cookies are dying?
Third-party cookies are already gone in Safari and Firefox and headed out of Chrome, so cross-site tracking as a foundation is retiring. The practical response is a stack of first-party signals: logged-in identity, server-side event collection, consent-managed UTMs, and self-reported attribution on forms. Pair that with incrementality tests and marketing mix modeling for the channels you cannot track at the user level. The goal is not to recreate deterministic tracking; it is to make budget decisions with enough signal to be directionally right, more often than the competition.
What is marketing mix modeling and how is it different from attribution?
Marketing mix modeling is a top-down statistical approach that regresses spend across channels against aggregate outcomes like revenue or pipeline, controlling for seasonality and external factors. It does not need user-level data, which is why it is having a renaissance as cookies disappear. Attribution works at the touchpoint level and answers "which journey closed." MMM works at the budget level and answers "which spend moved the needle." Mature analytics teams run both: MMM for annual planning, attribution for weekly channel optimization, and incrementality tests to reconcile the two.
How often should we audit our attribution setup?
Instrument once, then audit the pipes quarterly and the model annually. The quarterly audit checks UTM taxonomy, event coverage, duplicate tracking, and consent compliance against a written spec. The annual review revisits the model itself: is first-touch still the right default, are new channels like podcast and community showing up correctly, and has your sales motion changed enough to require a different lens. Document every change in a shared attribution runbook so finance, marketing, and sales share one source of truth instead of three competing dashboards.
