Marketing analytics is the strategic measurement discipline that turns disconnected marketing signals into a clearer picture of what is working, what is not, and where to invest next. It starts with the questions the organization needs answered, establishes whether the underlying data can be trusted, and ends in decisions rather than another dashboard.
One View
Paid media, search, website and conversion data brought together.
Full Funnel
Performance measured from first interaction through conversion.
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Reporting built around continuous measurement, not one-time snapshots.
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A selection of the specialists who lead and shape this work, supported by a broader multidisciplinary team.
Marketing analytics is the discipline of understanding how marketing activity contributes to meaningful business outcomes. It sits above implementation and reporting. Its job is to decide what deserves to be measured, judge whether the available data supports a conclusion, compare performance across channels that play genuinely different roles, and interpret the result in a way that changes what the organization does next. The chain it follows is simple to state and difficult to maintain: marketing activity produces reliable data, analysis produces insight, insight produces a decision, and the decision produces an improvement worth measuring again.
Measurement is difficult for structural reasons, not because teams are careless. Customer journeys cross devices, sessions, and weeks. Some of the most valuable actions happen on a phone call or in person, where no tag is watching. Privacy controls and consent legitimately limit what can be observed. Every platform scores itself using its own rules, its own lookback window, and its own definition of a conversion, so two accurate systems routinely disagree. And the metric that looks most impressive is frequently the one furthest from anything the business recognizes as a result.
So we hold a few positions firmly. Data quality comes before analysis, because a confident recommendation built on broken tracking is worse than no recommendation. No single platform is treated as the whole truth. Context is part of the metric, since a cost per click or a conversion rate means nothing without the objective, audience, channel, and timeframe attached. Channels are judged against the role they were built to play, because an awareness campaign held to high intent search metrics will always look like a failure. And analysis has to end somewhere useful. If a review does not produce a view on what to continue, change, test, stop, or fund, it was reporting wearing an analyst's coat.
The question is not how many clicks. It is what the marketing produced.
We are platform-independent. Tooling is chosen per engagement, and every account is client-owned.
Google Analytics 4Google Search ConsoleGoogle Tag Manager
Looker StudioGoogle Ads
Meta
Microsoft AdvertisingEight failure patterns we see repeatedly, and what we change about each.
If none of these describe the situation, we will say so before a proposal is written.
Spend is spread across search, social, programmatic, and organic, and each channel reports its own success. Nobody has reconciled them into a single account of what the marketing produced.
The reporting arrives on schedule and the numbers are accurate, and yet the same questions come up every quarter, because the reports describe activity rather than explain results.
A budget decision is coming and the supporting evidence is a set of platform screenshots that disagree. The decision gets made on instinct and defended afterward.
The valuable action happens on a phone call, in a showroom, or weeks later in a CRM. Website conversions alone describe a fraction of what the marketing is doing.
Someone noticed the analytics numbers do not match the ad platform, or that a conversion count changed after a site release, and confidence in the whole setup quietly collapsed.
Awareness work, demand capture, and local visibility are judged with one conversion metric, which makes the upper funnel look like a failure and the lower funnel look like genius.
The instinct when marketing performance is unclear is to collect more of it. Another platform, another dashboard, another weekly export. It rarely helps, because the problem is almost never a shortage of numbers. It is that nobody has decided which numbers are supposed to indicate progress, whether the underlying tracking can be trusted, or what a given figure means in the context of the objective, the audience, the channel, and the timeframe. So we work in the other direction. Start with the questions the organization needs answered, establish which signals can answer them, verify that those signals are being collected correctly, and only then analyze. A small set of trustworthy measurements that someone acts on beats a comprehensive set that nobody reads.
More data does not automatically produce better decisions. It usually produces longer meetings.
Fig. 02
Every platform reports the part of the journey its own tag could observe. Ad platforms credit the click or the view they served. Analytics credits the last source it could see. Search Console describes the query, the CRM describes the outcome, and the call record describes the conversation none of them captured. Reconciling those into one account, adding the context of objective, audience, and timeframe, and analyzing the result is what separates a decision from an export.
Platform exports are the input to analysis, not the conclusion of it.
A review should end in a decision: continue, change, test, stop, or fund differently.
Seven stages, run in order. Measurement design is agreed before any budget is committed.
Scoped per engagement. Most programs use four or five of the capabilities below.
Establishing what the marketing is meant to accomplish, how progress will be judged, and which signals are actually capable of indicating it.
A short, defensible set of indicators tied to objectives, with diagnostic metrics kept separate from the ones used to make decisions.
Comparable evaluation across paid, organic, local, and offline activity, using shared conversion definitions rather than each platform's own scoring.
Analysis of campaign, audience, placement, and creative performance against the role that campaign was actually built to play.
Site behavior read as marketing evidence: where attention holds, where journeys stall, and which pages do the converting.
Form, chat, and call measurement treated as one lead picture, including the calls that never appear in a website conversion count.
Where a store exists, product, category, and revenue analysis alongside the marketing that drove it, rather than in a separate commerce silo.
Source and medium analysis that survives contact with reality: consistent tagging, direct and referral traffic explained, self reported channels reconciled.
Identifying where intent is lost, and whether the loss is a targeting problem, an experience problem, or a measurement problem.
Behavior across segments, devices, geography, and new versus returning, so performance differences can be explained rather than averaged away.
Cost per meaningful action by channel, campaign, and audience, with the quality of the resulting action weighed alongside its cost.
Evidence for where the next dollar should go, including what is saturated, what is underfunded, and what produces volume without value.
Trend and seasonality analysis over meaningful windows, so normal fluctuation is not mistaken for a result and a real shift is not missed.
Judging whether the data can be trusted before it is used: tracking coverage, conversion definitions, duplication, filtering, and known blind spots.
A working view of how credit is assigned across touchpoints, what each platform's rules do to the numbers, and where interpretation must stay directional.
A written account of what happened, what it means, what remains uncertain, and what we recommend doing about it.
Fig. 03
Not every metric deserves equal attention. Exposure is the easiest signal to generate and the furthest from the objective. Business outcomes are the hardest to capture and the closest to what the organization actually counts. Most reporting inverts this, leading with the numbers that are easy to produce. The correct hierarchy differs by client and by campaign, and not every organization measures revenue, so the top of the ladder is defined during the Define stage rather than assumed.
Exposure and engagement explain movement. Conversion and outcome justify budget.
An awareness campaign and a high intent search campaign should not share one KPI.
Not sure whether your numbers can be trusted
Send us access to what you already have. We will tell you what is being tracked, what is missing, where the numbers disagree and why, and which of your current metrics are worth making decisions on. No obligation afterward.
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Good marketing begins and ends with reliable data. Clean instrumentation, honest attribution, and reporting an executive team can defend, established before the spend and revisited after it.
The Wheel Shop
Retail and e-commerce. Advertising.
The automotive category looks like one audience and behaves like several. A shopper pricing custom rims is nothing like a driver who needs rim repair today, and neither searches the way a tire or alignment customer does. The program had to win the searches with immediate intent, send each customer to the right action, and measure results in calls and form submissions rather than clicks.
Conversions from paid search
Insurance is one of the most competitive categories in paid search, and HUB International served industries whose needs had almost nothing in common. A business researching employee benefits needed a different message than one evaluating cyber liability, and the budget had to stay clear of claims, careers, and consumer traffic.
Professional services | Advertising
10
Campaign themes structured by insurance need
Professional services
Advertising
HUB International
Writing from the team running this work, published as the field changes.
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Marketing analytics is the practice of measuring marketing activity and interpreting it, so an organization can tell what worked, what did not, and where to invest next.
It defines what to measure, checks whether the data is trustworthy, analyzes performance across channels, and turns the findings into marketing decisions.
Web analytics measures behavior on the website. Marketing analytics measures the whole system around it: channels, campaigns, website behavior, and business outcomes.
Track a hierarchy from exposure to engagement to intent to conversion to business outcome, weighted toward whatever your organization actually counts as a result.
Agree on shared definitions, tag traffic consistently, reconcile conversions in one place, and evaluate each channel against the role it plays rather than one universal KPI.
A KPI is a small set of measurements chosen in advance because they genuinely indicate progress toward an objective and should influence what you do next.
Yes, with reliable tracking you can see which sources and campaigns are associated with leads, calls, and revenue, though no platform can assign perfect credit for every interaction.
Each platform uses different attribution rules, lookback windows, and timestamps, so accurate systems still disagree. Pick one reconciled source for decisions and use the rest diagnostically.
Operational checks run frequently, performance analysis monthly, and strategic review quarterly. Reviewing slow moving channels too often turns normal variance into false conclusions.
Reporting states what happened. Analytics explains why it happened and what should change as a result.
Marketing analytics
Tell us what you are running, what you are measuring, and the question you cannot currently answer. We will look at how performance is tracked today, where the picture breaks down, and what it would take to get a version of it you could make a budget decision on.
Call (800) 505-7570. Measuring marketing performance since 1998.
