Customer satisfaction metrics are quantitative and qualitative measures used to assess how well a business meets customer expectations, typically tracked through scores like Net Promoter Score, Customer Satisfaction Score, and Customer Effort Score. Used well, they give marketing and commercial teams a direct line of sight into the health of the customer relationship, not just…
Customer satisfaction metrics are quantitative and qualitative measures used to assess how well a business meets customer expectations across products, services, and interactions. The most commonly tracked include Net Promoter Score, Customer Satisfaction Score, and Customer Effort Score, each capturing a different dimension of the customer experience. Used well, they tell you where you’re losing…
Channel partner analytics is the practice of measuring marketing performance across indirect sales and distribution partners, resellers, affiliates, and co-marketing relationships, so you can see which partnerships are generating real commercial value and which are simply generating activity. Done well, it gives you a clear view of revenue contribution, lead quality, and marketing efficiency across…
The metrics that matter in assessing integration performance are the ones that connect tool behaviour to business outcomes: data completeness, event accuracy, conversion fidelity, and the degree to which your integrated stack is telling a coherent story across channels. Most teams measure whether their integrations are technically live. Fewer measure whether they are commercially useful….
Advertising effectiveness measurement is the discipline of determining whether your advertising actually changed business outcomes, not just whether people saw your ads, clicked them, or converted in a way your platform reported. Done well, it separates the activity that drives revenue from the activity that simply consumes budget. Most measurement frameworks in use today do…
Marketing metrics and KPIs are only as useful as the questions you ask of them. A metric in isolation tells you almost nothing. A metric in context, compared against a baseline, measured over time, and connected to a business outcome, tells you something worth acting on. The difference between those two things is where most…
A data-driven attribution model uses machine learning to assign fractional credit to each touchpoint in a customer’s conversion path, based on the actual contribution of each interaction rather than a fixed rule. Unlike last-click or linear models, it weights touchpoints differently depending on how much each one influenced the final outcome, using patterns observed across…
Attention metrics measure whether an ad was actually seen and processed by a human, not just whether it was technically served. That distinction matters more than most media plans acknowledge. Viewability told us an ad had the opportunity to be seen. Attention tells us whether anything happened as a result. The gap between those two…
GA4 is not a new coat of paint on Universal Analytics. It is a fundamentally different measurement system, built on a different data model, with different defaults, different logic, and different implications for how you interpret your marketing performance. If you migrated to GA4 and simply recreated your old UA reports, you did the migration…
Cookieless attribution is the process of measuring which marketing activities drive conversions without relying on third-party cookies to track users across the web. It uses a combination of first-party data, privacy-preserving APIs, modelled signals, and statistical inference to build a picture of marketing performance that is both compliant and commercially useful. The shift has been…
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