The decisions above the tooling. Which metrics deserve attention, how measurement work should be organised, and what changes now that AI does a growing share of the analysis.
17 articles
AI is transforming analytics from retrospective reporting to proactive intelligence. This guide explores the four pillars of AI analytics, compares platform capabilities, and provides a practical roadmap for implementation.
Every company claims to be data-driven. Few actually are. This guide walks you through the five stages of data maturity, provides a quick self-assessment, and offers concrete recommendations for moving from reactive firefighting to transformative business leadership.
Every analytics investment starts with a promise of better decisions and better outcomes. But when the CFO asks for proof, most teams struggle. This guide provides a complete framework for calculating analytics ROI with step-by-step methodology and industry benchmarks.
The analytics landscape has fundamentally shifted. Third-party cookies are dying, regulations are tightening, and consumers are paying attention. This guide shows you how to build an analytics practice that respects user privacy while still delivering the insights you need to grow.
Most organizations are stuck at stage 1 or 2 of analytics maturity: they can report what happened but cannot predict what will happen. Here is how to move up the maturity curve.
Target famously predicted customer pregnancies from purchase patterns. While that example is extreme, the principle applies everywhere: behavioral data reveals intentions before customers act on them.
Most companies have a decisions problem, not a data problem. When 91.9% of firms invest in data initiatives but only 23.9% describe themselves as data-driven, the gap is not technology. It is the connection between metrics and the decisions they should inform.
The best analytics does not just tell you what happened on your website. It tells you who your customers are, what they need, and when they are ready to act. That shared conviction is what brought Sandstorm Analytics and KISSmetrics together.
Lifetime value is revenue per period multiplied by how long the customer stays. Retention is the multiplier. These six strategies raise LTV by extending customer lifespan and growing spend inside it, with the measurement approach for each.
Strategic planning in marketing runs in five stages: situation analysis, objective setting, strategy development, tactical execution, and measurement. This guide walks each stage, the frameworks that sharpen it, and the data that keeps it honest.
The customer life cycle has six stages: awareness, acquisition, activation, retention, revenue, and referral. Each has its own entry event, its own metrics, and its own failure mode. This guide covers all six and the transitions between them.
Having data is not the same as making decisions. Most teams drown in dashboards while struggling to translate numbers into action. This guide gives you a framework to close the gap.
Every analyst has heard "just pull some insights." This guide gives you a framework to decode what stakeholders actually need and deliver analysis that drives real decisions.
The best analysis in the world is worthless if nobody acts on it. This guide teaches you how to structure findings as stories that executives understand and act on.
Turning a business question into a data query means resolving five things the asker left unsaid: the entity, the metric, the timeframe, the comparison, and the threshold. Then the query is the easy part.
AI is not coming for your job. It is coming for the parts of your job you do not like. This guide separates the hype from reality and shows analysts how to adapt.
Being mentioned by ChatGPT is a proxy. Someone arriving from ChatGPT and signing up is the outcome. Both are worth measuring, but only one goes in the board deck.
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