Playbooks
The full playbooks — deep content and courses.
Each playbook is the complete operational fix for one of the 25 obstacles to customer data maximization: the moves, the sequence, and the metric to watch. Read free — then book a call when you want it built.
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Fragmented customer data
The operating sequence to fix data silos for good: name one master per data domain, get the connecting keys right, then appoint someone to own quality. Ownership first, tooling second.
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Identity resolution
Below 60% identity coverage, personalisation and attribution are guesses. This is the operating sequence to fix it: verify email at capture, build a deterministic spine, then measure coverage and supplement.
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Data quality, the AI multiplier
Poor data quality costs the average company 12.9 million dollars a year and stalls AI projects. This is the operating sequence to fix it: clean on collection, monitor at ingestion, tie quality to a revenue use case with an owner.
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Data governance as enablement
The operating sequence for turning governance from a compliance tax into a revenue enabler: reframe the mandate, govern 20% fully, hand it a commercial owner, expand from the win.
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Consent as a data type
Half of companies say privacy rules made personalisation harder. This is the operating sequence to fix it: model consent on the profile, enforce it everywhere downstream, and store the rule not the build.
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The cookie reversal
Google keeping the cookie in Chrome changes nothing about the right move. This playbook restarts any paused first-party work, moves collection server-side, and makes the data survive the next reversal.
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Multi-touch attribution
Replace broken multi-touch attribution in three steps. Fence it to tactical reads, stand up incrementality testing for budget calls, and add marketing mix modelling for the full picture. A measurement decision, not a new tracking build.
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The dark funnel
Measure the dark funnel in three steps. Add self-reported attribution to every form, cross-reference against pipeline quarterly, fund channels on what closes.
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Cross-channel measurement
The operating sequence for measuring offline and digital together: clean the historical data, build a marketing mix model, then settle the arguments with geo-lift experiments.
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Retiring the MQL
Retire MQL volume as a board metric in three steps. Cut the vanity number, install a revenue-integrated scorecard, and measure the buying group. A reporting decision, not a system build.
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The activation gap
Close the activation gap in three steps. Name the decision and owner for every report, push data into operational tools, and measure actions taken per insight produced.
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The personalisation perception gap
A practical sequence for closing the personalisation perception gap: rebuild the scorecard on customer signal, segment on intent, and prove relevance with one metric owners trust.
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Personalisation without the creepiness
A practical sequence for personalising without crossing the creepiness line: write rules of use, make the value exchange visible, and police the line before send, not after the complaint.
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Real-time personalisation
A practical sequence for real-time personalisation that pays for itself: name the moments where speed changes the outcome, fix the record they fire from, then build real-time only there and leave the rest on batch.
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Channel consistency
A practical sequence for keeping messaging consistent across every channel: name one source of truth, back the self-service buyers prefer, and add channels only for new reach.
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Martech underutilisation
You use 49% of your martech. This is the operating sequence to get the rest: audit against real use, cut the redundant, and fund the people who run what is left. No new tools, no 12-week project.
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Composable CDP
Your CDP became a copy that drifts. This is the operating sequence to replace it: make the warehouse the single store, resolve identity deterministically, and precompute audiences so the view runs the system. No bigger CDP required.
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Integration debt
The operating sequence to pay down integration debt: set one shared data model, buy API-first with interoperability as a rule, and test every tool against your live stack with a proof of concept before you sign.
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Martech stack ownership
Name one owner, build one live inventory, make utilisation a performance objective. The operating sequence to fix the martech ownership gap that split funding creates, in weeks.
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The buy-a-tool reflex
A procurement discipline for marketing teams: default to what you own, require a business case that names the revenue at stake, and run competitive proofs of concept instead of vendor demos.
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The generative AI ROI gap
An operating sequence for measurable AI: pick use cases by business outcome, set the metric and baseline before you start, then split quick-win and agentic work onto different timeframes.
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AI data readiness
The operating sequence for making data ready before you scale AI. Audit readiness first, fix the foundation in order, then scale the model on data you trust.
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AI trust and hallucination governance
The operating sequence for deploying AI you can trust: stand up governance as a function, set provenance and human review on customer-facing output, and prove explainability.
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Team enablement
Fund enablement as core spend, mandate AI and data fluency as a baseline skill, and train on the stack you actually run. The operating sequence to close the skills gap, in weeks.
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Data leadership that lasts
Partner instead of police, fix the process behind the data, and get finance to certify the value. The operating sequence that turns a churning data role into one that lasts past year two.
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