How can Kleene.ai help with digital attribution?

Kleene.ai measures cross-channel performance using long-term journey data to gain unbiased visibility into channel effectiveness and revenue impact.

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Industry challenges

Limited LTV insights

Slow reporting cycles

Cross-channel attribution gaps

Poor visibility into CAC & LTV

CRM-to-revenue disconnect

Reactive budget allocation

Weak cohort analysis

No predictive forecasting

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Which channels is last-click over-crediting this quarter?

KAI

Last-click Data-driven Share of credited conversions

Channel	Last-click	Data-driven	Change
Paid Search	38%	26%	−32%
Paid Social	12%	19%	+58%
Email	9%	14%	+56%
Direct	17%	13%	−24%

What this means

Paid Search brand terms are taking credit for conversions that started on Paid Social.
Email assists 2.3 journeys for every one it closes, so last-click barely sees it.

Recommendation

Shift roughly £48k/month out of brand search into Paid Social prospecting and hold Email budget flat. Reallocations at this size typically recover 10–25% of wasted ad spend.

Same question, asked from your own tools

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You Which channels is last-click over-crediting this quarter?

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How Kleene.ai solves this

With ELT infrastructure and AI analytics in one integrated system

A team that works inside your business

Data analysts and engineers that build, run in interpret your models for you

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use cases

Popular use cases for marketing teams

True ROAS across paid channels

Connect Google Ads, Meta, TikTok, GA4, CRM (HubSpot/Salesforce), and revenue data to match ad spend to closed-won revenue and identify which campaigns actually drive profitable customers.

CAC by channel & cohort (LTV-Based)

Unify ad platforms, CRM, subscription or ecommerce revenue, and churn data to calculate CAC and LTV by channel and acquisition cohort – and shift budget toward the highest long-term value sources.

Marketing pipeline forecasting

Combine live ad spend, pipeline stage conversion rates, historical close rates, and sales cycle length to predict next-quarter revenue impact before increasing or reallocating marketing budget.

Activate KAI Analytics for marketing

With ELT infrastructure and AI analytics in one integrated system

Segmentation

Track monthly customer movement across value-based RFM segments, enriched with geodemographic and transactional data. Quantify value gained or lost as customers shift between segments — enabling smarter retention and acquisition decisions.

Media Mix Modeling

Use 24+ months of sales, seasonality, weather, and channel data to isolate true media impact. Optimize budget allocation and improve marketing ROI.

Digital Attribution

Measure cross-channel performance using long-term journey data. Gain unbiased visibility into channel effectiveness and revenue impact.

Demand Forecasting

Project demand up to 18 months out and rank the drivers behind it, including seasonality, events and weather. Test scenarios before committing to a plan.