Move budget to where the chain actually breaks.
The Markov chain model tells us how essential each channel is to the path that ends in a conversion. Comparing channel credit against current channel spend reveals where you're structurally over- or under-invested.
Currently 42.9% of paid budget, but Markov credit is 19.9%. Last-click gives it 22.4% — which is roughly aligned.
Currently 10.7% of paid budget, but Markov credit is 3.3%. Last-click gives it 3.8% — which is roughly aligned.
Currently 0.0% of paid budget, but Markov credit is 14.6%. Last-click gives it 9.9% — which is meaningfully off.
Currently 0.0% of paid budget, but Markov credit is 9.5%. Last-click gives it 18.5% — which is meaningfully off.
| Channel | Current spend | Share of spend | Last-click share | Markov share | Implied ideal | Δ Recommendation |
|---|---|---|---|---|---|---|
Meta Ads | $751,065 | 42.9% | 22.4% | 19.9% | $348,398 | -$402.7K |
Referral | — | — | 9.9% | 14.6% | $255,112 | n/a |
Organic | — | — | 18.5% | 9.5% | $166,152 | n/a |
LinkedIn Ads | $186,685 | 10.7% | 3.8% | 3.3% | $58,182 | -$128.5K |
Direct | — | — | 12.3% | 6.5% | $114,727 | n/a |
TikTok Ads | $108,924 | 6.2% | 2.8% | 7.5% | $131,650 | +$22.7K |
Google Ads | $648,998 | 37.0% | 29.0% | 36.1% | $631,919 | -$17.1K |
Snapchat | $56,794 | 3.2% | 1.3% | 2.6% | $46,327 | -$10.5K |
This isn't a forecast. Reallocation recommendations are derived from the Markov chain credit distribution, which captures how essential each channel is to the journey reaching a conversion. The implied dollar amounts assume CPA holds steady — in practice, marginal CPAs degrade as a channel scales, so use these as directional guidance, not a budget commitment.
In production this view would also incorporate a scaling-elasticity modelper channel (CPM curves from the Ads APIs, saturation in audiences) before publishing the dollar figures. The Markov layer answers where credit really lives; the elasticity layer answers how far you can push it.