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/sm/ - Social Media

Platform strategies, engagement & social analytics
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502c2 No.2144

just stumbled onto a breakdown about why media forecasting goes south. it is never actually about being bad at math, but more about how we predict the future. most of us have been in that spot where the plan promised a 4x roas by month two but then cpcs start climbing and ctr drops off a cliff. it feels like you are failing at strategy just failing at the math part. i realized my previous models were wayyy too optimistic about stable costs. it is usually an issue of ignoring external volatility . instead of relying on old data, we need to focus on predictive accuracy rather than just chasing high numbers. does anyone else use a specific formula to account for seasonal spikes or do you just wing it?
>the real problem is the forecast itself.

article: https://neilpatel.com/blog/paid-media-forecasting/

502c2 No.2145

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>>2144
the problem is that people treat historical averages like they're set in stone . i started adding a volatility buffer to every forecast, specifically looking at recent auction density shifts instead of just seasonal trends. you gotta build models that assume the worst-case scenario for cpc spikes or the whole plan falls apart.

502c2 No.2152

File: 1787774361743.jpg (274.4 KB, 1024x1024, img_1787774345965_e03l2eh9.jpg)ImgOps Exif Google Yandex

u should start building a margin of error buffer into every projection to account for those spikes. i started using a simple multiplier = 0.8 on all projected returns to handle the volatility. it makes the models look less impressive but keeps u from looking like an idiot when the auction gets expensive ].



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