PPC Performance Forecasting: Predicting Realistic Results

Most PPC forecasts are just wishful thinking disguised as data - they’re not based on real evidence. For proper, smart forecasting, you need real data - historical performance, seasonality patterns, and realistic assumptions used to predict future results. Forecasts are never perfect, but a bad one is worse than not having one at all. Instead of guessing, use a model - even a simple one.

Build Forecasts from Historical Data

Start with your historical spend versus return data from the past 3-6 months. If you spent $10,000 and generated $40,000 in revenue, your baseline ROAS is 4:1. Use this as your starting point for future projections.

Factor in seasonality and sales cycles that affect your business. Retail sees spikes in November and December. B2B often slows in summer and around holidays - these are consistent patterns. Look at year-over-year data to identify these patterns and adjust forecasts accordingly.

Project traffic changes using keyword volume tools like Google Keyword Planner. If search volume for your main keywords increases 20% next quarter, you can expect roughly 20% more impressions and clicks.

Apply your average cost per clicks and conversion rates to estimate performance - if your keywords typically cost $5 per click and convert at 3%, you can calculate how many conversions to expect from any given budget.

Create Realistic Scenarios

Estimate best-case, worst-case, and most likely scenarios for each forecast - but be realistic. Best case might assume 20% improvement in conversion rates and 10% lower CPCs. Worst case might assume 15% higher CPCs and 10% lower conversion rates. Most likely assumes performance similar to recent averages.=