👯 Plan across multiple models

If you have multiple models with Recast, you can use the planning tools to optimize and forecast across all your models. This will help you see the outcome of your marketing budget across all your KPIs and maximize the sum of your dependent variables across all your models.

To optimize across multiple models:

  1. Navigate to the optimizer tool in the Recast app
  2. Pick which models you want to include in your optimization by selecting the checkboxes next to the model name.
  3. Click “show spikes” to adjust the dates when you expect an anomaly in your spending or dependent variable.
  4. Input a multiplier in the input box.
  5. Set your objectives
  6. Set your constraints
  7. Upload your budget and run the optimizer


The output of the multi model optimization includes a breakdown of spend and expected ROI per channel. This is visible both as an aggregate of all your models and per model. This tells you how much you are spending in each channel towards the ROI driven by each dependent variable. You can also see how much you are spending and your ROI overall. Additionally, can see spend over time, daily and cumulative optimized outcomes aggregated over all your models as well as for each model.

To Forecast across all your models:

  1. Navigate to the forecaster tool in the Recast app
  2. Select the models you would like to include in your forecast by clicking “Forecast across multiple models” and checking the box next to the models to include
  3. Input the model multiplier
  4. Select the timeframe for your forecast
  5. Select an option for how to create your budget. Click here to learn more about the different options for how you can upload a budget to the forecaster.
  6. Limit spend in your lower funnel channels (Learn more about lower funnel settings.)*
  7. Name your forecast*
  8. Select ‘Run Forecast



The multi-model forecaster outputs the total spend, in-period return and ROI for each channel across all your models. You can also see how this is broken down by each model you have aggregated in your forecast using the tabs at the top of the results. In the aggregate tab, you will see the results of all your subsequently selected KPIs in terms of the initially selected KPI. At the bottom of the page, you can see the uncertainty contribution of each channel in your mix.

The graphs show:

  • How much you will spend over time in each channel towards all your KPIs
  • Expected KPI numbers over time both as aggregate measures over all your KPIs and for each KPI
  • A forecast of the aggregate return over all the channels present in all the models selected during the timeframe of your forecast.


Q. What is the model weight multiplier?

The multiplier has to be greater than 0. The multiplier reflects the relative values of the modeled dependent variable in terms of either revenue or CPA depending on the KPIs of each of your models and how each of your models are weighted. e.g. if a “signup” is worth $100 the multiplier would be 100 and we would optimize 1_revenue + 100_signups. If both your models are CPA models, the multiplier could simply be a ratio of how valuable one conversion is compared to the other. The default multiplier is 1 which assumes that each dependent variable is ‘worth’ $1.

Q. What will happen to the constraints setting if models have differing most recent dates of data?

You can set the constraints only in the dates that overlap between all models. So the first date will be the maximum of the allowed start dates of all included models.

Q. If both models are conversion models, then we should put a multiplier in that is some approximation of revenue per conversion like an LTV?

The conversion model weights could be LTV approximates or just a ratio of how valuable one conversion is compared to the other.

Q. What do I do if one of my models uses revenue and one uses CPA as the KPI?

For cross model optimizations, the model weight multiplier handles the conversion between revenue and CPA.. For example, if a “signup” is worth $100 the multiplier would be 100 and we would optimize 1_revenue + 100_signups. You could just as easily set the revenue model weight to be 0.01, implying you need $100 of revenue to equal one conversion.