Campaign Effectiveness
How we used counterfactuals and Bayesian structural time series to understand campaign effectiveness
Problem:
The Client, in B2B software space, was running promotional campaigns in 4 different markets and wanted to understand whether the campaigns were leading to increment in Leads.
Solution:
- The concepts of counterfactuals and Bayesian structural time series were applied to understand whether the additional leads were due to the campaign and if yes, how much was the incremental lift in leads.
- If the algorithm observed increased leads (or other KPIs) during the intervention period (campaign duration period) vis-a vis the hypothetical scenario wherein no campaign was run, then the algorithm assigned a probability of this increment happening due to the campaign.
Impact
15% lift observed in Unique Leads
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