Applying AARRR in Practice

# Applying AARRR in Practice Dave McClure's AARRR framework (Acquisition, Activation, Retention, Revenue, Referral) forces you to examine the entire customer journey. ## AARRR stage aggregation in Python ```python import pandas as pd # Channel-level AARRR pipeline dataset data = { "channel": ["Paid Social", "Organic Search", "Referral"], "acquisitions": [10000, 8000, 3000], "activations": [1800, 3200, 1650], # D1 value moment "d30_retained": [270, 1120, 825], # D30 retention "paid_converted": [120, 480, 450], # Revenue stage } df_aarrr = pd.DataFrame(data) # Calculate stage conversion rates df_aarrr["activation_rate"] = df_aarrr["activations"] / df_aarrr["acquisitions"] df_aarrr["retention_rate"] = df_aarrr["d30_retained"] / df_aarrr["activations"] df_aarrr["pay_cvr_overall"] = df_aarrr["paid_converted"] / df_aarrr["acquisitions"] print(df_aarrr[["channel", "activation_rate", "retention_rate", "pay_cvr_overall"]].to_string(index=False, formatters={ "activation_rate": "{:.1%}".format, "retention_rate": "{:.1%}".format, "pay_cvr_overall": "{:.1%}".format, })) ``` ## Takeaway AARRR helps spot whether your acquisition engine is merely filling a leaky activation bucket.