# Step 1: Convert all interactions to a list
journeys = campaign_data.groupby('customer_id')['channel'].aggregate(
    lambda x: x.tolist()).reset_index()

# Step 2: Add last interaction as 1 or 0 event representing activation
activation_results = campaign_data.drop_duplicates('customer_id', keep='last')[['customer_id', 'activation']]
journeys = pd.merge(journeys, activation_results, how='left', on='customer_id')

# Step 3: Add start and end states based on whether customer activated
journeys['path'] = np.where(
    journeys['activation'] == 0,
    journeys['channel'].apply(lambda x: ["Start"] + x + ["Null"]),
    journeys['channel'].apply(lambda x: ["Start"] + x + ["Activation"])
)

journeys = journeys[['customer_id', 'path']]

# Get overall activation rate
total_activations = journeys['path'].apply(lambda x: x[-1]).str.match('Activation').sum()
activation_rate = total_activations / journeys.shape[0]