Customer journey mapping and connecting sentiment signals to business KPIs

1. List every touchpoint in the first 90 days (welcome email, setup wizard, first login, tutorial, support check-in). 2. Gather sentiment data for each touchpoint from sources like post-interaction surveys or support chat logs. 3. Plot the average sentiment score against the cohort's churn rate at day 30, 60, and 90. 4. Identify the touchpoint with the steepest sentiment drop and correlate it with a spike in churn.

1. Map the journey stages: Awareness, Consideration, Purchase, Delivery/Use, Post-Purchase Engagement, Repeat Purchase. 2. For each stage, define the primary channel and the key sentiment signal (e.g., website review sentiment, email open/click sentiment, support ticket sentiment). 3. Calculate the average sentiment score for customers who become high-LTV vs. low-LTV. 4. Create a sentiment funnel visualization showing how positive sentiment decays (or builds) from Consideration to Repeat Purchase, pinpointing the stage with the largest 'sentiment leak' that impacts LTV.

1. Aggregate 12+ months of sentiment data from all touchpoints and quantify it using a weighted sentiment score. 2. Perform regression analysis to establish the statistical relationship between the composite sentiment score and the leading indicator of churn (e.g., usage decline, support ticket volume). 3. Translate this relationship into a 'Revenue at Risk' model: e.g., a 0.5-point drop in composite sentiment predicts a 2% increase in churn next quarter, equating to $X revenue loss. 4. Structure the board presentation: Start with the 'Revenue at Risk' headline, show the historical correlation chart, present the predictive model, and propose the $2M investment as a direct mitigation of that financial risk with projected ROI.