Enabling localized teams (e.g. the ecommerce team) to pull specific and hyper-contextual data that informs decisions.
Consider the below example:
Context: Sales are increasing over time. But retention rate for new customers is ~15% over a 3-month period.
Decision: To reduce churn, the ecommerce team decides to compare cohorts of customers with higher retention rates vs lower retention rates.
Success criteria: An increase in lifetime value after 3 months.
PULL ANALYTICS OUTCOME
Our hypothetical team notices that lifetime value is closely correlated to the second purchase. When customers make a second purchase, not only do they spend more, but they’re more likely to make a third, fourth, etc. purchase.
Our heroic team digs deeper and notices that customers who purchase product X have higher 1st-to-2nd repurchase rates. They also notice that second purchases tend to happen 4-6 weeks after the first purchase.
Armed with this data, the savvy team adjusts acquisition activities to focus on product X. And doubles down on weeks 4, 5 and 6 for retention activities.
SUMMING UP: PULL ANALYTICS
Whilst it’s a simple example, this kind of insight is rarely surfaced in off-the-shelf dashboards. Deep (profitable!) data exploration requires self-serve investigation by subject matter experts such as your ecommerce team.