With most businesses already ‘sold’ on the importance embedding data into marketing practices, ecommerce businesses need more pragmatism.

The reality is that many companies start their analytics journey unsure how to create and nurture a data-driven culture. This post will focus on the first step to data maturity: making sense of customer data with automated reports. 

There are TWO ways to get data into the hands of people making decisions: push and pull reporting.

To make the most of their data, ecommerce organisations need to do both.


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.


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.


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.


The goal of pushing data is to make sure that everyone in a team or company is aware of a set of core metrics (KPI’s). 

These metrics should closely correlate to desired business outcomes. They should also be metrics that can be optimized so that data is actionable.

It’s important to send this data to stakeholders on a cadence that matches decision-making processes. This usually takes the form of custom reports or dashboards that can be easily referenced on any given day, week, or other time period.

Commonly, dashboards are bookmarked, emailed to inboxes, or shared on messaging platforms such as Slack.

Dashboards require maintenance (such as when data pipelines are updated, or when business requirements evolve), so that they always represent a ‘source of truth’ for core business metrics.


It’s crucial only to push data that changes behavior. Focus on giving stakeholders starting points that prompt further investigation and action.


Push Analytics (automated reports) provide value by surfacing the performance growth metrics without data wrangling.

Inevitably, these reports invite follow-up questions (even when they closely align with business outcomes). Such questions can be answered with Pull Analytics to drive real outcomes.

This is why push and pull analytics need to be combined. Only then can data be operationalized on a consistent basis.

Push/Pull analytics is the first step on the journey to becoming truly data-driven. At a high level, businesses need to:

1. store and centralize data from multiple sources into a data warehouse (this is a data engineering task);

2. combine and model the data to make it analysis-ready (analytics engineering task);

3. surface the data into data visualisation tools (analytics engineering task).

Host Digital builds data pipelines & bespoke dashboards for ecommerce marketing teams and institutional investors; then we actively help to operationalize this data. Lean more about our Data-As-A-Service offer and schedule a dashboard demo here: