Analytics And Data

Analytics And Data

Let’s have a quick conversation about your analytics and data.

If your business is running without the full benefit of a well-run data and analytics program there is usually one of two issues:

  1. You don’t know what to measure and/or don’t have time or resources to measure it if you did. As a result, you measure nothing and make business decisions with “gut instinct.”
  2. You measure everything and are overwhelmed with an avalanche of data. As a result, business decisions aren’t made due to “analysis paralysis.”

As with most things, the optimal level of access to data is somewhere in the middle.

Whether it’s a lack of data or a glut, your organization needs to organize its metrics into two groups:

  1. Key Metrics – A small number of metrics that are critical to meeting business goals. These metrics are monitored regularly.
  2. Drill-Down Metrics – A larger number of metrics that are only analyzed if a Key Metric changes significantly. These metrics are granular in comparison to the Key Metrics they are associated with.

For example, a company might be monitoring a Key Metric called ‘New Website Visitors’.

If that metric changes significantly, the analyst would investigate why by analyzing the Drill-Down Metrics of ‘Traffic by Channel’ or ‘Share of Search’.

Key Metrics should be represented by data dashboards that can be monitored at a glance.

Analytics and Data Dashboard

Key and Deep Dive Metrics should be organized by the different stages of the marketing and sales funnel.

Organizing business metrics in different stages of the funnel so that issues can be identified and investigated quickly.

Create categories of metrics such as:

  • Top of Funnel Metrics (TOFU Metrics) – Metrics that track the goal of raising awareness for your company and the solutions it provides.
  • Middle of Funnel Metrics (MOFU Metrics) – Metrics that monitor the health of marketing campaigns designed to generate leads.
  • Bottom of Funnel Metrics (BOFU Metrics) – Metrics that measure the conversion of leads into customers and existing customers into more valuable buyers.
  • Retention and Monetization Metrics – Metrics that monitor the companies ability keep existing customer and encouraging people who have bought to buy more frequently.

Let’s look at the analyst toolkit. 

When attempting to explain the cause of a significant shift in a metric, the data-driven organization views the data through four equally important “lenses”:

  • The Historical Lens – How does the current data compare to the baseline in the past? For example, does this metric always drastically improve during the Christmas holiday?
  • The External Lens – What changed externally that may have impacted this metric? For example, has a new law been passed that has drastically altered this metric?
  • The Internal Lens – What changed internally that may have impacted this metric? For example, is this metric being impacted by a new source of traffic being purchased by your Acquisition Team?
  • The Contextual Lens – Is the data being viewed in the proper context? For example, am I comparing raw numbers to percentages? Are there a few outliers that are dramatically skewing this metric?

Here’s a breakdown of the terms I’m using (in case you’re not sure what some of these mean).

  • Analytical Decision Making – The process by which a hypothesis is formed around an anomaly in the metrics. That hypothesis is then used to exploit the benefit of a positive shift or correct a negative shift in a metric.
  • Key Performance Indicator (KPI) – a metric that demonstrates how effectively a company, department or campaign is meeting business objectives.
  • Variation – A visualization of metrics usually contained on one page or screen and, ideally, updated in real time.
  • UTM Parameters – Tags added to a URL that communicates the campaign, source, medium and other information about a website visit to Google Analytics.