Understanding Analytics

Analytics Overview

Lime Light Analytics is designed to support the need to gain critical business insights without exporting data and manipulating it. Quickly answer macro and micro questions such as:

  • What is Gross Revenue by subscription cycle over time?
  • How does the paid search channel Customer Lifetime Value (CLTV) compare to affiliate channel CLTV?
  • Has Customer Churn rate from Initial to Recurring billing decreased from Q1 to Q2?
  • Is a specific source of traffic bringing higher quality customers to the business than others?
  • etc.

Through several unique dashboard reports and advanced filtering, Lime Light Analytics is specifically designed for presenting advanced business intelligence. Analytics will not leave questions about key metrics unanswered. Now address questions in one place with Lime Light Analytics!

Data Elements, Metrics & KPIs

Unfortunately, it’s not uncommon for the terms (data point, metric and KPI) to be misunderstood. The terms are often used interchangeably and the problem is, all three are very different—and using them correctly makes a difference.

  • A Data Element is a single identifiable data measure such as revenue, visitors, customer acquisition cost, etc that is used to create a metric.
  • A Metric is a result of a quantitative calculation or aggregation using data elements. For example, metrics include Average Order Value (AOV), Decline Rate, Refund Rate, Net Profit, etc.
  • A KPI (Key Performance Indicator) is an actionable scorecard that helps to keep strategy on track. They help enable businesses to manage, control and achieve desired results.

To get real power out of the data in Lime Light, it’s important to understand the relationship between data elements, metrics and KPIs, and how to use each of them.

Metrics Vs. KPIs

Metrics provide information that can be digested.

KPIs offer comparative insights that guide future actions.

Metrics are extracted and organized by activity or process.

KPIs are initiated by high-level decision makers.

Metrics can be viewed historically, but do not identify the future action.

KPIs incorporate goals and objectives.

Metrics are static, and once extracted do not change.

KPIs can be evaluated and reset over time.


Setting up Analytics

If the heart of Lime Light Analytics is the connection to the database, the brain is in the proper configuration. One without the other just doesn't thrive! The engineers at Lime Light have to take care of the connection from the production data and the Analytics/reporting database. Proper configuration relies heavily on setting up campaigns, gateways, expense assumptions, channels & verticals, and the other components of the Lime Light platform properly.

  • Production data is routinely updating the Analytics Database. This is done through a daily ETL (Extract, Transform, Load) so the most stable of technology and processes updates the data.
  • The ability to tie expenses to marketing efforts, make attribution inherent in Lime Light Analytics. The Channels and Verticals features add critical attribution characteristics to campaign data. The Expense Assumption functionality ties expenses to marketing activity and determines attribution profitability.

Attribution is critical as it identifies the specific marketing channels and verticals that are contributing to order volume and equally important… profitability. This layer of attribution lives within Lime Light Analytics if configured properly. 

  1. To set-up Channels, refer to Channels.
  2. To set-up Verticals, refer to Verticals.
  3. To set-up Expense Assumptions, refer to Expenses.


Acquisition Date Vs Transaction Date

Note: Data is organized and presented in two primary ways, including by:

  1. Acquisition Date; and
  2. Transaction Date. 

Acquisition Date organized the data by the date that the original order was placed. This allows Analytics to present data in an appropriately connected way. Specifically, Acquisition Date associates Re-bill and Recurring transactions (Cycles 1…X) to the Initial transaction that initiated the order.

When calculating many metrics, disassociating the recurring transaction types from the date of origination (the Initial transaction) causes arithmetic inconsistencies and brings incorrect and misleading results - and ultimately incorrect business decisions.

As an example: metrics that incorporate time-phasing and transaction type correlation into the calculation: think Re-Bill rate or Chargeback Rate; they both associate a transactional event to other components of the overall order within a timeframe.

That being said, there are instances where you may want to simply see the count, or value, or count by transaction type, of transactions within a period. When this type of analysis is performed it will be noted as such.

Data presented based on Transaction Date will organize the data by the date in which the order was placed without an association between Re-bill and Recurring transactions (Cycles 1…X) and the Initial transaction (Cycle 0) that initiated the order.

You may find the following Help Center articles relevant to Analytics helpful.

  1. Using Filters
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