Analytics and Business Intelligence – Introduction

The data you need to manage a business falls into two broad categories: data you know you don’t know, and data you don’t know you don’t know.

Type 1: Data you know (but probably don’t know well enough)

Descriptive Analytics use Key Performance Indicators (KPI).  Typical examples are leads, prospects, new customer growth, customer acquisition cost, etc. Online businesses are interested in website traffic patterns, time on site, customer retention, lifetime customer income, etc.
One reason investments in big data fail to pay off, is that most companies don’t do a good job with the information they already have.

Representative applications

Regardless of your business model, you should be interested in what the market and your customers are saying online, i.e. social business.

Social Business Tracking

Aggregation, Curation and Presentation

Data is just numbers but information is actionable. Several powerful applications allow you to capture and format data from various sources into actionable charts and presentations.

Maintenance and Real time analytics

Trend analysis help you fight fires but real-time monitors are smoke detectors – and there is wide range depending on your business objectives.

Type 2 – Data you don’t know that you don’t know

Predictive Analytics is exposing trends you don’t track, correlations you didn’t know existed, Connections you don’t know you have, and risks and opportunities that may be obscured.

Applications include:

  • Lead Scoring Uncovering additional leads from collateral data
  • Product Planning Identifying new markets for existing products and new products for existing markets
  • Networking Identifying new relationships from collateral data;
  • Dynamic Customer Profiling Real time behavior for online businesses.
  • Examples:

    Future Topics

    This series will continue on PageMill Marketing, Results Exponetialized.

    • Data Science – Big Data Case Studies
    • Marketing Automation – Integrated Platforms for Inbound Marketing, e.g. eMail, Lead Nurturing, etc., with interfaces to CRM
    • Big Data Case Studies
    • The leap from Data Science to Market Intelligence