Getting Started with Digital Analytics

What is digital analytic?

  • Using data to drive change. The change desired is based on individual or business objectives

Why digital analytics is important?

  • The traditional sales funnel is very linear, it assumes customers will appear at one place, and guides them in a funnel until a conversion(purchase). In the digital world, a purchase can happen at any point of a decision path. The control is shifting from firm to customers.
The tradition sales funnel
The Traditional Sales Funnel
Affect of digital tools on marketing and sales
  • The traditional sales funnel breaks apart in the digital world. Four trends that is changing the landscape of traditional sales funnel.
    1. Internet
      • User can access any information of products and services from computer with internet connection. They can compare prices very fast.
    2. Mobile devices and applications
      • Coupled with internet connection, customer can search and compare product and service price/review/history, 24/7
    3. Cloud computing
      • Firms can ignore infrastructure maintenance/setup/security/cost, and just focus on analyzing data and application.
    4. 3D printers
      • This is more related to product development. Customers can print their own spare parts/components and devices, without purchasing from firms.
The change of customer behavior because of digital trends
The change of customer behavior because of digital trends
  • Hence we need to analyze more on the customer(behavior), rather than the channel they are accessing a site.
  • A marketer should understand the customer behavior and anticipate where the customer can appear, and what message to they need to hear.
User is in control
User is in control

Definition of Digital Analytics

Avinash Kaushik's( a digital analytics evangelist) definition of Digital Analytics is:

Digital Analytics is the analysis of qualitative and quantitative data from your business and the competition to drive a continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes(both offline and online).

Quantitative Data
  • This data is generated when user uses a site. Example are page visits, geographic location.
  • Previously GA can only collect data from web sites. Now it can collection from various devices and applications used by users. This gives a holistic point of view of how a user behaves and we target according to their behavior
**Quantitative data generate when user use a site**
Example of quantitative data generated from user activity
Quantitative Data
Example of quantitative data generated from user activity from different locations
Qualitative Data
  • Data that explain why a user behaves in certain ways. For example, survey, and polls.
  • Show how a user experiences your site, that you cannot get from quantitative data.
Qualitative Data
Example of qualitative data are survey, polls, and user reviews
Outcomes
  • Before we do some data analytics, we need to understand what is our ultimate business objectives and outcomes, and how do you expect to measure those outcomes.
  • Its important to have a clear measurement strategy to guide your implementation strategy, and your data analysis.
  • For a online world, the 5 common business objectives are:
    1. E-commerce:
      • Sell more products & services
    2. Lead generation:
      • Collect user information for sales team to connect with potential leads.
    3. Content publisher:
      • Encourage, engage customers & frequent visitation
    4. Online info/support:
      • Help user to find information they need at the right time.
    5. Branding
      • Drive awareness, engagement, loyalty.
Five common business objectives
5 common business objectives
Business Type and Their Desired Outcomes
Business Type and Their Desired Outcomes
  • There are keys action on a site or mobile site that can be tied back to the business objectives.
  • When an action indicate a business objective is fully meet, we call them macro conversion
  • Some actions don't not indicate a business objective is fully meet, but they indicate their are getting near to the business objectives. We call them micro conversion
  • We need both micro and macro conversion to better understand how the desired outcomes are being meet.
Macro and micro conversion
Example Of Macro and Micro Conversion In A E-comerce Site
Continual Improvement
  • Data can be used for continual improvement for your business. The continual improvement process can be broken to five steps are:
    1. Measure
      • Collecting the data needed to answer your business questions. Ex, How many people are completing the customer journey? Where along the journey we are losing or retaining customer
    2. Report
      • Package the data in a readable format, and get data out to the decision makers. So they can have the information they need to do business decisions. Ex, distributing pre- made dashboard
    3. Analyze
      • Identifying large trends. It can also be complex( identifying deep segmentation of your data), or a competitive analysis(Compare your result with industry benchmark)
      • We will create hypothesis that match our expectation, and figuring why the numbers do or do not match our expectation.
      • When unexpected things happen in your data, analyze helps us to figure out why.
    4. Test
      • We try different solution to the problem, that we identified during the analysis.
      • This is important because it takes out opinion from the decision making process to discover improvement opportunities.
    5. Improve
      • We repeat what we learn and we improve
The process of continual improvement
The Process Of Continual Improvement

Read further: