Landing page optimization

Landing Page Optimization (LPO, also known as WebPages Optimization) is the process of improving a visitor’s perception of a website by optimizing it’s content and appearance in order to make them more appealing to the target audiences as measured by target goals such as conversion rate or other.

Multivariate Landing Page Optimization (MVLPO) is Landing Page Optimization based on an experimental design.

here are three major types of LPO based on targeting:

• Associative Content Targeting (also called ‘rules-based optimization’ or ‘passive targeting’). Modifies the content with relevant to the visitors information based on the search criteria, source, geo-information of source traffic or other known generic parameters that can be used for explicit non-research based consumer segmentation.
• Predictive Content Targeting (also called ‘active targeting’). Adjusts the content by correlating any known information about the visitors (e.g., prior purchase behavior, personal demographic information, browsing patterns, etc.) to anticipated (desired) future actions based on predictive analytics.

There are two major types of LPO based on experimentation:

• Close-Ended Experimentation exposes consumers to various executions of landing pages and observes their behavior. At the end of the test, an optimal page is selected that permanently replaces the experimental pages. This page is usually the most efficient one in achieving target goals such as conversion rate, etc. It may be one of tested pages or a synthesized one from individual elements never tested together. The methods include simple A/B-split test, multivariate (conjoint) based, Taguchi, Total Experience testing, etc.
• Open-Ended Experimentation is similar to Close-Ended Experimentation with ongoing dynamic adjustment of the page based on continuing experimentation.

This article covers in details only the approaches based on the experimentation. Experimentation based LPO can be achieved using the following most frequently used methodologies: A/B split test, Multivariate LPO and Total Experience Testing. The methodologies are applicable to both – close-ended and open-ended types of experimentation.

A/B Testing

A/B Testing (also called ‘A/B Split Test’): a generic name of testing a limited set (usually 2 or 3) of pre-created executions of a web page without use of experimental design. The typical goal is to try, for example, three versions of the home page or product page or support FAQ page and see which version of the page works better. The outcome in A/B Testing is usually measured as click-thru to next page or conversion, etc. The testing can be conducted sequentially or concurrently. In sequential (the easiest to implement) execution the page executions are placed online one at a time for a specified period. Parallel execution (‘split test’) divides the traffic between the executions.

• Pro’s of doing A/B Testing:
  • Inexpensive since you will use your existing resources and tools
  • Simple –no heavy statistics involved
• Con’s of doing A/B Testing:
  • It is difficult to control all the external factors (campaigns, search traffic, press releases, seasonality) in sequential execution.
  • The approach is very limited, and cannot give reliable answers for pages that combine multiple elements.

MVLPO

MVLPO structurally handles a combination of multiple groups of elements (graphics, text, etc.) on the page. Each group comprises multiple executions (options). For example, a landing page may have n different options of the title, m variations of the featured picture, k options of the company logo, etc.

• Pro’s of doing Multivariate Testing:
  • The most reliable science based approach to understand the customers mind and use it to optimize their experience.
  • It evolved to a quite easy to use approach in which not much IT involvement is needed. In many cases, a few lines of javascript on the page allows the remote servers of the vendors to control the changes, collect the data and analyze the results.
  • It provides a foundation for a continuous learning experience.
• Con’s of doing Multivariate Testing:
  • As with any quantitative consumer research, there is a danger of GIGO (‘garbage in, garbage out’). You still need a clean pool of ideas that are sourced from known customer points or strategic business objectives.
  • With MVLPO, you are usually optimizing one page at a time. Website experiences for most sites are complex multi page affairs. For a e-commerce website it is typical for a entry to a successful purchase to be around 12 to 18 pages, for a support site even more pages.

Total Experience Testing

Total Experience Testing (also called ‘Experience Testing’) is a new and evolving type of experiment based testing in which the entire site experience of the visitor is examined using technical capabilities of the site platform (e.g., ATG, Blue Martini, etc.).^[1]

Instead of actually creating multiple websites, the methodology uses the site platform to create several persistent experiences and monitors which one is preferred by the customers.

• Pro’s of doing Experience Testing:
  • The experiments reflect the total customers experience, not just one page at a time.
• Con’s of doing Experience Testing:
  • You need to have a website platform that supports experience testing, (for example ATG supports this).
  • It takes longer than the other two methodologies.

Multivariate Landing Page Optimization (MVLPO)

The first application of an experimental design to website optimization was done by Moskowitz Jacobs Inc. in 1998 in a simulation demo-project for Lego website (Denmark). MVLPO did not become a mainstream approach until 2003-2004.