Landing Page Optimization
Multivariate Landing Page Optimization

To my surprise, there is no entry for LPO or MVLPO in Wikipedia. Here is a rough draft. Please, comment.
Alex

See also: [Search Engine Optimization], [Social Media Optimization]

Definition of Term
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.

Background
A recent study by researchers in Canada showed that the snap decisions Internet users make about the quality of a web page have a lasting impact on their opinions. They also reported that impressions were made in the first 50 milliseconds of viewing^[1]. These findings underscore the importance of creating the most appealing landing pages for ROI.

In addition to obvious targets such as home pages, other parts of a website may also be affecting the goals such as conversion rate. According to MarketingSherpa data, the average ecommerce shopping cart has a 59.8% abandonment rate^[2]. Many website designers do not consider these pages important. A simple improvement to this infrequently changed area (vs. the front page) could bring a substantial improvement to revenue per visitor (RPV) and ROI in general^[3].

Description
In a wide interpretation, there are five major approaches to LPO:

Associative Content Targeting

(also called "rules-based optimization" or “passive targeting”). Provides relevant information to the visitors 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”). Correlates 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.

Consumer Directed Targeting

(also called “social”) allows the consumers to adjust the relevance of publicly available information through a mechanism based on reviews, ratings, tagging, referrals, etc.

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 may include simple A/B-split test, multivariate (conjoint) based, Taguchi, 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 last two of the approaches, while the first three entries could be better classified as targeting methods rather than optimization.

Simple LPO involves a series of one or more disconnected A/B tests, each representing a “slot” on a page template where content can be placed. This approach generally is very limited, and cannot give reliable answers for pages that combine multiple elements.

MVLPO 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.

MVLPO can be executed in a live (production) environment (e.g., Google Website Optimizer^[4], Optimost.com, etc.) or through a market research survey/simulation (e.g., StyleMap.NET^[5]).

In Live Environment MVLPO, a special tool makes dynamic changes to the web site, so the visitors are directed to different executions of landing pages created according to an experimental design. The system keeps track of the visitors and their behavior (including their conversion rate, time spent on the page, etc.) and with sufficient data accumulated, estimates the impact of individual components on the target measurement (e.g., conversion rate). This approach is very reliable because it tests the effect of variations as a real life experience, generally transparent to the visitors. The drawbacks of the approach are the typically high cost, the complexity involved in modifying a production-level website, and the long time it may take to achieve statistically reliable data caused by variations in the amount of traffic, which generates the data necessary for the decision. This approach may not be appropriate for low traffic / high importance websites when the site administrators do not want to loose any potential customers. Many of these drawbacks are reduced or eliminated with the introduction of the Google Website Optimizer – a free DIY MVLPO tool that made the process more democratic and available to the website administrators directly.

Simulation (survey) based MVLPO is built on advanced market research techniques. In the research phase, the respondents are directed to a survey, which presents them with a set of experimentally designed combinations of the landing page executions. The respondents rate each execution (screen) on a rating question (e.g., purchase intent). At the end of the study, regression model(s) are created (either individual or for the total panel). The outcome relates the presence/absence of the elements in the different landing page executions to the respondents’ ratings and can be used to synthesize new pages as combinations of the top-scored elements optimized for subgroups, segments, with or without interactions. This survey approach using statistically designed combinations turns out to be much faster and easier to prepare and execute in many cases compared to the live environment optimization. It also addresses the issue of low traffic websites. Furthermore, the survey method may produce more robust and rich data because of a higher control of the design. The drawbacks of the approach include the possible bias of a simulated environment as opposed to a live one, and a necessity to recruit and optionally incentivise the respondents.

MVLPO paradigm is based on an experimental design (e.g., conjoint analysis, Taguchi method, etc.) which tests structured combination of elements. Some vendors use a full factorial approach (e.g., Google Optimizer that tests all possible combinations of elements). This approach requires very large sample sizes (typically, many thousands) to achieve statistical importance. Fractional designs typically used in simulation environments require the testing of small subsets of possible combinations. Some critics of the approach raise the question of possible interactions between the elements of the webpages and the inability of most fractional designs to address the issue. To resolve these limitations, an advanced simulation method based on the [Rule Developing Experimentation paradigm] ([RDE])^[5] has been introduced. [RDE] creates individual models for each respondent, discovers any and all synergies and suppressions between the elements, uncovers attitudinal segmentation, and allows for databasing across tests and over time.

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

Some of the companies currently providing MVLPO in one form or another:
Google
Offermatica
Optimost
SiteSpect
Mmetrics
Widemile.
Moskowitz Jacobs Inc. (RDE based).

References
^[1] Lindgaard G., Fernandes G. J., Dudek C. & Brown J. Behav. Inf. Technol., 25. 115 - 126 (2006).

^[2] Can Multivariate Tests Reduce Your Shopping Cart Abandons? Real-Life Results... MarketingSherpa, October 3, 2006
(https://www.marketingsherpa.com/barrier.html?ident=29725)

^[3] Andy Theekson. Rocket Conversion Rates With Multivariate Testing.
(www.ezinearticles.com/?Rocket-Conversion-Rates-With-Multivariate-Testing&id=554332)

^[4] Google Website Optimizer ( http://services.google.com/training/websiteoptimizeroverview/#slide=1)

^[5] Howard Moskowitz and Alex Gofman. Selling Blue Elephants: How to make great products that people want BEFORE they even know they want them. Wharton School Publishing, 2007.


External Links
http://services.google.com/training/websiteoptimizeroverview/#slide=1
http://www.the-dma.org/cgi/dispnewsstand?article=5275
http://www.stylemap.net/
http://www.websiteoptimization.com/speed/tweak/blink/
http://www.optimizeandprophesize.com/
http://ezinearticles.com/?Rocket-Conversion-Rates-With-Multivariate-Testing&id=554332


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Internet advertising and promotion
Internet terminology
Search engine optimization
Internet marketing by method