Conversion optimization
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In Internet Marketing, Conversion optimization is the science and art of enhancing a visitor's online experience with an advertiser's offering when they visit a web site or see an online ad - with the goal of transforming the visitor into a customer.
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[edit] Web Origins
Conversion optimization was born out of the need to define the new type of customer that surfaced as a result of the web. Internet marketers had to figure out what web surfers were thinking when browsing a site and how the site could influence them to perform specific actions. Two of the pioneers of conversion marketing, Bryan Eisenberg and Jeffrey Eisenberg, recognized this emerging field and quickly became an integral part of its development. Through the creation of concepts such as Persuasion Architecture, the Eisenberg's soon became two of the biggest names in the conversion optimization field.
[edit] Why conversion optimization
Frequently, when marketers target a pocket of customers that has shown spectacular lift in an ad campaign, they belatedly discover the behavior is not consistent, with online marketing response rates fluctuate widely from hour to hour, segment to segment and offer to offer.
This phenomenon can be traced to the inability of humans to separate chance events from real effects. Using the haystack process, at any given time marketers are limited to examining and drawing conclusions from small samples of data. However, psychologists (led by Kahneman and Tversky) have extensively documented tendencies which find spurious patterns in small samples, thereby explaining why poor decisions are made. Therefore, statistical methodologies can be leveraged to study large samples and mitigate the urge to see patterns where none exists.
These methodologies, or “conversion optimization” methods (x), are then taken a step further to run in a real-time environment. The real-time data collection and subsequent messaging as a result, increases the scale and effectiveness of the online campaign.
[edit] How conversion optimization works
Conversion optimization platforms for Content, Campaigns and Delivery, then need to consist of the following elements:
[edit] Data collection & processing
The platform must process hundreds of variables and automatically discover which subsets have the greatest predictive power, including any multivariate relationship. A combination of pre- and post-screening methods is employed, dropping irrelevant or redundant data as appropriate. A flexible data warehouse environment accepts customer data as well as data aggregated by third parties. Data can be numeric or text-based, nominal or ordinal. Bad or missing values are handled gracefully. Data should be geographic, contextual, frequency, demographic, behavioral, customer, etc.
[edit] Optimization goals
The official definition of “optimization” is the discipline of applying advanced analytical methods to make better decisions. Under this framework, business goals are explicitly defined and then decisions are calibrated to optimize those goals. The methodologies have a long record of success in a wide variety of industries, such as airline scheduling, supply chain management, financial planning, military logistics and telecommunications routing. Goals should include maximization of conversions, revenues, profits, LTV or any combination thereof.
[edit] Business rules
Arbitrary business rules must be handled under one optimization framework. Some typical examples include:
- Minimum (or maximum) weights for specific offers
- “Share of voice” among all offers
- Differential eligibility for different offers
- Mutually exclusive offers
- Bundled offers
- Specified holdout sample
Such a platform should understand these and other business rules, then adapting targeting rules accordingly.
[edit] Real-time decisioning
Once mathematical models have been built, ad/content servers use an audience screen method to place visitors into segments and select the best offers, in real time. Business goals are optimized while business rules are enforced simultaneously. Mathematical models can be refreshed at any time to reflect changes in business goals or rules.
[edit] Statistical learning
Ensuring results are repeatable by employing a wide array of statistical methodologies. Variable selection, validation testing, simulation, control groups and other techniques together help to distinguish true effects from chance events. A champion/challenger framework ensures that the best mathematical models are deployed always. In addition, performance is enhanced by the ability to analyze huge datasets and to retain historical learning.
