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Conversion optimization was born out of the need of lead generation and ecommerce internet marketers to improve their website's results. As competition grew on the web during the early 2000s, Internet marketers had to become more measurable with their marketing tactics. They began experimenting with website design and content variations to determine the layouts, copy text, offers and images will improve their conversion rate. Many practitioners have contributed to the field, including Bryan and Jeffrey Eisenberg, Anne Holland, Tim Ash, Ayat Shukairy, Jonathan Mendez, Khalid Saleh and Chris Goward.
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, 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.
How conversion optimization works
There are several approaches to conversion optimization with two main schools of thought prevailing in the last few years. One school is more focused on testing as an approach to discover the best way to increase a website, a campaign or a landing page conversion rates. The other school is focused more on the pretesting stage of the optimization process. In this second approach, the optimization company will invest a considerable amount of time understanding the audience and then creating a targeted message that appeals to that particular audience. Only then willing to deploy testing mechanisms to increase conversion rates. The article "a case against multi-variant testing" outlines some of the reasons testing should not be the only component in conversion optimization work.
Elements of the test focused approach to conversion optimization
Conversion optimization platforms for content, campaigns and delivery, then need to consist of the following elements:
Data collection and 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.
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.
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.
Real-time decision making
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.
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.