Conversion rate optimization

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In internet marketing, conversion optimization , or conversion rate optimization (CRO) is a system for increasing the percentage of visitors to a website converted to customers, or more generally, take any action desired on a web page. Usually referred to as CRO.

Video Conversion rate optimization

History

Online conversion rate optimization (or website optimization) is born from the needs of e-commerce marketers to improve their website performance after the dot-com bubble. Because competition grew on the web during the early 2000s, website analytics tools and awareness about website usability prompted internet marketers to generate benchmarks for their tactics and improve the user experience of their websites.

In 2004, the new tool allowed internet marketers to experiment with website design and content variations to determine which best-performing layouts, copy texts, offers, and images. This form of optimization was accelerated in 2007 with the introduction of free Google Website Optimizer. Today's optimization and conversions are a key aspect of many digital marketing campaigns. A research study conducted among internet marketers in 2014, for example, shows that 59% of respondents think that CRO is "important to their overall digital marketing strategy".

Conversion rate optimization shares many principles with direct response marketing - a marketing approach that emphasizes on tracking, testing, and continuous improvement. Direct marketing was popularized in the early 20th century and supported by the formation of industrial groups such as the Direct Marketing Association, formed in 1917.

Like modern day conversion optimization, direct response marketers also practice separate A/B testing, response tracking, and audience testing to optimize email, radio and print campaigns.

Maps Conversion rate optimization

Statistical significance

Often, when marketers learn about improvements in ad campaigns, they find inconsistent customer behavior. The online marketing response rate fluctuates widely from hour to hour, segment to segment, and offers to offer.

This phenomenon can be traced to the human difficulties of separating accidental events from real effects. Using a process of haystack, at any given time, marketers are limited to checking and drawing conclusions from small data samples. However, psychologists (led by Daniel Kahneman and Amos Tversky) have documented the tendency to find fake patterns in small samples to explain why poor decisions are made. Statistical methodology can be used to study large samples, reducing the urge to see patterns where none exist.

This methodology, or the "conversion optimization" method, is then taken a step further to run in a real-time environment. The collection of real-time data and subsequent messaging increases the scale and effectiveness of online campaigns.

Achieving statistically significant results alone is not enough. Practitioner conversion optimization should ensure that their sample size for variables is important. For example, a test may appear statistically well before the seasonal factor (time of day, day of week, year-round) has been adequately reflected in the sample data. One variation may attract one more season than the other and eventually mislead the result.

It is equally important to understand how the various segments influence the test and the results. Different user segments (e.g., device type, location, new visitor vs. return visitors) will respond differently to each variation. Analyzing results without taking into account different segments can cause a significant increase for one segment; or many variations can offset poor results for other segments. For example, increased desktop conversion rates may offset a decline in conversion rate on mobile devices. In this case, only the desktop version should be declared a 'win' test.

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Methodology

Conversion rate optimization aims to increase the percentage of website visitors taking certain actions (often submitting web forms, making purchases, signing up for trials, etc.) by methodically testing an alternate version of a page or process. Thus, a business can generate more leads or sales without investing more money for website traffic, thereby boosting its marketing profit on investment and overall profitability.

The conversion rate is defined as the percentage of visitors who completed the goal, as determined by the site owner. Some testing methods, such as separate testing or A/B testing, allow one to monitor titles, copy, images, elements of social evidence, and content that helps convert visitors into customers.

There are several approaches to conversion optimization with the two main schools of thought that have prevailed in recent years. One school focuses on testing to find the best way to improve a website, campaign, or landing page conversion rate. Other schools focus on the pretesting stage of the optimization process. In this second approach, the optimization company will invest a lot of time to understand the audience and then create targeted messages that appeal to a particular audience. Only then will it be willing to deploy a testing mechanism to increase the conversion rate.

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Test-focused approach elements

The conversion optimization platform for content, campaign and delivery consists of the following elements:

Data collection and processing

The platform must process hundreds of variables and automatically find out which subset has the greatest predictive power, including any multivariate relationships. A combination of pre-and post-filtering methods is used, dropping irrelevant or excessive data as appropriate. A flexible data storage environment accepts customer data as well as data collected by third parties.

This means it's important to make sure the data is 'clean', before doing any data analysis. For example, remove activity from bots, staging websites, or misconfigured tools like Google Analytics.

Data can be numeric or text based, nominal or ordinal. Bad or lost value is handled gracefully.

Data can be geographic, contextual, frequency, demographic, behavioral, customer-based, etc.

Hypothesis

After data collection, forming a hypothesis is the next step. This process forms the foundation of why change is made. The hypothesis is based on observation and deduction. It is important that any hypothetical situation can be measured. Without this there is no conclusion that can be derived.

Optimization targets

The official definition of "optimization" is the discipline of applying advanced analytical methods to make better decisions. Below this framework, business objectives are explicitly defined and then the decision is calibrated to optimize the goal. The methodology has a long record of success in various industries, such as airline scheduling, supply chain management, financial planning, military logistics and telecommunications routing. Goals should include the maximization of conversion, revenue, profit, LTV or any combination thereof.

Business rules

The arbitrary business rules must be handled in an optimization framework. Using such a platform requires that one should understand these and other business rules, then adjust the appropriate targeting rules.

Real-time decision making

Once the mathematical model is created, the ad/content server uses the viewer screen method to place visitors into segments and choose the best offer, 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.

Statistical learning

Ensure results are repeated using a variety of statistical methodologies. Variable selection, validation testing, simulation, control groups, and other techniques together help distinguish the true effects of accidental events. A champion/challenger frame ensures that the best mathematical model is always used. In addition, performance is enhanced by the ability to analyze large data sets and retain historical learning.

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See also

• Audience screen
• Behavioral targeting
• Conversion Marketing
• Conversion rate
• Digital marketing technician
• Direct Marketing
• Internet Marketing
• Multivariate testing
• Promotions
• Separate test
• Web personalization
• A/B Test
• User Intent
• Search engine optimization

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References

Source of the article : Wikipedia