Conversion rate optimization
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In internet marketing, conversion optimization, or conversion rate optimization (CRO) is the method of creating an experience for a website or landing page visitor with the goal of increasing the percentage of visitors that convert into customers. It is also commonly referred to as CRO.
- 1 History & Web origins
- 2 Why conversion optimization
- 3 Statistical significance
- 4 How conversion optimization works
- 5 Conversion optimization skills & disciplines
- 6 Elements of the test focused approach to conversion optimization
- 7 See also
- 8 References
History & Web origins
Online conversion-rate optimization (or website conversion-rate optimization) was born out of the need of e-commerce Internet marketers for lead generation and 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 which layouts, copy text, offers and images will improve their conversion rate.
Conversion-Rate Optimization shares many of its basic principles with Direct Response Marketing – an approach to marketing that emphasizes tracking, testing and on-going improvement. Direct Marketing was popularized in the early twentieth century and was supported by the formation of industry groups such as the Direct Marketing Association which formed in 1917.
Like modern day Conversion-Rate Optimization, direct response marketers also practice A/B split-testing, response tracking and audience testing to optimize mail, radio and print campaigns.
Why conversion optimization
Conversion-rate optimizations used to make marketing campaigns more efficient. By increasing the number of respondents that take action a business is able to grow their business quicker, improve their marketing return on investment and increase their overall profits.
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. Online marketing response rates fluctuate widely from hour to hour, segment to segment and offer to offer.
This phenomenon can be traced to the difficulty humans have separating 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. 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.
Reaching a statistically significant result in itself is not enough. Conversion optimization practitioners must ensure that their sample size takes into account important variables that impact their tests. For example, a test may appear statistically significant well before seasonal factors (time of day, day of week, time of year) have been adequately reflected in the data sample. This is important because one variation may appeal to one season more than others and ultimately misguide the result.
It is equally important to understand how various segments affect tests and results. Different user segments (e.g. device type, location, new vs. returning visitor) will respond differently to each variation. It’s important to understand this and interpret results accordingly. Analyzing results without accounting for different segments can cause a significant improvement for one or many variations can offset poor results for another segment. For example, uplift in desktop conversion-rate could offset a decreased conversion-rate on mobile devices. In this instance, only the desktop version should be declared a ‘winning’ test.
How conversion optimization works
Conversion Rate Optimization seeks to increase the percentage of website visitors that take a specific action (often submitting a web form, making a purchase, signing up for a trial, etc.) by methodicallytesting alternate versions of a page or process. In doing so, businesses are able to generate more leads or sales without investing more money on website traffic, hence increasing their marketing return on investment and overall profitability.
A conversion rate is defined as the percentage of visitors who complete a goal, as set by the site owner. Some test methods, such as split testing or A/B testing, enable one to monitor which headlines, copy, images, social proof elements and content help one convert more visitors into customers.
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 would it be willing to deploy testing mechanisms to increase conversion rates.
Conversion optimization skills & disciplines
Conversion-rate optimization is a multifaceted discipline. Conversion-rate optimization professionals will typically have a deep understanding of the following strategies and tactics:
- Conversion-oriented web design
- Landing pages
- Split-testing (A/B Split testing, multivariate testing)
- User surveys & feedback
- User experience
- Tracking and analytics
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 may be geographic, contextual, frequencial, demographic, behavioral, customer based, etc.
After data collection, forming a hypothesis is the next step. This process forms the foundation of why changes are made. Hypotheses are made based on observation and deduction. It is important that each hypothetical situation be measurable. Without these no conclusions can be derived.
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
- Pricing Strategy
- Up-sells, down-sells and cross-sells
- Mutually exclusive offers
- Bundled offers
- Specified holdout sample
Using such a platform entails that one should understand these and other business rules, then adapt 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.
- Audience screen
- Behavioral targeting
- Conversion Marketing
- Conversion rate
- Direct marketing
- Internet marketing
- Split testing
- Multivariate testing
- Digital marketing engineer