Performance Optimization

Table of Contents

This article will focus on Performance Optimization. Moreover, this is a sub-article of the main E-commerce implementation and the below links provide access to the main article and sub-articles.

When starting your e-commerce store for the first time you will face many challenges. Moreover, challenges can be almost zero sales, low traffic, and server issues, etc. In such conditions, it is always best to consult a more experienced store owner who can point out e-commerce optimization mistakes that you should avoid.

This article will discuss the key elements of e-commerce optimization.

Success Indicators Measurement

Generally, KPI is a type of performance measurement that helps you understand how your organization or department is performing. Moreover, a good KPI should act as a compass, helping you and your team understand whether you’re taking the right path toward your strategic goals. Hence, to be effective, a KPI must:

  • Well-defined and quantifiable.
  • Communicate throughout your organization and department.
  • Crucial to achieving your goal. (Hence, key performance indicators.)
  • Applicable to your Line of Business (LOB) or department.

Statistical Analysis

Most importantly, statistical analysis is a component of data analytics. Moreover, the goal of statistical analysis is to identify trends. A retail business, for example, might use statistical analysis to find patterns in unstructured and semi-structured customer data. Furthermore, that can use to create a more positive customer experience and increase sales.

Whether you are working with large data volumes or running multiple permutations of your calculations, statistical computing has become essential for today’s statistician. Moreover, popular statistical computing practices include:

  • Statistical programming: From traditional analysis of variance and linear regression to exact methods and statistical visualization techniques. Furthermore, statistical programming is essential for making data-based decisions in every field.
  • Econometrics: Modeling, forecasting and simulating business processes for improved strategic and tactical planning. Furthermore, this method applies statistics to economics to forecast future trends.
  • Operations research: First of all, Identify the actions that will produce the best results. Moreover, results based on many possible options and outcomes. Furthermore, Scheduling, simulation, and related modeling processes use to optimize business processes and management challenges.
  • Matrix programming: Powerful computer techniques for implementing your own statistical methods and exploratory data analysis using row operation algorithms.
  • Statistical visualization: Fast, interactive statistical analysis and exploratory capabilities in a visual interface. Moreover, this may be used to understand data and build models.
  • Statistical quality improvement: Generally, a mathematical approach to reviewing the quality and safety characteristics for all aspects of production.

A/B Tests and Experiments

A/B testing known as split testing or bucket testing. Moreover, it is a method of comparing two versions of a web page or app against each other to determine which one performs better. Furthermore, AB testing is essentially an experiment where two or more variants of a page are shown to users at random. Besides, statistical analysis is used to determine which variation performs better for a given conversion goal.

Why Should A/B Test

A/B testing allows individuals, teams, and companies to make careful changes to their user experiences while collecting data on the results. Moreover, this allows them to construct hypotheses, and to learn better why certain elements of their experiences impact user behavior. In another way, they can be proven wrong—their opinion about the best experience for a given goal may prove wrong through an A/B test.

More than just answering a one-off question or settling a disagreement. Furthermore, this can use consistently to continually improve a given experience. And also improving a single goal like conversion rate over time.

For instance, a B2B technology company may want to improve their sales lead quality and volume from campaign landing pages. Therefore, the team would try A/B testing changes to the headline, visual imagery, form fields, call to action, and overall layout of the page.

First of all, testing one change at a time helps them pinpoint which changes had an effect on their visitors’ behavior and which ones did not.  Furthermore, they can combine the effect of multiple winning changes from experiments to demonstrate the measurable improvement of the new experience over the old one.

most importantly, this method of introducing changes to a user experience also allows the experience to be optimized for the desired outcome. Moreover, can make crucial steps in a marketing campaign more effective.

most importantly, checking market ads help marketers to learn which version attracts more clicks. Moreover, testing the subsequent landing page, they can learn which layout converts visitors to customers best. Finally, overall spend on a marketing campaign can decrease if the elements of each step work as efficiently as possible to acquire new customers.

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