A Archives - Piwik PRO

A/B testing

Fri, 29 Mar 2019 07:40:23 +0000

A/B testing, often called split testing, is used in marketing, web design, and product development to optimize performance and improve user experience. This statistical approach involves comparing two versions of a webpage, app, or any other digital asset to determine which performs better regarding a specific goal, such as conversion, click-through, or user engagement.How A/B testing works

Create hypotheses: Before starting an A/B test, marketers and product owners should formulate hypotheses about what changes could lead to improved performance. This could involve altering headlines, images, call-to-action buttons, or layout designs.
Segment your audience: Divide your audience into two groups: one interacts with Version A (the control) and the other with Version B (the variation).
Run the test: Execute the test over a sufficient period of time to gather meaningful data, ensuring that external factors remain consistent.
Analyze results: Once the test concludes, analyze the performance of both versions. Look at metrics such as conversion rates, user behavior patterns, and engagement levels.
Implement changes: If one version significantly outperforms the other, implement the winning version as the new standard, further optimizing for better results over time.

Benefits of A/B testing

Data-driven decisions: It provides concrete data that guides marketing strategies, reducing guesswork.
Improved conversion rates: Businesses can increase conversion rates effectively by identifying which elements resonate better with users.
Enhanced user experience: Understanding user preferences helps to create more engaging and efficient digital experiences.
Cost-effective: Rather than overhauling an entire marketing strategy, A/B testing allows for incremental improvements to be made.

Best practices for A/B testing

Test one element at a time: Focus on one variable at a time to accurately determine what change is causing any performance variation.
Use a large enough sample size: Ensure your test reaches a statistically significant sample size for reliable results.
Be patient: Allow enough time for the test to account for user behavior variations over different times or days.
Document and learn: Keep track of the changes and the outcomes to inform future tests and strategies.

Conclusion

A/B testing is essential for businesses looking to enhance their digital presence and satisfy their customers. Companies can make informed decisions that boost performance and foster growth by systematically testing variations and learning from the results.

Using A/B testing effectively can help you effectively achieve business objectives and success in digital marketing by improving your understanding and application.

To learn about the best tools for A/B testing, read our article: Best 10 A/B testing tools for 2023: Google Optimize

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Acquisition reports

Fri, 24 May 2024 09:00:08 +0000

Acquisition reports in web analytics offer valuable insights into the sources of traffic on your website. These reports help you understand the effectiveness of your advertising, social media engagement, and search engine visibility.

They typically divide traffic into five categories:

Campaign: Visitors who arrive at your site after clicking a link with added parameters.
Organic search: Users who find your site through search engines like Google (also known as organic traffic).
Direct traffic: Visitors who type your website URL directly into their browser.
Referral: Traffic from users who click on links from other websites leading to your site.
Social media: Visitors coming from social media platforms like Facebook, Twitter, or Instagram.

By analyzing these sources, you can determine which channels are most effective in driving traffic to your website.

Learn more about improving your digital marketing with analytics from this post: Digital marketing analytics: The beginner’s guide to data-driven marketing success

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Action

Fri, 29 Mar 2019 07:42:37 +0000

An action is any activity registered by the Piwik PRO analytics platform, e.g. a visit, Page view , download or event.

You can learn more about actions from the article What is a Piwik PRO Action and What Does It Mean For My Business?

Adobe Analytics

Mon, 21 Aug 2023 09:47:55 +0000

Adobe Analytics is part of the Adobe Experience Cloud. This analytics platform facilitates marketers’ work by enabling them to improve all the website’s important marketing indicators.

Adobe Analytics also comes with a robust set of tools, including Reports & Analytics, Analysis Workspace, Report Builder, and Ad Hoc Analysis. This set of products allows marketers to apply detailed segmentation and real-time analytics across different marketing channels.

The foundation of Adobe Analytics is the SiteCatalyst solution by Omniture, which was acquired by Adobe in 2009.

Adobe features

Adobe Analytics offers a wide range of built-in features and integrations, but the implementation and customization options may be challenging if you are not a power analytics user. Adobe Analytics can be integrated with other Adobe marketing products like Adobe Campaign or Adobe Target. In addition to advanced segmentation and real-time automation, it offers ad-hoc analysis. Moreover, with Adobe you will have extensive online documentation at your fingertips, and you can reach out to the community. But Adobe Analytics support does not provide implementation services or training.

Adobe charges based on every 1,000 server calls (page views + downloads + custom events) generated from the client’s website.

Its customers include Sony, Asos, and B&Q.

Adobe Analytics benefits:

A bespoke solution allowing you to fully customize your data collection and reporting.
Vast features and functionalities. Particularly when it comes to segmentation capabilities, it offers flexibility and customizability.
Fully-featured ecommerce tracking.
Real-time reporting capabilities, including a real-time API.

Adobe Analytics disadvantages:

One of the most expensive solutions on the market.
Requires technically skilled analysts to use all the features and set the platform up correctly.
Doesn’t integrate well with Google products.
Doesn’t offer personalized support and onboarding.

Affiliate marketing

Thu, 04 Jun 2020 05:45:34 +0000

An advertising model where a company (an advertiser) pays a commission to third-party publishers (affiliates) that drive visitors to the company’s website by merchandising a product or service. In this model an affiliate network plays the role of the intermediary that measures transactions and facilitates payments or other activities between advertisers and publishers.

The affiliate landscape employs different channels to market products, such as:

Social networks
Search engines
Digital banners
Blogs

The affiliate publisher is a website, usually with high traffic volume, which earns a commission when its visitor is redirected to an advertiser’s website and completes a transaction.

To get more information about the affiliate marketing, check out the Piwik PRO blog:

How to analyze multi-channel attribution in affiliate marketing using raw data

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Aggregated data

Wed, 06 Nov 2024 14:07:55 +0000

Aggregated data refers to the data created through aggregation performed on raw data. Such aggregations can be done by web analytics tools or, in more complex approaches, using BI tools to pull data from a data warehouse or other sources. Aggregated data is most commonly available in the UI of analytics platforms as reports, making it more accessible.

Aggregated data is readily interpretable through data transformation and analysis, making it easier to identify patterns, trends, or relationships. Visualizing the data doesn’t require extensive technical resources and skills. On the other hand, it’s not as flexible as raw data, making it more difficult to perform advanced statistical analysis.

Learn more:

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AI and privacy

Mon, 13 Nov 2023 21:49:14 +0000

Developing a large language model such as ChatGPT requires gathering vast bodies of text through a process called web scraping. These datasets ingest details from open online sources such as social media profiles. If data is pulled from publicly available sources, it is in the scope of privacy laws. AI is now regulated by standard privacy laws, like the General Data Protection Regulation (GDPR) and similar law regimes.

GDPR places various stringent obligations on any organization storing, transmitting, or performing analytics on personal data. The most fundamental issue under GDPR is identifying a legal basis for scraping the personal data of millions of people without their knowledge or consent. This matter has been subject to heavy regulatory and judicial scrutiny across Europe, and there’s no simple solution in sight.

It is still unknown how GDPR will apply to generative AI, but some decisions have been made. ChatGPT was temporarily banned by the Italian Data Protection Authority over incorrect results and a lack of lawful grounds for the processing, as well as the mismanagement of children’s data. Google then had to postpone the EU launch of its competitor Bard over similar privacy challenges.

You may also like:

Data privacy laws in the United States and how they affect your business

11 new privacy laws around the world and how they’ll affect your analytics

Data privacy breach

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Analytics dashboard

Fri, 29 Mar 2019 07:44:35 +0000

A user interface with a collection of widgets, it provides an overview of reports and metrics to help you quickly visualize and review data. It lets you keep track of numerous matrics at once and observe connections between various reports.

Analytical dashboards can contain reports like:

Page view in time
Traffic by channel and localizations (countries, cities)
Conversion by channel and localizations

A useful analytics dashboard should be clear and include some conclusions and action points.

More about Analytics Dashboards on the Piwik PRO blog:

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Analytics suite

Tue, 23 Jul 2019 08:24:07 +0000

Analytics suite is a group of integrated applications used for a wide range of purposes, such as collecting, measuring, and analyzing data. In the next step, the stack lets you visualize gathered information and share with different stakeholders.

The analytics suite enables users to solve different analytics use cases without advanced technical knowledge. Such a stack helps companies to run in-depth analysis, better act on data and ultimately improve collaboration and decision-making processes.

Piwik PRO Analytics Suite consists of the following modules:

Find more information on making decisions with the right data, read the Piwik PRO blog:

7 steps to make data-based decisions with web analytics

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Anonymized data

Tue, 05 Mar 2024 20:27:08 +0000

Anonymized data is a type of data that has been processed to remove any personally identifiable information (PII) or personal data. Such data is often used in research, analytics, and other data-driven activities, as well as for compliance with privacy regulations.

According to GDPR, anonymized data has been altered in such a way that it can’t be used to identify a specific person. Since anonymized data can’t be restored, it isn’t considered personal data under GDPR. This means it is exempt from GDPR.

Some examples of compliant data anonymization methods include:

Randomization:
- Noise addition – Where personal identifiers are expressed imprecisely, for instance: height: 180 cm → height: 320 cm
- Substitution – Where personal identifiers are shuffled within a table or replaced with random values, for instance: ZIP: 10120 → ZIP: postcode
Generalization:
- Aggregation – Where personal identifiers are generalized into a range or group, for instance: age: 30 → age: 20-35

Removing any identifiable information from a dataset allows for meaningful analysis without compromising the privacy of individuals.

Examples of use cases for anonymized data include:

Measuring the effectiveness of marketing campaigns.
Analyzing the behavior of website or mobile app users.
Analyzing trends and patterns.