D Archives - Piwik PRO

Dark patterns

Wed, 24 May 2023 10:19:50 +0000

Dark patterns are deceptive user experiences that take advantage of people’s habits of using websites and apps to trick them into doing something they did not intend to do.

Dr Harry Brignull, an UX designer, coined the term on July 28, 2010, with the registration of the domain darkpatterns.org (now deceptive.design), a site devoted to naming and shaming deceptive user interfaces.

Brignull identified 12 categories of dark patterns:

Friend spam happens when someone asks for your contacts under false and good pretenses but then sends spam to them, claiming it’s from you
Forced continuity occurs when your free trial ends, and you get charged, so it is a hurdle for you to cancel
Ads disguised as different kinds of content or navigation within the website, so you click on them
Confirmshaming, which is an act of guilting the user into opting in for something by shaming them into compliance
Bait-and-switch is when you set out to do one thing for a given outcome, but a different and undesirable effect happens instead
Hidden costs, which make something appear cheaper only so at the last step of the checkout process, you are told of the additional charges
Roach motels, which are designed to make it very easy for you to get into a certain situation, but then hard to get out of it, like a subscription
Privacy zuckering, named after the Meta CEO, is used to trick you into publicly sharing more information about yourself than you want
Misdirection, which intentionally focuses your attention on one thing to distract you from something else
Price comparison prevention is when the retailer makes it hard to compare prices of items, so you cannot make an informed decision – thankfully, this is mostly outdated now.

Each of the above practices have now become recognized by various legislative bodies. For example, European Data Protection Board, which published a special guidelines of using dark patterns in social media platforms.

You may also like:

The post Dark patterns appeared first on Piwik PRO.

]]>

Data activation

Tue, 05 Mar 2024 20:55:46 +0000

Data activation is the concept of unlocking customer insights to inform business decisions and marketing activities and provide better customer experiences. Data activation comes after data collection, warehousing, and management. It shifts the focus from simply understanding data to taking action on it.

Through activations, companies can target their desired audiences more effectively and personalize the experience for users at different stages of the funnel. Data activation gives all teams within an organization the ability to tie their efforts to business outcomes.

You can activate data with a customer data platform (CDP) by sending it to various tools, such as:

Ad platforms like Google and Facebook Ads
Email marketing platforms
Marketing automation tools
Analytics and business intelligence (BI) tools like Microsoft BI or Tableau
Personalization software
Customer support platforms, such as chatbot or live chat
CRMs

Examples of data activations include:

Send a push notification to re-engage customers who haven’t interacted with your business recently.
Send a time-limited promo code to apply to a user’s subscription so they have more time to try your product for less money.
Show ads with product recommendations based on customers’ latest purchases.
Retarget visitors who didn’t complete a given funnel.
Send an email to a user with items from their abandoned cart.

Data anonymization

Fri, 19 Jul 2019 12:45:49 +0000

Data anonymization is the use of one or more techniques designed to make it impossible to identify a particular individual from stored data related to them. That kind of data bring significant benefits to businesses collecting data since anonymous data is not personal data for the purposes of GDPR.

Anonymous data doesn’t require any additional safeguards to ensure its security. Among other things, this means that, you don’t need to get consent to process it and it can be exported internationally.

In terms of GDPR we can anonymize data such as:

login details
device IDs
IP addresses
cookies
device type
language preference
time zones

Example methods of data anonymization:

Noise Addition – example: height: 180 cm → height: 320 cm
Aggregation – example: Age: 30 → Age: 20-35
Substitution – example: : 10120 → ZIP: postcode

To get more details on data anonymization, read the Piwik PRO blog:

The post Data anonymization appeared first on Piwik PRO.

]]>

Data clean room

Tue, 11 Apr 2023 17:37:09 +0000

A data clean room is a piece of software that allows organizations to bring data together for joint analysis under defined guidelines and restrictions.

The process involves two entities – such as an advertiser and a publisher – who upload their first-party data to the data clean room. Then, the data undergoes security and privacy-protection measures, such as pseudonymization, restricted access, or noise injections, that remain in place to ensure none of the parties can access personal data or data of the other party. Both parties get information in the form of cohorts and aggregated reports. The data can then be activated for various advertising and marketing efforts. For example, it can be applied for targeting a specific demographic, audience measurement, and analysis.

Data clean rooms contain aggregated, anonymized user information considered non-personally identifiable information (non-PII). The involved contributors can benefit from the information in the joint data set from two or more parties without gaining access to specific data about individual users. At the same time, they cannot export or copy the data set of the other contributors, and no one outside the data clean room can access the user-level data.

The demand for data clean rooms is growing due to privacy regulations, the deprecation of third-party cookies, and the need for advertisers to better target users.

Read more about privacy-friendly solutions to marketing:

Privacy compliance in 2023 – the experts’ take on marketing and data protection

The post Data clean room appeared first on Piwik PRO.

]]>

Data controller

Tue, 16 Apr 2019 10:27:57 +0000

A data controller is a person or organization that determines the use of personal information. The role of the data controller can be shared by many people or organizations.

Rights and responsibilities of data controllers include:

establishing a lawful data process and observing the rights of Data subject s (including collecting data subject Consent s and requests)
issuing instructions on data processing (including appointing employees to serve as point of contact) for the Data processor – the data processor should handle data exclusively in the manner prescribed by the controller.

Read more about data controller on the Piwik PRO blog and in the resources section:

The post Data controller appeared first on Piwik PRO.

]]>

Data democratization

Wed, 23 Oct 2024 11:07:37 +0000

The basis of data democratization lies in making data accessible to all employees within an organization, regardless of their technical expertise. It aims to empower non-technical users by providing intuitive tools and resources that help gather, analyze, and interpret data with ease. By breaking down silos and fostering collaboration, organizations can enhance the quality of decision-making processes. Effective data democratization leads to improved business outcomes as employees across various departments can leverage insights derived from data analytics.

For more insights on data in digital analytics, check our series:

The post Data democratization appeared first on Piwik PRO.

]]>

Data-driven attribution

Fri, 11 Aug 2023 10:38:02 +0000

A data-driven attribution model uses machine learning to track user data across multiple touchpoints.

A data-driven attribution model assigns credit to each touchpoint without using a predefined model, unlike rule-based attribution models. Instead, it uses machine learning technology to create a custom model for each business based on data that reflects actual customers’ journeys.

Traditional rule-based attribution focuses on conversion paths.

In contrast, data-driven models consider both converting and non-converting paths. This enables marketers to assess how each touchpoint increases the likelihood of a customer converting rather than allocating credits to conversion path touchpoints based on predefined rules.

For example, GA4’s default attribution model is data-driven. Data from your Google Analytics account is analyzed using machine learning algorithms. Conversion credit is assigned to different touchpoints based on their actual impact.

An advantage of data-driven attribution is that it automatically adjusts attribution weights as your marketing landscape changes. However, a marketing attribution model that changes the weighting it applies to email marketing interactions from one year to another can result in useless comparison reporting. You will never know if the increase in conversions attributed to that channel was due to your increased spend or Google’s change in attribution.

You may also like:

Google Analytics and multi-channel attribution Google Analytics alternatives – free and paid Piwik PRO vs. Universal Analytics & Google Analytics 4

The post Data-driven attribution appeared first on Piwik PRO.

]]>

Data export

Wed, 06 Nov 2024 13:58:47 +0000

Data export refers to transferring data collected through analytics platforms to external systems for further analysis, reporting, or storage. This capability is crucial for businesses seeking to leverage their analytics data beyond the confines of the platform itself.

Companies can typically use two types of data exports:

Raw data export involves transferring data in its most granular form. This allows analysts to access detailed information about user interactions, enabling in-depth analysis and customized reporting beyond what is available in the analytics platform. For instance, Piwik PRO Analytics Suite allows you to export raw data to platforms like BigQuery, facilitating advanced data processing and analysis.

Aggregated data export provides summarized information, which is easier to manage and interpret and can be helpful for high-level overviews but lacks the detail necessary for deep analysis. Many analytics platforms offer this export type as a standard feature, allowing users to download reports in formats like CSV for easy sharing and visualization.

Businesses can utilize data export for various purposes:

Custom reporting: Users can create tailored reports that combine data from different sources, improving the quality of their analysis and decision-making processes.
Data integration: Exporting data allows companies to merge analytics insights with other platforms or databases, providing a more holistic view of business or marketing performance.
Long-term storage: By exporting data, organizations can retain historical records beyond the retention limits set by analytics platforms.
Advanced analysis: Analysts can use exported raw data for machine learning, predictive analytics, and other advanced analytical techniques that require detailed datasets.

The post Data export appeared first on Piwik PRO.

]]>

Data freshness

Thu, 25 Aug 2022 12:41:31 +0000

The freshness of your data in analytics describes how long it takes to collect, process and deliver data to your reports. If that process takes 20 minutes, then data freshness equals 20 minutes.

Different types of reports have different requirements for data freshness. If you are, for example, measuring ad performance on your website or app, daily updates might be sufficient.

Fresh data will help you better monitor your site’s performance and identify trends, patterns, and interesting changes much closer to when they happen, so you can make faster decisions. The freshness of your data is an important aspect of data quality.

For example, with a platform like Google Analytics 4, you wait up to 8 hours for your reports. And, you can expect sampling, which can hurt your reports.

In Piwik PRO you can count on data freshness every 30 minutes. There is no sampling. In other words, even if your website records hundreds of millions of actions per month, you still have smooth access to complete and quality analytics data sets.

The post Data freshness appeared first on Piwik PRO.

]]>

Data import

Wed, 06 Nov 2024 13:56:49 +0000

Data import refers to transferring data from various external sources into an analytics system for analysis and reporting. This capability is crucial for organizations seeking to integrate disparate data sets into a cohesive framework.

Key benefits of data import include:

Comprehensive analysis: By merging data from multiple sources (like CRMs, email marketing tools, data warehouses, ecommerce platforms, ad management software, and others), organizations can gain a more comprehensive understanding of customer behavior.
Enhanced reporting: Data import capabilities enable the generation of detailed reports that incorporate both online and offline data, improving the accuracy and relevance of insights derived from analytics. It enables better segmentation and targeted marketing efforts.
Dynamic updates: Many platforms offer features to automatically synchronize data, ensuring access to the most up-to-date information. This way, companies can enrich and update data in real-time and have it available for analysis without manual intervention.

Businesses can use data import capabilities in tools such as analytics platforms or customer data platforms (CDPs).

Learn more:

The post Data import appeared first on Piwik PRO.

]]>