Aug 6, 2024 414 reads

The Use of First-Party Data in E-commerce Analytics

First-party data is the information a company collects from its customers and through its own channels. This data is typically gathered through customer interactions, website visits, transactions, and other direct engagements. Here’s why first-party data is so valuable.What is first-party data?First-party data is the information collected directly by a company from its own customers and target audience. This data comes from visits to your website, purchases, and other user interactions. It is considered the most valuable type of data for your business because it comes directly from the source, making it accurate and reliable.Why should we use first-party data? Accuracy and Reliability: Since first-party data comes directly from your customers, it is the most accurate and reliable form of data. It best reflects customer behavior and preferences. Data Control: First-party data is fully under your control, giving you greater oversight in terms of data privacy and security. This makes it easier to comply with privacy regulations like KVKK and GDPR. Personalization: The data collected from your customers allows you to offer more personalized and relevant content. This enhances the customer experience and boosts customer loyalty. Competitive Advantage: You gain access to exclusive data that your competitors can’t access. This makes your marketing strategies and business decisions more effective. So what is third-party data?Third-party data is information collected, aggregated, and sold by entities that are different from the original data source. In digital marketing and data analytics, third-party data is typically sourced externally and may include a broad range of demographic, behavioral, and interest-based data about individuals. This data is gathered by data brokers, aggregators, or other third-party organizations specialized in collecting and selling data.Recently, there have been limitations on third-party data usage, particularly in performance marketing and e-commerce analytics. KVKK requires user consent for data collection, which affects the creation of third-party cookies. Mozilla Firefox and Apple Safari have implemented features like Intelligent Tracking Prevention (ITP) and Enhanced Tracking Protection (ETP), which block third-party cookies by default. Google Chrome planned to phase out third-party cookies by 2022, but this deadline has been pushed to the second half of 2024, aiming to strike a balance between user privacy and an ad-supported web. Safari and Firefox offer advanced privacy to their users by blocking third-party cookies by default.However, with Google’s recent A new path for Privacy Sandbox on the web announcement, a new update has been introduced stating that the blocking of third-party data and cookie deprecation will now be left to user choice.The main reason for this update is the combination of default cookie blocking in browsers, pressure from ad-tech platforms, and underwhelming performance in the tests conducted under the Privacy Sandbox API.For more comprehensive and technical e-commerce analytics, contact us

Jul 18, 2024 641 reads

Preparing for Privacy Sandbox: What is Storage Access API?

Chrome is gradually phasing out support for third-party cookies to reduce cross-site tracking. This creates a challenge for sites and services that rely on cookies in embedded contexts for user journeys like authentication. The Storage Access API (SAA) allows these use cases to continue while limiting cross-site tracking as much as possible.What is the Storage Access API?The Storage Access API is a JavaScript API for iframes to request access to storage permissions that would otherwise be denied by browser settings. Embedded elements with use cases dependent on loading cross-site resources can use this API to request access from the user when needed.If the storage request is granted, the iframe will be able to access cross-site cookies, just like it would if the user visited that site as a top-level context.While it prevents general cross-site cookie access often used for user tracking, it allows specific access with minimal burden on the user.Use casesSome third-party embedded elements require access to cross-site cookies to provide a better user experience — something that will no longer be possible after third-party cookies are disabled.Use cases include: Embedded comment widgets that require login session details. Social media “Like” buttons that require login session details. Embedded documents that require login session details. A top-level experience delivered within an embedded video player (e.g., not showing ads to logged-in users, knowing user caption preferences, or restricting certain video types). Embedded payment systems. Many of these use cases involve maintaining login access within embedded iframes.Using the hasStorageAccess() methodWhen a site first loads, the hasStorageAccess() method can be used to check whether access to third-party cookies has already been granted.// Set a hasAccess boolean variable which defaults to false. let hasAccess = false; async function handleCookieAccessInit() { if (!document.hasStorageAccess) { // Storage Access API is not supported so best we can do is // hope it's an older browser that doesn't block 3P cookies. hasAccess = true; } else { // Check whether access has been granted via the Storage Access API. // Note on page load this will always be false initially so we could be // skipped in this example, but including for completeness for when this // is not so obvious. hasAccess = await document.hasStorageAccess(); if (!hasAccess) { // Handle the lack of access (covered later) } } if (hasAccess) { // Use the cookies. } } handleCookieAccessInit();

May 24, 2024 1343 reads

Write e-commerce Purchases to Firestore with sGTM

Server-side Google Tag Manager (sGTM) offers enhanced flexibility and security for tracking and handling data in your e-commerce applications. One powerful application of sGTM is writing purchase data directly to Firestore, Google Cloud's NoSQL database. This blog post will walk you through the process of setting up sGTM to capture e-commerce purchases and store them in Firestore.Step 1: Creating a New Server-side Google Tag Manager (sGTM) TemplateIn this step, you'll create a custom template in your server-side Google Tag Manager (sGTM) container. This template will define the logic for capturing e-commerce purchase events and sending the data to Firestore. By creating a reusable template, you streamline the process of handling and managing purchase data across your e-commerce platform.You can use this code to accessing write and read data to Firestore from sGTM.const Firestore = require('Firestore');const Object = require("Object");const getTimestampMillis = require("getTimestampMillis");let writeData = { timestamp: getTimestampMillis()};if (data.customData && data.customData.length) { for (let i = 0; i < data.customData.length; i += 1) { const elem = data.customData[i]; if (elem.fieldValue) { writeData[elem.fieldName] = elem.fieldValue; } else { Object.delete(writeData, elem.fieldName); } }}const rows = writeData;Firestore.write('', rows, { projectId: '', merge: true,}).then((id) => { data.gtmOnSuccess();}, data.gtmOnFailure);Step 2: Configuring the Firestore DatabaseOpen Google Cloud Console: Navigate to the Firestore section. Create Database: Follow the prompts to set up a Firestore database in "production mode" or "test mode" based on your requirements.You may also create a new rule like this while you are running test mode:  rules_version = '2';service cloud.firestore { match /databases/{database}/documents { match /{document=**} { allow read, write: if request.auth != null; } }}Now you can send the any purchase data to Firestore like this:  Contact us for more use cases using server-side Google Tag Manager. 

May 7, 2024 1071 reads

Preparing for Privacy Sandbox: What is CHIPS?

With Google's postponement of the Cookie phase-out process to the first quarter of 2025, many brands and digital marketers must take full advantage of the Privacy Sandbox and the transition process.That’s why in the first post of our new blog series, we’ll briefly introduce CHIPS within the scope of the Privacy Sandbox.CHIPS, or Cookies Having Independent Partitioned State, gives developers the ability to include cookies in partitioned storage, enhancing user privacy and security through separate cookie jars for each top-level site.Without partitioning, third-party cookies allow services to track users and combine their information across unrelated top-level sites — a practice known as cross-site tracking.Browsers are moving toward phasing out unpartitioned third-party cookies. When third-party cookies are blocked, CHIPS, Storage Access API, and Related Website Sets will be the only options for reading and writing cookies in cross-site contexts like iframes. (We’ll cover those later.)Partitioning is currently supported only in Chrome and Edge browsers. It’s not yet available in Firefox or Safari.For example, imagine you have a website named retail.example and you want to integrate a widget hosted on support.chat.example, a third-party service, to support your users via chatbot.Many embedded chat services already rely on cookies to track user actions.Without cross-site tracking adjustments, support.chat.example typically has to find more complex alternatives to store those cookies. Another option might involve embedding it on retail.example with higher privileges—such as access to authentication cookies—which comes with security and legal risks due to potential exposure of PII (Personally Identifiable Information).This is where CHIPS comes in, offering an easier way to continue using cross-site cookies without the risks associated with unpartitioned cookies.So, Where is CHIPS Used?CHIPS generally applies to any cross-site scenario where subresources require a session or persistent state specific to the user’s interaction within a single top-level site. Common use cases include: Embedded third-party chat widgets Embedded third-party map widgets Embedded third-party payment method widgets Subresource CDN load balancers Headless CMS providers Third-party CDNs using cookies to serve gated content depending on authentication status on the first-party site (e.g., social media profile images hosted on third-party CDNs) Remote API endpoints that depend on cookies Embedded ads that need publisher-specific state (e.g., capturing ad preferences per website) More info:https://developers.google.com/privacy-sandbox/3pcd/chips If you’d like to learn more about cookieless measurement beyond Privacy Sandbox, take a look at our AnalyticaHouse Cookieless project.

Apr 25, 2023 1583 reads

Business Intelligence Reporting: Revolutionizing Digital Marketing Strategies

Are you leveraging the power of data to optimize your digital marketing strategy? Business intelligence (BI) reporting is an essential component for any business to grow and thrive in today's data-driven world, especially in the digital marketing field. This article will explore the ins and outs of business intelligence reporting, its importance for digital marketers, and how it can be used to make data-driven decisions using tools like Looker Studio, BigQuery, GA4, Google Ads, and Facebook Ads. The Concept of Business Intelligence ReportingBusiness intelligence reporting refers to the process of collecting, analyzing, and presenting actionable insights from data to support decision-making in a business setting. It helps organizations gain a better understanding of their performance, identify opportunities for growth, and pinpoint areas that need improvement. In the digital marketing arena, BI reporting is critical for optimizing marketing campaigns, measuring ROI, and ensuring that resources are allocated effectively.Importance of Business Intelligence Reporting in Digital MarketingOrganizations must be agile and responsive to keep one step ahead of the competition given how quickly the digital marketing landscape is changing.  Business intelligence reporting plays a pivotal role in achieving this by: Guiding Marketing Strategies: Data-driven insights can reveal trends and patterns that inform the direction of your marketing efforts, enabling you to make better decisions and achieve desired results. Improving Campaign Performance: Analyzing performance data allows you to identify high-performing channels and campaigns, which can then be scaled to maximize ROI. Enhancing Customer Targeting: By analyzing customer data, marketers can develop a deeper understanding of their target audience, enabling them to create more personalized and relevant messaging for improved engagement. Boosting Budget Efficiency: Through BI reporting, marketers can identify underperforming campaigns and make necessary adjustments, thereby reducing the wastage of resources and ensuring that marketing spend is allocated optimally.Tools for Business Intelligence Reporting in Digital MarketingTo harness the power of business intelligence reporting, digital marketers need a suite of tools that allow for seamless data collection, analysis, and visualization. Some of the most popular tools for BI reporting in digital marketing include: Looker Studio: A visualization tool by Google, Looker Studio allows users to create interactive reports and dashboards that consolidate data from various sources like Google Analytics, Google Ads, and BigQuery. With its user-friendly interface, digital marketers can easily visualize and share insights with their teams. BigQuery: A Google Cloud Platform service, BigQuery is a powerful data warehousing solution that enables users to store, manage, and analyze large volumes of data. It is particularly useful for digital marketers who need to process and analyze vast amounts of data from multiple sources quickly. GA4 (Google Analytics 4): As the latest iteration of Google Analytics, GA4 offers advanced features and machine learning capabilities that enable marketers to track user engagement, conversions, and other important metrics across their digital platforms. Google Ads: The go-to platform for running search and display advertising campaigns, Google Ads provides detailed performance data that can be analyzed to optimize campaigns for better ROI. Facebook Ads: With billions of users worldwide, Facebook is an essential platform for digital advertising. Facebook Ads Manager offers comprehensive reporting and analytics tools to help marketers track and optimize their campaigns.Best Practices for Enhanced Decision-making with Business Intelligence ReportingTo make the most of business intelligence reporting in digital marketing, marketers should adopt the following best practices: Integrate Data Sources: Consolidate data from multiple sources, such as Google Ads, Facebook Ads, and Google Analytics, to create a comprehensive view of your marketing performance. This holistic approach enables marketers to derive meaningful insights and develop well-informed strategies. Set Clear KPIs: Establish key performance indicators (KPIs) that align with your marketing goals and objectives. Monitor and analyze these KPIs regularly to measure progress and identify areas for improvement. This ensures that your marketing efforts remain focused on achieving the desired outcomes. Automate Reporting: Leveraging tools like Looker Studio to automate the reporting process saves time and ensures that data is consistently updated and accurate. This allows digital marketers to focus on analyzing insights and making data-driven decisions rather than getting bogged down with manual reporting tasks. Utilize Advanced Analytics Techniques: Employ advanced analytics techniques, such as predictive modeling and machine learning, to uncover hidden patterns and trends that can inform your marketing strategies. These cutting-edge methods can provide a competitive advantage by revealing untapped opportunities and enabling more precise targeting. Foster a Data-driven Culture: Encourage collaboration and data-driven decision-making across your organization by sharing insights and fostering open communication. This creates an environment where everyone is working towards the same goals and using data to make informed decisions. Continuously Optimize: Regularly review and analyze your BI reports to identify opportunities for improvement and optimization. Adjust your marketing strategies and tactics accordingly to maximize ROI. By staying agile and adaptive, digital marketers can ensure their campaigns remain effective in the ever-evolving digital landscape. ConclusionBusiness intelligence reporting is a game-changer for digital marketing professionals, enabling them to make data-driven decisions that drive growth and success. By leveraging tools like Looker Studio, BigQuery, GA4, Google Ads, and Facebook Ads, digital marketers can gain a comprehensive understanding of their performance, optimize campaigns, and allocate resources more effectively.Embrace the power of BI reporting and watch your digital marketing strategy reach new heights. In an increasingly competitive digital marketing environment, businesses cannot afford to ignore the insights and advantages offered by BI reporting. The ability to stay agile and adapt to market changes will be the determining factor in the success of your digital marketing efforts. Embracing the power of business intelligence reporting will equip your organization with the tools needed to navigate the complexities of the digital marketing landscape and achieve new heights of success.As digital marketing continues to evolve, so will the capabilities of BI reporting tools and technologies. Stay ahead of the curve by continually investing in your knowledge and understanding of BI reporting and analytics. Doing so will ensure that you remain well-prepared to capitalize on the ever-changing opportunities presented by the digital marketing world.

Apr 24, 2023 1209 reads

Universal Analytics Attribution Models

Attribution models are essential tools in digital marketing for understanding the impact of marketing activities, optimizing budgets, developing strategies, gaining customer insights, measuring performance, evaluating different channels’ contributions, and making data-driven decisions.By choosing the right attribution model, you can make your marketing efforts more efficient and effective, thereby increasing the success of your marketing strategy.To select the appropriate attribution model, you need to understand the models available in Universal Analytics and Google Analytics 4.What Is Attribution Modeling?Attribution modeling is a method used in digital marketing and advertising to assess the contribution of various marketing channels during a customer’s conversion journey. It determines which channels were most effective before a user completes a purchase or conversion.In digital marketing, multiple channels—organic search, paid search, social media, email marketing, referral traffic, content marketing, and interactive ads—often contribute in sequence to a single conversion. Attribution models evaluate these complex interactions to reveal each channel’s weight in driving that conversion.In short, attribution models provide valuable insights for budget allocation, strategy optimization, channel performance analysis, conversion path tracking, and understanding customer behavior.Why Are Attribution Models Important in Digital Marketing?When used correctly, attribution models offer marketers critical analytical insights into the effectiveness and return on investment of their campaigns. This empowers them to understand performance, optimize budgets, refine strategies, gain customer insights, measure ROI, evaluate channels, and make informed decisions.Common Attribution ModelsGoogle Analytics provides several built-in attribution models: Last Interaction (Last Click) Last Non-Direct Click Last Google Ads Click First Interaction (First Click) Linear Time Decay Position-Based Last Interaction (Last Click)The Last Interaction model attributes 100% of the conversion value to the final click or interaction before conversion, ignoring all earlier interactions. It assumes the last touchpoint is the most important, but it can overlook the influence of previous channels in the customer journey.Last Non-Direct ClickThis model gives full credit to the last click or interaction that was not “direct” traffic, and it ignores any direct visits. It’s useful for understanding the impact of non-direct channels, but like Last Interaction, it disregards earlier touches.Last Google Ads ClickThe Last Google Ads Click model assigns all conversion credit to the last Google Ads click. It’s ideal for measuring paid search performance but ignores contributions from other channels.First Interaction (First Click)First Interaction attributes all conversion value to the very first click or interaction in the customer journey. It highlights awareness channels but overlooks any later touches that may have influenced the final conversion.LinearThe Linear model distributes credit equally across every touchpoint in the conversion path. It provides a balanced view but may mask the true impact of especially crucial interactions.Time DecayTime Decay gives more credit to interactions closer in time to the conversion. Early touches get less weight, emphasizing the last steps in the journey.Position-BasedThe Position-Based model assigns 40% of the credit to both the first and last interactions, and the remaining 20% is evenly split among the middle interactions. This highlights both awareness and conversion touchpoints but gives less weight to in-between channels.Which Attribution Model Does Universal Analytics Use by Default?By default, Universal Analytics uses the Last Interaction (Last Click) attribution model. That means every conversion is attributed to the last channel the user clicked before converting, and all other channels are ignored.In our next article, we’ll examine Google Analytics 4 and its attribution model options.