Web Analytics: Data for Digital Marketing

**Chapter 5: Data for Digital Marketing Analytics** **The importance of data:** Data is the essential raw material for digital marketing analytics. It is crucial for understanding customer behavior, identifying trends, optimizing strategies, and achieving marketing goals. It comes in numbers, text...

**Chapter 5: Data for Digital Marketing Analytics** **The importance of data:** Data is the essential raw material for digital marketing analytics. It is crucial for understanding customer behavior, identifying trends, optimizing strategies, and achieving marketing goals. It comes in numbers, text, images, audio, video, or any other digital representation. In the modern era of technology, data is an exceptionally valuable resource, almost as precious as gold. It can provide valuable insights, facilitate informed decision-making, enhance customer experiences, and drive innovation to new heights. Businesses that expertly collect, manage, and apply data to their operations can gain an in-depth understanding of their customers, optimize their processes, personalize their offerings, and improve their performance across the board. However, it is critical to adhere to responsible data practices, ethical values, and privacy regulations to maintain trust and comply with legal requirements. Data is a dynamic and evolving resource that demands careful handling, analysis, and ethical considerations to make the most of its benefits. **Reasons why data is so important:** - **Strategy formation and Decision Making**: Utilizing data moves decisions from intuition to evidence-based, ensuring marketing strategies are aligned with actual audience behaviors and preferences. - **Comprehending the needs and wants of consumers**: Data offers a deep dive into demographic and behavioral details, enabling the creation of tailored content and targeted campaigns, which heightens engagement and conversions. - **Efficient Resource Allocation**: Analytics reveal the effectiveness of marketing initiatives, ensuring budget is allocated to high-ROI channels and strategies, maximizing returns. We look at three types of data in terms of their availability. Organizations use Web Analytics to collect first-party data on their digital properties. In recent years, companies have formed data partnerships and alliances to understand their target audience better and improve their advertising efforts. This approach combines first-, second-, and third-party data resources, allowing organizations to leverage identity resolution technologies to create a unified view of their customers. By doing so, businesses can better comprehend customer behavior, preferences, and interactions across various touchpoints **3 Types of data:** 1. **First party data:** This type of is collected by issuing the first-party cookie to the Web browser of a visitor to a website running Web Analytics software. Other applications are running on a site serving first-party cookies as well. Most websites issue first-party cookies for applications and login information that takes place on their website. It is also directly obtained from a company's customers or audience. It includes customer demographics, purchase history, website behavior, preferences, and contact information. - A cookie is a small file of letters and numbers that is downloaded on to your computer when you visit a website. Cookies are used by many websites and can do a number of things, remembering your preferences, recording what you have put in your shopping basket, and counting the number of people looking at a website. - They send this information to the site owner so they can use the information to send you advertising, improve the way their site works, improve the page layouts etc. - Once created, cookies are stored in a file on your hard drive or browser, depending on your operating system and the browser you use. 2. **Second party data:** Second-party data refers to the first-party data of another organization or business, which is shared or purchased through a mutually beneficial partnership or arrangement. It allows businesses to access valuable insights and reach a broader audience beyond their own customer base. For example, many online banking and credit card customers provide first-party data that is then provided to their business partners (the terms of this arrangement are usually appear in terms of service of the website). 3. **Third party data:** Third-party data refers to data collected by external sources not directly affiliated with a particular business or organization. It includes information about consumer behaviors, interests, demographics, and online activities. Over the past 25 years, third-party data has been widely used for audience targeting and ad personalization. However, privacy concerns, regulations, and changes to browser policies limiting third-party cookie tracking have impacted its availability and usage. The industry has shifted towards more privacy-centric practices and a focus on first-party data. **Data collection ecosystem:** There are so many ways to gather audience data that it is mind boggling and it continues to get faster and more sophisticated every year. The data that is collected by consumers is then anonymized, normalized, de-duplicated, aggregated, repackaged, and sold to advertisers and publishers (and pretty much anyone else willing to purchase it). **Data Lakes:** Data Lakes are a customizable, general purpose data store where information is saved in its original format. By placing data into the Lake, it becomes available for analysis by everyone in the organization. The Data Lake is usually formed with a Hadoop cluster with several nodes that holds all the information that populates Salesforce, Marketo, and Adobe Analytics. Data Lakes solve this problem by leaving the information intact in Hadoop in an unstructured (or minimally structured) state, ready and waiting to be analyzed. **Data lake governance**: The data must be clean, reliable, and available when it is needed. Updated campaign codes are required. Attribution models in Web Analytics need to be chosen with care. "Lookback Window" that is long enough to capture enough of the customer lifecycle to provide meaningful marketing attribution. The right team is required that has the combination of skills, talents, and a love for data and problem-solving. **Data lake issues:** While Data Lakes solve some data collection and storage issues, its utility creates other problems. **Security and access control**: Captured data goes into the Data Lake with no oversight of the contents. The security capabilities of Data Lakes are still immature. **Data quality**: There is an inability to determine data quality, or annotate the findings by other analysts or users that have found value from the same information in the lake. Without descriptive metadata and a way to maintain it, the Data Lake risks becoming a data swamp. **Third-Party Data and Analytics:** Web Analytics, an example of first-party data, provides data on what is happening in the organization or business website by placing cookies into a visitor's browser. Web Analytics often lacks competitive or industry data outside of the tracked website. Most organizations would like to know how well they are competing in the major areas connected to their business goals and revenue with a few exceptions. Taking competitive analysis information into consideration, Google Analytics provides anonymous benchmark data collected that can be useful to provide some idea of how well a website is performing against others in similar industries and locations. It also provides second- and third-party data from the DoubleClick Ad Network. Other platforms, such as IBM Coremetrics and Adobe Analytics, include industry-wide metrics gathered from customers running the analytics platform. **Third party ecosystem/ Digital data landscape** **Third-Party Data Aggregators (What They Do):** Every third-party data aggregator has their way of combining data that is why it is better to assemble the data side by side *but not intermix it,* which will degrade data quality. - Aggregate behavioral data of audience members from online publishers, such as the *New York Times*, and Web portals, such as Yahoo, that repackage and resell them to other organizations. - Provide data collection scripts for publishers to run on their sites, allowing third-party data aggregators to collect data from publishers and portals as part of an ad network (e.g., Audience Science) or revenue share agreement (e.g., Oracle BlueKai). - Provide third-party cookie lists for sale to advertisers and agencies that are used for ad targeting. The cookie list is available for bidding within minutes (particularly for in-market audiences) and has a shelf-life of up to two months. **Third-Party Data Tools Useful for Digital Analytics:** - **ABI/INFORM Global:** Finds articles from trade journals and magazines, scholarly journals, and general interest magazines covering accounting, advertising, business, company information, industry information, management, marketing, real estate, economics, finance, human resources, and international business. - **Academic OneFile:** Articles from magazines and scholarly journals from a wide range of subjects. - **Business Monitor Online (now called BMI Research):** Might be useful to find out about a market in each area. - **eMarketer:** eMarketer takes data from all over the digital marketing, world, re-charts and organizes it for reference and publication. - **Gartner:** Analysis of IT markets for hardware, software, IT services, semiconductors and communications. Reports on IT issues in ten industries including education, banking, retail, healthcare, and manufacturing. - **Grolier Online:** Useful for definitions of topics along with building citations. - **IBISWorld:** Reports on more than 700 industries plus specialized analyst reporting. - **Kanopy:** Might be useful as a source of stock educational videos. - **American Fact Finder:** Find out details about demographics in a zip code in the US. - **Business Insights: Essentials**: Good for SWOT analysis of companies, perhaps of industries, also allows keyword search. Provides industry reports that might be useful. **Web Data Collection Issues:** There are some problems with web data collection such as: - Users in Web Analytics platforms are not identical to people. Users are cookie value (first-party data), and an accurate count of Web users is not the same as actual users as the same user might be accessing the website through multiple devices (each having its own IP address and cookies). The remedy for this problem is to have users log in to the site, so it is counted accurately, but that is not always possible. - Web Analytics can no longer capture the variety of traffic produced, by the customer's journey. In fact, no single, piece of software does it all in one place. - Meaningful information is hard to isolate due to excessive amounts of generated data (refer to the Metropolitan Museum example earlier in this chapter). It is also tempting to use the wrong key performance indicators, such as likes, shares, and followers, because they are easier to gather than others which might be better suited but are harder to collect. - To reinforce the confusion about which metrics to use, there is no consensus on the right metrics for consumer activities on the Web. **Combining First and Third-Party Data Case Study, The Informatica.com Data Lake:** - A Data Lake is a storage repository holding vast amounts of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a Data Lake uses a flat architecture to store data. - It is not hard to understand why Data Lakes are so attractive to organizations. Building a modern data warehouse is a very expensive undertaking that doesn't have a high success rate. - Before beginning to improve the quality of analytics to uncover actionable insights, the data and marketing automation in place should be re-examined and, perhaps, reconsidered and replaced when needed. Next, website and Web Analytics need to be set Up to connect with the Data Lake. **Digital Advertising Types:** Modern technology has changed the way consumers purchase products and services online. **Digital**: Graphic ads appearing next to content on webpages, IM applications, email. **Video**: Ads appear in the video before, during, or after the video plays. One of the fastest-growing; opportunities online today. **Mobile**: Ads used on mobile devices, such as cell phones or tablets are growing, quickly. **Search:** Ads are placed and ranked by search engines on webpages that show associated results from the user's search engine queries (can be combined with display networks-such as Google's). **Social:** Produce content that users will share with their social network. **Native:** A form of social media advertising Bat matches the shape and function of the platform on which it appears---looks similar to user's post or newsfeed item but is an ad. **3 Pillars of the Digital Marketing at Informatica:** **Pillar 1:** Adobe Analytics which is part of the Adobe Marketing Cloud (Google Analytics now has a similar cloud offering connected to Google Analytics, but it was not available at the time of this case study in fate 2015). **Pillar 2:** Marketo Marketing Automation used for tracking known visitors, email, and automating our nurture flows, (we chose Marketo). **Pillar 3:** Salesforce CRM for tracking the sales opportunities all the way through to revenue, which is a vital part of any big data marketing program. **Summary:** - This chapter covered the data for marketing and digital analytics. The digital analytics and marketing world have undergone significant changes in terms of how data is used over the past 25 years. - Data is key for analytics and digital marketing. There are different types of data. Understanding the differences between first, second, and third-party data is essential. - While technological improvements allow organizations to collect vast amounts of information, they have not learned how to analyse or use most of it. While the approach any given organization takes with Big Data is unique, they should expect to see increased revenue as an outcome. To gauge success, organizations should be able to drive business decisions with the data and reap the benefits.