How 5 Top Retailers Implement Adobe Analytics

July 19, 2021 Jack Vawdrey

If you’re a digital marketer or analyst working in the retail sector, getting a sense of how other prominent online retailers implement Adobe Analytics can help uncover opportunities for you to improve your own implementation.

Using our own TagDebugger (a freemium tool for debugging analytics requests), we analyzed the Adobe Analytics implementations of the following 5 retailers:

  • CVS Health
  • Costco
  • Lowe’s
  • Verizon
  • Walgreens

From our analysis, we’ve summed up which data points these retailers capture in Adobe Analytics and why.

The information included here was gathered from publicly available data and not directly from the above organizations. Our analysis is presented in aggregate to respect each company’s hard work in creating stellar implementations and strategies. 

See how we collected this data.

Note: The insights in this article were gathered from public data, not from any team member at any of the above organizations.

The Pillar Metrics of Digital Marketing and Analytics

As complicated as a digital analytics implementation can be, the goal of all tracking boils down to optimizing four pillar metrics of digital marketing & analytics. Namely:

  • Average Cost per Visitor
  • Traffic Volume
  • Conversion Rate
  • Average Order Value

Each of these metrics represents a lever that a digital marketer can pull to profitably grow top-line revenue, based on the following criteria.

IF 

Average Cost per Visitor < Conversion Rate * Average Order Value

THEN 

Pay for more Traffic Volume

ELSE

  • Improve Conversion Rate, OR
  • Increase Average Order Value, OR
  • Lower Average Cost per Visitor

In other words, the goal of digital analytics is to answer the question, “If we put a dollar in, are we getting more than a dollar out?” Complex tools like Adobe Analytics enable analysts to answer this question not just for web visitors as a whole, but for segments sliced in as many ways as the analyst can conjure up.

Below are some examples of dimensions that 5 of the top retailers use to segment their visitors and products in order to better understand which lever to pull.

Note: Because Adobe Analytics tracks campaigns based on a campaign id (instead of by separate campaign parameters like in Google Analytics), and because we’re working from public data, we’re not able to offer any insight on which campaign variables these retailers track.

 

Page Data

Page data refers to data collected about a given page on a site. Tracking a page’s metadata is helpful to understand what types of pages visitors land on or navigate to before making a purchase.

Analytics dimensions for page data

Page identifier. A page identifier is a unique string that represents the page a visitor is viewing. It could represent a product category (e.g. electrical_supplies) or another classification of page.

Page classification. By classifying pages as a category, super category, product detail, or other type of page, analysts can determine which types of pages visitors view before making a purchase.

Page taxonomy. Page taxonomy gives a top-down view of performance down the hierarchy of a website’s product divisions. For example the taxonomy of men’s white Nike basketball shoes could be: Men’s > Sports > Basketball > Shoes > White Nike Air Max.

 

Visit Data

Visit metadata refers to data collected about an individual visit to the site, such as the time the visit occurred and the type of device. Understanding the nature of a visit that converts can inform how a web design team prioritizes its optimizations (such as for device type) and how digital marketing prioritizes budgets and schedules for paid media.

Analytics dimensions for visit data

Date/Time. Date/time is an obvious data point an analytics team would want to have, as it helps to determine when visitors are most active on the site. Some applications of this data point include:

  • Determining what times of day, week, month, and year have the highest purchase volume
  • Determining when paid media is the most cost effective (either due to a higher conversion rate/average order value or a lower cost per visitor)
  • Determining what times of day/week email marketing campaigns are most effective

Day of week. For simplicity, several of the implementations reported the day of the week (i.e. Monday, Tuesday, etc.) as its own variable, separate from the date/time. Doing so gives analysts the opportunity to recognize time-related purchasing patterns and make recommendations to marketing. Other implementations delineated between weekdays and weekends, providing another dimension on which to analyze purchasing patterns.

Last visit. The space in between visits can reveal various aspects of your audience:

  • How sticky is my audience in terms of frequency of visits?
  • On average, how frequently do visitors visit the site? How often do I need to change the site?
  • Are repeat buyers frequent or infrequent visitors to the site?

Device type. The type of device a visitor uses impacts behavior. For example, are you more likely to make a purchase on your desktop vs. your smartphone? Understanding how device type influences acquisition costs, conversion rates, and average order value will inform audience targeting for paid media, as well which experiences you should prioritize for A/B testing and optimization.

Authenticated vs. unauthenticated visits (i.e. guest or logged in). Whether or not a visitor is logged in during a visit is a strong frame through which you can analyze conversion behavior. For example, with authenticated users, developers can make robust personalized experiences from past purchase data, which in theory should improve conversion rates/average order value. 

By segmenting authenticated and unauthenticated users in analytics, analysts can determine if a personalized experience produces a significant difference towards improving conversion rate/average order value.

 

Visitor Data

Visitor data covers information about the individual navigating through your site. Data about the visitor enables analysts to determine the most profitable segments of their audience based on geo, language, and other qualities.

Analytics dimensions for visitor data

Location details. Understanding a visitor’s location provides a dimension on which to prioritize marketing spend. For example, if a specific geo shows above average profitability, the marketing team could prioritize which geos to target.

In cases where the retailer also has a brick-and-mortar presence, an analysis of location data can also inform regional preferences for given products, which the marketing team can incorporate into online and offline media alike.

Language code. While the general user experience of a website should be identical across languages in terms of layout and features, the nature and quality of web copy in various languages (and the quality of translation) can impact conversion rates. Tracking language codes can reveal weaknesses in web copy for a given language.

There may also be underlying cultural differences between speakers of different languages, which could affect purchasing patterns. Segmenting by language can reveal these differences and help marketers prioritize product promotion in the various languages.

New or repeat visitor. In general, knowing what percent of your visitors are new vs. returning can give you a sense of:

  • How far-reaching your marketing efforts extend
  • How loyal your visitor base is

When paired with campaign data, this dimension can help you better understand which channels are best driving new visitors to your site and which are bringing people back.

Prospect or customer. Whether or not a visitor is a current customer or a prospect will largely affect behavior. For example, a current customer may navigate to your site often to review the status of an order or log into a customer portal. A prospect, on the other hand, may be interested in browsing your inventory. Being able to separate the two segments can reveal opportunities to convert browsing prospects or improve the user experience for customers so they will purchase again in the future.

Customer category. As with the dimension tracking if a visitor is a prospect or customer, being able to categorize visitors based on other dimensions can help analysts separate behaviors and recommend changes to marketing/engineering. One way to segment customers would be for Business vs. Consumer customers, whose conversion behavior will likely differ.

 

Product Data

Tracking product data enables analysts to lend insight to marketing teams about which products they should market on the site (such as on home and category pages), off the site but online (via display ads and organic/paid social), and offline (i.e. traditional media like television, signage, and print).

Analytics dimensions for product data

Product category. A product’s category provides a higher-level view of which segments of product tend to perform best in terms of:

  • Traffic volume: Which product categories attract the most page visitors?
  • Conversion rate: Which categories tend to convert well in a given time period?
  • Average order value: Which categories do customers tend to include in above-average cart sizes?
  • Average cost per visitor: Which categories are the most costly to attract customers to?

Product name. At a microscopic level, tracking the product name enables analysts to see which products best convert and which don’t. But a full product name can reveal more than just performance for a specific SKU. A robust product name is multidimensional, providing multiple frames of analysis. For example, consider the following product name and the sub-dimensions an analyst could analyze:

Product Name: Weber Genesis II E-315 Black 3-Burner Liquid Propane Gas Grill


Sub-dimensions

  • Brand name: Weber
  • Model name: Genesis II
  • Color: Black
  • Number of Burners: 3
  • Fuel type: Liquid Propane

Of course some of these sub-dimensions are interdependent—you couldn’t reasonably compare the performance of a black grill to black shoes—but they do still open the opportunity for robust analysis.

Product SKU. The most microscopic way of analyzing product performance, product SKUs are helpful to simplify analysis of performance within a category or sub-category (like grills). Marketers should promote the best performing SKU in their internal and external campaigns.

 

Browser Data

Browser data offers basic information about how your website appears on the visitor’s device.

Analytics dimensions for browser data

Screen width. Tracking screen width is largely about determining whether the site has similar performance across screen sizes. If visitors with screen widths <400px are significantly less likely to convert, then the engineering team will likely need to revisit the design for that screen width. In most cases, screen widths were represented as enum values (e.g. mobile, tablet, desktop) as opposed to absolute widths (e.g. 403px, 517px, etc.)

Title tag. Depending on how it’s structured, the title tag could serve multiple functions for analysis. For example, it could contain a product name and category, or highlight a recent tentpole event.

 

Site Version Data

Understanding which versions of your site you’re serving can help you test new versions or device-specific versions against other versions to determine potential improvements.

Analytics dimensions for site version data

Web version served. In some cases, a website will serve a different version of the site depending on the device. Segmenting analysis by the version of the website can reveal performance gaps that marketing and engineering should address. Some questions an analyst might look to answer with this dimension include:

  • Does using the same creative on desktop and mobile inhibit the experience for one experience or the other?
  • How have our page speed optimizations on the mobile version affected performance?
  • How does the alternative mobile layout perform compared to the desktop version?

Release version. After the engineering team makes updates to the website, analysts will want to determine the effects of those changes on traffic volume (think SEO), conversion rate, and average order value. Ideally, the engineering team will serve the new version of the site (after significant testing in staging) to only a portion of web visitors to analyze performance.

 

Managing the Complexity of a Growing Implementation

As you build out your Adobe Analytics implementation to track more dimensions for robust analysis, keeping a handle on the growing complexity can be challenging. Especially when  multiple teams manage different aspects of the site, periodic updates  can result in your tracking breaking down, leaving gaps in your data and inhibiting analysis.

ObservePoint’s Technology Governance solution enables you to run regular audits of your site, testing that your Adobe Analytics variables are always present and functioning as expected. Schedule a demo to learn more.

About the Author

Jack Vawdrey

A former student and present enthusiast of the humanities, Jack Vawdrey uses his love of language to explore the role of marketing and analytics technology in business. Jack joined the ObservePoint marketing team in August 2016 as Managing Editor and now serves as a Content Marketing Consultant. Adamant about automation, Jack writes to educate the analytics and marketing community about the role of tag auditing and data governance in the enterprise.

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