If you are tracking user interactions, either on-page or server-side, you are likely to have at least some sessions that are not associated with a landing page. This can happen when a session includes no pageviews or when the first hit in a session is not a pageview.

There are three potential causes to sessions with no landing page in Google Analytics (Universal Analytics; GA4 is different). All are connected with event tracking. 

To troubleshoot this problem, always start by creating a segment for sessions where the landing page is “not set.” Once you have done that you can begin to identify the likely culprit. 

Here are the three main potential causes:

Server-side events

Events delivered server-side (via GA’s Measurement Protocol) often create a distinct session. Because this session does not take place in a browser or follow a pageview, the session does not get attributed to a landing page.

How to troubleshoot: Apply your “not set landings” segment to an event report such as “top events.” 

You might now be able to easily recognize which events are server-side. If they are, that is the likely cause and you can consider this to be expected behavior. Your “not set” landing pages are not a sign of a tracking problem.

Conversely, if the associated events are events that take place on pages — you can add “page” as a secondary dimension to determine this — this is not likely to be the problem. That’s because events that cause not-set landing pages are not associated with pages at all.

Session refresh

When you are tracking on-page events such as video plays and downloads you will sometimes see sessions with not-set landing pages due to “session refresh.” 

For instance, when a user has left a page open in the browser for longer than the session timeout period (usually 30 minutes) and that user returns and starts interacting again, a new session will begin in Google Analytics — starting with the first hit delivered beyond the timeout. 

When that first hit is an event rather than a pageview the session will report a landing page “not set.”

How to troubleshoot: Apply your “not set landings” segment to the audience overview report. 

If session refresh is causing this issue, that segment will include a disproportionately high percentage of “returning users.” That is your first hint that session refresh is the cause.  

You can dig a little deeper using the user explorer report. Observe the actions taken by users who have sessions with no landing page. 

If they have a normal-looking session (such as one that starts with a pageview) and then the first hit in their next session is an event, and it is more than 30 minutes later than the prior hit, this is likely to be a session refresh.

Firing order

A simple implementation mistake also can result in not-set landing pages. 

On-page events should not fire before pageviews. However, there are many cases where certain events fire upon page load.

For example, a third-party service might populate additional audience info in an event; a split-testing service might fire an event to indicate that a test experience has been entered; or a scroll tracking event might indicate 25% scroll on page load. 

If any of these types of events happen to fire before the pageview tag on the page, that can cause the session to begin without a landing page value.

How to troubleshoot: Follow the steps above, regarding session refresh.

The post ‘Not Set’ Landing Pages Getting You Down? appeared first on Empirical Path.

Google recently announced the rollout of Google Analytics 4, the future version of its analytics platform. Our analytics team has compiled a few key initial questions (and answers!) about GA4, with more to come.

• Do I need to start over on the analytics implementation we just did recently?
• I heard this version of GA4 previously referenced as App+Web. Since our organization doesn’t have a mobile app, do I still need to migrate?
• Does the new GA4 have any sampling?
• What happened to engagement metrics such as bounce rate and time on page/site?
• How is the new engagement time metric calculated?
• Can I use GA4 to build audiences for remarketing across the Google Marketing Platform, Salesforce Marketing Cloud, etc.?
• Is there a paid (360) version of GA4 or is there only a free version?
• I don’t have GA360. Can I export GA4 data to BigQuery for free?

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The post Google Analytics 4 Frequently Asked Questions appeared first on Empirical Path.

All of the changes in the digital privacy landscape over the past few years have been challenging to follow. Digital marketers are scrambling to keep up with a variety of issues, including the death of third-party cookies in many browsers, user opt-in requirements for cross-app and website tracking with the release of iOS 14.5, and the continued rollout of regional privacy regulations. 

At Empirical Path, we recommend a few steps to adapt to these changes and those on the horizon.  

Adopt Site-wide Tagging and First-Party Cookies

Following the death of third-party cookies in Safari and Firefox and the increasing number of users opting out of third-party cookies in other browsers, conversion tracking has moved to first-party cookies, which are set in the domain of the host website. A solid first-party tracking solution starts with site-wide tags. Examples of site-wide tags (which means tagging all pages of a website) include — for the Google stack — Google Tag Manager with conversion linker, gtag.js, or site-wide Google Analytics with GA imported conversions. For ad platforms such as Facebook, this means site-wide pixels. 

With site-wide tags, the ad click data is captured in a cookie on the user’s landing page after a click-through from an ad. The data is then stored so it’s available to be appended to conversion events on conversion pages. Both Google and Facebook ads have moved to setting first-party cookies, though Facebook ads set both first- and third-party cookies. While some browsers (namely Safari) have decreased the lifespan of first-party cookies, the first line of defense in preserving measurement today rests on building a solid site-wide first-party cookie tracking solution.

Invest in First Party Data

Because third-party marketing such as remarketing to your site visitors across ad networks is now limited, our general advice is to instead invest in augmenting first-party data. In addition to first-party cookie collection, incentivising users to register, log in, and opt-in to sharing contact information such as an email address, provides an opportunity to engage in a dialogue directly with current  and potential customers. This data also can be uploaded to ad networks to create “lookalike” audiences to target.

Customer Data Platforms (CDPs, such as our partners Tealium or Segment) provide a platform for managing all customer data, such as site activity, CRM data, and sales data, in a single location, leveraging all of these data points for additional marketing. Alternatively, customer data can be imported into Google Analytics to add customer attributes to a customer identifier. We recently spearheaded GMP audience creation based on nuances like current products subscribed and customer buckets, combined with recent site activity for more targeted and powerful marketing.

Embrace Conversion Modeling and Modified Tracking Methods

It’s important to keep up to date as marketing platforms evolve their methods to report conversions.

Google now integrates “consent mode” in Google Tag Manager and gtag, which provides the ability to adjust the type of data sent to Google tags based on a user’s consent status. This allows Google to continue collecting privacy-compliant data (including “cookieless pings” for GA4) without violating user privacy preferences. Google ad tags can leverage consent status to estimate conversion volume loss due tracking preferences. Modeled (estimated) conversions can also fill in gaps when view-through and cross-device conversions can’t be collected due to browser limitations.

So what is conversion modeling? Google explains it as a way to estimate directly unobservable data; for instance, data protected by user privacy settings or technical limitations. These estimated conversions are then included in campaign conversion totals in order to improve campaign management.

Google is also rolling out enhanced conversions to provide a way to send hashed first-party data (like an email address or phone number) matched against a Google account, rather than cookies, for conversion tracking. This method can vastly improve view-through attribution, which is usually reliant on third-party cookies.

Facebook’s Aggregated Event Measurement represents that platform’s workaround for privacy-compliant conversion tracking on Apple devices. Follow their instructions to set up a limited number of key conversion events and rank them in order of importance. Also note that given the shortened lifespan of cookies, some of Facebook’s attribution windows (28-day click-through, 28-day view-through, and 7-day view-through) are no longer available.

Building on User Trust 

Above all, be as transparent as possible about the data you collect and its uses, then give users the ability to opt out. If a user ID is collected with analytics, you may need user consent and you definitely need to acknowledge this and any plans to link user activity to CRM data in an updated privacy policy.

Consider regional regulations and adjust data collection and settings as needed. For example, Google Signals (which allows sites to both access cross-device reporting for Google’s signed-in users and market to them) can be managed by country or state to limit data sharing for specific locations.

The web has always changed and will always change, presumably for the better for all of us. Understanding the rules of the road when it comes to analytics and data analysis is a tool for deeper understanding, something we at Empirical Path work hard to provide to our clients every day. Staying a step ahead of those changes is the key to continuing to refine the disparate flow of user touches into a clear, actionable picture for your organization.   

The post A Better Understanding of Digital Privacy appeared first on Empirical Path.

Google Analytics recently announced the rollout of Google Analytics 4, the future version of its analytics platform. The new GA4 integrates innovative features to the core of the reporting suite that marketers in the finance sector will find particularly compelling. In particular, new capabilities — such as more extensive cross-device tracking, machine learning, and a revamped reporting interface —  all represent opportunities that savvy finance marketers would be wise to leverage. 

Enhanced identity resolution

The current version of Google Analytics forces users to choose between two distinct views of their data. Analysts can use the “user ID” reporting view to analyze logged-in user data, or the standard reporting view which leverages device / cookie level view to identify a user. Both of these views present challenges for digital marketers. The “user ID” view presents data only from the subset of users who are in a logged-in state, while the device/cookie level view treats every new instance of a cookie from a device or browser as a new user, which results in significant overcounting. Users who delete their cookies regularly are also likely to be double-counted and can overstate the volume of “new” users.

For industries that tend to have a longer sales cycle — including insurance, investing, lending, and banking — this problem is exacerbated. Users are more likely to perform research from more than one device or to have cleared their cookies at some point during their journey, making it difficult to accurately attribute conversions to initial campaigns. Google Analytics 4 offers a potential solution by blending user identifiers and using the best-known identifier — whether that’s a customer ID or a cross-device user identified by a Google account, or if neither option is available, then by the device. The resulting reports present a more accurate view of true website users.

Privacy-first design

A broad shift toward greater individual privacy has created headaches for digital marketers, who now face a higher level of regulatory scrutiny.  However, compliance issues are likely familiar to finance professionals. In an industry accustomed to constant monitoring by the Financial Regulatory Authority (FINRA) and other regulatory bodies, maintaining compliance standards is paramount. Consumers are more focused on digital privacy than ever, leading regulators and business leaders to craft new policies. Legislation like the European Unions’ General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) and moves by browsers to limit the lifespan of third-party cookies have all curtailed marketers’ ability to collect and store consumer data while granting users more control over where and how their data is used. Google Analytics 4 is designed with this new landscape in mind and offers features like machine learning to augment collected data, helping to offset some of the data losses resulting from users opting out and the deprecation of third-party cookies. 

Cross-device attribution

Enhanced identity resolution will dramatically improve user reporting and provide more accurate attribution for any marketer in a sector with a long sales cycle.  If a user with a Google account (who has not opted out of ‘ads personalization’) clicks on an email from a mobile device, then returns to browsing, only to submit a lead form later from a desktop, the marketing team will be able to connect that lead back to the original email. Ultimately, it will improve marketers ability to attribute impact and to assign resources more efficiently. 

Simplified data model and BigQuery export

The financial industry has been ahead of the curve in its investment in proprietary data lakes, but integrating analytics data with the existing iteration of Google Analytics can be challenging for non-360 customers limited to API feeds. The updated platform includes a simplified data schema as well as the ability for non-Google Analytics 360 customers to export granular event-level data to BigQuery, making it easier to analyze, query, and transform the type of data that financial marketers capture. 

What’s next? 

Changes to critical tools like Google Analytics can create headaches, but GA4 brings with it a host of new opportunities for financial marketers to strengthen their existing strategies. The key for most will be to look for ways to leverage a new and expanded toolkit to build reporting that accommodates that natural length of the financial sales cycle and to use that reporting to build deeper relationships and greater understanding of the finance consumer. Contact us if we can help.

The post For Finance Marketers, Google Analytics 4 Presents Big Opportunities appeared first on Empirical Path.

Even before the world was gripped by the COVID-19 pandemic, most industries were seeing customer interactions shift from in-person and telephone to digital. The pandemic has accelerated this trend, and financial institutions are no exception. When it comes to attracting, retaining, and serving clients, the creation of efficient, effective digital experiences is more important than ever. And the foundation of any digital strategy is built on a mastery of marketing analytics. 

In a recent study published by eMarketer, 73% of consumers reported themselves more likely to access digital banking services online in April 2020 as a result of the pandemic. This shift to digital platforms has led the financial services industry to increase its digital advertising spend by 9.7% in 2020, with projected spend reaching $19.62 billion. For those dollars to deliver results, however, financial marketers will need to enhance their ability to leverage marketing analytics to optimize campaigns and build better, more efficient digital experiences to capture and retain these new users. 

For financial institutions, success in digital means focusing on conversion optimization across all channels. Product managers need analytics to guide them as they build, measure, analyze, and refine digital experiences that not only obtain new customers and members but also improve retention rates by delivering customers the services they need as directly as possible. Analytics help marketers to determine what content, messages, and targeted promotions will attract new customers and deepen relationships with existing customers. They also provide insight that can be used to build better forms and applications, reduce friction, curb abandonment, and drive higher conversion rates. 

To help financial marketers begin to leverage marketing analytics, Empirical Path has distilled some of the key insights we gained from working with clients across industries, as well as our deep relationships with clients in the financial services sector. Here are a few important opportunities to consider when optimizing digital experiences for your customers and members: 

Cross-Device and Cross-Property Tracking

Most financial services have a longer-than-average consideration cycle, so it’s likely that your potential customers will be researching your products and services across multiple devices. That’s why it’s critical to be able to identify and track users as they migrate from mobile devices to desktop, ensuring that they receive the right messages and promotions in the right sequence to create the optimal conditions for conversion. The latest rollout of Google Analytics 4 brings with it significant enhancements to cross-device attribution that makes it easier to trace users across lengthy sales cycles using a blend of user login, Google Signals, and device IDs. 

It’s equally important that financial marketers be equipped to view users’ data holistically across all properties. As product teams roll out new and enhanced mobile apps to complement existing online services, it’s critical to be able to track and aggregate identified user data across both mobile apps and the web. Users that begin their journey on the web should be able to complete a seamless process on mobile or vice versa. This entire journey should be mappable so that more comprehensive and efficient user experiences can be created over time. Siloing user data risks creating wildly divergent experiences on web and mobile applications that can frustrate consumers and lead to churn. 

Segmentation and Attribution

Understanding a long path to purchase requires careful attention to attribution. All external links should be tracked using UTM codes, including social media, targeted emails, paid and organic search, and advertising creatives. Differentiating between all your external links and tracking link-specific traffic will help your team to ascertain which messages and channels are driving the users to visit your pages and which pages and content are most attractive to them once they arrive. Marketers should focus on building an attribution model that best reflects their most likely path to purchase. For services with longer consideration paths, it may be advantageous to pursue a multitouch model to derive additional insight about which messages are most influential to the purchase process and which lower-consideration services may benefit from a more direct model, such as simple last-touch. Empirical Path’s attribution specialist, Lachlan Brown, can help you to build a model that best fits your business and customers

Although it’s critical to understand how users arrive on your pages, it’s equally important to understand who those users are. Financial services are highly personal and vary widely depending on the consumer’s circumstances and life-stage. Recent graduates in their first jobs need radically different services than older customers who are in the latter stages of retirement or even estate planning. Segmenting your audience by key traits and demographic characteristics allows you to deliver the right messages to the right audiences and reduce waste. Teenagers will likely be unmoved by home loan products in the same way that seniors most likely won’t need student loans. Tying this demographic data into your internal personalization software and CMS tools will allow you to create more relevant experiences that extend all the way from consumers’ first contact with your ad to their time on your site. 

Testing and Optimization 

Building new digital experiences means developing pages of new content, forms, and calls to action as well as building new customer journey funnels to drive potential consumers from consideration to conversion. But with a flood of new potential customers comes new sets of needs and preferences that marketers may not anticipate. It’s critical for digital teams to build up the tools and tactics necessary not just to assemble new content but also to constantly improve it through testing and optimization. 

In its simplest form, this could involve A/B testing two or more options for headline copy or content to see whether there is a meaningful difference in performance. To find the optimal combination needed to produce results, more complex products and services may require applying a multivariate testing model (MVT) to test not only which content and copy works best alone but also to see how various pieces of content work together. Some combinations of content and CTA may be more potent than others, and MVT allows marketers to test a wide variety of combinations to quickly land on the most effective versions of each piece of content. Of course, none of this is possible without capturing informative analytics that reflects performance as well as progress toward a set of decisive marketing goals. 

Data Export and Transformation

Capturing the right data is key, but it’s equally important to be able to export, manipulate, and transform that data across a variety of tools and platforms. The latest edition of Google Analytics provides some assistance in this area, making exports to popular data warehouses like BigQuery simpler and more efficient. Google offers a host of tools for capturing and interpreting data, but it’s often necessary to combine data sets and perform complex calculations — two functions that are not easily completed within the Google Analytics environment. 

Once web analytics are exported, they can be more easily integrated with other forms of marketing-adjacent data, including profitability and performance statistics and pricing data, as well as the product of other business analysis tools. This will enable digital teams to perform critical calculations like mean and standard deviations, which can help marketers build probabilistic models to guide future campaigns. 

The Big Picture

Digital experience is only going to become more central to the delivery of financial services. Building a great digital experience is the key to attracting customers during the current crisis and to retaining them into the future as services across every industry become more integrated and digital. Analytics provide marketers and digital product teams with the insights to give customers what they want, meet their expectations, and anticipate their future needs. As a flood of new finance industry customers adopts digital tools and services for the first time, some of the existing assumptions about customer needs will need to be updated to reflect the needs of these new audiences. Analytics is an essential tool to do this. Fortunately, Empirical Path is ready to help. Contact us today to speak with a measurement expert.

The post Experience Is Everything: How Financial Marketers Leverage Analytics to Make Better Digital Experiences appeared first on Empirical Path.

By now you’ve probably heard about a new version of Google Analytics, called GA4. It has been more than seven years since Google introduced its last platform overhaul with Universal Analytics. The new GA4 transforms how we look at analytics by marrying user data from mobile apps and traditional desktop websites. Here are some of the advancements we’re most excited about at Empirical Path and advice for enabling them.

You should expect new features and more useful insights with varying degrees of implementation complexity. To get ready, Empirical Path is testing key features of GA4 before introducing them to our clients. We recommend that our clients begin planning around these change requirements in 2021. Even if you take “baby steps” toward GA4 at first, having that conversation early will help Empirical Path create a proper migration roadmap to meet your needs.

So what’s new? First, Google Analytics is building on the App + Web property that has been in beta since last year. The new Analytics uses machine learning to automatically produce helpful insights into your customers and their behaviors. GA4 by design will help you learn what your customers are doing on your properties regardless of platform, be that a mobile app or via a browser. (If you’re already using App + Web this change may not be such a leap.) 

Secondly, the industry writ large is bracing for increased regulation around privacy, so identifiers such as 3rd party cookies will be increasingly less reliable as users opt out. To prepare for this reality, Google Analytics will make it easier to combine multiple sources of user identification into a single reporting view. These sources include User IDs from known or logged in users, Google’s cookie, and data from users willing to allow Google to collect data for advertising purposes, i.e. Google Signals. . The goal is to follow your customer from acquisition (via an ad, an app download, or perhaps a referral) to conversion and retention, even if this journey includes multiple devices. This new way of looking at the customer journey will be reflected in GA4 reports.

Rather than emphasize tracking pageviews and sessions, GA4 provides reports on events and users regardless of the platform on which they interact with your brand. It will be easier to follow user funnels in terms of navigation steps from beginning to end and thus better understand how and why a conversion happened. The goal is to narrow down which channels on which platforms, app or web, are turning leads into customers, and to maximize each.

Google promises that its machine learning models will alert you to trends in your data automatically, helping forecast demand and even predict what customers might do next. For instance, predictive metrics can highlight the potential revenue of specific types of customers and increase engagement with them, improving their experience and making more sales.

Some additional benefits of GA4 include:

• Simplified reporting interface (less overwhelming but perhaps harder to recreate current standard reports)
• Many typical user actions will be tracked automatically, so less-complex instances are easier to set up
• A more insightful and flexible conversion funnel and also content pathing (user journey) reports
• Export to BigQuery of raw event data with streaming data export and regional data storage options, previously only available to Analytics 360 customers
• Better and more automated remarketing audience building and publishing (but just for Google Ads for now)

Timeline

The challenge for many GA users will be making the shift to event-driven reporting, which Empirical Path partners like Mixpanel and Amplitude have long done. Seeing the customer as a series of decisions rather than disconnected actions is the key, but that means many traditional reports and interfaces analysts rely upon today may not cleanly translate into the new Analytics.

It seems clear that Google will not end support for its existing Analytics system. But it’s equally clear that Google wants to incentivize adoption of the new GA4 by providing compelling reasons to switch, such as machine learning for predictive analytics and estimating opted-out users. The good news is that the free version of GA4 currently includes all of these innovative features, including BigQuery export.

The way forward Empirical Path recommends is for clients to set up “dual tracking” by running Universal Analytics and GA4 at the same time for a while. Importantly, the ultimate move to GA4 will be a clean break from the previous way of reporting. Google thus advises that clients start early with GA4 in order to build historical data. 

Empirical Path is broadly advising our clients to migrate to GA4 in stages. The first stage is basic creation steps, such as importing current Google Analytics property settings, if tagged with the gtag.js global site tag. If tagged with Google Tag Manager, you will need to create new GA4 tags within GTM. If tagged with analytics.js you will need to migrate to the global site tag or, preferably, GTM. The good news: There are a lot of custom events such as document downloads and scroll tracking that can now be turned on without additional tracking effort. 

Every organization should consider advice in order to maximize the power of the new GA4 for its specific needs. For some clients, the path could be deliberate and take longer. For some app-driven businesses, there may be incentives to move faster to take advantage of new tools.

Empirical Path is prepared to fully brief you on what steps are important to take sooner than later and to help your analytics team prepare for a better, more useful Google Analytics. To get started, or if you would like to keep up-to-date on GA4 and receive timely advice on migration, please let us know by filling out our contact form. We look forward to hearing from you.

The post Looking ahead to the new Google Analytics: first steps appeared first on Empirical Path.

Differentiating prospects from members in marketing analytics reports is not only achievable, it is not hugely difficult, and is a game-changer for marketers, especially for SaaS companies. 

If your marketing site is visited by existing members looking for a login link, you have probably at least scratched your head about how this extra noise affects your conversion rate; perhaps you have even tried to filter out this member traffic without satisfactory results. Here’s why most attempts to do this don’t have the intended result, and how you can achieve the ultimate in segmentation and filtering by prospect and member type, using Google Analytics as an example.

Who needs this level of segmentation?

This will help any online service where registered members can access their login page via the marketing site or just consume content on that site. These might be B2C, or B2B tech offerings, or perhaps most in need of this level of segmentation are two-sided marketplaces. Not to mention Ecommerce sites, which can benefit tremendously from member vs. new shopper segmentation. 

The exact solution, while easily replicable, should be tailored to your particular business model and site structure. For example:

1. Simple Membership: visitors consist of prospects and existing members (also applicable in traditional ecommerce)
2. Trial > Subscriber Membership: visitors consist of prospects, trialees, and subscribers
3. Distinct Audience Types: visitors may consist of small-business prospects  and enterprise prospects, small-business members and enterprise members
4. Two-sided Marketplaces – visitors may consist of buyer prospects and seller prospects, buyer members and seller members

In case you are wondering; yes, you should be able to segment all of the above.

What is the purpose?

At a minimum, you need a reporting view that filters out returning member sessions. With this, you can at least get a more accurate understanding of purely prospect traffic trends, conversion rate and behavior.

At most, you may want the gold mine of filtered views that isolate particular prospect types and even particular member types in addition to a unified or rollup view where you can analyze your traffic mix trends. This is particularly handy in two-sided marketplaces, where each audience affects the trends and behaviors of the other.

Example: Most of the recent growth from Branded PPC is driven by seller-prospects while other prospect types have stayed fairly steady and a moderate number of returning members, more buyers than sellers have used paid ads to return to the site.

Why don’t the usual approaches to this filtering and segmentation work?

The most common things we have seen businesses do to try to exclude returning members are:

1. Using exclude filters to remove traffic to the members only area, usually a specific hostname or directory in the url.
   
   This does not work because this sort of filtering is done at the hit level. The effect is that hits in that section of the site are excluded from the view, but their sessions are still included when the user visits the marketing site to login.
   
   It actually makes matters worse because you can no longer detect, using segments for example, what portion of traffic to the marketing site is also going to the membership site. Furthermore, it removes your ability to analyze post-signup behavior for prospect sessions that convert. Not to mention, in the Ecommerce scenario, this is likely impossible as members and prospects are usually using the same site for their shopping experience. Lastly, it inflates session count if you ever combine the data across views.
   
2. Using separate properties for marketing site vs members-only areas, creates the same problems as separate views, plus can actually prevent attribution for sessions that start on the marketing site and then login to the members-only zone.
   
3. Filtering based on a user-scoped custom dimension that is set when the user reaches the membership area comes closer to the mark, but also does not have the intended effect on its own because it filters out ALL member sessions, including the sessions in which prospects convert which are arguably the most important sessions to include.
   
4. Segments can sometimes be used to exclude returning member sessions in the simplest model, but even there it has minimal usefulness and distinct disadvantages. Any meaningful analysis over time will be subject to sampling (in GA Free) which diminishes its usefulness and this same logic cannot be achieved within Google Analytics to filter a view.

How can this be accomplished with Google Analytics?

The key is a session-scoped custom dimension (a custom dimension that remembers the last value received in a session, applies it to the entire session and can be filtered on). This can be done similarly using Adobe Analytics visit expiring, last value, eVars, or Adobe also allows you to use more complex criteria combinations without sampling, making segments a more viable option in that tool for some sites.

Using logic in your Google Analytics implementation — most easily accomplished using Javascript in Google Tag Manager — we track the users last and most-specific indicators of prospect and or member type on every hit of their session, delivering the most specific value known at the time of the hit into the custom dimension. Our approach is to treat all sessions as “prospect until proven otherwise.”

For the simplest sites, that might be as easy as hits anywhere on www are prospects (until proven otherwise). For the more complex sites with multiple prospect types, we add custom logic to deduce a prospect type based on the types of pages the user is viewing or actions they are taking during their session. For example, in a two-sided marketplace, a user visiting www might be treated as a nonspecific-prospect at first but then, when they access a seller specific page, they should be treated as a “seller prospect” from then on, even if they return to a more generic page after that.

Remember that session-scoped custom dimensions apply the last value received in the session to the entire session. The key in preserving the prospect status for prospect sessions that convert to become members is in NOT overwriting the custom dimension any time after the user is determined to be a member in their signup session. In this solution, when returning members access the membership area after first going to the marketing site, they might be treated as a candidate for their first few hits if they are on a new device. But as soon as they reach the membership area and are determined not to be a new signup, the custom dimension would be overwritten, identifying them as a returning member and that is the value that will stick for the session. 

Whether your spread of audience types is simply “prospect” and “returning-member” — or maybe it is “seller-prospect,” “buyer-prospect,”,”seller-trial-member,” “buyer-trial-member,” “seller-subscriber-member,” “buyer-subscriber-member” — imagine what you could achieve if you could isolate each in a Google Analytics view and report the mix of these on a trendline!

Contact us to tailor a solution for your business. Empirical Path is a digital measurement consulting firm that partners with Google Analytics, Adobe Analytics, product analytics tools and CDPs to help clients better track and improve their online acquisition and user experience. We’ve helped membership clients with SaaS (Bill.com, Egencia), publishing (Business Insider, The Motley Fool) and marketplace (Roadie, Vroom) models. 

The post How to Distinguish Prospects from Returning Members on your Marketing Site appeared first on Empirical Path.

Marketers often struggle to visualize online and offline customer interactions together for a more complete view of the customer journey. But they owe it to their stakeholders to track the full funnel. More importantly, they must act on those insights to drive value.

As Deloitte describes in their report, Digital CRM 2.0, “The dependence on customer insights and the responsibility for (at least) interactions with the customer make analytical know-how a top priority, even beyond the current CRM structure.” Fortunately, the integration of CRM and web analytics platforms can provide these insights and support targeted campaigns to drive that long-term value.

The Business Imperative for Web Analytics and CRM Integrations

Marketers rely on both digital and non-digital channels to acquire and engage customers. Understanding the full cycle of lead generation and conversion is invaluable. When analytics can tell the whole story of the digital and non-digital experience, it enables both sales and marketing to become more data-driven. This is critical to the personalized experiences prospective customers have come to expect.

Web analytics platforms provide valuable insights, such as how a visitor arrives on a website, what pages that visitor consumes, and what actions that visitor takes while there. But web analytics alone cannot provide marketers with answers to business-critical questions, including

• How are contacts in the sales funnel using the site in their decision-making processes?
• How valuable is a given lead, and did it convert?
• What is the complete picture of a customer’s online and offline activity? 

Integrating CRM with web analytics can provide the answers to these questions and enable marketers to target similar segments of converters. Here’s a closer look at the business capabilities and benefits these integrations provide.

Achieving a More Complete View of Activity Across Your Marketing Channels

Integrating CRM with web analytics allows marketers to factor offline and online data into their attribution modeling, providing a more complete view of related activity across marketing channels. This fulfills a critical business imperative: assimilating online and offline customer data in insightful and actionable ways.

Integrating Google Analytics 360 with Salesforce Marketing Cloud and Sales Cloud, for example, enables a bi-directional flow of data, making customer insights more actionable for marketing and sales. Marketers can import data like firmographics, opportunity status and closed revenue from Salesforce Sales Cloud—including offline data—and link it to Google Analytics 360 data about the same person to improve bid optimization and create unique audience segments. Meanwhile, Salesforce Marketing Cloud leverages email, ad, and site data from Google Analytics 360 as well as data from Salesforce Sales Cloud to improve direct marketing channels (e.g., email, SMS) to those new and existing segments.

In one scenario a sales team may receive leads via CRM, then learn more about those leads through related web analytics data. Studying the results of email blasts compared to social outreach, and which results came first, can help them better understand the source and needs of those leads, then improve the contextual relevance of their outreach. Marketers can leverage these insights to determine how to get more similar leads—they can target or customize the user experience via A/B testing, for example, leveraging this newfound intelligence to drive better UX performance.

What’s more, marketers achieve a better understanding of customer journeys, helping them improve campaigns of all kinds—whether that’s through contact forms and lead nurturing, or even offline purchases. Marketers also benefit from a more complete understanding of how well and in what ways their marketing campaigns perform, including perspectives on which campaigns are driving the best quality leads and customers. That’s because they can visualize individual customer paths, from awareness to conversion and retention.

Driving Sophisticated Marketing and Customer Success 

Perhaps the greatest business benefit of integrating CRM with web analytics is the timely and actionable insights they produce together, allowing marketers to develop and execute new strategies quickly as opportunities arise. The leading, agile marketing capabilities they support include: :

• Better decision-making based on deeper, strategic customer insights
• Attribution across all touches
• Smarter, more targeted marketing based on robust analytics
• More effective prospecting based on a richer variety of customer profiles
• Increased marketing automation driven by data marketers can trust

Next Steps

Although web analytics is critical—indeed, digital initiatives are foundational for much of modern marketing—it alone does not provide a complete picture of customers and their behavior. Companies need to understand the context of customer desires and behavior to inform ongoing decision making in marketing. This applies to all dimensions of marketing and customer engagement, from content development and ad placement to overarching strategic initiatives.

CRM and web analytics integrations accelerate a more data-driven approach to customer engagement and retention—one that informs more successful brand initiatives by prioritizing customer insights for strategic action. The analytics and marketing experts at Empirical Path can help you develop the tools you need for a deeper, more actionable understanding of your or your clients’ customer behavior. Contact us today for a consultation and start overcoming CRM and web analytics integration hurdles now.

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In digital marketing, UTM stands for Urchin Tracking Module. Urchin was the name of Google Analytics before Google bought it. In practice UTMs are parameters that you add to the end of all possible links that send traffic to your website or app. They look like the bolded parts this example tracking URL:

http://www.your-website.com/?utm_medium=social&utm_source=linkedin&utm_campaign=Oct2020-utmarticle

While there are numerous answers to what they do, a helpful way to put it is, that by using consistent and scalable UTMs across all available sources of incoming traffic, you’ll accomplish the following:

• Clean reporting in Google Analytics (and Amplitude, Mixpanel, Parse.ly, Woopra among other tools) for dimensions like Medium, Source and Campaign, and possibly others, depending on how deeply you create your UTM taxonomy
• Actionable cross-channel reporting and analytics in Google Analytics, including with some offline marketing and/or sales reporting
• Actionable marketing attribution reporting, including both single- and multitouch-attribution models, both in Google Analytics and some MTA platforms

There are, however, myriad challenges for organizations to overcome to get the full benefit of consistent and scalable UTM tracking, such as:

• Less than 100% compliance across teams or individuals creating inbound URLs. Compliance includes both “are they’re being implemented at all?”, as well as “are they being done consistently both within and across marketing channels?”
  •  Even small inconsistencies such as capitalization (Email vs. email) can wreak havoc on clean, actionable reporting.
• Lack of a centralized process and/or tool(s): As you might guess, having a well-documented and accessible Standard Operating Procedure, as well as a shared tool to both create and save tracking URLs will go a long way towards solving the previously mentioned challenge! We’ll go over some of the best practices for such a tool below.
• Tagging internal promotion links (setup Enhanced Ecommerce instead)
• Over-engineered UTM naming taxonomy: In some cases there’s a desire, usually coming from analytics, to get really granular with the naming convention, including requiring all standard + custom UTMs, and multiple parts within UTMs such as utm_campaign and utm_contn. The problem with this approach is two-fold:
  • There may not be enough data at these more granular levels to allow for statistically significant cross-channel findings.
  • The added complexity of such a process may lead to lack of compliance. While compliance can be improved through oversight, it’s always worth asking if the juice is worth the squeeze!
• Switching  the values for medium and source. This is one of the most common mistakes we see when helping clients. Simple examples are using ‘facebook’ or ‘twitter’ as the medium value, and ‘social’ as the source. It should be the other way around, with medium being more closely aligned with channel, and source being the actual name of the referring… source of traffic!

In this section we’ll look at some best practices which will help you overcome some of the common obstacles to business value from UTMs.

• Use a shared online tool to create and store tracking URLs, primarily for traffic sources other than paid media such as Google Ads or Facebook
  • Google Sheets can be a great platform, mainly for it’s relative ubiquity for online collaboration, and similarity to MS Excel in terms of ease of use. A few features to consider building into your tracking URL generator: 
    • Make sure to use drop down lists for medium values and any other where you want to enforce naming standardization.
    • Require that utm_medium, utm_source, and utm_campaign are filled in at a minimum
    • Include error checking and/or coding to handle space characters. For example if someone enters ‘spring sale’ as part of a parameter, the tracking URL should insert either a hyphen (spring-sale) or the URL encoding character which is ‘%20’ for a space (spring%20sale).
    • Use bit.ly or another URL shortener. There are multiple solutions posted online on how to call the bit.ly API from a spreadsheet, or use a Google Tools add-on such as the one mentioned in the bit.ly documentation.
    • Make a separate version (tab) for distinct channels. This can be helpful if, for example, different teams create far more links than others, to make it easier to manage and reference links for a particular channel. 
  • Document your taxonomy and the SOP. You’ll want to assign ownership of UTM / tracking URL compliance to an individual. Additionally you should run frequent reports out of Google Analytics including all the incoming UTM values by segmenting in only New users. While it’s virtually impossible to achieve 100% clean reporting for UTM values, by getting all parties bought into the process and managing with clear ownership, you can get the main elements needed for insightful and actionable insights!
  • Make sure to update Google Analytics default or custom channel definitions as needed once you have reached agreement on a shared taxonomy and accompanying processes.  (mostly when using new medium values)
  • Create a scalable taxonomy that supports actionable insights, both for media optimization as well as cross- or omni-channel campaigns.  While this can be a complex undertaking and will be the sole subject of a subsequent post, here are a few tips:
    •  Use existing naming / Internal campaign codes: If, for example, you already use a campaign naming taxonomy that contains internal codes and structured placeholders for internal reporting, why not use that in Google Analytics as well to make it that much easier to blend GA with internal/financial reporting? For example you might have an internal campaign naming structure as follows:

 utm_campaign=Event ID-BudgetOwner-MarketName-Date

• Such a naming convention brings up an interesting issue often encountered in this process: utm_campaign values that differ significantly from actual campaign names in Google Ads or other digital paid media platforms. While this will be the subject of a subsequent post in this series, the short answer is to create your base tracking URLs in a separate system, including the UTM values based on the taxonomy, and then use tracking URL templates or macros in the respective ad servers to append actual names of Campaigns, Ad Groups, Placements, etc.
  • There will be a need to create custom UTMs for the Corporate campaign name vs. the paid media campaign name, and possibly others! Here’s a link to the Google Analytics documentation for creating custom UTM parameters: https://support.google.com/analytics/answer/1033863?hl=en
• Another UTM value that can be used effectively for cross-channel and/or multi-touch attribution analysis is the utm_content parameter, typically used to name or describe the creative associated with a click.
  • An example cross-channel use case is to measure the impact of a promotion (e.g. a sale) that’s going out via multiple paid channels. Use this in the utm_content field, and hopefully with a structure that’s consistent across channels, for example:

 utm_content=Promo-Pricepoint-AdType-AdSize

Creating and implementing a naming taxonomy and tools/processes to use Google Analytics UTM parameters is a vital step towards realizing business value from attribution. The best practices in this article have helped many of our clients achieve measurable success and we hope you’ll realize the same! If not, send us a quick note to let us know how we can help:

The post Best Practices for Using UTMs to Create Business Value appeared first on Empirical Path.

Whether you are marketing to a payer, a practitioner, or a patient, mapping and monitoring their progress through the online customer journey helps you discover gaps, opportunities, and pain points hiding in your online experience.  Web and app analytics data alone never paint the entire picture of your total customer experience.  Using those tools to chart user flow across multiple sessions, devices, and marketing touchpoints can greatly inform a complete customer journey map.  Use online analytics to enrich the full story and back it up with data. Your analytics tool can identify unique customer types and note the different (or the same!) ways they go about completing tasks or moving through funnels.

Execute on the Basics of Attribution

Regardless of industry, sector or size, answering customer journey questions begin similarly— where and how the user started the journey. It is critical for all Google Analytics users to be able to answer this question with accuracy and relevancy. 

Lucky for the digital marketing world, the attribution standard of UTM tagging is an extremely simple, free and effective way to unpack fundamental attribution answers. 

If unfamiliar with UTM tagging, see this as a reference.This plug-and-play open source solution is designed to be a relatively free-text method to give the marketer flexibility in meaning when done with precision. 

To exemplify this effectiveness, here is an example of where we have inherently told Google Analytics where and how the session began (email newsletter), what campaign this content pertains to (July 2020 Feature Launch), and specifically which click contributed towards the visit.

In light of this flexibility, we recommend as many marketers in the organization collaborate in establishing a comprehensive UTM naming convention. 

You may be an organization currently paying for a marketing service automating UTM parameters by default. There is a chance there are syntax / taxonomy changes your organization would like to make within the configuration. 

You may be a small healthcare organization wanting to save a nickel on a tool to automate yet still want to leverage UTM tagging—Google’s free Campaign URL builder makes that an easy task. Simply input the landing page URL, the respective values your organization has established per source, medium, campaign, term, and content and include within marketing content embedded links. 

Taking the time to establish a consistent and organizationally-understood UTM taxonomy is a low-cost-high-reward essential to set the stage for uncovering future customer journey and/or conversion questions. 

A couple things to keep in mind with UTM tagging: 

• Be sure to only use UTM tagging on external content. One common mistake is to include UTM tagging internally within the site—Unfortunately, Google will interpret these clicks as new sessions and will lead to inaccurate reporting. 
• For medium,there are several predefined variable names Google Analytics recognizes and in most cases it’s in your best interest to adhere where possible. If you opt to go rogue, traffic may end up separated out from its natural habitat.

Lastly, UTM tags are only as good as how often they are being used. The more consistent your organization makes use of them, the better reporting you will have in the long term. 

Discover Actual Customer Journeys with User Explorer and Behavior Flow Reports

Instead of guessing, actually see how your most valuable or most typical customers are interacting with your site over time.  Visit the User Explorer report in Google Analytics and filter the list of anonymized users by segment to get your candidates.  Start at the top of the list, sorted by revenue or number of sessions and spend a little time looking through session history for a few different ID’s to get a real-world sense of how your users are or are not accomplishing tasks on your site. These anonymized IDs within Google Analytics comply with regulations and do not include Personally Identifiable Information (PII). 

Google Analytics User Explorer Reports are pure gold for turning up real-world user journeys.

The Behavior Flow report visualizes the path users travel from one page to the next. This report can help you uncover what content keeps users the most engaged. It can also help identify potential content issues. By analyzing the behavior flow, you should be able to see which paths through your site are the most popular so you can confirm whether those are the paths that you actually want users to follow. 

Top Conversion Paths Report Reveals Marketing Touchpoints

When it comes to understanding how your marketing efforts are drawing users through the customer journey, the Top Conversion Paths report in Google Analytics is another great place to derive useful insights by toggling to different goals and to uncover common paths. Discussions about first vs last vs distributed touch attribution models have their place when it comes to budget allocation, but for customer journey mapping, you can dispense with all that and focus instead on what mix of channels and campaigns most often result in a success for you and your users.

Like with the User Explorer Report, there are often surprises and “aha” moments when reports don’t exactly match our preconceptions.  Occasionally, especially for this report, those “aha” moments are realizations that your campaign tracking and attribution data maybe isn’t in great shape.  That’s never a happy insight, but it is definitely a useful one.  It’s also not that uncommon.  Let us know how we can help.

Assuming your analytics data is in good shape and mapped to channel categories and groupings that are meaningful to your business, these reports help marketing teams avoid tunnel-vision and embrace the impact of marketing mix and attribution over time.

Google Analytics provides a rich set of reports that should be a starting point for understanding and charting your customer journey.  Instead of guesswork, opinions, and over-reliance on anecdote, begin to understand the customer experience with real-world insights based on solid, validated web and mobile analytics data. This will not only provide near-term value in its own right, but will also help lay the foundation for future steps on an Attribution roadmap. 

Empirical Path Expert Consultants Can Help

Medical marketing is evolving and embracing more advanced digital strategies. Tracking your customer journey can help discover gaps, opportunities, and pain points hiding in your online experience. If you’re a healthcare marketer, contact us to discuss your most pressing measurement challenges.

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