Analytics Fireside Chat

Analytics Fireside Chat: Mai Alowaish, InfoTrust

Mai Alowaish, Industry Team lead at InfoTrust, shares insights about cross-device analytics and tactics for capitalizing on the latest trends in web analytics.

Diana Daia
September 14 · 5 min read

Take a deep dive into cross-device analytics with Mai Alowaish, Industry Team lead at InfoTrust and mentor at the Digital Analytics Association.

Who is Mai Alowaish

Mai Alowaish is a digital marketing consultant and industry team lead at InfoTrust for eCommerce & retail, specializing in complex and cross-device analytics for websites and mobile applications. With over a decade of experience in digital analytics and eCommerce applications, Mai has led implementations for a variety of analytics solutions for online retailers, financial institutions, airlines and more. Mai is an active member and mentor with Digital Analytics Association (DAA), and the recipient of the 2019 DAA President’s Award.

You’re a successful analytics consultant focused on challenging yourself professionally. What advice would you give to fellow analytics practitioners to drive more impact?

First, stay curious.. curiosity begets creativity and it will challenge you to dig into the data and develop innovative ways to solve problems and answer questions.

Second, keep learning. Continuing education gives you a competitive edge in the market. The options are now endless to learn new skills or achieve certifications through learning in different mediums that fit any schedule and lifestyle.

Every action that the user took toward the conversion tells a piece of the story and helps analysts draw personas of the users, map conversion funnels, and attribute conversions to marketing efforts.

You have gradually focused your career path on cross-device and mobile app analytics. Why is it important to get a 360-degree view of the customer journey in 2020? How can organizations leverage app and cross-device analytics to better understand the customer journey?

With the increased use of mobile applications and IoT devices, analysts must now consider data sources beyond just web analytics to be able to tell the entirety of the customer story and understand his journey across channels and devices. As analysts, we don’t only focus on what occurred when the user first visited the mobile app and browsed products for several minutes. Nor do we solely look at what his or her actions were during the final minutes spent on the website completing the purchase. Rather, we focus on the process at both the micro and macro levels along the path to the conversion. Every action that the user took toward the conversion tells a piece of the story and helps analysts draw personas of the users, map conversion funnels, and attribute conversions to marketing efforts.

You’ve been working closely with businesses within Commerce and Retail. What are the common analytics challenges that these industries are facing?

Retailers always aim to know their customers and to build relationships with them, and they learned over the years that behavioral data about users is significantly better than demographics for predicting things like purchases, upgrades, and churn when it comes to eCommerce. The recent changes in privacy laws, such as the GDPR, CCPA, ITP and ETP, have dramatically affected third-party tracking which previously allowed marketers to see customers on different channels. This has made it difficult for retailers to stitch together activity and create personas. As a result of the new regulations, retailers now only have the option of first-party data or data they directly own about their audience.

Another challenge that these restrictions introduce is the limitation on cookie persistence. Without the same cookie persisting, if a user does not convert within the same lookback window as the cookie expiration date, his subsequent sessions that lead up to the session with conversion will be lost or unattributable. This means that analysts will likely see a drop in credit that can be applied to upper-funnel channels and more given to lower funnel or direct conversions, not showing the full value of early channels in the customer journey that influenced conversions.

From your experience implementing complex analytics configurations, what steps should companies take when they’re building the analytics foundation for the marketing data?

The key is to structure tracking around the customer and plan to measure and analyze across the full user journey, consolidating data from different platforms to gain a unified view of the customer.

This does not mean that you should ignore metrics and actions specific to the platform experience. Rather, find those platform-specific metrics and track them, as they are valuable when optimizing the user experience for each device. These data points will serve as subsets that will not impact the overall merger of event data across platforms and devices.

The key is to structure tracking around the customer and plan to measure and analyze across the full user journey, consolidating data from different platforms to gain a unified view of the customer.

What are your 3 key tactics for creating a winning tracking strategy for marketing campaigns?

  1. Start with the overall business objective, and drill down to metrics.
  2. Make the user your basic unit for tracking, not session or interaction.
  3. Make your tracking strategy a living document by revisiting your analytics strategy regularly and continuously updating it.

You’ve been discussing at great length about Google’s App and Web. How does it help companies get better insights into the entire customer journey?

App+Web makes cross-device implementation much easier and enable analysts to combine data from websites and mobile apps, without the extra effort of trying to make those pageviews and screen views somehow work together or unifying event tracking structure to get some cross-platform insights. While app and web platforms may differ, many of the KPIs and business reporting needs are the same. In addition to having a unified model, App+Web also allows for numerous integrations with other platforms (and more coming soon) to add more insights into the user journey.

Many analysts and data scientists today say that they spend more time cleaning data instead of analyzing it. Why are we struggling with ensuring data quality? What are your tips for improving the quality of campaign data?

Historically, integrating data meant identifying commonalities between platforms and creating coherent data sources. This process wasted substantial time in organizing, cleaning, and formatting the data into a machine-readable format to generate rolled-up metrics and dashboards. However, this process did not provide actionable insights on how interactions from different devices collectively lead to conversions. When data are reduced to commonalities, the richness of the customer’s journey is lost.

My advice is to plan your measurement for a cross-device strategy and ensure that the tracking is unified across all platforms and centered around the user rather than the interaction. When measuring an interaction, focus on the metrics that measure the value of this interaction as part of the overall journey. This means that all user actions must follow the same architecture regardless of the platform or device. Then, analysts will have a richer dataset and are able to examine the user and the actions he or she took along the way, regardless of the platform.

When measuring an interaction, focus on the metrics that measure the value of this interaction as part of the overall journey. This means that all user actions must follow the same architecture regardless of the platform or device. Then, analysts will have a richer dataset and are able to examine the user and the actions he or she took along the way, regardless of the platform.

If you were to predict the biggest future analytics trend, what would that be?

Automated insights, as event-based models simplified data collection, the technology now is ready to perform analyses at a massive scale in no time. While this is already available in many platforms, it’s not widely used by analysts; many analysts are spending their time crunching numbers instead of designing datasets. I think with unified models that enable automated insights, the analysts’ time in the future will be better used making quicker decisions that support business objectives.

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