Strategies to Amplify your Testing Program
As a digital marketer, you search far and wide for optimization ideas – both within the organization, as well as across the competitive landscape. After all, a robust testing program cannot have too many hypotheses in the backlog. However due to limited team bandwidth, time constraints and other competing initiatives, building a lengthy backlog of ideas is often easier said than done.
Let’s examine some potentially overlooked tools, capabilities and strategies to help fuel your testing and optimization program today!
Maximize the Visitor Profile
If you’re reading this then you’re probably already familiar with the Adobe Target profile, or as Analytics Demystified describes it, “the most valuable component of the Adobe Target platform.” Profile attributes can be defined by virtually any data passed to Target and are great tools for expanding the segmentation and audience building efforts for A/B testing purposes. Profiles can be defined in a variety of ways, one of which is by leveraging profile scripts directly in the Target UI. These profiles scripts are essentially logic to characterize the data visitors send to Target when they interact with your website.
Below are some great examples provided by Analytics Demystified, that can be building blocks to A/B testing and optimization use cases. Profile scripts are created directly in Target by navigating to Audiences > Profile Scripts.
| Details | Profile Attribute Name | Script |
|---|---|---|
| This profile attribute retains the current visit number of the visitor. | visitnumber | if(user.sessionId!=user.getLocal(‘lastSessionld’)) { user.setLocal(‘lastSessionld’, user.sessionid); return (user.get(‘visitnumber’) / 0) + 1; } |
| This profile attribute will associate the IP address with the visitor thus enabling you to target activities to certain IP addresses. | ip_address | user.header(‘x-cluster-client-ip’); |
| This attribute increases with each purchase as defined by impressions of the ‘orderConfirmPage’ mbox which typically exists on thank you pages. | purchasefrequency | if (mbox.name == ‘orderConfirmPage’) { return (user.get(‘purchasefrequency’) | 0) + 1; } |
| One of my favorites as it allows you to QA tests without having to repeat entry conditions of the tests. Simply use the letters “qa” as part of your query string and this profile is set to true! Very popular attribute. | qa | if (page.param(“qa”)) { return “true”; } |
| Day of the week. Helpful such that it highlights the incorporation of standard javascript functions. | day_of_visit | if (mbox.name == “target-global-mbox”) { var today = new Date().getDay(); var days = [‘sunday’, ‘monday’, ‘tuesday’, ‘wednesday’, ‘thursday, ‘friday’, ‘saturday’]; return(days[today]); } |
| This attribute sums up the total revenue per visitor as they make multiple purchases over time. | amountSpent | if (mbox.name == ‘orderConfirmPage’) { return (user.get(‘amountSpent’) || O) + parseInt(mbox.param(‘order Total’)); } |
The Almighty Google
An awesome tool to pair with your onsite Adobe Analytics data, Google Trends can help prioritize testing initiatives by shedding light on interests by geographic region or trends over time. A practical example of this is A/B testing copy, or testing the navigation menu on your website. A financial institution, for instance, may find the below Google search interest chart valuable in determining what verbiage would resonate strongest with its visitors.
Similarly, drilling into the relative interest by subregion for a particular search term can be valuable for use cases involving geotargeting, or where unique experiences are necessary due to psychographic differences.
As of December 2021, Google had an online search market share of roughly 86%. With a plethora of filtering options and historical data from 2004 to present, consider tapping into this massive source of information when building your testing hypotheses.
Untapped CRM Data
If you have an extensive database of customer data that is yet to be leveraged onsite, consider onboarding this data directly into Target via Customer Attributes. CRM data, for example, can be onboarded directly to the Experience Cloud for activation in Target activities and A/B tests.
To illustrate a CA use case, consider an online publisher that collects valuable member data in their CRM system. The unique member identifier can be onboarded (and hashed if necessary) in addition to other unique member data points such as a “campaignID” associated with an email campaign. These attributes can be translated into profile parameters for audience building in Target, and leveraged onsite to deliver consistent, retargeted experiences.
Learn more about Customer Attributes and how to get started with the Adobe Enterprise Cloud People service here.
Mobile Web for the Win
It’s no secret that mobile has become the preferred channel for many consumers, but it’s still pretty staggering to consider the rate that these smaller screens have overtaken the traditional desktop experience. Over the past decade, for instance, mobile traffic has propelled from roughly 9% of total website volume to about 58% of total volume. The chart below – courtesy of StatCounter – helps illustrate this rapid migration to mobile.
But despite this rise in mobile volume, many optimization and testing tools are still natively designed to build desktop experiences first, with mobile capabilities presented as ancillary or secondary features. Perhaps due to this common solution design or merely the familiarity of desktop in general, it often feels as though smaller screens are overlooked in the test ideation process. Consider expanding your mobile optimization efforts, and exploring creative and navigational tests that are specifically curated to improve the user experience on these devices.
The “Global Maximum” Approach
My last suggestion for optimizing your testing program is undoubtedly the most abstract. Rather than a particular tool or resource, I’m suggesting a change in mindset and strategy. This method requires taking a step back, assessing your overall business objectives and determining how your optimization program is delivering on those goals. Instead of optimizing elements within predefined parameters, or “locally”, consider optimizing on a broader scale, or “globally”.
As Ted Vrountas of Instapage explains, “Refining your page will only get you to what’s called the ‘local maximum’ — which, in post-click landing page A/B testing terms, means the highest-converting version of your original page. Exploring completely different alternatives, on the other hand, can get you to the ‘global maximum’ — meaning, the highest-converting page possible.”
To simplify with an example, the “local maximum” may be the best performing hero imagery on a product page, while the “global maximum” may be a redesigned product page with no hero imagery, or a redirect that bypasses that page altogether. Below is a cool representation of refinement (local maximum) versus exploration (global maximum), also detailed by Cara Harshman of Optimizely here.
TL;DR
Intimidated by the daunting task of consistently growing and maintaining a healthy optimization backlog? You’re not alone.
Despite our best intentions, it’s common to overanalyze the testing process and confine ourselves within a “legacy mindset”. Past, known successes become the basis for all new hypotheses, rather than unique and novel exploratory brainstorming. It is, after all, way more efficient and convenient to leverage past wins when developing new ideas. With this approach, however, long-term growth is limited.
Consider leveraging underutilized tools or capabilities to unleash the full potential of your optimization program. You may be surprised what you discover.
