Analytics for Target: Deeper Customer Insights for Deeper Personalization

The better you know your customers, the better you can personalize the experiences you deliver them through your A/B testing and experience targeting activities. If you have Adobe Analytics and Adobe Target and use the server-to-server integration for reporting known as Analytics for Target (A4T), you know that it does just that — it lets you gain deeper insights and apply that knowledge to your personalization and optimization work.

In this post, I want to dive into the technical aspects of the integration along with some key benefits it offers. Finally, I want to leave you with a few ideas to get you thinking about how you can use it.

The server-to-server integration explained

As solutions of Adobe Experience Cloud, Analytics and Target both leverage the common visitor ID of the People Core Service. This ID lets you track individual visitors across the devices they use and all your digital touchpoints — on-site, off-site, in-app, and cross-channel. It also enables the server-to-server integration between Target and Analytics, which supports synchronous data sharing between the solutions and lets you evaluate your manual A/B testing or experience targeting activities in Target using Analytics as your data source.

When it comes to looking at the results of your personalization and testing activities, just how important is it that you use a tight server-to-server integration of your personalization and analytics solutions rather than two loosely integrated solutions? It’s critical. It avoids data discrepancies. When you have two different point solutions collecting data and reporting, you can easily run into discrepancies of up to 30 percent. One system can actually show one experience as the winner, while the other system can show another. Here’s why that can happen:

Location of the data collection script. To collect data about the visitor and the visit, your web page runs a script as it loads in your visitor’s browser that makes a call to send data to the tool. The script for the optimization tool typically runs at the top of the page, while the script for the analytics tool runs near the bottom. If the user closes a page before it hits the analytics script — instant data discrepancy — something you can avoid by having a common ID and a tag management solution to easily deploy your optimization and analytics tools.

Different time zones. What if the time zone differs in the two tools? Results could get attributed to Monday in one tool, but Tuesday in the other — an issue that compounds the wider your analysis window gets. Your results can end up being way off between the two tools.

Cookie expiration. To track visitors from visit to visit, personalization and analytics tools use cookies. If cookie expiration dates differ between the tools, a visitor’s cookie may expire and drop off that visitor in one tool, but not the other. Consider a visitor who makes several visits over time and eventually reaches the end of a purchase funnel. The visitor may have the same cookie at the end of the funnel with your personalization tool, and so appears as a single visitor. In your analytics tool, however, the original cookie expires before the visitor reaches the end of the funnel. Your analytics tool assigns the visitor a second cookie, and it’s with this cookie that the visitor reaches the end of the funnel. In the analytics tool, your visitor appears as two visitors.

Bot filtering. Bots — like shopping comparison bots that crawl the web to collect data — may appear as website visitors. To get more accurate visitor counts, solutions filter out these fake visitors with rules. These rules can differ between solutions, which can lead to differences in visitor counts and any results dependent on that measure.

If you have tens of thousands of visitors, these discrepancies can add up — leaving you unsure that you’ve based your decisions on the right data. How do you know which solution is your source of truth? When you use the exact same data in both solutions, you simply don’t face these issues — the data used is consistently based on the same assumptions.

Benefits of A4T

But A4T offers benefits well beyond eliminating data discrepancies. It lets you:

Launch Target activities more quickly. When setting up an activity in Target, you must take the time to select any audiences to include and metrics for measuring results. If an audience doesn’t already exist, you’ll need to create it if you want to review results after the activity concludes. With A4T, choose Analytics as your reporting source on a per-activity or default basis, select the Analytics report suite for the activity, and choose a primary success metric. Done. It’s a fast and simple setup that makes all the segments and metrics in your report suite available for robust analysis.

Think bigger about your activity. How many times have you run a test only to have an executive ask after the test concluded, “So how did that perform with new visitors?” Or, “How many visitors clicked the product details link after viewing a video about the product?” To provide that answer, you either had to include new visitors as an audience or click the product details link as a success metric when you set up the activity — or you would have to go back and run a new test. With A4T, all the metrics and segments associated with the report suite are available for retroactively evaluating your results. Perform ad hoc analysis galore — no crystal ball required.

Analyze results more deeply. With A4T, you can leverage Analysis Workspace in Analytics to evaluate your activities at a much deeper level. For example, you can create a project in Analysis Workspace that shows you where along a registration flow visitors fell out. In other words, where the visitor didn’t make it to the next step versus those who fell through and did make it to the next step. With just a click, you can make an audience of those visitors who fell out, share it with the Marketing Cloud, and create experiences in Target to re-engage that audience.

Synergize your analytics and optimization teams. Your optimization and analytics teams may fall under the same organization and even have the same manager, or they may be siloed. If they’re siloed, think about the value of tighter collaboration. You share the results from your testing and personalization activities with the analytics team — the people who live and breathe data. They examine those results in depth and provide explanations you may not have considered. You’re both looking at the same data set, but viewing the reporting in your preferred tool. On the flipside, when the analytics team finds something that stands out — perhaps an extremely valuable audience — they can share that with you. You can run a test to validate their finding or create an offer specifically for that audience. It’s synergy at its best.

Powerful and easy to deploy

So now you know why A4T is so powerful. It’s also easy to deploy. You just need to have the Adobe Target and Adobe Analytics SKUs (not SiteCatalyst or Test&Target) and have the Marketing Cloud Visitor ID service provisioned with user permissions set up. One recommendation: to manage data collection and easily implement and update the various libraries, deploy the Marketing Cloud Visitor ID service, Analytics, and Target using Adobe dynamic tag manager or the next generation tag management service Adobe Launch.

Get started using A4T

Once you’ve deployed A4T, you can use it in countless ways. As a retailer, in Analysis Workspace create that audience of customers who fell out of your purchase funnel and retarget them with a discount offer. In travel and hospitality, don’t just consider the impact of an offer on bookings for all your customers, see which ones were more likely to respond to the offer — those who paid with cash, cash plus loyalty points, or loyalty points only. If a group didn’t respond well, create a new offer for them.

There’s really no limit to what you can do. A4T unlocks the power of Analytics for your manual A/B testing and experience targeting activities in Target with rich analytics insights and a consistent data source.