Discover the power of Adobe Analytics' Attribution Panel and Lookback Window to better understand your customer journey.

When I first thought about the attribution panel and  lookback window, I was immediately reminded of the concept of 'time travel’; then, of course, I was also reminded our typical response to many new tools like these is to simply put off trying to use it, because they look so complicated.

I mean honestly, just look at all those options, switches, panels, readouts, and knobs. And seriously, let’s talk about those complicated flashing lights, hoses, gauges…. WAIT!! This is not the time to get distracted talking about time machines, we just don’t have the time… or do we?

I will admit the  attribution panel  is a fairly complex tool; however, our typical job as analysts, day in and day out, is to use another one of our favorite and highly complex tools to also take a look at what happened in the past. That tool is called  Adobe Analytics! So yes, to answer our very relevant question, I believe these two things say we have plenty of time.

Therefore, why should we allow something like a little fear to stand in the way of such amazing, sophisticated, and powerful tools like these that literally allow us to look  backward  in time, each and every single day?

After all - this is TIME TRAVEL, folks!! We’re all about that kind of stuff. Right???!!

So, what are we waiting for - a shiny metal car, a police box, or a vintage telephone booth using the wiring of an old umbrella as its antenna to show up on our doorstep?

No! We’ve got something even better, so let’s strap in and hang on!

Well… you get the idea.

Now that we’re all excited about time travel, let’s take a deep breath, step back a little, establish what the  attribution panel   really is, and break things down a little bit:

Figure 1 - Numbers displayed inline with text further below

In  attribution, simply consider how events/actions might be caused by an individual, several individuals, or one of any number of different events over time.

According to Adobe,  attribution  gives analysts the ability to customize how  Dimension  items receive credit for  success events.

In fact, rarely is any given customer journey truly linear and even less often predictable. More so, each customer will proceed at their own pace; often, they might double back, stall, drop out, or engage in other non-linear behavior. These organic actions make it difficult or practically impossible to know the impact of marketing efforts across the customer journey. It also hampers efforts to tie multiple channels of data together.

That’s right. Leave your “domino” analogies at the doorway and open your minds to concepts more along the lines of the butterfly effect and string theory - but like everything else, we need to start with some of the basics.

Attribution models

When we use the  attribution panel, we may begin to observe several different things. For instance, the  attribution models  demonstrate to us how our  conversions  (i.e., ❶  success metrics) may be distributed across  hits  in any given group.

Simply put, if  10 people  press a  BIG RED BUTTON  to step through a door, our  attribution models  are going to tell us which of those  10 people  we want to assign “credit” - or even better said, how  much “credit” we want to assign them - for pressing said button.

Keeping this in mind, here are a few examples of how the ❸  attribution models  might affect those  10 people:

• First touch: This model works exactly like it sounds by giving  100% credit  to the  first  person who walked through the door. Marketers are more likely to use this approach for tactics like  social media  or  display; however, it is also a great tactic to often use for on-site product recommendation effectiveness.

• Last touch: This tactic also works exactly like it sounds, but instead gives  100% credit  to the LAST person who walked through the door. This model is typically used to analyze things like  natural (organic) search  and other  short-term  marketing cycle campaigns.

• Linear: This model distributes equal credit across EVERY SINGLE PERSON who walked through the door.

• U-Shaped: This approach assigns  40%  of the credit to the  first person  in the door, spreads  20%  of the credit across  everyone in between, and then gives  40%  to the  last one  through. This model will most often be used in situations when you have a  long conversion/sales cycle  containing  several touchpoints  along the way. In this case, your goal is to primarily highlight the  first  and  last  marketing tactics that contributed to the customer converting.

• J-Shaped  and  Inverse J:

  • Think about  U-Shaped, but instead this model assigns  60%  credit to the  last person  walking through the door,  20%  to the  first, and then  divides  the remaining  20%  across  everyone else  in the middle.  Inverse J  does the exact opposite.

    The goal here is to put most of your emphasis, either at the  beginning  or the  end  of your campaign; however, you still want to assign a certain amount of credit to the contributing item on the opposite end while acknowledging the “little guys” along the way.

• Time decay: Now, I would be remiss if I didn’t share this one. This model literally has a half-life that exponentially decays - over time! In this case, the  default  parameter for this model’s half-life is  7 days. The way it works is to then apply  weight  to each  marketing channel,  based on the amount of time  that passes after the  initial touchpoint  and when the customer converts.

  Time decay  and  U-shaped attribution models  are both typically used to measure longer-termed campaigns, but as you can see, they have slightly different goals, based on how they ultimately  weigh  the value of the outcome.

• Custom: You pick and choose who’s going to get credit. It’s your campaign!

For additional information about these and other attribution models, click here

To make this even more interesting, let’s talk about turning back the clock!

Lookback windows

Now it’s time to start taking your mind to the next level. This is where we literally add the time travel element to our analysis - and again, we are beginning with the basics.

Adobe  defines ❹  lookback windows  as “the amount of time a conversion should look back to include touch points. Attribution models that give more credit to first interactions see larger differences when viewing different lookback windows.”

In other words,  lookback windows  determine the time period during which  conversions  are considered and provide  context  to the attribution analysis.  Adobe Analytics  offers three types of  lookback windows:

• Visit lookback window:  Looks back to the beginning of a  visit  when a conversion happened, providing insights into the immediate interactions leading up to conversions.

  Remember this is typically the shortest  lookback window  to use.

• Visitor lookback window:  Looks at all  visits  back up to the first of the month within the selected  date range, offering a much broader view of the customer’s interactions and helps identify patterns over time.

• Custom lookback window:  Allows you to expand the  attribution window  beyond the reporting  date range  up to a  maximum  of  90 days. It provides  flexibility  in capturing touchpoints that occurred  outside  the selected  date range, ensuring a comprehensive analysis.

By adjusting a given  lookback window, analysts may then examine the impact of one or more touchpoints within specific time frames and gain greater insights into how different durations affect attribution results.

Bringing it all together

So, what does all this mean to us as analysts?

The  attribution panel  and  lookback window  give us the power to look beyond the mundane, surface-level data, and dive deeper into the customer journey. By understanding which touchpoints had the greatest impact on  conversions, we can make informed decisions about our marketing strategies and allocate resources more effectively.

Remember, after you have your  attribution models  and  lookback windows  selected, you may still further manipulate your data by filtering it with a ❺  segment, or any other component you wish at this point. Furthermore, after the panel is rendered, you have all the functionality of a traditional Workspace at your disposal.

Finally putting it into practice

Now that you’ve got the concepts down, imagine you’re running a marketing campaign and trying to determine which channel is the  most effective  for driving conversions. With the help of the  attribution panel, not only can you see the  last touch, but also the  first touch,  same touch, and any other  model  you choose to determine which  channels  are the  most effective  in driving your  conversions. Then, this information can be used to  optimize  your campaigns and improve overall performance simply by turning back the clock with the  lookback window  of your choice!

Now that you’ve seen what it can do, don’t be fooled or intimidated by the seemingly complex features of the attribution panel.  Face it.  Embrace  it.  Understand  it.
BUT MOST OF ALL -  Use it to your advantage.  The  attribution panel  and  lookback window  are the keys to unlocking a deeper understanding of your customers and their journey with your brand.

Now, we can travel “back in time” with confidence and use the power of our trusty time machine (a.k.a.  Adobe Analytics) to make data-driven decisions.