In a recent blog post, we announced the new, free Adobe Target Value Navigator, a unique tool that helps companies discover the most impactful optimization methods to drive conversion rate lift on various pages throughout their websites. In this post, we’ll take a look at the math behind the tool and provide insight into the results that users might see.

In developing this tool, our engineers worked closely with Adobe Consulting Services to put together typical median lift ranges our consultants have observed over nearly a decade of engagements with hundreds of organizations of all sizes. The algorithms behind the tool are built on top of unique lift ranges for each company objective and website page. Where a company will fall in those ranges depends on the website’s total conversions, the optimization tactics deployed and the resources available within the organization. This tool asks these important questions and returns the potential lifts within the lift range that our consultants typically see.

Step 1 of the Value Navigator (see screen shot below) asks users to specify their ultimate optimization goal (for example, increasing revenue per visitor) and rate their company in terms of optimization sophistication (level of resources, executive involvement, etc.). Certain goals are more likely to impact a website’s conversion rate than others. For instance, a goal of increasing revenue per visitor is likely to generate more lift than simply increasing overall website traffic. This step is the ultimate starting point for the algorithm. It is what we used to decide which range they should start in. Also, more sophisticated organizations consistently place higher on the conversion rate lift curve—the more testing you do, the more lift you’ll see. For the algorithm, this means the more sophisticated the organization, the further up the lift scale it will slide.








Conversion rate lift ranges are also affected by the specific targeting methods a company intends to implement, such as automated decisioning and targeting, multivariate testing, or recommendations and cross-selling. This data is collected in Step 2 (see screen shot below). Simply put, the more methods you use, the higher conversion rate you’ll see. We used targeting methods as our standard deviation for the algorithm. The more methods a company implements, the larger swing in conversion rate it will see.







The pages on a website on which targeting will be implemented is a critical factor in the equation, because some pages will have a bigger impact on your conversion rate. For example, different optimization methods tend to work better on a product page versus a home page or landing page. Our equation employs a multiplier based on the potential impact of targeting on a particular page type (3x for a product page, 2x for a home page, etc.).







Once all the inputs are gathered (based on targeting objectives, methods and sophistication), the tool finds and returns the appropriate lift range. In the accompanying screen shot of Step 3, we see that based on the data this user has entered, our equation indicates that using automated decisioning and targeting on product pages, landing pages, and the home page has the potential to increase average conversion rate lift by 111%.

The Adobe Target Value Navigator has been very popular since its launch, and many organizations have used the tool to compare lift ranges and identify the optimization methods that are likely to be most effective. A common takeaway is that although A/B testing is sometimes a great choice for a particular goal or page, there are many other capabilities of Adobe Target that can yield higher lift ranges in certain scenarios. There are many targeting methods, and this tool will help you decide which optimization techniques are best for each of your pages.