Over the past year, I've spoken and written about monetizing and prioritizing opportunities identified through analytics to ensure your efforts are focused on the right opportunities.
Part of prioritizing opportunities is forecasting how much you can increase or lower a metric. I'm often asked, "How can I determine potential lift so I can prioritize opportunities based on the greatest impact to my business?"
Before you can forecast the lift, however, you must assign a value to the desired site behavior. For example:
These are just a sampling of desired behaviors you may want to define. Step back and think about what behaviors visitors can perform on your site that positively affect your business. Yes, this is a very selfish way to look at a visit, and we always want to consider what's important based on visitor needs, but starting this way allows you to understand your business drivers' value.
Once you've identified the desired behaviors and assigned a value to each, you can forecast the changes' impact. Unfortunately, there isn't a specific equation to determine how much you can realistically lift a specific key performance indicator (KPI).
First, learn everything you can about visitor behaviors around the desired behavior you are trying to affect. We break this research up into three categories:
When you look at this data, it's helpful to look at visitor segments. You may find what works for some visitor segments won't work for others. You don't want to change something that's already working really well for one group.
With more detail in the three categories of understanding, you should be able to better understand the problem behind the desired behaviors you are trying to improve. Start off with conservative changes.
Let's say your site focuses on generating leads and currently has a visit-to-lead conversion rate of 3 percent. You do some research (behavioral, attitudinal, and competitive) and find a few potential issues. You wouldn't want to forecast the impact for 5 percent. Instead, you select a range based on what you see in the research, then tune that over time as you run tests. You may put a range together, such as 3.25 percent to 3.75 percent in 0.1 percent increments, to understand the value. Again, you can tune this over time, but from a prioritization standpoint you want to be conservative and look at a range you think you can realistically hit through one or two tests.
A word of warning: Don't increase visit-to-lead conversion in the above example at the cost of reducing lead quality. You can surely increase conversions by giving away an iPod to every fifth person who registers. But you will most likely be driving unqualified leads, greatly reducing lead value. You must consider and measure the outcome of such things.
As you do more of these, you'll get better at determining the potential lift based on the contributing factors you see during your research. You will be able to tighten the forecast range in terms of change.
The key is to start prioritizing opportunities based on monetized values and quickly move into testing different ideas through either simple A/B tests or more advanced multivariate tests using tools like Offermatica. Forecast the potential lift you think you can realize from the different opportunities. You'll nail some and miss others, either forecasting too high or too low. That's OK. Small changes can often lead to a big result in terms of monetized value. You forecast change to help prioritize your opportunities based on the greatest impact, as well as to help get your organization to realize the potential in opportunities that are being identified and to drive them to act on those opportunities.
Shoot me an email and let me know how it goes.
As the Chief Performance Marketing Officer for POSSIBLE, Jason supports the agency's global Marketing Sciences and Media Services programs.
His primary role is to help POSSIBLE teams and clients use data to craft digital strategies that attract, convert, and retain customers - maximizing ongoing ROI across paid, earned, and owned channels. He believes that brands can better serve their customers by understanding audience behavior, and that messaging should be targeted to individual customers through the use of testing, behavioral targeting, and CRM initiatives.
Jason has written extensively about digital analytics, optimization and digital strategy, including an ongoing column at ClickZ.com. He is the co-author of "Actionable Web Analytics: Using Data to Make Smart Business Decisions," which is one of the leading texts in the field of digital analytics. His client roster includes Microsoft, Nike, Nokia, Dell, Ford, Sony, PayPal/eBay, P&G, Alcoa, Expedia, Mazda, Intel, and Motorola, and more. Jason is a frequent speaker at conferences and seminars around the world ranging from the Cannes Lions, Adobe Omniture Summits, eMetrics, SES, ad:tech, BazaarVoice, and many other WPP events.
Follow him on Twitter @JasonBurby.