Pricing Page Predictive Optimization
Pricing pages are undeniably vital to your maximizing your bottom line.
Potential customers on pricing pages are further down the conversion funnel than the average blog reader. They are likely gathering information to make a purchase decision, or at least are at the consideration stage.
Here are ways that FunnelEnvy can use predictive optimization to drive more revenue through your SAAS pricing page.
#1: Optimize through personalization
In B2B SAAS, customers expect experiences that are tailored to their needs. It’s no longer enough to just have one pricing page experience and hope it resonates with every unique customer. That being said, it’s imperative you identify accounts coming to your site and tailor experiences for them.
Let’s use Crunchbase as an example of how to accomplish pricing page personalization for Enterprise customers.
The first step is identifying a visitor. We recommend using a reverse IP Lookup tool like Clearbit to tell us the visitors’ domain. Once Crunchbase has a known Enterprise account is visiting their site, they can change the experience to be more tailored.
Let’s use Microsoft as the visitor on Crunchbase’s site. As we can see below, the headline is generic to every visitor.
Yet, for Microsoft, they are interested in learning about new companies that should be on their radar for acquisitions. With this information, Crunchbase could tailor their pricing page headline.
Learning about the “IT Ecosystem” is what Microsoft cares about, so they should change the pricing page accordingly.
Taking things a step further, Crunchbase could even mention Microsoft by name. Data shows that mentioning a company by name can increase conversions by 10-15% for named accounts!
#2: Reduce options for Enterprise leads
We see pricing pages like the above from Crunchbase all the time. In an effort to improve conversions through offering multiple price points, they actually stunt conversions. Here’s why:
A: Multiple options increases friction for a customer to make a decision
If you’re in the business of driving conversions: competing calls to action, audiences, and buyer journeys on a single page has a really negative impact on your conversion rate.
There are too many options for users to take, often resulting in analysis paralysis. In the example above, three separate plans targeting different users presents scenarios where customers do not know what to choose.
B: Multiple options leads Enterprise customers to pick cheaper option.
When a Microsoft VP is on Crunchbase.com, the goal is to maximize revenue through a sales conversation. However, given three options, the VP will likely click the ‘free’ option or $29 pro plan for the simple reason that it’s cheaper.
C: Multiple options anchors your customers to a lower price point
If a customer is finding value in a $29 plan, it’s going to be quite difficult to upsell them on a six-figure contract if the value between the two isn’t great. While offering Enterprise customers a $29 plan might work for some companies that know they can get a bigger deal down the line, most companies will find this transition difficult.
Therefore, our solution is to identify Enterprise accounts, and then show them one optimized path towards a sales conversation. In the Crunchbase example, they should remove the two lower price points, or simply ditch the table altogether.
#3: Emphasize features based on audience profile
SAAS marketers should know which enterprise accounts are visiting their site so they can emphasize the most relevant features for those accounts.
For example, consider highlighting various integrations based on a customer’s tech stack.
In the visual below, the CRM integration listed would be dynamic based on the customer’s tech stack. In this case, it’s Salesforce, but this would change depending on the visitor.
#4: Use user behavioral data to tailor 1:1 experiences
Behavioral data can be anything from what type of white papers a customer reads, what internal pages they’ve visited, the types of emails they’ve opened, etc… By using this behavioral data we can gain key insights into what features or products customers are interested in and then serve the most relevant experiences.
Again let’s use Crunchbase as an example.
The image below is seen at the bottom of Crunchbase’s pricing page, below their pricing table.
While this feature is less relevant to their general audience, there is a subset of users who have shown interest in this product given their time spent on the ‘Crunchbase for applications’ page. If we can use behavioral data to determine their interest in this product, it should be emphasized at the top of the page.
Using FunnelEnvy’s prediction model, customers don’t even have to explicitly express interest in a product/feature for our model to know they are likely to purchase it. Using machine learning, the FunnelEnvy platform analyzes similar customer profiles and purchase history to predict interest levels. From there, the platform dynamically emphasis that particular product/feature.
