How SAS Uses SAS® CXA to Increase Marketing Response RatesDecember 11, 2013 by Janna Finch
First-generation Web analytics technology collects basic information on website user behavior, such as clicks, page views, bounce rates, downloads and conversions. However, this data is limited and anonymous, and doesn’t deliver the deeper intelligence needed to engage customers in meaningful ways.
Enter SAS, a leader in business analytics software and services, and SAS® for Customer Experience Analytics (CXA). An intelligence tool that tracks every user interaction on a website, CXA helps develop a more nuanced understanding of customer behavior that can be applied to marketing efforts to improve results.
CXA collects anonymous visitor clickstream data in real-time via a cookie—a piece of data stored in a user’s web browser that records browsing activity.
Clickstream data includes pages viewed per session, time on page, referring URLs, downloads, on-site searches and off-site referring search queries. While this is similar to popular free analytics programs like Google Analytics, CXA takes customer behavior a step further by tracking everything a customer does and sees, including hovering their mouse over a selection and each keystroke in a form.
With CXA, once a visitor completes a form on a website, they’re no longer anonymous—their historical clickstream data is linked to their personal information and stored in a centralized database of online and offline customer data.
CXA is making a measurable impact in SAS’s in-house marketing programs. Here, we highlight how SAS uses CXA to optimize website performance and increase marketing response rates.
Locate and Remove Conversion Barriers
Because CXA collects data on every visitor interaction, SAS can analyze all paths users take on their website to locate any possible issues that may be deterring customers. When campaign conversions are low, SAS turns to this pathing data to determine why visitors aren’t converting.
SAS Marketing Services Director Jennifer Chase describes how her team used pathing data to improve the conversion rate of a high-priority Visual Analytics campaign:
“We offered an onsite demo of our Visual Analytics product. After six weeks, we noticed that 81 percent of visitors to the Visual Analytics page abandoned the demo registration process. We analyzed the pathing and conversion data, and when we looked at the sequencing of pages of all who had made it through and compared it to the individuals who stopped at certain stages, we found where we had dropoffs.”
The marketing and web teams determined that the demo registration process required too many clicks, so they designed a new customer experience. They shortened the path to the demo and made design changes to include more obvious calls to action. By reducing the number of clicks in the signup process from four to two, signups increased 32 percent.
Create High-Performing Outbound Email Lists
Email campaigns that are timely and relevant are those most likely to convert visitors into customers. To this end, SAS uses CXA’s real-time visitor behavior data to create targeted, high-performing outbound lists for each campaign.
Once customer data is gathered with CXA, the company creates targeted email lists based on customer persona (e.g. company size and job title), past behavior (e.g. event registration and software purchases) and recent website searches.
SAS then uses marketing automation software to communicate with these visitors and make decisions based upon the results. As Senior Manager of Database Marketing, Matt Fulk, explains, “CXA is the listener and marketing automation is the logic.”
For example, in the Visual Analytics campaign mentioned earlier, SAS used CXA data to build a targeted list of users who failed to register on its Visual Analytics pages. SAS then sent an email to this group that contained a direct link to the registration page. As a result, an additional 6.6 percent of visitors registered.
“We wouldn’t have been able to add this strategy or lift conversions without being able to analyze the inbound traffic on the Visual Analytics product page,” Fulk says.
In another email campaign promoting a webinar on high-performance data mining, the baseline list response rate was 3.5 percent, while the list created with CXA data had a response rate four times that, at 14 percent. Fulk says that CXA’s main advantage is its ability to provide the most recent and relevant user data, which helps SAS’s marketing team improve how they communicate and engage with customers.
“Before CXA, all we could see in the data was the last thing the customer did with us—but what if that was months ago? Now we have a real-time view of customer behavior when they visit our site, and this enables us to send more targeted messaging in return,” he explains.
Improve Lead Scoring to Determine Sales Readiness
CXA data also helps power lead nurturing campaigns. Precise lead scoring is essential to respond to leads appropriately, and CXA web behavior data is indispensable for this. There are two types of data SAS uses to run lead nurturing campaigns: explicit and implicit.
Explicit information is that collected from website visitors via contact forms, and includes job title, job function and company size. Implicit data, on the other hand, is essentially customer behavior, which Fulk says is “the most important factor to assess a lead’s sales readiness.”
Prior to CXA, the extent of implicit data SAS was able to collect was limited to customer activity such as event registrations and downloads. As a result, the company relied primarily on explicit data to drive lead nurturing campaigns. With CXA’s extensive implicit behavior data, SAS can use predictive analytics to identify website visitors that are most likely to become sales-ready. Fulk explains:
“Our model is to look at all of the leads from the previous year and determine the level of engagement that each lead had on the site before they were passed on [to sales] as leads. We found that the leads that had spent a certain amount of time on the SAS website researching our products are more likely to convert to new sales opportunities. The key is being able to explore the data and target leads [based on] actionable behaviors.”
When a lead receives a “sales ready score” based upon their level of engagement, their information is automatically passed on to the sales team to begin the sales process.
Example lead analysis showing leads by date and status and conversions by month.
SAS then receives feedback from their sales force on the quality of leads that were sent. They review which leads have the highest conversion rates, which need to be nurtured and which simply aren’t ready. By identifying which behaviors correspond with sales readiness, SAS can hone their scoring for improved lead quality.
“We are constantly reviewing and updating our scores based on feedback from sales,” says Fulk. “CXA is the centerpiece that has helped us simplify hundreds of different scores down to one score, and has made lead nurturing more intelligent and easier to manage.”