Find the Right Attributes in Visitor, Lead, and Customer Scoring

Mark  Ryan

September 21, 2012

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Visitor scoring can be a highly valuable tool in gauging the success the digital channel, ongoing optimization, and the proficiency of a web team.  Good scoring systems will enable your team to quantify a visitors interest in your product, how close they are to making a buying decision, and how close of a match they are to the ideal customer profile.  As visitors perform actions on your site (i.e. use calculators), consume content (i.e. videos), and perform tasks the visitor starts to accumulate 'points' to contribute to an overall score of the visitor.

One of the hardest elements of a good scoring/grading system is determining which actions/content should contribute to (or reduce) a score. Let's look at one data point - Pageviews. Is it safe to say that a visitor that looks at more pages than the average visitor is more likely to become a lead and then a customer and then a long term customer? For most companies, the answer is we don't know. So what is the best way to figure out which data points are confidently influential to a score?

Your web analytics platform should be able to tell you which pages, phrases, actions have the highest conversion rates and the highest value. From these reports, every company can start to construct an initial scoring system. Over time, your team can start to add or eliminate datapoints as well as revise the influence that specific content/actions have on a score. The following is a list of datapoints that are often used in scoring/grading systems.

  • Specific Keyphrases:  Visitor vocabulary determined from external search, internal search, knowledgebases, chat tools, and/or ticketing systems can often help determine the level of the prospect visiting the site (i.e. 'can I qualify for a mortgage' vs 'how do I get the lowest mortgage rate').
  • Attributes from IP Address:  There are several services on the web that can tell you company name, company revenues, company location, number of employees, and DnB score from an IP address.
  • Tool Usage:  Interested and determined prospects will often use tools on the sites such as ROI calculators, product wizards, and search filters.
  • Multi-Channel Visitors:  Visitors that engage your organization in multiple channels (i.e. email, social, mobile) are typically more serious about understanding your offering.
  • Referrer Sources, Partner Sites:  Often visitors that come from (or visit) a partner during a sales cycle convert at higher levels.  Especially if the partner helps qualify the visitor.
  • Frequency:  Individuals and companies that are involved in a multi-visit sales cycle (i.e. B2B software) often increase their frequency of activities/visits when they are getting close to a buying decision. In cases where the product/service is a big ticket items the increase in frequency can come from multiple visitors.
  • # of Visitors from an Organization: Medium to large size organizations often require multiple influencers before a decision is made. Seeing that multiple visitors from the same organization are visiting and consuming content about the same (or similar) products can be a good indicator of interest.

Some data points are also used to track which actions can negatively influence a score. For example, if a visitor spends more time on the careers section than on the product section or the visitor has not visited the sites in months, these types of activities might signify a poor prospect. Too much visitation may imply the visitor is incapable of making a decision. In some cases, the scoring can be conditional. For example, if a visitor to a mortgage refinance page has visited every week for several months, it is likely that the visitor will not convert until the mortgage rates decrease to a specific level. A good analyst can often find values in the data the strongly imply the visitor will never convert.

In any scoring system it is important that the data points be reviewed by the team on a periodic basis so that the accuracy of the scoring system can be improved. Over time, the team will be able to determine which fields have no influence over accuracy and which fields have the most importance.

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