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Analyzing the behaviors of leads by integrating Salesforce data with Google Analytics.

  • Mark Ryan
  • January 26, 2016
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In planning or optimizing a site, it’s important to find the most efficient way to generate the best business results. When looking at the granular performance of each area of a website, it’s not uncommon to find that ~20% of the site is doing ~80% of the work. Knowing the difference between what generates poor-quality leads and high-quality leads helps Web teams focus in the right areas during strategy and design. Knowing how to plan and build a site that is optimized for all phases of the sales cycle, such as product research, lead generation, lead nurturing, and sales assistance, helps the web team focus in areas that are high impact.

Website owners that are focused on increasing lead generation on a site are often faced with the issue how to sort the high-quality leads that sales seeks from the mass of inbound leads. This is because sites that focus strictly on lead generation often generate leads that do not convert well. This can start a debate in which marketing thinks the sales team should be working harder to convert the leads and sales thinks that marketing should be generating better leads.

Being able to generate a comprehensive view of how qualified prospects (and unqualified prospects) behave on websites throughout the sales cycle is difficult to do. This is partly because the tools to track users throughout these phases are disparate.

  • Web analytics tools (i.e., Google Analytics and Adobe) are often used primarily to analyze traffic generation and lead generation.
  • Marketing automation tools (i.e., Marketo and Eloqua) and email tools (i.e., Silverpop) are often used to analyze lead nurturing behaviors before a successful sales contact is made.
  • CRM tools (i.e., Salesforce and NetSuite) are used to read lead activities while the lead is being closed (or not closed).

We are always able to look at how users and organizations use search engines and websites to learn about products, solutions, and vendors. But there are important elements in how offline activities (i.e.,sales calls) and personalized digital campaigns (i.e., email campaigns) change the way prospects/leads consume content and utilize tools on a website. These Web pages and Web tools also affect how users react to offline activities such as sales pitches. Combining these datasets from tools, such Google Analytics and Salesforce, always brings about great insights on why websites sometimes produce leads that are difficult to convert and why some leads convert to customers easily.

Consider the following funnel.

An effort to redesign or optimize a site can affect any (or potentially all) of these numbers and ratios. But to be efficient with a budget we must decide which area of focus will yield the best results (i.e., more customers). There is a scenario in which a slight drop in the lead generation ratio (~2%) and a slight increase in the % of qualified leads (~2%) will increase the total number of converted customers. By focusing on the quality of leads we can allow the sales team to focus on fewer leads but convert them at a higher ratio. To do this, we need to know if and how we can identify the behaviors of qualified and unqualified leads in the Web analytics data.

Integrating Data

As we start to analyze the various behaviors of prospects on the website throughout the sales cycle we need to identify which phase visitors are in (i.e., awareness, nurturing, closing) and to do this we need unique identifiers at the user and company level to connect Web analytics data with marketing automation and CRM data. These unique identifiers will be used to track users across website visits, email opens/clicks, and sales meetings, as well as track groups of visitors from the same company. In the awareness phase of the sales cycle we do not have personal information such as email address, so we use the unique user ID provided by the Web analytics data, which is typically cookie based. Within a platform such as Google Analytics we can use the unique user ID within the platform and store it with a Custom Dimension. After the user has filled out a lead form, the user ID can be passed to Salesforce and/or the marketing automation platform.

Once we have a clear point of integration between all platforms used throughout the sales cycle we are able to analyze different phases of the sales cycle such as:

  • How prospects research before filling out lead forms, how groups of prospects at the same company consume content and which tools/pages convert qualified visitors the best
  • How leads come back to the corporate website and view different pieces of content as they communicate with the sales team
  • How leads interact with nurturing tools and content to strengthen their preference for an organization or products
  • How the website and email campaigns are utilized during the end of the sales cycle when the lead converts to a customer and how the Web behaviors of leads are affected by offline activities (i.e. sales meetings)

With a firm grasp on these Web behaviors throughout the sales cycle, we are able to understand which areas of the site can be improved to generate the highest converting leads (most qualified customers). We also know which areas benefit the most from advanced functionality such as personalization, platform integration such as triggered nurture emails, and offline integration such as sales calls. With this information we can increase prospect-to-lead ratios, nurture leads to higher scoring leads, and improve the lead-to-customer closing ratios. This level of analysis and action is superior to simple web site analysis and is only achieved when we bring these disparate data sources together.

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