Salesforce Lead Scoring: How to Build a Seamless Model with Pardot and Einstein

January 3, 2018 Karina Dalhunova

How do you know when a lead is sales-ready? Which criteria do you use to determine whether a lead is qualified or not? How do you decide when it’s time to assign a lead to a sales rep? Unless you implement some sort of automated lead scoring and routing program, all these tasks will be hard to achieve. Proper Lead scoring will tell you when it’s time to reach out, ensuring a streamlined lead flow, efficient Salesforce lead management process and increased sales effectiveness. In other words, lead scoring can keep you from wasting your time and money on leads who are currently not ready to make a purchase. 

Lead Scoring in Salesforce

Lead scoring is a shared sales and marketing methodology that helps to rank leads according to their ‘sales-readiness.’ This indicator varies depending on the interest a lead shows in your business, a lead’s current place in the buying cycle as well as its fit with your defined buyer persona. Below, you can see how establishing a lead scoring system can benefit your company:

  • Identifying ‘hot’ leads that can be fast-tracked to sales reps
  • Distinguishing leads that need further nurturing
  • Evaluating and refining your sales process
  • Tracking the engagement of your leads and customers over time

You can always purchase a sophisticated marketing automation (Marketo, HubSpot, etc.) that integrates with Salesforce to handle lead scoring, or you can try to set up lead scoring using built-in Salesforce functionality (workflow rules). The former can be an expensive investment; the latter will eat up a lot of your time while providing only limited visibility into your leads’ actions. Unless you can track your lead activity in real-time (website actions), it will be challenging, or even next to impossible, to assign accurate scores to leads. That is why Salesforce offers Pardot, a marketing automation platform that comes with the ability to score leads based on certain actions they take on your website or emails you send. Once you start using scoring categories in Pardot, you can add this information to your lead and contact page layouts in Salesforce. Now, let’s take a closer look at how you can handle lead scores in Pardot.  

Custom Scoring Models in Pardot

In Pardot, you can define the ‘weight’ of various actions a lead can take in the sales funnel and assign corresponding point values to them. When a lead reaches a specific threshold value, it means the lead has become ‘hot’ enough for a sales rep to reach out.

To construct your lead scoring model, you can use numerous parameters, including:

  • Page views
  • Site search
  • Downloads
  • Email open rates
  • Email clicks
  • Searches
  • Landing page visits
  • Webinars or videos viewed
  • Link clicks, etc.

Note: When creating a lead scoring model, you should distinguish between a lead’s interest in your products or services and a lead’s intent to make a purchase. Indeed, the fact that someone has downloaded your presentation doesn’t mean that your sales rep should pick up the phone and call that person right away. You need to determine which actions define active buying behavior and assign higher score values or even trigger alerts to them.

Once you’ve defined score values for all lead actions according to their relative importance, you can include them in your custom scoring model that will indicate a lead’s interest level as well as a lead’s degree of activity within a certain period. Using this information, sales reps can prioritize their time and resources by focusing only on ‘hot’ leads, thereby increasing conversion and close rates.

Lead prequalification in Pardot

If your company typically sells to and prefers to deal with a specific type of customers, you can also use the Pardot lead grading system, which is the best way to determine whether a particular lead is a good fit for you. Using lead grades, you’ll see how well a lead matches your ideal customer profile (ICP). Pardot automatically evaluates leads based on a number of implicit factors and assigns a letter grade (A – F) to them. Below you can see sample criteria that you can use in your lead grading system:

  • Firmographics:
    • Location
    • Industry
    • Company Size
    • Revenue
    • Job Title, etc.
  • Technographics:
    • Web-based technology usage (eg. marketing automation, ATS, analytics)
    • Backend technologies (eg. CRM, ERP, database)

Reinforcing Lead Scoring in Salesforce

Merging lead scoring and lead grading results can ensure that sales reps will get qualified and sales-ready leads. However, to make the most of this tandem and create a perfect Salesforce lead scoring formula, you should do a bit more. In particular, Salesforce consulting experts advice the following:

  • Define what a qualified lead looks like as well as the conversion point when it should be passed to Sales. For this purpose, you can have a look at leads that became customers and analyze their behavior, including the content they were interested in, the number of steps involved to convert from a lead into an opportunity, etc.
  • In case you have multiple products and services, create a separate scoring model for each product line or service.
  • Use negative scoring and score degradation to exclude non-targeted website visitors, such as job seekers or writers.
  • Regularly review your lead scoring criteria and reevaluate your scoring model.

Predictive Lead Scoring with Einstein

Nailing down a consistent lead scoring formula is a tricky process that often results in a careful ‘try and check’ process with manual review and reevaluation of your lead scoring criteria. And this is where predictive lead scoring comes into play and shows its beauty. Using predictive lead scoring powered by artificial intelligence (AI) and machine learning (ML), marketing managers don’t have to figure out what criteria should be included in a lead scoring formula or how much weight to assign for each property − the system does all the research and calculations automatically. Thus, sales and marketing managers can eliminate constant scoring formula reviews, decrease the number of evaluation inaccuracies and save up their time for other primary activities.

To make all these benefits available to users, Salesforce introduced Einstein AI, an add-on component that represents a predictive lead scoring approach. Einstein lead scoring uses the existing fields on the lead record to find data points that have been responsible for the lead’s conversion. Then, the engine automatically adopts the best model to score leads. A correlation between new lead attributes and those of historical leads defines a lead score in Salesforce Einstein. So, the higher the score, the more likely a lead will convert into an opportunity.

One Final Note

Lead scoring and grading bring numerous benefits for marketing and sales teams, including:

  • Enhanced lead management processes
  • Higher lead quality
  • Improved lead nurturing
  • Better sales productivity

Since there’s no universal lead scoring formula suitable for any company, you’ll need to create a custom model based on your industry-specific parameters, targeted customer profile and other valuable criteria.

Featured Image Source: Target Goal Aiming Dartboard by 41330 CC0 Pixabay

About the Author

Karina Dalhunova

Karina is Salesforce CRM Observer at ScienceSoft, a software development and consulting company headquartered in McKinney, Texas. As a passionate Salesforce advocate, Karina is raising awareness of the platform and its possibilities among users as well as helping companies to optimize their marketing campaigns, increase sales and revenue using Salesforce.

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