Understanding navigator Scoring: How to construct a Scoring Framework

If you’ve ever swiped through a dating app, you recognize amount isn’t everything. It’s enjoyable to have a lot of options, but they’re not all going to be life-associate material. 

Finding your best match in a pile of dating app profiles is sort of like spotting a faithful ecommerce customer in a sea of casual online shoppers. Dating might always be challenging, but when it comes to business, there’s an alternative to manual sorting: navigator scoring is a way for companies to identify prospects with the highest chance of converting. In essence, it’s a way to make sure the best prospects sort to the top.

tiny business owners can’t afford to waste period on impoverished leads, so here’s how to use navigator scoring to focus your marketing efforts on the highest standard leads.

What is navigator scoring?

navigator scoring is the procedure of ranking potential customers, or “leads,” based on the probability they will convert to paying customers. In the navigator scoring procedure, a business identifies the behaviors and attributes that indicate a potential customer is likely to make a purchase, then assigns a point worth to each characteristic or signal. The business combines these points to form a potential customer’s navigator score, which represents the overall navigator standard. 

Calculating a potential customer’s navigator score helps you determine next steps. For example, you might prioritize leads with powerful conversion potential by asking your sales and marketing teams to reach out to them personally. You might decide that approaching low scoring leads, on the other hand, is a waste of period.

navigator scoring models

• Demographic data
• corporation requirements
• Engagements
• Website behavior patterns
• Spam contact detection

Your navigator scoring model encompasses the types of data you’ll collect and analyze in order to assign points to leads. These models can include explicit customer data (data the customer enters themselves, like an email address) or implicit data (insights the business draws by observing customer behavior, like engagement with a brand’s social media account). 

Companies often use multiple navigator scoring models at the same period. These are some ordinary ones:

Demographic data

Demographic data includes basic information like age, gender, location, and job title. Companies can add demographic fields to navigator capture forms to collect some of this data. Analyzing demographic data can reveal how well a user aligns with your ideal customer, and it can assist you identify ineligible customers. 

If your locally sourced food product is only available for delivery within the Pacific Northwest, for example, you could assign a negative point worth to respondents who live outside of this region. 

corporation requirements 

A corporation requirements model scores leads based on how well they align with your customer base. This model involves identifying your minimum requirements for buyer eligibility and collecting data to verify users satisfy these conditions. This structure aims to reduce the number of irrelevant leads passed to your marketing and sales teams. 

For example, if you run a B2B corporation concentrated on tiny businesses, you make a navigator capture form with fields for intended product use and corporation size. To score leads, you could assign a higher point worth to responses indicating professional use at a corporation with fewer than 100 employees. You might also assign a negative point worth to users who indicated they were interested in your product for personal use. 

Engagements

This model uses engagement data to assess buyer yield. Engagement models assign higher point values to customers who regularly interact with your content by evaluating data points like email open rates, click-through rates, and social media engagement. 

Website behavior patterns

Website behavioral data reveals how a user interacts with your website. This model involves tracking online behavior patterns such as how many pages a user visits and how long they spend on each page. It can also consider a user’s visits to high-worth pages like a checkout or pricing page. With this model, a business might assign a high point worth to users who have visited the website more than twice and spent more than 30 seconds on a product page. 

Spam contact detection 

The spam detection model prevents you from passing along low-standard leads to your sales throng. This model involves identifying and assigning a negative point worth to fake leads. Spam detection tools flag suspicious behaviors like entering form responses with multiple consecutive letters or submitting invalid email addresses. 

Spam detection can also detect dubious keywords and flag submissions that include profanity, celebrity names, or names of fictional characters. For example, a business might use this model to automatically assign a negative point worth to leads with email addresses like [email protected] or [email protected]. 

How to score leads manually

1. Identify key navigator attributes
2. Assign point values
3. make tiers and determine next steps
4. Refine your scoring procedure

From pop-ups asking for an email address to referral programs to in-person sign-up sheets, there are multiple ways to capture leads, and you can even use navigator production tools to expedite the procedure. Once you have data on potential customers, you can use a scoring framework to assist you identify the individuals with the strongest navigator conversion potential. Here’s how to do it:

1. Identify key navigator attributes 

Using data like history conversions and trade research, identify both the traits and behaviors that might signify a standard navigator. Determine the characteristics associated with your ideal customer profile (like age, gender, and profession), and consider any requirements that limit your eligible customer base. 

Make sure to identify customer behaviors, too: If a customer signed up for SMS marketing, for example, they might be a standard navigator.

2. Assign point values

Develop a scoring structure and assign values to each attribute. Businesses often choose a 100-point scoring structure. Assign high point values to behaviors that indicate solemn yield, and consider using negative scoring by applying negative points to disqualifying actions. 

This habit helps to weed out low-standard leads. The objective is to generate scores that reflect how likely a customer is to convert. For example, if your ideal customer is a mom who likes DIY car repairs, you might assign a higher numeric worth to female-identified leads and a negative worth to users who don’t own automobiles. 

Make sure to use history conversion rates. For example, if you recognize that 50% of leads who add a product to their cart complete up making a purchase, and 20% of leads who open your marketing emails convert, you’ll assign a higher number to leads who’ve added a product to their cart than you will to leads who’ve clicked through your emails. 

3. make tiers and determine next steps

make a structure to categorize your leads. For example, a corporation scoring leads using a 100-point scale might classify leads with a score of 40 or less as cold, 40 to 70 as warm, and above 70 as warm. 

These categories will determine your next steps and assist you decide how to way the leads. For example, a business might have sales reps call the best leads, add warm leads to an email list so that the marketing throng can continue to nurture the connection, and decide to ignore the cold leads for the period being. 

4. Refine your navigator scoring procedure

navigator scoring requires you to choose and rank several data points, but you might not always select and score those data points correctly the first period around. Maybe leads with a low-scoring attribute ended up converting, or vice versa. Tweak your navigator scoring procedure to ensure the best results.

navigator scoring tools

Developing, using, and updating a manual navigator scoring structure can be period-consuming and complicated, so a business might use a navigator scoring tool to simplify the procedure and ensure more accurate results. Predictive navigator scoring tools use machine learning technology to collect data throughout the customer trip, parse through that data, and qualify leads. 

You can also use navigator scoring tools to automate sure actions based on a potential customer’s navigator score. For example, a navigator scoring tool could send a conversion-concentrated email to high-scoring leads. 

Here are a few popular tools that can assist with navigator scoring:

• Hubspot. Hubspot lets you toggle between predictive and manual navigator scoring, letting you harness the power of predictive navigator scoring while retaining the ability to customize your structure. Hubspot’s marketing software suite also has navigator production functionality for seamless integration across your sales pipeline.

• Pipedrive. Pipedrive is a sales management and CRM (customer connection management) software that can autofill information about leads by searching the web. It can then qualify those leads and automate tasks like sending marketing emails to promising prospects.

• Leadsquared. Leadsquared ranks leads with three scores: navigator standard (fit with ideal customer), overall navigator score, and engagement score. navigator score is cumulative, adding up all of a navigator’s yield-showing activities, while engagement measures recent activities, based on a timeline you specific. 

• Apollo.ai. Apollo.ai uses your business’s historical customer data to make navigator scoring models with AI. You can still customize your ranking criteria, and, if you’re curious about how the AI determined a navigator’s score, you can view a packed breakdown of how the score was calculated.