Lead scoring helps
businesses focus their resources on leads that are most likely to convert. It’s a
model used by marketers to assign pointsto leads based on specific
criteria. This helps both sales and marketing teams identify their most important
leads and decide the right time to trigger an MQL to SQL handoff.
Different lead scoring models are appropriate for different use cases, but many
marketing and sales teams are unsure of what their options are. In this blog,
we’ll demystify lead scoring by looking at common lead scoring models, when to
use them, and the best practices for applying them.
Lead Scoring Models
How you choose to lead-score depends on your definition of
a good fitting customer and the data points you have available. Here are some
common lead scoring models to consider:
Demographic Lead Scoring
This lead scoring model
is best for businesses whose main objective is to find leads based on a
particular industry, geographic location, company size, or job title. Demographic
lead scoring relies on clearly defined buyer personas informed by past data.
For example, your
company may see a pattern in the data that shows female chief marketing
officers at Fortune 100 SaaS companies in the Northeastern US are your biggest
Once the personas are pinpointed,
scores can be assigned based on how well the attributes of each lead fit into
that ideal persona. If you’re scoring leads on a scale of 1-100, you could
break down each element like this:
- Critical attributes –
These fit your buyer persona exactly and would carry the most weight in your
lead scoring. You may assign a point value of 10-15 points to leads for each critical
attribute they meet.
- Important attributes –
These are demographics that may be important, but don’t fit your buyer persona
exactly. For these, you’d assign a lower point total, somewhere between 5-9
points for matching attributes.
- Influencing attributes –
These attributes may carry some weight with your ideal buyer persona but are
much farther away from your dream demographic. Influencing attributes may add
only 1-4 points to a lead score based on their lesser importance.
Company Lead Scoring
This lead scoring model is
most often used by B2B companies targeting specific industries, company sizes,
or company types. First, the company must review its data to uncover the company
size/type/industry it’s finding the most success with. After that, it’s
relatively easy to get a hold of the data to inform the rest of the model.
Researching on the
company website, via online business directories, social media pages, annual
reports, press releases, or government databases are all great places to find the
demographic company information to start scoring these types of leads.
Point values in this
model would correspond to the attributes of the company that are the most valuable
to your business. For example, let’s say you want to target banks and credit
unions with an annual revenue of $5 million. Do the products or services you’re
offering match banks and credit unions equally, or should one segment carry a
heavier point value than the other?
Should leads with less than
$5 million in revenue still receive some points or should they receive zero
points because, historically, you do not turn a profit with institutions in
that revenue range? These are the types of discussions you must have internally
to ensure the company lead scoring model is as precise as possible.
This is a common lead
scoring model for eCommerce sites that want to gauge how online behaviors
influence conversion. For example, how many pages did a lead visit before
making a purchase? Other relevant behaviors to track would be adding items to
the cart, initiating the checkout process, signing up for a newsletter,
watching product videos, or reading blog posts.
Web analytics and
tracking tools inform this lead model by monitoring and recording user
interactions in real-time. As data is aggregated about each lead, patterns emerge
about the types and frequency of behaviors they’re exhibiting. This can be used
to develop behavior-based lead scoring models based on the cumulative data. The
model can be automated within an ecommerce platform or CRM system.
Assign scores to each
behavior based on their importance and relevance to your sales funnel. For
example, actions like adding items to the cart or initiating checkout might
receive higher scores than merely visiting the homepage.
thresholds to categorize leads into different segments, such as "Highly
Engaged," "Moderately Engaged," and "Low Engagement." Insights
on engagement can then be used to inform targeted marketing campaigns and
automated emails to re-capture the low engagement leads.
Email Lead Scoring
Businesses that rely
heavily on email marketing as a lead generation tool focus on this lead scoring
model. When email communication is a key pillar in your lead generation
process, it’s important to know how well it works. What are the open rates? Click-through
rates? Conversion rates? Unsubscribes?
Scoring models for email
should get more granular than simply rating open rates. Behavior patterns like
forwarding or sharing emails or engaging with personalized recommendations can
be strong indicators of buyer intent. These attributes would warrant higher lead
scores. Historical data about your email communications is also crucial for
building out email lead scoring.
Social Media Lead
Brands whose primary
sales channel is social media rely most heavily on this lead scoring model. Post
click-throughs, retweets, and shares are all intent data that should be ranked.
Social lead scoring should also consider the frequency of interactions, conversion
actions like clicking through to the website, consistency of brand mentions, referral
sources, and how location and demographics align to your target audience.
Social media lead
scoring helps you identify leads that are actively engaged with your brand on
social platforms, allowing you to tailor your marketing efforts to those most
likely to convert. Assigning lead scores for social media profiles requires
historical data about how those profiles interact with your business and the types
of content they share.
Predictive Lead Scoring
In some lead scoring
models, humans are using fixed data to organize a lead scoring plan. With predictive
lead scoring, a wide range of data sources reveal complex patterns that would be
challenging for a human to identify on their own. Organizations with access to
machine learning algorithms and predictive analytics use this model to score
leads based on historical data and trends that previously led to sales.
Predictive lead scoring is highly adaptable and scalable, as algorithms can
change based on how lead behavior is evolving. To do it well, though, the data used
must be clean and regularly updated. Integrated data is also useful for this model
because it creates the most holistic view of lead behavior and enhances accuracy.
Scoring Best Practices
forget about Negative Scoring
Negative scores should
also be considered, especially for leads that fill out forms with fake
information or non-business email addresses like Google or Yahoo. Keep
negative scoring attributes in mind as you develop your lead scoring models.
your lead scoring approach
If your company offers
more than one product or service, you may want to utilize multiple lead scoring
models. Don’t box yourself into one type of lead scoring, especially when you’re
not sure which one will yield the highest results for your business.
your magic number
Lead scoring is about more than just assigning scores, you
must have a establish what those scores mean. Use scoring thresholds to determine
which leads require immediate attention and which should be nurtured further. The
thresholds will differ based on the type of scoring model you’re using, but the
point is to establish a magic number that helps your team decide if that lead should
be sales qualified or simply nurtured.
set it and forget it
Lead scoring is a delicate art. You must regularly evaluate
and update processes in response to market changes, product or service changes,
and other evolving factors. Regularly updating your lead scoring process will lead
to more successful results.
it a team effort
scoring qualification criteria should be defined collaboratively between sales
and marketing. Not only does this ensure alignment, but it keeps both teams
focused on activities meant to achieve the same outcomes. Part of your lead
scoring discussion should also cover the lead handoff process between marketing
and sales. Which information should be passed between the departments to
facilitate the most meaningful conversations?
it easier with workflows
you’ve scored high value leads, you need the ability to take immediate action.
Workflows that run through your CRM or marketing automation can guide leads
through the appropriate buyer journey with useful and personalized information
tailored to their needs. Initially building the workflows may take some time,
but it’s much faster to tweak and alter these automations as your lead scoring
models evolve later.
compliance in mind
your lead scoring practices comply with data privacy regulations and
requirements. Make sure you handle lead data responsibly and with care.
Document the process
the criteria, point values, and other information related to all lead scoring
models so you can go back and evaluate how they’ve changed over time. This will
help you build the highest performing lead scoring models.