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Lead generation is crucial for business growth.
And since nobody likes to waste time on unqualified leads, most businesses have a process in place for evaluating leads and identifying the most qualified ones.
This process is called lead scoring.
A typical lead scoring process involves scanning all incoming leads, either manually or using dedicated software, and assigning them a score based on various factors.
Factors can range from explicit factors (i.e., demographics and firmographics information received from the lead) to implicit factors (i.e., actions performed by leads and inferred information).
The lead scoring model is what determines the logic behind the scoring process. It is based on a pre-set combination of factors that imply a certain lead will convert.
For example, a company might decide that a qualified lead is any 'Director at a US-based technology company, with 200+ employees, who downloaded a whitepaper and logged in to his freemium account more than 3 times a week'.
Users meeting these conditions will be classified as qualified leads and routed to the right sales team.
It's important to note that businesses grow and evolve all the time, and the lead scoring model from last year may no longer be relevant and, as a result, it could be sabotaging your customer acquisition efforts, by excluding qualified buyers or including the wrong prospects.
For these reasons companies review their lead scoring models a few times per year for efficiency – and so should yours.
When a sales team starts receiving a lot of leads on a daily basis, it becomes difficult to tell which potential customer should be contacted first.
Lead scoring enables sales teams to prioritize the leads with the highest likelihood to convert before prospects with low conversion likelihood.
Without lead scoring, you are running the risk of putting hours into the wrong prospects and ultimately misusing resources that can be put into closing deals and generating revenue.
Lead scoring is the sure way of determining where the sales team should invest its time and effort.
Traditionally, lead scoring is based on a one-dimensional approach, where marketers rank leads based on a predetermined set of actions that display customer intent.
For instance, a lead that downloaded a white paper and visited the pricing page 3 times would get a higher score than a lead that never downloaded a white paper and visited the pricing page just once.
However, a set of predetermined actions doesn’t show the full picture.
In reality, many non-obvious, hidden factors are driving prospects’ certainty and likelihood to convert. If no one accounts for them, they would not be factored into the scoring model.
All in all, manual, rule-based scoring is limited to the subjectiveness of whoever sets it up.
A more holistic and objective approach is predictive lead scoring, which takes into account many different factors and detects hidden correlations that can help us understand what really causes leads to convert.
Predictive lead scoring solutions leverage machine learning and automation to identify your ideal customer types fast, and in a non-biased way.
The solution uses historical data to identify all the different attributes of your qualified leads and then applies predictive modeling algorithms to analyze your open leads, to ultimately find correlations and predict which prospects will likely convert.
Such deep, comprehensive analysis results in a lead scoring system that helps your go-to-market teams improve their prioritization, response time, and overall efficiency.
Building a lead scoring model, and fine-tuning it over time, can be a time-consuming operation that involves multiple stakeholders. Automating the process using predictive lead scoring will significantly shorten the time it takes to analyze the quality of leads, allowing go-to-market teams to spend this saved time pursuing their best leads and delivering value to them.
Predictive lead scoring systems are powered by data pulled from various sources, enabling the analysis to extend beyond your CRM and Marketing Automation data to external data enrichment sources and product usage systems. Using this approach, a richer buyer profile is created. Additionally, the use of machine learning has better chances to qualify leads in an objective, non-biased manner, as opposed to manual scoring that is prone to human error and bias.
Companies collect more data than ever, and if you had to compile and assess all this data manually, it would be a slow and tedious process. A predictive lead scoring algorithm is able to classify leads almost instantly, enabling sales representatives to conduct faster lead follow-ups, from the moment they sit in their chairs in the morning.
Even the world’s best sales and marketing teams are still human. People have their own experiences, biases and preferences that can shape their unique definition of a ‘qualified lead’.
When perspectives differ, you run the risk of having diverse opinions and inaccurate scores that can lead to prospects slipping through the cracks.
Predictive lead scoring is a data-driven methodology that removes bias, guesswork and judgment, by generating predictions based on cold data. This leaves more space for data-led decisions, making your sales efforts more focused.
When marketing teams generate leads and not seeing positive conversion rates from sales, or the sales department keeps getting terrible leads, it can tax both organizational harmony and business outcomes.
Predictive lead scoring enables sales and marketing functions to have a standardized, unbiased definition of qualified leads. Such alignment will result in better, more qualified leads and thus empower teams to work better together towards meeting their objectives.
By now you’ve heard about predictive lead scoring and how it works. Now comes the all-important question – how do you get started with it?
Beyond the technology used, it’s crucial for organizations to ensure their culture and employees are aligned with such change.
It’s worth taking some time to explain and brief teams on the benefits of predictive lead scoring and allow them to understand the advantages of adopting such a system.
Then, it’s time to pick a software solution with capabilities that fit your needs. Of course, this would depend on your organization’s needs, digital literacy, and data literacy skills.
For business teams, you would want to consider a vendor that provides a no-code, self-serve solution, that will be easy for non-technical employees to manage and operate successfully without having to depend on technical teams.
Putting the time into researching and implementing the right tool will be well worth your time, as it will improve and automate manual, error-prone processes and drive revenue-focused efficiencies.
Forwrd is a no-code, self-serve predictive analytics platform that specializes in predictive lead scoring and can help you automatically detect your best leads and route them to reps, right inside the tools they use daily, like Salesforce, Gmail, and more.
Want to see how Forwrd can help you implement an automated predictive lead scoring system in your business?
Book a quick demo today to see Forwrd in action.
