Lead Scoring Tools: Nected’s Algorithm For Business Success

This blog helps you understand lead scoring, algorithms, models and why you should choose lead scoring tools like Nected to help grow your organization.

Lead Scoring Tools: Nected’s Algorithm For Business Success

Prabhat Gupta

11
 min read
Lead Scoring Tools: Nected’s Algorithm For Business SuccessLead Scoring Tools: Nected’s Algorithm For Business Success
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11
 min read
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Identifying the most promising leads is one of the most crucial steps for any business. Lead scoring tools are the ones which help companies identify their value. Segregating all the available prospects based on their value addition to one’s business can become painstakingly difficult if not handled properly.

In this blog, let us have a look at lead-scoring tools, algorithms, and methodology that help us identify, engage, and convert the most valuable prospects.

Understanding Lead Scoring

The dynamic landscape of modern businesses operates with abundant data and diverse interactions with consumers from all demographics. Scoring your leads is as important as generating them in the first place, as you need to allocate your time and resources to your prospects accordingly.

In simple terms, lead scoring is the process of assigning scores to your leads based on how likely they are to become your customers. Needless to say, every business can have different processes with completely different criteria to consider. Factors such as the business model, the intended target market, the products and services provided, etc. influence the design process for an “ideal lead scoring algorithm” for any business.

Lead Scoring Algorithms

Traditional Lead Scoring Algorithms utilize scores assigned to leads based on predetermined criteria. These criteria include both explicit and implicit data inputs. Explicit data is the data provided by the prospect such as industry, service area, budget, etc., while implicit data is a reflection of the user behavior, which includes factors such as web visits, click-through rates, repetitive visits, etc. The above image depicts the characteristics of implicit and explicit data. 

The cumulative score from all these criteria is assigned to the lead and then the lead is considered for a follow-up if this score meets a certain threshold. We can keep in mind some basic points and factors while designing such an algorithm for any brand, and while measuring and tracking it over time. 

Making the Ideal Lead Scoring Algorithm

While there are several points to be considered depending on the business itself, we can start creating any such algorithm with some of the most basic yet most important factors: 

  1. Prospect demographics - This would include most of the explicit data received from the lead. This includes (not limited to), factors like age, location, job title, current company, organization type, language, etc. This easily filters out potential spam and gives us a clear view of market-qualified leads. For example, an industry professional working on a project that can utilize your services and having the budget for it would be a much-desired lead compared to some individuals just checking out the website.
  1. User engagement - A user who is actively engaging with the brand, both directly and indirectly is much more likely to convert to a full-time user. For example, a user subscribing to your newsletter, interacting on your brands’ social media, participating in brand surveys, and attending brand seminars is much more likely to convert and thus should be highly scored by your algorithm.
  1. Behavior - This mostly concerns the users' implicit data. It is extracted by analyzing the leads' behavior, such as website visits, resource downloads, links opened in emails, etc. It gives us a really good idea about the user's level of interest in the brand and their engagement with the products and services. This metric also gives us a good idea for the scoring metric as a user who is subconsciously interested in the brand is much more likely to be a future user. 
  1. User fit - Including all other miscellaneous information, we can try to build up a profile of the lead in question and estimate how good a fit they can be compared to an ideal consumer for the brand. This can be done by including points like the source from where they discovered the product, their responses in brand surveys, etc. 

Keeping Track of Your Algorithm

As depicted in the above image, Keeping track of your algorithm and regularly tracking it over time is as important as its design. To assign scores to all of a brand's leads, the algorithm needs to be constantly fed input in real-time, on which it operates. It then assigns them scores based on those criteria. For example, if we have criteria for leads that visit our website via LinkedIn, then we assign 5 points to user A who was redirected from LinkedIn, while no points to user B who came from, say YouTube, for this particular criterion. 

This can be implemented by either coding API endpoints, connecting databases, and then calculating scores on the backend, followed by updating the new scores for the users, or simply by using lead scoring tool providers like Nected.

The interface provided by Nected is easy to use and directly connected to the brand database. 

  • The no-code parameter list enables users to easily define lead scoring conditions and assign the corresponding scores. 
  • Users can then create, update, or delete such criteria and finally select the database to which these changes should be reflected. 
  • With the help of secure APIs provided by Nected, the changes are worked on and reflected in real time.

Users can easily set up basic lead-scoring algorithms using Nected’s no-code tool in just 15 minutes (watch demo).

There are further steps to be taken after assigning scores to the data. The first step usually involves categorizing the leads based on their scores in different criteria. We can further use this segregation to decide a threshold for users on the database. 

For example, if around 60% of good leads come from a total score of 800, while 15% of poor leads come from the same score, you’ll consider the score of 800 to be nearer to your desired threshold.

Tracking and analyzing your algorithm over time is as important as creating it in the first place. The criteria for scores need to be updated with the ever-changing market dynamics and the algorithm needs to be updated with each such iteration. Usually, it is a good practice to update and check your algorithm at least once a quarter.

You can also get a good idea of when the strategies need to be updated based on patterns from the scores you receive, for example, if a pattern of points is being repeated in several thresholds, then it means that the system might not be exactly accurate and you may need to check your lead generation strategies.

Lead Scoring Models Used by Businesses

Generally, Lead scoring models are categorized into two major categories -

Traditional Scoring Models

When implementing lead scoring for the first time, many companies utilize a conventional approach. Every lead is assigned a score based on predefined criteria. Both explicit and implicit data are included in this criterion. The information provided by the prospect—such as the industry, company, service area, title, budget, etc.—is the emphasis of explicit data.

The behaviors of the prospect are reflected in implicit data (web visits, click-through rates, repeat visits to the same sales page, etc.). You assign a number to every criterion. The prospect's lead score is derived from the sum of its numerical values.

The lead is forwarded to the sales team for follow-up if the score reaches a predetermined level that was decided upon by both sales and marketing. If not, the lead is nurtured further (according to their level of interest) until their lead score rises.

Predictive Scoring Models

Here, an algorithm is used by the predictive lead scoring approach to assess lead quality and the prospect's propensity for making a purchase.

This algorithm integrates behavioral and historical data from your CRM combined with big data to produce the optimum lead profile. To identify which inbound leads are most likely to convert and which require additional time, they are then assessed about this ideal lead profile.

Once you have enough information on how potential customers engage with your marketing before making a purchase, predictive ratings might be helpful. The score will rely more on big data to predict the likelihood of closing till there is sufficient data.

Multiple Scoring Systems that help in Scaling a Business

As brands scale, they need to cater to multiple lead-scoring systems, which allows them to qualify different sets of users in different ways. We can do it in some of the following ways -

  1. Fit vs Interest - For example, suppose your sales team wants to assess clients based on two factors: is a contact located in the appropriate area? The appropriate sector? the appropriate role?) as well as the degree of interest (e.g., how involved have they been with your web content?). To prioritize outreach to contacts whose values are high in both categories, you can build an engagement score as well as a fit score if both of these traits are important to you.
  1. Multiple Personas - Assume that your software company sells two distinct kinds of software to various customer types through separate sales teams. One lead score for a buyer's fit and another for their interest in each tool might be created. Then, you would route leads to the appropriate sales teams using these corresponding scores.

Why It’s Crucial To Choose Automated Lead Scoring Tools?

Manual lead scoring exists, but is a cumbersome process and hardly used in the fast-paced industry. There are several tools available out there, which make your life simpler, such as the no-code tool provided by Nected.

Nected, embracing a low-code/no-code philosophy, revolutionizes lead scoring by providing an easy-to-use tool that connects to the brand database and manages all your custom rules in real time.

Try Nected for free and ace the lead scoring game.

Conclusion

The best lead-scoring program for your business requires a deep understanding of your target market, your company objectives, and your sales strategy. A lead's likelihood of becoming a customer fit, engagement,  behavior, and demographics. Your sales team will be able to focus on the most promising leads and Prioritize their efforts through lead-scoring algorithms, which will ultimately drive conversion rates and revenue for your company. 

In conclusion, developing the best lead-scoring system for your company requires continuous improvement and refinement to build your unique brand. It is not a one-size-fits-all approach. Hence we should identify the criteria based on our brand's profile, and keep it evolving with time.

FAQs

Q1. What is lead scoring, and how does it benefit my business?

Lead scoring ranks leads based on characteristics and behavior and helps prioritize resources on leads more likely to convert. It improves conversion rates and streamlines the sales process.

Q2. How do lead scoring tools work, and what features should I look for?

Lead scoring tools use algorithms to assess lead potential by integrating with CRM and marketing systems. Look for customization, integration capabilities, behavior tracking, lead segmentation, and scalability in a lead-scoring solution like Nected.

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