Traditional Vs Predictive Lead Scoring - The Right Choice For Your Company? - Ciente

For many businesses today, the biggest goal to be a success is to generate a lot of leads. However, a lead goes through a long journey from being interested to actually signing up as a customer.

As per recent statistics, the average conversion rate from leads to customers is only 5%.

Does this mean that businesses will always have 95% of their leads unconverted? Not exactly. With advances in technology, marketing tactics have also evolved. From social media publishing software to advanced analytics tools, we have a wide range of options to choose from today. Additionally, managing leads has also moved from the traditional ways of gathering leads, cold calls, and scoring the leads based on outdated, manual techniques to better, more effective ways, including automated lead scoring and lead scoring predictive analytics.

In this article, we’re discussing lead scoring and comparing the traditional method to the predictive method.

But first, what is lead scoring?

Lead scoring is one of the most efficient ways to measure the quality of leads, which allows businesses to reduce the conflicts between the sales and marketing efforts. Specifically, it is a process of assigning a numerical value or rank to a lead based on a set of criteria and their likelihood to convert into a paying customer of the company.

Lead scoring allows companies to use their budget and efforts more efficiently by only focusing on leads that matter and will be highly likely to take action (such as a purchase, a sign-up, etc.). It helps businesses answer questions such as “Which leads should be followed up with immediately?”, “Which lead is showing higher buying signals?” and “How do we know which lead is qualified?”.

It can be done with two methods – traditional and predictive. Let’s talk about them in detail.

What is traditional lead scoring?

Traditional lead scoring, or manual lead scoring, used to be an efficient method of finding the best and most profitable leads for a business before the introduction of machine learning solutions. The method is called “traditional” because it relies on your team’s collective experience, common assumptions, and historical trends that apply to your business.

There’s no single proven or effective method of defining the criteria of traditional lead scoring. The scoring model is designed purely on assumptions. Here’s a simple breakdown of the key steps involved in lead scoring:

Identifying Key Characteristics

The sales and/or marketing team of a business first decides the factors that they believe indicate readiness of a lead to convert. It is generally a mix of demographic, firmographic attributes, and behavioural actions. The demographic/firmographic attributes may include anything from the industry that a lead belongs to, the revenue they make in a year, to geographic location and company size.

For example, if you are targeting companies or decision makers of companies belonging to the SaaS industry within the New York area and with a 500+ employee base, the scoring will be high for the leads that specify these details. If the lead’s geographic location is in the LA area, they will have a lower score. Similarly, if they don’t belong to the SaaS field, their score will be low as it makes them less likely to go for your services.

Further on, the behavioural actions typically include criteria such as “clicked the landing page link”, “initiated checkout”, “requested a product demo”, or “opened an email”.

Assign Point Values

As specified previously, lead scoring is a numerical method. Therefore, you will manually assign a numeric score to each of the criteria.

Following up on the example above, the score can be:

SaaS Industry – +20 Points
New York Location – +15 Points
500+ Employee Base – +20 Points
Opened An Email – +15 Points
Requested a Demo – +30 Points

Create The Formula

So, when any new lead enters your database, you’ll have a set formula based on the point values above that will automatically score your leads before they’re contacted. The system will check the criteria they meet, the total points, and the final score based on that.

So if a lead is from the SaaS industry, based in New York, part of a 1000+ employee base, and opened all the emails and even signed up for a demo, they’re the highest-scoring lead and must be regarded with the most attention by the sales team.

Set Thresholds

Based on the final score, you may set different thresholds that prioritize the leads. For example, a lead that comes within the 80-100 score must be immediately contacted, and then you can further move to leads that fall under the 50-80 and lower score thresholds.

Pros and Cons of Traditional Lead Scoring

What is predictive lead scoring?

Predictive lead scoring takes the assumptions out of the picture. It is a modern, AI-powered approach that uses up-to-date machine learning capabilities to decide which leads should be prioritized and which ones are less likely to convert.

So, instead of a team deciding on the factors to qualify a lead, predictive scoring pulls data from multiple sources, analyzes your business’s historical data to figure out patterns from past customers, and calculates the score automatically, without human intervention.

Machine learning is used in this method as it can process thousands, and even millions of data points, and identify the clear signs of conversion specific to your business.

The Process of Predictive Lead Scoring

Gathering Data

The first step in predictive lead scoring is pulling together a big dataset that contains past leads and opportunities, the conversion rate of those leads, and how they interacted with your business. This dataset can include anything from demographic data, behavioural data, to engagement data and purchase history.

This is a key step because it is the foundation on which the machine learning techniques will be based.

Training the Machine Learning Model

Once the data is gathered, the system will analyze all the information to look at statistical patterns that help qualify a lead. For example, the patterns can tell what the high-converting leads had in common, which combinations of actions can be predicted, and if there’s any data that was overlooked in the process (such as the timing).

For example, the model may find out if a lead downloaded more than 3 resources within 15 days, or if they viewed the pricing page twice but didn’t check out, and if there are any specific demographic or firmographic data that makes them more likely to buy.

Score New Leads Automatically

After the past data is analyzed, the machine learning model uses the insights and analytics from it to score a new lead. It will compare the patterns of the past leads with those of the new ones to assign a score.

Now, this score is usually not like the traditional methods (like +10,+20, etc.). It is more likely to be a probability of conversion. For example, Lead A has a 50% chance of conversion based on their patterns, Lead B may not convert as it has only a 5% chance and similarity to highly converting past leads. This will help your sales team decide the priority of contact.

Continuous Learning and Refinement

You may think that the model only takes into account the past data and analyzes new leads based on that. However, predictive lead scoring evolves constantly. It keeps changing its scoring technique based on how the new leads are also performing.

For example, if a product was purchased more in the first week of the month, but now the leads have a higher chance of conversion in the middle of the month, the model will automatically update its criteria. This is also applicable if your product has evolved over time.

The Pros and Cons of Predictive Lead Scoring

The Pros

Objective and data-driven

There’s no guesswork or assumptions in predictive lead scoring. Every criterion is data-based and not based on personal anecdotes or gut feelings. This results in better-qualified leads and a higher likelihood of better conversion rates.

Scalability

There is no limit to the number of leads or data points in predictive lead scoring. Machine learning models can take millions of data points into account before building qualification criteria. From CRM, MAP, ad platforms, to billing systems, product telemetry, predictive lead scoring, AI can analyze everything and put it into context, which would be impossible if done manually.

Higher Accuracy

The average conversion rate of leads with predictive lead scoring is 15% as this method is more accurate and fact-based. Studies also show that predictive models constantly outperform manual scoring as they process dozens of data points in real time. So, if there are any early warning signs that may help you upsell or re-prioritize leads, the model will predict them.

The Cons

Highly Dependent on Data Quality and Quantity

Predictive lead scoring isn’t built for businesses that don’t have proper data in place. Any inaccuracies, such as missing data, duplicate records, or inconsistent tracking codes, can entirely break the system, leading to miscalculations and inaccurate predictions.

Complexity & Cost of Implementation

Since this method involves handling and managing data accurately, only qualified data engineers can stitch the sources together with the additional help from data analysts. Not only will this cost more, but it will be more complex to set up, especially for smaller firms.

Change Management

Not only is the method laborious to set up, but there is also the challenge of building trust. Sales teams may not trust the model and still rely on gut instincts and familiar patterns to predict conversion rates, even if they may be inaccurate. Additionally, if the business changes in any way, such as introducing a new product, new pricing, or situational changes, the model will produce incorrect results that may not be useful.

When does predictive lead scoring work best?

Predictive scoring can be highly beneficial for your business if:

You have lots of data about leads and customers
You want to automate your lead processes and scale lead qualification to find higher converting leads
Your sales cycles are very complex and can be better managed with a machine learning model
You have a CRM and marketing stack that is capable of integrating these models

Traditional Lead Scoring vs Predictive Lead Scoring

Aspect	Traditional Lead Scoring	Predictive Lead Scoring
Setup	Manual rules and assumption-based data points	ML-driven, automated data based on analytics and patterns
Data	Limited (explicit fields, behaviors) such as demographic/firmographic and behavioural patterns	Large, multi-source datasets that aren’t limited to demography or behaviour
Accuracy	Depends on subjective assumptions	Based on patterns from real outcomes and past lead data
Maintenance	Needs manual updates	Can auto-adapt as data changes
Use Case Fit	Small teams, simple processes	Larger teams, complex funnels, rich data