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Predictive Versus Conventional Lead Scoring

New technologies have eliminated the need for firms to make educated guesses about which leads are viable and which ones have become cold. In this RisePath article, we’ll talk about traditional and predictive lead scoring techniques and how both can completely overhaul the marketing and sales operations of your company.

Reviewing lead scoring

Lead scoring has a significant role to play in your company. Simply said, it is the act of giving each of your leads a score. The score is based on a number of variables, including demographics, how leads engage with your company through different marketing channels, and the likelihood that they will become paying customers.

The lead scores can be on the positive or negative side of the scale, or how likely they are to convert more or less. The current top customers can be found after you know the lead scores.

Your most loyal clients are your net promoters since they are more likely to recommend you to others and buy from you again. You can then modify your campaigns to target leads that need more nurturing before converting using the lead ratings.

Your marketing and sales teams’ output can also be increased because you are aware of which leads need extra care. Always keep in mind that obtaining leads isn’t as important as turning them into paying customers.

Let’s now examine traditional and predictive lead scoring in more detail to aid you in selecting the approach that is best for your company.

Lead Scoring

Conventional lead scoring

Traditionally, lead quality has been evaluated in an effort to predict which leads will result in sales. Both explicit and implicit data are gathered and analysed.

Explicit data is gathered through online registration forms or other demographic data provided by prospects (job title, business, contact information, etc.). The prospect’s actions on your website and through other marketing channels, such as the number of page views, email open rate, click rate, and other metrics, are used to gather implicit data.

A score is then manually assigned for each of these data points by the marketers. To ascertain whether a lead is ready for a sale, they frequently employ the BANT criteria, which stands for BudgetAuthorityNeed, and Timing.

The following information may also be used to base lead scoring models:

  • Demographic information: Includes things like place, age, gender, and job title.
  • Internet usage: How users engage with your website
  • Engagement: the specific ways in which customers interact with your business through marketing channels.

Benefits of conventional lead scoring

Traditional lead scoring has been used by marketers for a long time to collect data, update and test the scoring system, evaluate the outcomes, and find better leads. The lead scoring process can be streamlined and linked with the sales team with the use of customer relationship management (CRM) systems.

Conventional lead scoring’s drawbacks

Traditional lead scoring is tried and true, but it is also overly simplistic and focuses too much on weeding out undesirable prospects rather than finding excellent ones. Finding and focusing on the best leads while improving the nurturing of the other prospects is the actual difficulty.

Traditional scoring relies on a small dataset that is manually gathered and handled by the marketing team, making it less adaptable for rapidly changing markets. This approach is also quite arbitrary since the marketing or sales team’s assessments of what constitutes a good lead.

Additionally, rankings are based on a relatively short dataset that emphasises prospect activity and their interaction—or lack thereof—with the website rather than their specific needs.

Repetitive duties and low conversion rates brought on by inadequate lead scoring processes can also demoralise your workforce, lead to missed opportunities and delay business growth.

What kind of business is this best suited for?

Traditional lead scoring is most advantageous for businesses that rely on sales representatives and their client knowledge to establish lead scoring methods.

Score predictions for leads

Predictive lead scoring is created by taking all the advantages of conventional lead scoring and combining them with the effectiveness and efficiency of machine learning algorithms.

Predictive scoring gathers and analyses massive data to assess the significant actions of current customers and potential leads rather than depending on limited datasets and the manual metrics of humans. In order to distinguish between leads who are more likely to be converted, retained, or make a purchase from the company, these data points are then evaluated on a scale.

By automating the discovery and conversion of sales for your company, predictive lead scoring enables you to better utilise resources and refocus efforts for a quicker return on investment.

Predictive lead scoring benefits

The advantages of predictive lead scoring surpass those of conventional lead scoring because of the following reasons:

  • On the basis of big datasets, it generates trackable measures.
  • It gives the marketing teams the chance to run more precise campaigns and enhance ROI.
  • It increases the efficiency of sales teams by directing their resources toward better leads and customers
  • It boosts conversion and purchase rates
  • It adjusts lead information profiles by comparing past and present clients
  • Is less prone to mistakes
  • It collects data-backed information to support decisions.
  • It reveals trends and relationships that you may have missed

Predictive lead scoring drawbacks

Predictive lead scoring is only as good and helpful as the data you have, which is probably more of a requirement than a drawback. You will want a significant amount of precise and organised data, as well as the required technology to manage it, in order to obtain accurate and insightful information on your leads.

What kind of business is this best suited for?

Predictive lead scoring is better suited for companies that utilise online behaviour and engagement models because it requires a large dataset to deliver the best insights.

Predictive lead scoring management needs a lot of knowledge and commitment in order to be truly effective, in addition to collecting a lot of data. Ask yourself the following questions before you make the change:

  • What will we do with this score?
  • What procedure modifications should we implement in order to provide this data and information to sales?
  • What barriers or impediments can limit the use of predictive ratings in sales?
  • What score boundaries ought sales prioritise?
  • Do we have the proper tools to coordinate sales and marketing efforts and manage lead disposition?

The powerful capabilities of unified CRM platforms like RisePath enable your company to improve customer experiences by streamlining marketing campaigns, gathering and presenting greater and better data, and streamlining data collection and presentation.

The importance of lead scoring for your company cannot be overstated. You may manage leads using data-driven decisions and increase campaign ROI by using the right scoring model.


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