Lead Scoring and Prioritisation

September 6, 2022

Sales Process

Lead Scoring and Prioritisation

If you're not using lead scoring, you're missing out on one of the most powerful tools in sales. Here's what you need to know about lead scoring and why it's so important.

Lead Scoring, Score, Lead Prioritisation, Priority, Lead Strategy, Lead, B2B Lead, Sales, Sales Strategy, Artificial Intelligence

What is Lead Scoring?

Marketing and sales teams commonly use lead scoring as a methodology to determine the likelihood of their leads making a purchase. So, what is lead scoring?

Lead scoring is a methodology marketing and sales teams use to evaluate and rank potential leads. This rack is based on their behavior, interests, and engagement with the company. By assigning a numerical score to each lead, based on factors such as website visits, email opens, and social media engagement, teams can prioritize their efforts and focus on leads that are most likely to result in a conversion or sale. The ultimate goal of lead scoring is to improve the efficiency and effectiveness of the lead qualification process. Therefore, helping teams to close deals faster and drive revenue growth.

The numerical score for each lead is often between 1 and 100.

So, are you ready to take your lead scoring to the next level? Try out Dealcode Sales AI message generator now and increase your lead conversion rates!

Why are Lead Scoring and Prioritisation needed?

You're probably an experienced B2B sales rep who understands lead generation and lead prioritisation. So, as a B2B seller, you have likely worked with sales representatives; most sales representatives used to evaluate leads based on experience and gut instinct—unfortunately, that combination of gut instinct and experience isn't insufficientManual processes make this method time-consuming in addition to yielding poor results.

Consistently lower than expected closed deals with gut instinct showed the need for a more robust evaluation methodology. At one point, this refined methodology needs to identify time-consuming, not-so-ready prospects so you can focus on the right ones. There is a need for lead scoring or prioritising to prioritise leads most likely to close.

In short, lead scoring lets you see how far down the sales funnel your prospects are so you know whom to focus on and what action to take to convert them into clients.

But how could we identify the prospects with the highest conversion potential? What is the best strategy for bringing the best lead to the surface? Lead scoring empowers frontline salespeople by guiding them to the best prospects and providing visibility into the performance of comparable opportunities.

Companies can achieve a typical impact of approximately 3 -6% improvement in Return on sales (ROS) by utilising rich, data-based guidance on deal prioritisation and insights that reflect the specific characteristics of the prospect.

How does Lead Scoring work?

Deal scoring uses four key steps to score deals in real-time:

  • Identifying key variables such as deal size, customer, and channel used by the prospect.
  • Segmenting deals using, for example, K-means and machine learning. K-means clustering is an unsupervised algorithm for organising large amounts of sales data to generate competitive insights about your company.
  • Scoring deals and providing a colour code defines a prospect’s quality.
  • Integrating scores into core sales processes, such as adapting incentives based on the quality of the deal and directing the deal to the appropriate level in the deal approval processes.
Machine Learning Lead scoring
Machine Learning Lead scoring

A machine learning algorithm determines the deal score — the higher the number, the better the value for you.

What are the Lead Scoring models?

Lead scoring models specify the context in which values are assigned to each lead. The lead scores are usually assigned on a scale of 0 to 100. There are two types of lead scoring:

  1. Explicit Scoring: The score is determined by acquiring information directly from the lead. The category includes attributes such as demographic and firmographic data, e.g., job title, region/location, and Company size.
  2. Implicit Scoring: Scoring attributes, such as online actions, are used to determine the score in implicit scoring, which is based on observed behaviors and inferred information.

Benefits to Lead scoring

Lead scoring offers the following advantages:

  • Lowers the resources used in the marketing and acquisition process.
  • Discover both unqualified and qualified leads.
  • The conversion rates and sales cycle needs less time.
  • Higher revenue achieved.
  • Sales reps learn about the types of leads they should focus on in the future.

Additional Insights on Winning probability in support of Lead Prioritisation

What happens if you use a winning probability? You are not only prioritising leads by a score but also calculating the likelihood that it will be won. Many tools assign this lead or deal score to prospects or leads; Dealcode is a unique Winning probability SaaS that uses machine learning and Artificial intelligence to give the likelihood of a prospect turning into a signed deal. The score increases with actions recorded in the CRM used by sales reps. Dealcode’s predictive lead scoring tool also uses these actions and the data-driven winning probability to give recommendations. If you use Pipedrive and Hubspot as your CRM and winning probability is in your interest, you should definitely check out Dealcode!

EASY TO SET UP TOOL

  1. Connect your sales sources, in Dealcode’s case - Pipedrive or CRM. The data is cleaned and normalised using Dealcode AI analytics tools.
  2. Dealcode’s ML algorithms find patterns in your sales data, customising and determining its winning probability. The model becomes better with each new course and data point (from the activity).
  3. Check the winning probability, prioritising what prospect to follow up on, increasing your ROS by approximately 30%.
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