Leads are an important factor for the sales pipeline of your B2B company. If you have a lead system and want to act systematically and controlled with the contacts, a lead scoring model is the best choice.
What is Lead Scoring?
Marketing and sales professionals use lead scoring to assess, differentiate, and qualify potential customers.
Every product has a specific market and is intended for a specific group of buyers. If you have different audiences for your products, a lead scoring model will help you nurture contacts and prioritize - and motivate - them for optimal sales and revenue.
Lead scoring helps you assess whether an existing contact is promising or not.
Lead scoring is the process during lead nurturing where you analyze individual leads for their match to your B2B audience and assign a value, usually numerical.
This process helps both the sales and marketing teams prioritize leads, respond to them properly, and convert ready-to-buy contacts into customers at the right time.
Source: Customer Profile from Pedalix Software
There are different types of lead scoring based on the type of information collected:
Behavioral data from the website
Email activity information
Social media activity information
1. Demographic information
Different demographic information such as age, gender, professional position, country and similar data can be collected through forms on your website.
This way, you can remove leads from your database by deducting points for profiles that fall into categories you can't or don't want to serve.
For example, if you only sell to a specific geographic location, you can give a negative rating to any lead that is outside of that city, canton, zip code, country, and so on.
2. Information about the company
Information such as company size, industry, type, etc. can help you decide which leads to focus on for sales. This helps you to evaluate, further process or delete leads.
3. Behavioural data
Activities of a user on your website can give you clues whether the contact is really interested in your product or not. Relevant information can be collected through forms and the number of page visits.
The category of pages visited on your site is also relevant. For example, a higher score can be assigned to leads who have visited relevant pages such as the price list, delivery terms or shipping costs.
If you notice that a contact does not visit your site for a long time, you can deduct the points again.
4. Email Activity Information
The response to your email campaigns can help you decide how to score a lead.
Open rates and click-through rates are good indicators of potential customer interest.
Furthermore, it is easy to observe which contents are well received by the contact and at what time he reads his mails.
Source: Personalized Newsletter from Pedalix Software
5. Information on social media activity
Social media channels are among the biggest informative platforms for customer management today. The way your customers or contacts interact with your brand through social media can help you score.
Interest from your leads can be in the form of views, clicks, likes, shares, followers or comments.
Based on these parameters, you can assign or subtract high scores to your leads.
Elements of an effective lead scoring model
Marketing and sales goals
Central scoring model
1. Marketing and Sales Goals
The goals of the marketing and sales teams must be aligned to ensure effective lead scoring and management.
There must be consensus on who qualifies as a good lead or a bad contact, or when a marketing qualified lead (MQL) becomes a sales qualified lead (SQL).
This synchronization between marketing and sales is very important for any business to ensure proper transition of leads from marketing to sales.
2. Implicit valuation
An effective valuation model must consider both implicit and explicit valuation data.
Implicit valuation refers to the interpretation of behavioral measures to assess whether the product is suitable for the potential customer and what his real level of interest is.
3. Explicit evaluation
Explicit evaluation of the use of demographic data as well as personal information refers to the data shared directly by the potential customer in order to infer their true level of interest.
4. Central scoring model
You should maintain a single, centralized lead scoring for all campaigns and projects. Using different scoring systems at the campaign level can reduce the number of leads and increase your time and effort without providing any further benefit.
5. Negative scoring
The scoring of your leads varies constantly as you interact with the marketing or sales team. Based on activities like unsubscribing from the company's own newsletter, spams, the number of effective leads you have is imm dynamic. Don't let this discourage you.
Source: Customer Journey Reporting from Pedalix Software
How to calculate a basic lead score
Here, let's take a look at how you can perform a basic lead score calculation. These are some of the simplest methods to calculate it:
Manual Lead Scoring
Logistic regression lead scoring
Predictive Lead Scoring
1. Manual Lead Scoring
This method is time consuming and labor intensive. Let's take a look at it step by step:
Calculate Lead-to-Customer Conversion Rate: The lead-to-customer conversion rate is equal to the number of new customers acquired divided by the number of leads generated. This factor should be your benchmark.
Choose different attributes for customers that can become quality leads: Determine behaviors that could turn potential customers into people who, for example, request a free trial of your software or request a sample shipment.
Evaluate the success rate of the attributes you assign: Calculating the lead score is important as it helps you determine your next action. Thus, you can determine the number of qualified leads based on the actions you have taken. Then, you can interpret the actual closing rates to evaluate them objectively.
2. Lead evaluation of logistic regression
The above method can be a good start, but there are more mathematically advanced methods like this that involve data mining techniques.
Data mining techniques are complex but often closer to actual rates. Logistic regression involves creating a formula in Excel that calculates the probability of converting a lead into a customer.
It is more accurate than the other methods. It is now considered more holistic because it takes into account how all customer attributes - industry, company size and others - interact with each other.
3. Predictive Lead Scoring
A good lead scoring system helps to optimize the lead handoff process, lead conversion rates and also productivity.
The above two methods are very time consuming. However, with regular feedback and interaction with your team, lead scoring can be improved and become more meaningful.
A technology-based system helps you build better relationships with your customers.
This is where predictive lead scoring comes in. Predictive scoring uses machine learning to analyze thousands of data points to identify your best leads.
First, it analyzes the information your customers have in common, as well as the ones that make them different. Then, a formula is created to categorize your contacts by importance based on their potential to truly become customers with you.
Lead scoring is an important tool for marketing and sales. However, it is also important to consider the various factors, sort out the parameters and determine how to use them properly.
It requires a certain amount of effort to get to grips with lead scoring and to organise yourself internally within the company.
However, it will quickly become apparent that the effort is worth it in any case. Increased sales are guaranteed if used correctly.