One of the biggest challenges facing Marketing Automation is managing databases: how to sort, classify, and segment prospects, leads, or clients, and when faced with such a large number of records, prioritize quality.
To achieve this, the implementation of the lead scoring technique is of great help to organize information, optimize database management, define the most appropriate strategies to follow for each segment, and therefore, improve the company's results.
What is Lead Scoring?
Definition of Lead Scoring or lead qualification, is a process shared by both the Marketing and Sales departments, which tries to automatically classify all the contacts in our database with the aim of segmenting through a scoring system, which is assigned to each of the leads, according to the degree of affinity of their ideal customer profile.
In other words, Lead Scoring is about scoring the lead's conversion potential and the relevance it could have for our company, in order to prioritize the efforts of the Sales department, improving its productivity and efficiency.
Types of Lead Scoring
There are two types of lead scoring: unidimensional scoring and multidimensional scoring. Each organization must select the lead scoring model that best suits its needs based on the specifics of its leads and the objectives to be achieved.
Retrospective scoring and predictive scoring are included in unidimensional scoring. Retrospective scoring groups together both explicit and implicit canadian hospitals email list data and assigns the lead a score between 0 and 100. While predictive scoring incorporates Machine Learning technology in the analysis of the lead's behavior and provides us with the probability of success in achieving a specific objective.
Multidimensional Scoring parameterizes different types of variables or dimensions, for example, engagement with the brand, the phase in the purchasing process, brand awareness and the degree of affinity with our ideal client.
The truth is that the more variables or dimensions we can incorporate into the model, the more accurate the results will be. And if we can also apply AI in parallel, we eliminate the possibility of inconsistencies appearing.
For B2B, a robust and flexible approach to lead scoring is essential as sales cycles are often longer and more complex. Integrate detailed behavioral data such as event interactions, technical content downloads, and webinar participation. Close collaboration between marketing and sales teams is key to continuously fine-tune criteria and ensure that the best-qualified B2B leads are translated into real business opportunities.
How to qualify leads?
The first thing we must take into account when designing a Lead Scoring is to identify the parameters that are going to be relevant for us in defining our ideal client. Choosing these attributes will also depend on the objective we have set ourselves. That is, if the objective of the strategy is to attract new business opportunities or loyalty, the criteria to consider will be different.
To do this, we will need to collect all the data that can help us identify the qualification and disqualification criteria for the lead.
It is important, first of all, to align the Marketing and Sales departments to generate a joint strategy and a qualification method that works for both. The sales department has key information on the characteristics or parameters of the leads that converted the most and therefore, also those that converted the least. Generating a Service Level Agreement (SLA) between both teams will also help us clarify when a lead is ready to be attended to by sales.
On the other hand, we incorporate explicit information, which corresponds to demographic and firmographic data identifiable through forms, questionnaires or data shared directly by the client, which will allow us to identify those attributes that best align with your Ideal Buyer Persona and assess the potential of the lead. For example, we might be interested in knowing their position within the company, its size, the sector where they operate... and in the case that our business is B2B we can rely on the BANT model (Budget, Authority, Need, Time).
Finally, lead insights are all the implicit data and analytics generated by the lead interaction and behavior tracking automations that measure engagement with our brand, as well as identifying the moment in the purchase cycle they are in. We can also obtain information about which attributes or parameters are most repeated in conversions, and what phase they were in. For example, how many visits they made to our page, which ones they spent the most time on, email opening rate, clicks, interaction with our social media profiles, ebook downloads, frequency of interaction.
And most importantly, by unifying all this information in a Lead Scoring matrix, we will be assigning the total score to each of the leads according to the attributes and their attributed weight. As a result, we will have created a true image of the value of each lead for our business, the engagement of the lead with our brand, and we will also know what stage of the customer journey they are in.
