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The aim of lead scorings is to qualify customer data. This applies to both new and existing customers. Lead Scoring starts at two points: implicit and explicit information. Sales and marketing are to be improved through qualified data.
The more leads accumulate in a company, the greater the risk of losing track of them. Potential is lost if the data can no longer be assigned appropriately. This is where lead scoring comes in: In most cases, a points system is used to qualify the data. The higher the score, the more relevant the customer is for a company's products or services.
The explicit information mentioned above refers to data such as the gender of the customer, their professional situation or where they are located. This is implicit information in relation to customer behavior. This raises questions such as customers' responsiveness to e-mails or their surfing behaviour on the company website. Implicit and explicit information is included in the evaluation.
The three pillars of lead scoring
Lead scoring can only work if an appropriate methodology is developed to turn the data collected into useful results. Three columns are used for this:
- Processes and
The human resources department is staffed by at least one specialist who is responsible for communication with marketing and sales. They takes care that only qualified leads are passed on to the sales department.
Especially when it comes to acquisition, many sales people repeatedly obtain unusable or inappropriate data that does not fit the customer. The specialist should prevent this. In the Processes area, definitions of leads are defined and passed on to Marketing and Sales. It defines which data is important and how it is classified. The Technology division is responsible for the installation of suitable software so that all data can be processed and passed on quickly and precisely.
Lead Scoring: Manual, automatic or predictive
There are three possible ways of introducing lead scoring. It can be introduced manually, particularly when it is first implemented and when companies do not have large financial resources. This approach only works if data collection and evaluation starts as early as possible. Later, it makes sense to switch to automatic data maintenance. The software is not usually self-learning, so the regular maintenance and updating of the set up rules must be done manually. The predictive approach evaluates additional data and is self-learning. This includes not only implicit and explicit data, but also data that can be obtained from third parties. This data are not only used to analyze customer behavior on your own website, but also to create a far-reaching digital fingerprint.
If the amount of data increases, manual evaluation becomes problematic, especially as regular new factors are added that are important for lead scoring. If you initially only collected data such as the customer's age or profession, further data can be added:
- When and how does the customer move around on the website, open emails, read and answer them?
- Is the ending of their mail a private provider or (like gmail, gmx and so on) is the company name to be found in it?
All this and other data can be used to draw conclusions, but there are limits to manual use. In these cases there is no getting around the predictive approach.
Predictive Lead Scoring differs from the conventional method only in the use of modern technologies and automated evaluations. This allows the creation of more comprehensive data collections and intelligent algorithms to collect, update and evaluate the data.
Since not only the amount of data, but also the evaluation possibilities have increased and will continue to increase, manual work with lead scoring will or has long since reached its limits. The "gut feeling" may work if there are only a few criteria and customers. But even smaller companies are already increasingly relying on predictive lead scoring. According to a study by Accenture, the use of data analyses tripled in 2014 compared to 2009. This path seems irreversible. However, false assumptions may be made between data analysis and valuation, as Pinterest has shown. Simply because users posted wedding photos on their profile, Pinterest assumed they were about to get married, which was not necessarily the case. Blind trust in data analysis is therefore not advisable.
Significance for online marketing
The use of lead scoring is helpful to cope with the growing data volumes and for gaining insights. The fact that not only large but also smaller companies use this methodology shows that the need for efficient data analysis is generally recognised.