Search relevant leads straight from the Internet database

The real effect of a SESSA implementation is the acquisition of information about the probability of a given company being interested in a product or service you provide. In other words, SESSA allows companies to identify leads with the highest potential. This form of information management has been shown to directly influence increases in ROI, thanks to the ability to pre-select clients according to conditions set by the user. SESSA can also be applied to different types of signals.

What distinguishes SESSA  from other lead selection techniques is that this analysis is conducted through the use of information publicly available online. This means that the purchasing of databases of prospective clients is no longer a necessity. Only up-to-date information found on prospective clients’ websites is analysed, which can be subsequently adjusted to different types of signals. 

A search is performed through the use of the Microsoft Azure infrastructure in the SaaS (Software as a Service) model. The way this infrastructure was organised allowed us to create and offer our clients the highest possible quality of service as well as the optimised reduction of costs (the client only pays for the IT infrastructure resources used). Additionally, an extra benefit achieved through the SESSA was allowing us to engage our long-term partner – Microsoft – into the project, which has in turn led to the enhanced development of co-operation with this worldwide business. 

Use cutting-edge technology and find your most prospective clients.

How does it work?

A COMPANY presents specific DATA: field of interest, product description, keywords defining the area of search. Users also are required to provide information about prior sales attempts, both successful and unsuccessful and preferably with a specified level of success/failure. The process of gathering data is then conducted by MakoLab in the following way: we conduct any necessary additional research associated with the solution and translate the information provided by the client into a form that is understood by the software and compliant ontology (intelligent dictionary) terms. A machine learning model is created and delivered to the Classifier together with a list of potential clients. The search takes place using the Microsoft Azure Software in a Service infrastructure model (SaaS).

 

Image 1: Scheme of operations conducted by SESSA

As a result, the COMPANY obtains a list of potential CLIENTS. The number of assigned stars specifies the probability of success with each client – i.e. the degree of lead-matching to the expectations presented at the beginning of the project. 

 

Image 2: Results of potential clients provided by SESSA

Results of SESSA efficiency tests

The financial institution at which the solution was tested provided a list of 110 companies. The client was searching for companies interested in the service of factoring.

SESSA detected:

* 1
6 companies with the probability of success: >80%,
9 companies with the probability of success between 60% and 79%,
* 1
2 companies with the probability of success between 40% and 59%.

In another Proof of Concept testing, 110 more companies were tested, 49 of whom were interested in the institution’s services.

SESSA and MakoLab’s model of searching allows for obtaining better results than from traditional processes of finding clients, and is also much quicker thanks to the scalable architecture of Microsoft Azure (a public or private cloud in the company’s server infrastructure). In addition, this model allows for optimising project cost.

Interested in solutions for Finance and Insurance? See what else we can do to increase your business.

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