Targeted response to the individual needs of customers
Data Driven Marketing & Sales
Targeted response to the individual needs of customersWith a data driven marketing & sales process, you can turn your customer data into targeted marketing and sales actions. A first step is to gather all this customer data: available customer data from the organization itself, combined with market data and demographic information. Obtain insight into who the customers are and what, when, where and how they purchase products and services is the basis for a targeted response to the future needs of existing and future customers.
Due to the granularity* of the data and insights, marketing actions can appeal to customers at an individual level, addressing them in a highly personalized way.
This highly individualized marketing & approach results in a significant impact on the ROMI and response rate of marketing actions. With the right tools, these actions can be automated to create an individualized, automated and real-time marketing plan. Data Driven Marketing & Sales focuses on customers and is a crucial part of an organization's customer-centric vision.
Realdolmen's Customer Centricity Department supports its customers in turning this data into actions and insights through the implementation of various tools in combination with a data driven approach.

Applications
Intelligent Marketing Automation
Give your organization the marketing tools to develop intelligent, efficient campaigns and take your marketing to the next level.More...
Automatic Marketing Qualified Lead Generation
Leads can be generated by collecting and analyzing the interactions and behaviors of customers at marketing touchpoints.More...
Opportunity Probability Scoring
Use historical data to score the current opportunities and enable the sales organization to work more efficiently.More...
Know Your Customer
Analyze all customer data, familiarizing yourself with your customers and their needs in order to target these precisely.More...
* Granular data is information that is divided into the smallest possible parts to make it more defined and detailed. The advantage is that the data can be molded into whatever shape the data scientist or analyst requires, just as grains of sand conform to the vessel they're in. Granular information can be combined and split back up in order to meet the needs of various situations.