Data Mining with Salesforce Customer Relationship Management
The world is now a global marketplace thanks to the Internet, and customer relationship management is a critical part of enforcing business efficiency to enable achievement of business objectives and set a company apart from the competition. Salesforce CRM tools and strategies will only be effective if you utilize the customer information generated to anticipate and fulfill their needs, eventually leading to higher profitability.
A few basic questions to ask yourself while interacting with the CRM include:
- What are your customers’ specific needs and pain points?
- Are they satisfied with your product and/or service, and if so how much?
- How can our products and/or service be improved?
- What new opportunities can I get from this information?
These questions, among many more, will help you develop a better CRM strategy based on extrapolative data mining models which are supported by the proper collection and analysis of important data. The following steps outline the basic processes involved in CRM data mining. These will be necessary in order to come up with a robust CRM strategy to meet business objectives:
- Definition of the business objective
- Construction of the marketing database
- Data analysis
- Visualization of the predictive model
- Exploration of the model
- Model implementation and monitoring
The first three steps need little elaboration, possibly because as an organization, you have already carried them out to an extent. Let us examine the last three steps:
Step 4: Visualization of the predictive model
The process of building a predictive data model is mostly iterative. At the start, you may need to build two or three models in order to weigh them against each other and find out which one best suits your business goals. In looking for the right data model, you will have to backtrack and make small changes in whichever step, including altering your problem statement.
The process of building a data model begins with a set of customer data whose results are already known. For instance, you can send a test email inviting a response to determine how many customers respond accordingly. You would them divide this information into two groups: those who responded and those who did not.
From the first group, you can extrapolate a desired model and apply it to the remaining data. Once the estimation and testing procedure is complete, you should be left with the model that best fits your business objective defined in Step 1.
Step 5: Exploration of the model
Evaluation of outcomes heavily relies on the degree of accuracy of predictive models. For instance, predictive models which are derived from the data mining process can be put together with domain expert’s insights and applied to large projects intended for a variety of people. How data mining will be used for an application is determined by the type of customer interaction e.g. do you contact the customer or do they contact you?
Step 6: Model Implementation and monitoring
Customer interactions are analyzed based on the different factors including origin of contact, channel used, type of message etc. from there, you can use your predictive model select a user sample who will be contacted from your current customer database. For advertising campaigns, you can match the profiles of potential customers with the qualities discovered by your predictive model to define the users who your will contact through the campaign.
In both cases, where data collected includes age, income, gender and other demographic-related details, but the predictive model demands ratios (e.g. age-to-income or gender-to-income), you will need to adjust your database before applying the model.
My name is Jenny Richards. I am a freelance content writer. Also, I have written many good,unique and informative articles on different categories like Technology, Business, Career, Salesforce, CRM etc.