Mining customer data is crucial for the success of any type of business. It promotes improved decision making, improved planning and forecasting, cost reduction, improved customer relationships, and new customer acquisition as well as retention. Data conversion companies offer data mining services, helping organizations gain valuable insights on their customers which can be used to boost their sales campaigns. Even so, every business should know how to ensure success with data mining:
- Clear understanding about business goals: The first step in data mining is assessing business’s objectives and needs. Resources, assumptions, constraints, and other factors need to be considered. Data mining goals are then created based on these aspects. This will allow a detailed data mining plan to be created to help the organization achieve its business and data mining goals.
- Collection of data from multiple sources: As much data as possible is collected from multiple sources and analyzed real time. For example, a clothing retailer would need to gather and analyze browsing and purchase histories to get an in-depth idea about a customer’s shopping habits. Customer analytics will allow the company to predict customers’ buying patterns and will allow it to send out marketing messages at the right time.
- Involve people who can make the most out of the information: Customer data mining can be very effective when handled by people who can make the most out of the information. A Tech Target article quotes Robert Johnson, CTO of Interana, a customer data platform company as saying, “The most successful organizations we see are the ones who are able to get this visibility into the hands of the business people who already have context about their products and customers.”
- Analysis of both internal and external data – Data mining experts analyze both internal and external data such as social media activity. Social media sources can provide valuable insights into customer opinions and behavior, and ignoring this information can affect the company’s marketing campaign.
- Clean, concise sampling strategy – Typically, data mining focuses on small simple subsets of data, though a powerful analytics platform can train a model from the entire population dataset using advanced techniques. Regardless of the approach, it is necessary to ensure clarity about why it is used.
- Use a holdout sample. A holdout sample is a reference sample used to assess the predictive performance of the model. If you are building models from old, inaccurate and inconsistent versions of data, they will require extra meticulous testing on new and unseen data to ensure that they work in the real world.
- Implement throwaway modeling – The first part of modeling process involves identifying the best predictors from a wide range of variables that is available. Throwaway modeling involves throwing in all the information, testing multiple models, and then refining the selection process. Implementing this in the initial part of the project will help increase the productivity later.
- Feed in fresh data regularly – The predictive model will not always fit real world data perfectly. To maintain the quality of model, it has to be fed with .fresh data on a regular basis – every month, week, day, or even every hour. This will help sustain the predictive validity of the model.
- Ensure findings are clear, accessible and usable – It is necessary that the knowledge or information gained through data mining process are presented clearly and are accessible and usable by stakeholders when they need it. Avoid statistical jargon, instead use pictures and graphs that can be easily understood by non-statisticians.
Business organizations use data mining to derive business insights and drive success in their projects. Partnering with an experienced data processing service provider can ease the task and ensure optimal outcomes.
