Atlanta, Dec 19, 2023 – There is a winning tandem of acronyms in the data collection world: CRM and ERP. Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems collect a wealth of data about customers and business operations.

Knowing how to properly leverage this data can provide competitive advantages and significant improvements in customer relationships. Let’s look at how we can evaluate and use this data effectively.

Nine ways to evaluate and maximize the use of data

  1. Customer segmentation: It allows grouping customers according to similar characteristics such as purchase behavior and history, geographic location, age, gender, interests, and income level. It also helps customize communications and offers.
  2. Purchasing behavior analysis: This is about identifying purchasing patterns and knowing which products are usually purchased together or usual purchase sequences. This makes it possible to create product bundles, offer customize discounts or suggest complementary products.
  3. Forecasting and trending: Data can be used to identify sales trends, customer behaviors and other key indicators. Machine learning algorithms are applied here to forecast sales and product demand.
  4. Marketing campaign evaluation: This process compares the results of different marketing campaigns and promotions and adjusts strategies according to the results.
  5. Customer experience optimization: Customer interactions with the company are analyzed, from support to sales to feedback. This data is used to improve customer experience, reduce points of friction, and resolve issues quickly.
  6. Integration with data analysis tools: CRM and ERP systems can be integrated with data analysis tools or specific solutions that provide deeper visualizations and analyses that can help uncover hidden information.
  7. Application of machine learning algorithms: These algorithms can identify patterns in the data that would not be evident during traditional human analysis. For example, machine learning can detect high-value customer segments that might have been overlooked or predict when a customer might consider unsubscribing from a service.
  8. Automation and customization: Data can be used to automate repetitive tasks and deliver personalized customer experiences. For example, CRM systems can automatically trigger emails or notifications based on customer behavior related to shopping cart abandonment or interaction with certain products.
  9. Improved data quality: Data should be reviewed and cleaned regularly to ensure that it is accurate and relevant. Data quality tools and techniques can be used to identify and correct errors, duplicates, and other problems. We will go deeper into that later.

As we have just seen, the data collected in CRM and ERP systems is extremely valuable. The key is to have a clear strategy on how that data will be used, to have the right tools to analyze it, and to be willing to adapt the approach based on the information gathered.

So, it is clear that a lot of unbelievably valuable data is collected, but …. maybe it is too much? There may come a time when we need to filter it to improve its quality.

Nine best practices to improve data quality

Data quality is essential to making informed decisions. Incorrect, incomplete, or irrelevant data can lead to poor decisions that can cost time, money and, in some cases, customer confidence. Here is a systematic nine-step approach to improving data quality:

  1. Definition and standards: It is necessary to identify which data are essential for the business. Not all data is worth the same. Standards should be established for that data. For example, how will addresses be recorded? What formats will be accepted for dates?
  2. Data audit: An initial audit is necessary to understand the current state of the data and to identify where the most serious problems occur: Is there missing data? Are there duplicate data? Are there inconsistencies?
  3. Data cleansing: Duplicate data must be removed. CRM tools and other database management systems often have functionalities that automatically detect and remove duplicate records. Inaccurate records also need to be corrected. This may require manual review, especially if the errors do not follow a consistent pattern. Missing data must also be filled in. This can be obtained through additional customer information, by purchasing external data, or by using tools that automatically fill in empty fields.
  4. Validation at entry points: Validations must be implemented at data entry points. For example, if information is being collected through a web form, it is necessary to ensure that mandatory fields are checked and validated before submission. Automated tools can also be employed to verify the authenticity of the data entered (e.g., email address validators).
  5. Training: If people are involved in data entry, it is advisable to train them on the importance of data quality and how to enter data correctly.
  6. Monitoring and maintenance: This involves establishing regular routines to review and clean the database. Data quality is not a one-time but an ongoing project. Therefore, it is advisable to implement monitoring tools that alert about possible data quality problems.
  7. Integration and automation: If several systems are used (CRM, ERP, sales systems, etc.), it is critical to ensure that they are properly integrated to avoid data silos and duplication. There is also the possibility of using data integration tools or middleware to automate data transfer and transformation across systems.
  8. User feedback: End users (e.g., sales or customer support teams) are often the first to detect problems with data. Therefore, a process should be put in place so that they can report and tackle these problems.
  9. Backups: Data must be backed up regularly and security measures must be implemented to protect it. Data loss or theft can be devastating to a company’s bottom line and reputation. Today, customer trust is harder to gain and easier to lose than ever before. If they find out that their data is wandering the web, the custodian company can say goodbye to the affected customers.

Adopting these practices certainly provides a solid foundation for data quality assurance. Once these processes are in place, managing and maintaining data quality becomes a much more manageable and less overwhelming task.

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