The customer data is being used by most teams in any organization today. We rely on customer data working in customer service; building CRM and marketing campaigns; measuring real time current and past results and trends; analyzing results to reveal tendencies, launch new products; refine business strategies to name a few.
This list can be endless but there is one element that is absolutely exclusive in all these processes. It is the data quality. Business decisions, customer loyalty and communication, growth strategy, quality improvement, etc. all depend on accurate, clean data. That’s why data cleansing is a prime and paramount process. it is specifically crucial when we talk about customer data. Why?
Because the call centre representatives can not call the same customer multiple times, marketing teams can not target the wrong audience sending promotions to invalid addresses, marketing campaigns can not be based on inaccurate customer analytics, etc. In other words, in order not to jeopardize customer loyalty and company’s brand, the data needs to be cleansed regularly.
What are the pros of customer data cleansing? Let’s consider just some of them:
– customer data cleansing ensures customer service efficiency and improves workforce productivity;
– customer data cleansing allows to get rid of errors, bugs and glitches in the datasets;
– customer data cleansing simplifies data migrations during servers, database upgrades and new systems’ implementations;
– customer data cleansing enables mistakes free business intelligence and, therefore, has an enormous impact on analytical and business decisions;
– customer data cleansing boosts customer loyalty;
– customer data cleansing enhances marketing campaigns effectiveness, driving sales and increasing ROIs.
Customer data cleansing procedure is a complex method. It should be performed timely and regularly to maintain healthy and quality datasets. Here are some steps of customer data cleansing.
- Deduping. The process of identifying and removing repeated, duplicated records by purging or merging pieces of data depending on the set up rules;
- Customer Data Validating. Customer data is prone to frequent ongoing changes. Data needs to be thoroughly checked up to make sure that all records are correct to be useful and workable;
- Appending missing data. Data input is subject to human errors and very often records have missing pieces. To make data complete all the gaps should be filled up and misplaced data repaired;
- Data formatting. Enabling similar, homogeneous and corresponding formats are key to quality data. Different formatting for the same fields lead to discrepancies and makes data analytics absolutely unworkable. Wrong formats fixing is also very time consuming for analytical teams.
- Verifying Customer Data. Customers are changing email providers, addresses, jobs, etc. Clean and accurate data means timely updated data. It is important to perform customer data updates regularly to avoid confusing scenarios, for instance, communicating to the wrong people.
Customer data cleansing is an effective way of keeping your customer dataset accurate and health. Regular customer data cleansing allows the advantage on the market and stimulates customer loyalty. Prosperous, competitive and successful business can not afford to skip data cleansing. Today it is a bare necessity of any organization’s health.