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Bad data is just that – BAD!! 

Let’s look at an example of bad data and its effects in action. 

James pored over the computer since morning trying to figure out something important. He wondered if his CRM system could give him some insights into the problem that he had. He was losing customers faster than he could acquire new ones. It was leading to a net drop in his sales acquisitions, because of the attrition rate of the customers. Suddenly, he got up from his chair and headed straight to Bob’s cabin.

“Hey, Bob, I guess I‘ve found out why we are losing customers,” he beamed. “That’s fantastic!” said Bob, as excited as a kid who had just opened the stomach of the superhero toy that he was playing with and was about to remove its batteries. ”We are losing customers because we are not doing anything to retain them,” James said.

The smile on Bob’s face vanished. ”Is this the finding that you have for me after the entire day’s research?” James nodded. ”And why do you suppose we’ve not done anything to retain our customers since we are well aware of this profound truth?” Bob asked half-curious & half-frustrated. ”Simply because our CRM does not flag dissatisfied customers when they are in the process of getting frustrated.” Bob looked at him & reclined back in his chair, understanding the value behind this statement.


Bad Data’s Reality Today

This is the problem most managers today are facing with their customers. It is a well-known truth that it is almost seven times more expensive to acquire a new customer than to retain an existing customer. In spite of this, companies are doing very little for ‘churn prevention.’ The CRM system has records of all the customers that it has acquired over the past years. However, it has no way of flagging a client who’s on his or her way out of the system.
The qualitative aspect of the data gets sacrificed for maintaining the functional aspect of it. A company has a CRM system for the administration of its customer data. Also, it has plenty of data describing the client’s behavior and personal data about each customerBut, and here lies the problem; it is unable to predict which customers are going to remain with the company & which ones are on their way out. This is where Data Mining/CRM cleaning services comes into the picture.

Data Mining for Bad Data

Data mining can create a prediction model. Such a model can analyze the data & throw up qualitative insights. These insights can help predict the probability of a customer’s next possible move. Additionally, you can create a pattern recognition platform on the basis of this model. This will reveal the combinations of attributes describing customer segments with a high probability of leaving the company. Obviously, the customers with the highest probability require analysis in more detail. Moreover, special offers are necessary to help retain these customers or to keep them returning.
If you are facing such problems concerning how your data can throw up important insights into customer behavior, look no further. We, at Bizprospex, have the appropriate solutions for you.
Therefore, a unique mix of manual & automated data mining techniques helps. Such techniques will ensure that your data is not bad & hence, does not affect your business adverselyThus the company can benefit from various advantages which will include a lower attrition ratio or reduced churn. Subsequently, the lower acquisition cost per customer owing to better data management, and higher turnover per client due to higher customer life. All this will lead to satisfied & loyal customers.
Don’t rely on bad data to help you in your business. Mine it, clean it & let it be an asset for you. Approach BizProspex’s data appending services, data mining services, AML sanctions list, PEP List, and Global sanctions database today to know more.


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