Email marketing has a capacity to connect uncountable consumers to a brand. It offers entrepreneurs the best alternative to promote scalability together with prompting through an irresistible discount.  

David Newman once claimed-“Email has an ability many channels don’t; creating valuable, personal touch-at scale.”

His saying was proven by the Radicati Group. It has surveyed to come across how effective the email marketing is. Its facts and statistics have confirmed that the global people send about 196 billion emails every day. Out of these, 109 billion emails are commercial messages or prompts.  

In essence, Emails personally reaches your target audience and informs. Thereafter, the connection builds up. But, the commercial message and the connection may have a rift in between. It’s a gap that emerges due to the problems underlying the email data, such as uninterested target audience, bad email addresses and erroneous IDs. This is why only a few business emails live up to your expectations. 

The only way to leap from the problematic email data is email data mining.    

Email Data Mining:

It determines discovering intended patterns in the large sets of email data. The methods like statistics, machine learning and database system provide you with the scope to explore online and offline data. Thereby, the data warehousing carries out ETL (Extract, Transform and Load) without any glitch.

In short, the data mining process prevents breaking the back of email marketing experts, as it ensures supply of viable and relevant data.

The most common challenges that disturb email marketing goals are:

• Duplicate IDs: They represent multiple similar ids that can be an outcome of merging databases ignorantly.   
• Dead IDs: They refer to the email addresses that do not exist now.
• Erroneous IDs: They are the email addresses that contain typo errors or space problem. 
• Abnormal IDs: The abnormal ids can be the addresses with the wrong email format. 

The foregone challenges could interfere with quick web research tricks that you want to achieve in a wink.  The bad email list could bounce back. Besides, your pitching could fall flat. Despite using the bag of smart online marketing tricks, the customers would not make calls. This is why the best research firms and outsourcing companies call for data cleansing method. It ensures filtering out the effective addresses. 

Importance of Data Mining for Email Marketing Using its Techniques:

The data mining is all about spotting unforeseen patterns. These patterns aim at tapping the intelligence to repair the dent in marketing or business strategies. The journey from detecting faults in data to discovering game changing patterns is difficult, but it proves revolutionary at the same time.

These methods make data mining important for email marketing:     

1. Clustering to determine single target group: It is the data mining method, parting similar and dissimilar data objects in different groups for cluster analysis. It supports data analysts to identify a given user group (as pensioners, teenagers, married, managers or so on)upon observing the common features, such as age, geo-graphic location, education and son on. This is how the online marketing team gets selective database for pitching to the right target audience. 
2. Regression to predict marketing strategies:  The regression process enables data miners to underscore the changes in abstracts, such as habits, customer satisfaction level and other factors to comply with the intended digital marketing goals like advertising campaign budget.
3. Classification to filter spam: It is a critical data mining method, which recognizes classes of customers and potential customers according to their reply. Apart from that, this segregation helps in detecting the impact of advertising on potential customers. This is how the marketing data analyst brainstorms marketing strategies that could maximize conversion rate, removing superfluous information underlying the recurring schemes.
4. Detecting anomalies to filter abnormalities: Anomalies represent oddities or abnormalities. A trivial mistake during data entry, for example, could lead to disastrous analysis. However, it will not take your life. But, the mistake could suffocate your database, breeding inconsistencies at source. To sort it out, the anomaly detection process is brought about. It basically targets unusual patterns that do not conform to expected consumer behavior (or outlier). Fraud detection in credit card transactions shows its perfect example.
5. Association rule learning to discover links between data: The association rule learning deals with the motive of creating interesting relations between diverse data. The marketing analysts are expert at deriving a relationship between strange datasets. Let’s say, 90% of customers buys a brand ‘XYX’ of hair oil repeatedly while the rest ones pick it just once. This statistical fact can be utilized for designing an impactful ad campaign/ business proposal/promotional strategy. 
6. Neural networks to automate learning: Being witnessed the AI (Artificial Intelligence) and ML (Machine Learning), the data mining companies search for data models to train algorithms. It is essential for making their computers, which manage their databases, to learn the links and set up relationship with each other. This is how their data mining software or applications learn to recognise and store unique patterns, which could stick around their marketing goals.  
7. Induction for predictive analysis: This method deals with “what-if” circumstances. It also carries the hint of the future, as what if this happens, what if that happens after this and so on.  This is how the analyst predicts through the big data or existing records to deal with the upcoming challenges. Let’s say, the data analyst derives all patterns that points at erroneous ids in the email list. Once fed into the application through codes, the application functionality will successfully navigate across the errors.
8. Data warehousing for processing email data: As aforementioned, data warehousing is vital to effectuate ETL process, which eventually takes care of the data management process. The big data mining is not easy. Diverse complexities lie there if you want to web scrape the email ids of specific criteria. It helps in extracting, capturing, transforming and loading the cleansed data for analysis.

These methods explain the importance of email data mining in the context of business and marketing. The analysts have many more concepts to achieve data mining goals. And, the inception of applications, AI and machine learning has simplified it to the extent where its viability exceeds the expectations of entrepreneurs.