Every business needs to grow and stand out in this competitive world. To do so, it has to adopt means to reach out to the public and gain their applause. Marketing the goals, objectives, and aspirations of the company enables customers to know about your business. Investment of time in the beginning itself to attain data quality in email marketing proves to be effective in the long run.
Before going into the details of the topic, let us first initially try to know what Email Marketing is:
Email marketing is a means adopted to post advertisements, sales, business requests, products, services, and discounts to the active customers in bulk through emails.
How about using Email Marketing software that makes things happen easily. O-post is one such simple, affordable, and an effective tool that helps create various email designs and templates. Moreover, the software supports simple UI and live support.
Email marketing Guidance
Email marketing can be guided in the best way by defining an effective strategy prior to its implementation. Following it is likely to generate the best results.
Let us look at a simple strategy below:
1. To start, one needs to have a list of emails to send to. So, for this, one has to define the target audience
- Set up goals
- Create a sign-up screen to generate emails
- Select an email campaign
- Adopt a proper plan and implement it
- Evaluate the results
Email Marketing ROI
Email Marketing is an effective channel in terms of ROI compared to other digital marketing channels. Though various digital marketing channels keep coming up, Email marketing still stands valuable. With Email marketing, businesses of all sizes have a significant return on investment. Statistics reveal that the average ROI for email marketing is 122%, four times greater than existing digital marketing channels.
Data quality in Email marketing?
With an increase in the users’ list, fake or incomplete data may creep in. Data profiling handles this and checks the accuracy of the data. Thus, time, money, and effort are saved, and the quality of the business is built.
Customer data quality metrics
Data quality metrics are the factors to measure the quality of the data.
Some of the customer data quality metrics include the following:
- Uniqueness: Every record has to be unique when weighed against other records in the database
- Completeness: Customer name, phone number and Email ID are the significant and mandatory parameters. Ensure all the mandatory fields are complete.
- Accuracy: Comparing the actual values against the registered value determines the accuracy.
- Consistency: various countries follow different formats relating to specific fields of the customer registration page. For example, DOB format in the US is MM/DD/YYYY, while in Europe, it is DD/MM/YYYY.
- Timeliness: How good is the customer able to update the facts specific to the change of his/her address is one example of Timeliness. If a customer can update the concerned authority about the new address on time, further operations relating to him seems to be smooth; otherwise, it affects the current workflow as the operations are carried out concerning the old data.
- Integrity: If the data satisfies all the above-mentioned customer data quality metrics when it shifts data from one system to another or when maintained on different systems, then we can say that the data is integrated
As new opportunities creep in, more data is likely to grow. Ensuring customer data quality metrics handles smooth email marketing. Consequently, it gains customer satisfaction, retention, growth as the Return of Investment.
Quality is what matters. Adopting efficient means to generate data quality can bring out best email marketing. Following Customer data quality metrics without fail enables quality data. Thus it generates huge ROI and customer satisfaction. Using the latest automated software and tools would be an added advantage for entrepreneurs. ONPASSIVE O Post is one such email marketing software to customize all your emails and send them in one click to a bulk audience adopting rich text formatting.