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Proven Company Data Enrichment Techniques You Can Follow Now!

Proven-Company-Data-Enrichment-Techniques-Infocleanse

Let’s take a step back before we jump into the various techniques you can leverage for effective data enrichment. Ask yourself whether you entirely grasp data enrichment and how it can benefit your organization.

In essence, company data enrichment is a set of processes that append or enhance existing data in your repository for narrower data sets focusing on accurate target groups.

In simple words, it improves your data for increasing the accuracy of your customer data and marketing strategy.

With the sales intelligence market projected to be valuated north of USD 7.35 billion by 2030, here is how you can get on top of your data enrichment woes.

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Proven Company Data Enrichment Techniques!

In this data-driven landscape, data accuracy is the name of the game. Hence, repeated data enrichment practices must be done to keep the data fresh.

Here are some updated company data enrichment practices to help you maintain and improve your data repository.

1.     Continually Ensuring Data Quality

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In this technological landscape, the needs and expectations of the consumer are in constant flux. This is why having an accurate customer database at all times is crucial.

If the data in your repository is suffering from data decay and isn’t continually updated, your decisions might be based on incorrect conclusions, hurting your business.

Continual gathering and updating of data are vital to set up a recurring streamlined data enrichment process. Leverage the aid of data appending services to amalgamate the multiple data sources of your company and view your repository through a holistic, condensed, and accurate perspective.

All this data will be more consistent since they are all produced by the same source.

Furthermore, here are some steps you can take to maintain your data repository’s health continually.

  • Constant data quality tracking encompassing every business application must be done regularly.
  • The established guidelines must be followed to the word while performing data cleansing practices, post which you can authenticate the data.
  • Leverage data validation services to further ensure data health and accuracy.

2.     Segment Data Accurately

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With the ability to leverage a plethora of company data enrichment tools, you can only go so far without innovation. You need access to data that your competitors might not have, and it comes in the form of first-party data.

By leveraging surveys and other first-party data gathering techniques, you can gather deep insights about the segmentation of your audience. Moreover, you get a bird’s eye view of the mitigation of individual issues within each segment.

You can strategize and entice them with upsells while providing top-notch customer service to utilize the data to its potential. As a result, constant company data enrichment is happening in the background.

Obtaining data this way might take a little more time, but it gives you access to data exclusive to only your company. This is the data you have obtained from your current customers and prospects.

3.    Gets Rid of Data Redundancies and Outliers

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With the sales intelligence market growing continually at a CAGR of 10.6%, there is no room for redundancies in your repository.

As data cleansing services grow increasingly efficient and easy to access, you must ensure that your repository is free of data decay.

Here are two tips that could come in handy while ridding your repository of redundancies and outliers.

  • Make sure you get rid of probabilistic duplications. These take the most effort to detect since they are essentially the same information masked behind misspelled names, email addresses, wrong phone numbers, etc.
  • Don’t forget about data deduplication. When a customer’s information is being updated, the system might log the data in a new record by mistake. This is where data deduplication enters the picture since it rids your repository of duplicate records.

4.     Introduce AI and ML Models

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AI and ML are among the two most commonly used upcoming pieces of technology to automate various operations across many industries.

Moreover, it would suffice to say that they make a significant contribution to company data enrichment processes as well. With the alarming growth in industry-wide applications of AI and ML, they can now be leveraged to make accurate predictive analysis models.

Wait, what is predictive analysis again? The predictive analysis leverages pieces of technology like AI and ML to predict future customer behavior based on data from the company’s repository.

By using AI and ML to create a tailored experience for the customer, constant updation and enrichment of data take place in the background.

Furthermore, this is not where the use of AI and ML in data enrichment stops. They can also be used to perform repetitive and mundane tasks such as eradicating duplicates, fishing for typos and other mistakes, etc.

Even if your data is too scattered and unorganized to sift through, you can sort it out by using AI and ML paired with a human-in-the-loop approach.

But there is one thing you must note about AI and ML. These are inexhaustible tools only as long as you use them wisely. Once you start entirely relying on them to organize your data and predict outcomes, the results will be studded with inaccuracies. Use them paired with other company data enrichment tools and techniques for the most accurate results.

Final Thoughts

Organizations and rival companies have started getting their company data enrichment in order with targeted marketing and a customer-focused approach.

In this data-driven era, it is vital to have relevant and accurate data to navigate your marketing efforts towards your short-term goals with higher efficiency.

As it was accurately put by Carly Fiorina (former C.E.O of Hewlett Packard),

“The goal is to turn data into information and information into insight.” 

Maintaining constant data accuracy has to be on your company’s list of objectives.

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