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Resources / Blog

What is Data Enrichment?

1st October 2019

Topic: CRM Enrichment, Data Enrichment, Data Monetization, Raw Data
  • What is data enrichment?
  • How is Raw Data used in The Data Enrichment Process?
  • What Are The Benefits of Raw Data?
  • Raw Data enrichment services
  • Data Enrichment case study: How we enriched 500k profiles
  • Data enrichment process in online marketing - conclusion

Data Enrichment is a process of merging selected information from your company with data from external sources. Transferred raw data from data provider is being processed and therefore, enriched data becomes complete, consistent, accurate and current. It’s all coming to improved data that can be integrated with compatible systems and used further for marketing, analytical or operational purposes. 

What is data enrichment?

Technically speaking, it is a set of information that was delivered from a certain data entity to the data provider and hasn’t been processed yet by machine or human. This information is gathered out of online sources to deliver deep insight into users’ online behavior. Thanks to this information marketers can easily build users’ segments, create personalized online campaigns and reach target users with an accurate message at the right time.

Check how the stream of raw data works.


How is Raw Data used in The Data Enrichment Process?

In the process of data enrichment, raw data is used to enhance, refine, or improve the quality of the existing data by adding more relevant information or attributes to it. This can involve supplementing the data with additional details such as demographic information, geographic data, interest, purchase intentions or any other pertinent data points that can provide a more comprehensive understanding of the subject of the data.

What Are The Benefits of Raw Data?

Raw data, while initially a collection of unprocessed code, holds immense potential when integrated with dedicated analytical algorithms and appropriate user profiles. Businesses can effectively integrate raw data and leverage its benefits for targeted marketing and in-depth analysis.

  • Enhanced Marketing Insights – raw data can be matched with user segments created by marketers, providing invaluable insights into user behavior before visiting a website. This integration enables businesses to gain a deeper understanding of their visitors’ online activities, empowering them to tailor marketing and advertising strategies more effectively.
  • Leveraging Data Science Expertise – To fully capitalize on the advantages of raw data, having data scientists within a company’s staff can be invaluable. Their expertise can unlock the full potential of raw data, enabling businesses to derive deeper insights and make more informed decisions.

Integrating raw data with analytical algorithms and user profiles offers businesses a wealth of opportunities, from refining marketing strategies to gaining comprehensive insights into user behavior.

Book a call to learn more about Data Enrichment

Raw Data enrichment services

Data enrichment is a pivotal process that involves enhancing and refining raw data with additional information to make it more valuable and insightful for businesses. By integrating trusted data from over 200 markets, organizations can gain a comprehensive understanding of their users, enabling them to create personalized customer experiences. This process aids in filling the gaps in users’ profiles, providing valuable insights for running personalized campaigns and increasing return on investment (ROI).

Through the integration of raw or segmented data, businesses can tailor their marketing efforts, understand customer needs, and run more personalized campaigns to improve customer experience and engagement. Ultimately, data enrichment empowers businesses with actionable, value-adding insights, contributing to their success in a data-driven landscape.


How to use Raw Data in practice?

Many businesses are unaware that incorporating raw data into marketing and advertising strategies can enhance their ability to reach the right target audience by understanding their specific needs. Furthermore, there are numerous examples of how raw data can be applied:
Customer Profiling and Segmentation

  • Utilize raw data to create custom customer profiles based on demographics, behavior, and preferences.
  • Segment the audience using raw data to target specific groups with personalized marketing messages.

Campaign Optimization

  • Analyze raw data from advertising campaigns to measure performance and identify areas for improvement.
  • Use raw data to optimize ad placements, targeting parameters, and messaging for better results.

Personalized Marketing

  • Leverage raw data to personalize marketing communications, such as emails, ads, and website content, based on customer preferences and past interactions.

Predictive Analytics

  • Use raw data to predict future trends and customer behavior, enabling proactive marketing strategies and personalized recommendations.

Attribution Modeling

  • Analyze raw data to understand the customer journey and attribute conversions to specific touchpoints, helping to allocate marketing resources more effectively.

Learn more about Data Enrichment

Raw Data can be used to observe users’ behavior and compare their characteristics, know their interests and intentions, to maximize the chance of conversion. You can use Raw Data for: 

  • targeting in programmatic advertising market,
  • building your own data tools,
  • preparing Big Data analysis to improve your product
  • to make a research before a new product release.

To learn more about data stream check 5 ways how your marketing can benefit from using raw data.

Data Enrichment case study: How we enriched 500k profiles

All the technical details mentioned above might sound complicated but in practice using a Raw Data is simple and easy. Also, support team is always happy to help and answer all the questions. For a better depiction of how Raw Data works, we will present an example of use by the client from the financial area.

Our client – one of the leading bank on Polish market –  was looking for additional data about users gathered in CRM database for both marketing and operational purposes., through Data Management Platform, was able to provide raw data that included the online behavior of current Bank clients. Data consulting and analysis was held by 3rd party – one of The Big Four.

Among the Bank goals were pointed 4 followings:

  • Enriching CRM by ingesting behavioral data about clients 
  • Boosting sales – cross-selling / up-selling
  • Building a better customer journey through personalized messaging
  • Developing Risk Analysing tools through an additional pool of data

To meet the bank needs, we used Data Stream and the execution included 5 steps: 

1. We made a secure cookie-matching implementation outside the transactional system.

2. We developed a secure data exchange stream between DMP and the Bank.

3. We executed Data Transfer with all processes being verified.

4. We performed a mapping process of Data with corresponding Bank user IDs.

5. We implemented data in the sales process (flagging users for Call Center and personalized contact), performed marketing analysis and credit risk tools.

As a result, enriched the bank’s CRM by providing a behavioral profile of almost 500 000 Bank clients. 

The Bank was able to analyze its clients, design personalized marketing scenarios (inc. call center personalized contact), design a better customer journey to increase engagement, discover what kind of customers they have, and perform deeper risk analysis.

Data enrichment process in online marketing – conclusion

To sum up, for whom Raw Data is dedicated? Companies of all sizes which have data scientists team in their disposal can gain from raw data the most. Depending on the company’s industry, raw data can be processed according to business needs. 

Data Stream allows raising the efficiency of marketing actions, e.g. to save up to 30% of the budget with successful remarketing. Raw Data enables the data science team to build their own data tools, including personalized experience and messaging, recommendation systems or machine learning algorithms.

Check how data enrichment works

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