Learn about the benefits that data enrichment can bring to help with your business growth.
In this era of digitalisation, businesses are collecting more data than ever before to stay competitive. From customer information to sales and marketing analytics, they are needed for informed decision-making. However, it is difficult to derive valuable insights from raw data and this is where data enrichment plays a significant role.
Data enrichment in brief Data enrichment is a method of enhancing raw data with additional information from external sources or third party data for improved quality and accuracy, thereby making it more insightful for analysis and decision-making. A wide range of techniques is used for this process. In short, it involves basic data cleaning and validation to advanced algorithms that enhance data with information such as demographics, geography, or psychographics.
Benefits of data enrichment The benefits of data enrichment are aplenty, and here are some of the common use cases:
1. Improved data quality Data enrichment is the process of adding missing or incomplete data to the data you have. By doing this process, you can ensure that the data you have is accurate and up to date.
For example, Uber constantly innovates its Uber Rider app through the learnings obtained from their data. With a large user base, many features and regions to support, they are not shy of data quality problems. Uber collects data from the mobile and service layers, structures and copies them over as offline datasets to measure the success of the solutions developed.
During the enrichment process, various raw datasets like riders’ location data and device information are integrated to improve their data worth and allow them to do funnel analysis on their Rider Shortcut feature. This exercise enabled them to leverage their enriched data to introduce features such as Ring Messages for proactive communication and streamline the cab booking process.
2. Better marketing effectiveness Sun Tzu’s famous quote, “Know your enemy and yourself, and you shall win a hundred battles without loss ” may be about winning wars and dealing with conflicts. However, this is relevant and even crucial to triumph in the world of business.
With additional information such as age group, gender, affluence, app categories, and geolocation, you can create more accurate customer profiles or buyer personas for segmentation, targeting, and even positioning. Besides, you can gain insights into numerous markets for expansion and your competitors. Furthermore, you can identify the right channels to reach your target customers, tailor your messages to specific audiences, and personalise campaigns to increase engagement and conversions. Ideas for new product innovation are also possible!
As brands became more cautious about spending, a popular social communication platform in the region admitted its spend for display advertising had declined. As a result, the company decided to provide additional service tools for "Persona Targeting" by taking into account customers' demographics, interests, behaviour, and purchase intent by collaborating with ADA in the second half of 2023.
Through our previous project, the conversion rate has increased by 85% after enriching their data with ADA's affluence attribute.
3. Improved customer experience By having a better understanding of target customers, you can personalise your offerings via targeted marketing campaigns and communications.
We all know that Netflix is famed for its content personalisation . The question is: How did they do it?
Netflix collects customers’ viewing history and external data sources like devices used, login location , and social media to recommend new content to each customer. By enriching its database, Netflix is able to predict its original content success rate and create personalised recommendations, be it based on themes, genres or actors. Their hyper-personalised approach has led to increased customer engagement and retention, leading to increased revenue.
4. Better risk management Financial crimes such as money laundering and fraud have been rampant in recent years and banks, charged with the responsibility of being the custodian of the public’s money, are actively finding ways to combat these delinquencies.
With additional information such as transaction history, geolocation data, IP addresses, and device information, one can detect patterns of suspicious behaviour and take counteractive measures against fraud which may cause hefty financial losses and even reputational damage.
As part of its mission to help businesses combat financial crime, SEON acquired Complytron to integrate multiple external data sources worldwide with its anti-money laundering, fraud, and data enrichment solutions for better real-time monitoring of financial transactions as well as detection of fraud and money laundering.
Furthermore, by combining internal data with external sources such as news and social media, organisations can gain a better understanding of market development, policy and regulatory changes, which can help mitigate risks and capitalise on previously unexplored opportunities.
Walmart leverages data enrichment to manage its supply chain operations. It pulls data from 200 sources such as economic, social media, telco and even weather patterns to make data-driven decisions which include predicting customer demand, adjusting its products’ pricing and inventory levels as well as optimising its operations.
Best Practices for Data Enrichment in Marketing Data enrichment is a valuable practice for enhancing the quality and relevance of customer data in marketing efforts. Here are some best practices, incorporating the use of data enrichment tool and data analysis:
1. Segmentation and Targeting Use data enrichment tools to segment your customer data based on various attributes, such as demographics, firmographics, behaviour, and preferences. This allows you to create highly targeted marketing campaigns that are more likely to resonate with specific customer segments.
2. Data Cleansing Before enriching your data, ensure it's clean and accurate. Data enrichment tools can help correct and validate existing customer data, such as email addresses, phone numbers, and addresses, reducing the chances of communication errors.
3. Personalisation As mentioned earlier, data enrichment can help you improve customer experience, and one way to do this is through personalisation.
Leverage enriched data to personalise your marketing messages. Tailor your content and offer to align with the enriched customer data, making communications more relevant and engaging.
4. Lead Scoring Use data enrichment to assign lead scores based on the enriched attributes. By identifying high-value leads, you can prioritise your marketing efforts and allocate resources more effectively.
5. Data Integration Integrate enriched data into your existing customer relationship management (CRM) and marketing automation systems. This ensures that all customer-facing teams have access to the most up-to-date and enriched data for consistent communication.
By following these best practices, you can harness the power of data enrichment to improve the effectiveness of your marketing efforts, deliver more personalised customer experiences, and drive better results.
Data enrichment for insights, trends, predictions, and more In a nutshell, data enrichment can help businesses obtain greater insights into their customers, market trends, and competitors. It also allows businesses to create more precise predictions, explore untapped opportunities, and develop innovative and personalised solutions.
ADA strongly believes in connecting analytics, marketing, commerce and customer experience for business growth. ADA has served clients from various sectors through a data-driven approach and has been recognised as Campaign Asia Agency of the Year in 2022 among the many awards received.
To learn more about how ADA works with brands on data solutions contact us
Frequently Asked Questions (FAQs) about Data Enrichment What is the goal of data enrichment? The goal of data enrichment is to enhance the quality and depth of your existing customer data. By adding valuable information and ensuring data accuracy, it enables more personalised marketing, improved customer segmentation, and better-informed decision-making, ultimately leading to more effective marketing campaigns and increased customer engagement.
How data enrichment and data cleansing is different? Data enrichment involves enhancing existing data by adding new, valuable information to it. In contrast, data cleansing focuses on cleaning and correcting data by identifying and fixing errors, duplicates, and inaccuracies. While data enrichment adds to the data's depth, data cleansing ensures its accuracy and reliability. Both processes are essential for maintaining high-quality customer data in marketing efforts.
What is an example of data enrichment? Suppose a sales team has a dataset containing customer information with basic details like names, company names, and phone numbers. Through data enrichment, the team can add additional details such as industry classifications, revenue figures, and recent company news. This enriched data allows the sales team to tailor their approach more effectively, identifying high-value prospects, understanding their specific industry needs, and staying informed about relevant events that might impact the sales process.
