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    We have access to greater volumes and varieties of data than ever before. Data streams are getting faster and bigger, but bigger isn’t always better. Data is only as useful as our ability to understand it and to identify the most relevant and highest quality data points to inform our business decisions. To ensure the highest data quality and relevance, data management best practices are implemented: verification, cleansing, database structuring, and data enhancement. What is data enhancement? Data enhancement, also known as data enrichment or data append, merges your existing data with the most relevant, authoritative, externally-sourced third-party data

    Data enhancement software appends data by adding context, such as behavioral data, demographic data, contact data like phone numbers and email addresses, and geographic data. Enriched data is more valuable and enables businesses to develop more effective and accurate business strategies by helping decision makers better understand their target market and their existing customers.

    How do data enrichment techniques actually work in practice? Read on to learn more about how some major industries enrich data to glean valuable insights and improve operations. 

    Data Enhancement Across Industries

    Consumer Goods

    Expanding D2C channels and optimizing existing B2B channels with data enrichment services is a major key to success in the consumer goods industry. Data enrichment companies help enhance internal data with external data, providing more context and deeper insights so decision makers can better understand how to develop the most effective pricing and promotion strategies, how to optimize field sales, and customers’ increasingly shifting buyer behavior. 

    External data signals such as income levels within a certain radius and web searches for coupons within a certain geolocation can enrich internal data and improve pricing and promotion strategies and ROI; external information related to the opening and closing patterns of stores and restaurants in a certain area will help your sales force better target these accounts; and external information regarding demographics and buying patterns will help inform data scientists’ segmentation strategies and targeting and communication with potential customers. 


    More customers are shopping online for products and services than ever before, and that influx in online presence provides an enormous opportunity for businesses to use data enhancement tools to enrich their internal data with external data related to consumer behavior. External signals such as social media, demographic, and geospatial help acquaint us with current and potential customers, and improve our understanding of broader trends in market data. Third-party customer data enhances business strategies, boosts sales, cuts costs, improves response rates, and fosters a better relationship with customers. 

    Some examples of data enhancements inin eCommerce include: reviews, pricing data, online presence, technographics, firmographics, consumer surveys, company profiles, employment data, real estate data, web analytics, professional profiles, market value data, and foot traffic/anonymous mobile GPS location data. All of these relevant third-party data signals can enhance internal eCommerce data, and improve predictions and ML model performance.

    Financial Service

    Financial service providers need to enhance their data in order to effectively mitigate risks, gain insights into their current customers, spark innovation, and better understand how prospects will impact their business. 

    There is a great deal of data involved in lending decisions. Third-party data is an important component in business credit risk assessment. Internal data alone is not sufficient to drive an accurate risk model. Data enhancement tools help enrich internal data with external data signals such as income, borrowing, payment history, assets, liabilities, web presence, and company credit history. . 

    Financial institutions can also refine customer lifetime value and churn analysis with data related to shopping behavior, social media activity, online search queries, impactful demographic information, and credit scores. 


    External data is extremely valuable in the insurance industry. So many factors need to be taken into account and a broad range of relevant data must be fed into Machine Learning algorithms in order to identify patterns, make accurate predictions, and mitigate risks, and reduce claims fraud. With such a wide variety of options available, providers should use data enhancement services to retain a competitive edge, increase policyholder satisfaction and loyalty, refine customer lifetime value and churn analysis, and improve customer conversions. 

    Some valuable external data signals that can enhance internal data in the insurance industry include: income, claims history, social activity, payment history compared to socioeconomic and income status, cost of maintenance compared to revenue and social trends, credit scores, economic, and biographical data. 


    The primary goals in retail are to drive growth and reduce costs. The best way to do this is to have a thorough understanding of customers, potential buyers, and the current market. Without enhanced data in retail, you’ll just end up throwing strategies at the wall to see what sticks. Data enhancement helps remove the guesswork and give us real insight into the minds of our customers and the intricacies of the market. 

    One of the most effective means of optimizing marketing efforts in retail is customer segmentation. Enhanced data helps guide what kind of content we send to different people, and even when to send it. Relevant external data signals such as personality, interests, habits, demographics, industry, and income all enrich internal data, such as existing customer files, and help tailor marketing and sales efforts to the needs of specific groups. This data can also help refine direct mail programs and improve ad retargeting. 


    In the technology industry, low quality leads are a big waste of time, money, and resources. Relevant external data is a major component in refining customer segments, enhancing predictive lead scoring models, and generating useful leads. External intent data helps tech companies understand what an individual customer, department, or company in a particular geolocation is most interested in.

    Interactions like social engagements, internet searches, webinars attended, whitepapers downloaded, and more can be consolidated and categorized into different topics, against which benchmarks can be created and surges in interest for any particular topic or product can be identified. From this we can glean insight into which factors lead to conversion at the start of the cycle. Data enhancement will result in a thorough customer profile that will help tech companies optimize marketing and sales campaigns with a more targeted, tailored approach.

    How to Leverage Data Enhancement and Enrichment Tools at Your Company

    To automate data enhancement and enrich internal data with reliable and relevant external data sources in real-time, organizations are turning to external data clouds . An external data cloud helps execute discovery, validation, and integration of third-party data sources into your Machine Learning and analytics pipelines. Automated data enhancement fuels modern industries and empowers leaders to make precise, data-driven decisions.