Data enrichment is a process of expanding old data with new information obtained from other sources of data. It does not only enhance the quality of data but also enhances the understanding of data for the right decision-making and strategic development. In this case, we describe the most common data enrichment techniques with an example of each approach to make the material easy to understand.
Description: Appending data can be defined as a process of extending various records already contained in the database with more information including contact details, demographic data, firmographic data and others. This data enrichment technique is effective in making sure that the businesses involved receive the most accurate and inclusive information.
Example: An organization in the retail industry appends customer email addresses and phone numbers to its database for more effective and directed marketing.
Description: Segmentation is the process of partitioning a set of records into different subsets according to certain characteristics, and can be based on demographic, behavioral or geographic classification. This technique can help businesses implement strategies that are more suited to each of the segments more effectively.
Example: A financial services company divides its customers by income and investment plans into various categories to provide solutions and advice.
Description: Demographic enrichment involves augmenting existing datasets with additional information about the individual such as age, gender, income, education level, and marital status. Through this technique, it becomes easier for businesses to determine the particular segments of customers to target and how to do it.
Example: This additional data can be used to make specific programs for advertisement and sales, like the advertisement of products as per the income group of the TG, or providing discounts to the younger generation.
Description: Geographic Enrichment includes address, zip code, geographical coordinates, and other attributes related to the geographic nature of the field. This technique is particularly vital for industries whose operations heavily depend on geographical data regarding supply chain, advertising, or delivering services.
Example: This data enrichment technique provides the precise location and zip codes. Therefore, the service can help in identifying efficient delivery routes, cut down on delivery time, and hence serve the customers better by delivering their consignments on time.
Description: Behavioral enrichment involves information about customers' behavior and activities including their purchasing patterns, website visits, social media activity, and product usage. This data enrichment technique gives a thorough understanding of customers, their needs, and their actions, informing the business strategies to adopt.
Example: An e-commerce platform augments its customer details data with details about the customers' past buying and searching behavior. It boosts the chances of the customer buying more and enhancing the customer experience as a result.
Description: Transactional enrichment extends the customers' transaction history, such as their frequency and amount of transactions, modes of payment, and the types of transactions they have made. This data enrichment technique assists in the determination of customer spending and the generation of client value lists for the businesses.
Example: This data enrichment service enriches its subscriber information database with transactional information, including subscription renewal rates and the methods used to renew subscriptions. Using this data, the service can monitor the level of subscriber loyalty and offer them specific benefits to keep them subscribing.
Description: Company profile enrichment is employed mainly in B2B environments and augments the data with the business domain, revenue, employee count, and size. This technique is useful in categorizing the company's B2B customers and thus marketing and selling to them appropriately.
Example: With this enriched data such as the industry type and the size of the company, the company can modify its sales proposals and commercial messages according to the needs and demands of the various kinds of industries and company dimensions, increasing the possibilities of a sale.
Description: Psychographic augmentation refers to the addition of information associated with the customers' LTV, beliefs, and preferences. This technique is more advanced than the usual demographic targeting since it helps to determine the customers' motivations and preferences.
Example: This data enrichment technique enables a travel agency to develop packages that will appeal to the customer who is inclined to adventurous trips or a luxurious vacation hence improving the satisfaction of the customers.
Description: Social media enrichment involves the enhancement of data profiled from social media platforms to profiles, interests, and activities. This method assists business organizations in obtaining a wider view of the customers' status on FB and their social conduct.
Example: A fashion brand can analyze the abovementioned-enriched data to find out the trend topics according to its customers and the most influential bloggers to collaborate in social media campaigns.
Description: With predictive enrichment, the organization is likely to analyze the results obtained from previous customer experiences and use these and other results to predict the kind of experiences the customer is most likely to have in the future. Applied to business, this data enrichment technique assists in identifying potential customer needs before they become a problem and in improving them.
Example: This data enrichment technique can help the company to find dangerous clients before and exhaust all the protections, which minimizes the risks and achieves the organizational stability of the financial resources.
Data enrichment techniques are viewed and applied as critical for refining the quality and value of the data to be used by businesses for decision-making purposes. Infobel Pro provides advanced data-relevant services which are outstanding products aimed at aiding businesses in enhancing their data. When these techniques are incorporated into data management, it is possible to get maximum value from the data and meet the organizational objectives. Each of them brings a different piece to the equation, making it simpler to decipher the customer needs, company requirements, and the competitors' moves.