Enrichment refers to adding something to improve the quality and value of something. In the case of data, enrichment means adding pieces of information to raw data that often lack the value needed to drive useful insights. Overall, data enrichment enhances the existing data while integrating additional contextual bits of detail to make more informed decisions.
This post will let you interact with the top data enrichment techniques for deeper insights.
So, let’s get started to discover the best data enrichment methods.
Top Techniques for Data Enrichment
Some very popular methods of data enrichment are given below.
- Geocoding and Location Data Enrichment
The very first technique or method is geocoding. Being related to geography, this method enables the conversion of any address into geographic coordinates. Overall, it is related to completing or adding location-based elections to make information more contextual and location-specific, such as demographics, nearby amenities, and regional locations. It is really helpful for logistics companies.
A study by Geospatial World indicates that location-based data increases sales by 15-20% for a business. And even, marketing campaigns involving geocodes are reportedly 60% more effective than those that don’t add these details.
Why It Matters
- Better Customer Insights: The specific location details help in locating customers and customizing offers or marketing strategies accordingly. Also, customers feel delighted on getting improved delivery services.
- Improved Targeting: It can specifically back local SEO efforts that enable businesses to discover demographic data and inquiries. The demographic details facilitate deriving effective marketing campaigns to increase footfall in the stores.
- Behavioral Data Enrichment
Considering the basics, it is related to behavioural data enrichment. It ensures adding information to existing intent-based data, whose enriched records guide in customizing support and offerings. Simply, the purchase history, web journey, and engagement components in insights guide in making effective targeting or retargeting strategies.
Salesforce findings state that 57% of consumers accept sharing personal data in exchange for personalized offers or discounts. It shows the significance of behavioural data for companies that use it to get insights into their intent and potential to buy.
Why It Matters
- Personalized Experiences: Behavioural data enrichments reveal the likelihood and intent of customers, which guide in defining personalized offers.
- Predictive Analytics: Behavioural insights can also help in foreseeing customers’ actions according to their product searches, cart, and other relevant details.
- Demographic Data Enrichment
Enriching demographic details involves adding gender, age, income level, education, and occupation to complete customer profiling. It can help in training machine learning algorithms that automatically represent customer profiles.
Businesses relying on demographic data for segmentation get 24% higher response rates in their marketing campaigns, according to Experian. This kind of segmentation enables them to personalize emails, which generates a 6x higher transaction rate, as per Marketing Sherpa.
Why It Matters:
- Market Segmentation: Demographic data sets the stage for segmenting their market more effectively, which ensures tailoring marketing strategies for various groups.
- Enhanced Customer Understanding: The knowledge of demographics establishes the customer base, which guides the creation of products and services that meet their expectations.
- Social Media Data Enrichment
Social media data can also be enriched by integrating data from different social accounts. Once done, it helps in understanding customer sentiments, preferences, and social interactions.
A Statista report states that 73% of marketers believe that social media has been “somewhat effective” or “very effective” for their businesses. Another review by Harvard Business confirms that social media data that is leveraged for insights increases customer satisfaction by 20%.
Why It Matters
Real-Time Insights: By processing real-time reviews, comments, and customer engagement data, it is easy to understand customer opinions and trends.
Brand Sentiment Analysis: Social media is mainly used to build reputation online. So, businesses use official social media accounts, such as Facebook, Twitter, Instagram, and LinkedIn, to manage their recognition, reputation, and customer satisfaction.
- Transactional Data Enrichment
Transactional data is concerned with transactions. These transactions may not be useful unless valuable details are added to the payment method, frequency of purchase, average transaction values, etc. These records guide predictions and hosting facilities to avoid waste and increase the inflow of money.
Bain & Company found that companies using enriched transactional data for CLV calculations experience 33% higher revenue. And a report by Forrester also states that cross-selling and upselling can increase sales by 10–30% by using transactional details.
Why It Matters:
Customer Lifetime Value (CLV): Customer Lifetime Value or CLV helps in understanding the transactional behaviour of customers. Businesses use it to discover and focus on high-value customers.
Cross-Selling and Upselling: With the records of transactions, identifying opportunities for cross-selling and upselling can be way easier through monitoring and analyzing previous purchase patterns.
- Third-Party Data Enrichment
What It Is:
Now comes third-party data enrichment, which allows integrating external data sources with internal sources of information. This combination guides market trends, industry benchmarks, and competitive intelligence.
Another report by Forrester states that companies leveraging third-party data for enrichment register a 10-20% maturity in their decision-making. And 65% of marketers utilize third-party data to win customer satisfaction through their insights (eMarketer).
Why It Matters
Broader Perspective: Third-party data can help in integrating more values so that a broader perspective can be highlighted, which helps businesses discover their position in the market.
Enhanced Decision-Making: Likewise, integrating external data with internal databases can guide informed decision-making and strategic decisions towards achieving end goals.
Conclusion
Data enrichment is extremely powerful, which increases the value of your existing databases. Techniques like geocoding, behavioral, and demographic data enrichment with social media insights, etc. are good at achieving values, and integrating records can let you emphasize deeper insights for making informed decisions. This post has various statistics to complement the use of data enrichment techniques. They help in improving targeting, satisfying customers to a great extent, and increasing revenues. Embracing these techniques facilitates a better understanding of intent, which guides one to achieve the top position in a highly competitive landscape. This is where data-driven solutions and results rule.
