The amount of data we produce each day is truly mind-boggling at 2.5 quintillion bytes at our current pace which is only set to increase with new developments in Internet of Things (IoT), quantum, edge and cloud computing. To put that into context, Google processes 40,000 searches every second with over 5 billion searches per day across all providers. Beyond that, every minute users watch over 4 million YouTube videos, send 456,000 tweets and post 47,000 photos on Instagram. All of this is before we even begin to talk about text messages, emails, Skype calls and Tinder swipes!

Every single one of these activities creates data and the challenge for organizations is how they organize the vast quantity of information into something that is useful. For example, is the same person posting a tweet and a photo on Instagram? What are your customers searching for on Google before they purchase? Businesses are creating data enrichment strategies to answer some of those questions.

What is data enrichment

Data enrichment is defined as merging data from third-party sources with an existing database of first-party customer or prospect data. The aim of data enrichment is to gain deeper insights into customer experiences and behaviours that could in turn allow improved segmentation, targeting and ultimately revenue.

Data enrichment can be part of data cleansing or done on its own in that it is the process of adding additional information to make records complete and more accurate whereas cleansing would looking at correcting the existing data by removing duplicates or correcting typos perhaps.

Key uses for Data Enrichment

Whist there are several applications for data enrichment processes give the huge amount of data to work with, many of the key ones lie within marketing and digital experience teams.

Lead scoring

With so much data available, it has never been more important to score your leads and customers. Enriched data provides far more insight into your customer and their attributes or behaviours. For example, you may be able to enrich transactional data with social media activity to give a wider scope of their network and activity. Some companies are even able to use social media images to enrich data with when customers tend to go out or get paid perhaps.

On a more standard level, you might enrich sales data with Google Analytics to show the device customers use to purchase and score them according to likelihood of conversion.

Customer Experience

Enriched data can allow you to better align journeys for the customer through personalization. For example, imagine you are a holiday company and start tracking your customers social activity. The enriched data picks up that your customers post a lot of Instagram pictures related to winter sports. The next time they visit your site, you can tailor the platform to what they are most likely to need e.g. most likely going somewhere wit snow! Targeted campaigns through data can be very cost effective and brilliant for improving customer relationships.


Clustering or segmenting customers is a great step in organizing your database and is made more accurate through data enrichment. Instead of standard segments by age or location you can start splitting customers into more granular groups such age & social media activity or location & device used. The improved accuracy again allows for better targeting of prospective customers and is key for businesses embarking on AI and machine learning strategies.

Improved web form conversions

Lead generation is a primary focus for marketing teams and any steps to make this easier are beneficial. Data enrichment tools have the capacity to fill in missing data, negating the need for customers to do it manually. For example, why ask for their name when they could just connect via Facebook and why should they manually enter their address when you can integrate a postcode lookup tool? Web form can stick to just the “must haves” and rely on enrichment for the rest, taking less time for the customer whilst improving data entry accuracy.

AI & Big Data

Big Data platforms and storage such as Hadoop and Amazon Web Services are designed for efficient enrichment. With the ability to process vast amounts of information quickly, these platforms ensure disparate sources are collated in a central location, whether they be structured or unstructured. Machine learning techniques (those that don’t require human intervention) rely on large volumes of data for accuracy and without enrichment, would not be successful.

How to enrich your data? 

There are multiple third party data sources that can be used, usually providers are split between B2B and B2C. For customized data enrichment, you may want to use a data cleansing company.