How to master database enrichment

Customer Intelligence 9 min read

Data enrichment is defined as merging third-party data from an external authoritative source with an existing database of first-party customer data. It's a general term that refers to processes used to enhance, refine or otherwise improve raw data. Brands do this to enhance the data they already possess so they can make more informed decisions.

All customer data, no matter the source, begins in its raw form. When this collected data flows into a central data storage, it often is ingested into the system in discrete datasets. What you often have when this happens is data being dumped into a data lake, or a data swamp, full of raw information that often isn’t useful outside of narrow contexts. For example if you don’t use the events collected on your website or in your app to make thoughtful decisions while composing your marketing campaigns. So making your data a valuable asset for your business.

By adding data from a third party, brands gain deeper insight into their customers’ lives. The resulting enriched data is richer and more detailed, which enables brands to more easily personalize their messaging because they know more about their customers.

Strong data enrichment processes are a key part of building the golden customer record or 360 Customer Profile. One dataset by itself, no matter how detailed, doesn’t include every piece of behavioral or transactional data needed to build a comprehensive single view of the customer. This is why data enrichment practices are vital to marketing’s long-term goal of delivering personalized experiences.

Not quite a perpetual motion machine

Much like every other aspect of data management, data enrichment isn’t something you can do once and then never do again. Customer data, no matter how detailed, is fundamentally a snapshot in time. Income levels rise and fall, marital status may change, and the type of car and physical address can alter. Even names may change, well nowadays email addresses do change pretty often.

Given the possibility of all these changes, data enrichment processes need to run on a continuous basis. The alternative is having outdated information that could lead to customers receiving irrelevant offers. Keeping all this information updated is a titanic undertaking, so it’s little surprise that more than 50 percent of businesses spend more time cleaning data than using it.

The time commitment for keeping data up-to-date is a strong argument for automating the process. Machine learning algorithms that run on a continuous basis can substantially streamline the data enrichment process because they can match and merge records much faster than a human data steward. This leads to a data enrichment process that runs 24 hours a day, seven days a week, and results in data that is always the most up-to-date it can be. Ultimately, this allows brands to maintain a high level of enrichment and keep the process moving forward in real time to enhance customer engagement.

Types of Data Enrichment

No individual dataset can provide a complete and holistic overview of a brand's customer. To properly understand your customer base, you will need to combine several datasets into a single enriched source. As businesses across the board try to introduce greater levels of personalization, the ability to meaningfully combine different data sources provides them with a powerful tool for enhancing their services or products.

There are a lot of types of data enrichment, as well as data collection techniques. However, there are two types of data enrichment that are far more prominent than the others. These are the two that you really need to know about.

Demographic Data Enrichment

This includes any type of data that allows you to more accurately define your users. For example, income level and marital status are both examples of demographic data. Combining demographic data with existing data about your customers will enable you to draw a holistic picture of exactly who they are.

The key to demographic enrichment lies in identifying exactly what you want to achieve with the data you have. For example, including databases with information about a user's credit rating can help you decide if you should provide them with a credit card offer.

Geographic Data Enrichment

This involves adding data about the geographical location of your users. It could be things like ZIP codes and other postal data, longitude and latitude coordinates, and home addresses.

Geographic data is easy to come by because there are many businesses that exist to provide such data. Geographic data enables businesses to offer a more personalized service. For example, retailers can use it to offer localized pricing, especially now since retailers can open up their shops again.

What Are the Benefits and Use Cases for Data Enrichment?

The main benefit of data enrichment is that it enables businesses to enhance both the value and accuracy of their datasets. Having high-quality and accurate data is essential if businesses want to be able to draw meaningful conclusions from it.

Enriching an incomplete database with data scraped from the right sources can instantly enhance its value. Not only does this make data more useful for the businesses who own it, but it can also enhance its shareability.

When businesses know they have a reliable means of enriching data, they can reduce the amount of data collection they need to undertake. For example, by enriching the data collected from customers during the signup process for a newsletter or website, businesses can ask their customers for less information.

The rapid growth of data enrichment tools in recent years shows how businesses are beginning to wake up to the potential of data enrichment. Data is a vital resource for SAAS businesses. Anything that can further enhance its value and usability is worth exploring. Data enrichment does exactly that.

Let's have a look at some enrichment use cases for your CRM. Whether Hubspot, Pipedrive, activecampaign or Teamleader, all of them have the same barrier.. It's not always possible to connect your own service to their platform without development knowledge.

Helpful CRM data enrichment use cases

1. Fewer form fields

Historically, a shortened lead capture form came at a high price. While creating shorter forms often increased conversions, it meant taking the risk of prolonging the buying cycle. This as either marketing would need to find new reasons to collect missing data or sales would have to uncover it themselves.

Nowadays, marketers no longer have to strike a constant balance between asking for the information required to determine lead quality and not turning away potential leads with long, daunting forms.

2. More personalization in every customer interaction

With enriched data, your view into your target audience grows exponentially. The process provides you all the information you need to create hyper-targeted customer segments. Thus ensuring that you're providing the right customer journey to the right company.

Today's sales and marketing boil down to the relevance of customer interactions. Back in the early days of data provisioning, just including a contact's first name in the salutation was enough. But now, your communications need to go beyond understanding your prospect's firmographics, technographics, and recent relevant events in their organization and tailor your messaging accordingly. And this is why data enrichment is the way to go.

3. Advanced account scoring

Even partially manual lead and account scoring processes are tedious and often more trouble than they are worth. Data enrichment can be a great way to remove yourself from the scoring process altogether by automatically categorizing prospect priority based on real-time enrichment data.

Let's illustrate this with an example: A lead comes into your contact database with nothing but a first name, last name, and personal email address. Without the process of data enrichment, your current lead scoring system is likely to give it a low score. With only scarce information to work with, the system won't be able to know if the lead has a high buying intent and product fit. With a real-time enrichment process in place, can link that lead to more company data-points. With a richer profile on the lead, your lead scoring system can reprioritize it and automatically pass it along to sales. And you don't have to lift a finger.

4. Improving the overall customer experience

All in all, CRM data enrichment improves your customers’ experiences while engaging with your company. Enriched CRM data means more accuracy in the insights and conclusions you draw from your existing database. This means that you can tailor your sales call script to be relevant for every prospect, increase upsell and cross-sell opportunities, and detect churn risks more efficiently.

With, your CRM will be up-to-date at all times, including all relevant information about recent changes in your existing customer's business. By identifying business signals among your existing customers and tracking the same, your customer success team can reach out to customers most likely to churn and assist them in getting the most out of your product.

5. Enabling machine learning technology

Through the power of artificial intelligence and machine learning, sales and marketing professionals can now offer increasingly personalized touchpoints with potential and existing customers in ways that previously would have required tremendous human resources and huge budgets.

One practical example is chatbots. They've turned the way businesses gather critical company data from site visitors on the head. Thanks to data enrichment possibilities, your team can acquire customer data through conversations with a bot. The data obtained from these conversations can then be enriched with existing information in your CRM. This brings us back to our first use case: There’s no need to offer self-serving lead capture forms in this day in age.

Isn't that too much for my company?

Data enrichment fosters meaningful customer relationships for all company sizes.

Enriched data promotes personalized communications and increases the likelihood of meaningful customer relationships and business opportunities. With relevant customer data, your business can develop communication strategies that meet customer preferences and needs. A customer is more likely to make a purchase when they feel that your company understands their needs.

Data enrichment maximizes customer nurturing

Data enrichment maximizes customer nurturing by identifying segments of customers to be nurtured. A segment offers value-driven information that has the potential to evoke a purchase.

Data enrichment boost successful targeted marketing

Targeted marketing is the future of marketing, and many businesses are realizing that a one-size-fits-all marketing approach does not work. They are turning to targeted marketing. For targeted marketing to be successful, an organization requires data enrichment to segment data effectively.

Imagine investing a huge sum of money on your contact list hoping to get customers and prospects only to discover that your contact list is outdated. Organizations cannot afford such losses. Data enrichment ensures you have a clean and accurate contact list to increase sales efficiency and boost ROI. Also, it offers opportunities for cross-sells and upsells because a business has the right data and knows its customers well.