Data Enrichment 101: How to Turn Raw Data into Qualified Leads

Data Enrichment 101: How to Turn Raw Data into Qualified Leads

How does a business decide what kind of buyers to target? How about what kind of ads to launch? The answer is simple—they rely on concrete data to make such informed decisions. 

Think of data as the fuel to every business’s engine, helping them better understand their customers, the market, the competition, the success of their operations, and so much more. 

The problem is, while all businesses are capable of producing tons of raw data from their everyday sales, marketing, and customer success efforts, oftentimes it’s not enough to paint the full picture. 

This is where data enrichment steps into play, enhancing the available raw data with additional insights that, together, transform data into meaningful decisions. 

Data enrichment provides businesses with actionable intelligence that empowers them to create more precise, personalized, and effective customer experiences. 

In this article, we’ll cover everything you need to know about data enrichment—how it works, why it’s important for all businesses, common use cases, and software to help you on this mission. 

What is data enrichment?

Data enrichment refers to the process of fusing raw data with external data sources with the main goal of creating more accurate customer profiles.  

Simply having the name and email of a potential lead is not nearly enough in today’s landscape to create a personalized buyer experience, which has become increasingly crucial in today’s customer-centric environment. By creating a data enrichment process, businesses can supplement that information with relevant details such as demographic, technographic, and behavioral data, to name a few. 

After all, the better a business knows its customers, the better it can connect with them via its sales and marketing efforts. 

That’s why the main goal of data enrichment is to transform incomplete or unstructured data into valuable information, allowing teams to fine-tune their strategies and engage customers with the right message at the right time. 

Data enrichment is one of the 3 key elements of transforming data into decisions, and some people even argue that all 3 fall within the wheelhouse of data enrichment: 

  • Validation → verifying that the existing data from internal and external sources is correct and updated at all times, avoiding any misleading conclusions or decisions. In the context of email outreach, for instance, validating your customer emails is crucial to ensure successful email deliverability and avoid the spam folder.  

email validation as a data enrichment aspect

  • Enrichment → supplementing existing (and validated) data with additional internal and external datasets, as well as filling in the gaps of any missing details. This could be integrating additional prospect information from dedicated contact databases, updating existing customer profiles with ​product usage data, and the list goes on.

B2B data enrichment is important

  • Contextualization → once you’ve established mechanisms to validate and enrich your data, the final piece of the puzzle is to make logical interpretations based on the complete datasets. In other words, explaining the ‘why’ behind the data, for instance, analyzing why certain prospects end up making a purchase while others don’t.

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Business importance of data enrichment

Rather than relying on general public information and/or one’s gut feeling, data removes all the guesswork in business operations to provide objective insights aligned with the reality of the market. 

what is data enrichment in matters of information

In a nutshell, enriched data empowers businesses to make more precise, revenue-driven decisions across the board, from generating leads to closing deals and retaining customers. 

On top of that, it’s also a valuable tool for:  

  • Lead generation → detailed and accurate data empowers businesses to create laser-sharp customer profiles to target with their sales and marketing efforts, as well as effectively segment them based on distinct data types like demographics, firmographics,  and potentially—purchase-intent.
  • Lead qualification → with more accurate customer profiles, businesses can better qualify and prioritize customers with data-driven lead scoring systems, which in turn improves ROI and limits resources on those who are unlikely to make a purchase. 
  • Personalization → data is the foundation of personalization, be it for inbound marketing campaigns or outbound sales outreach, providing teams with the necessary information to tailor the value proposition to their exact needs and wants, significantly improving the customer experience and boosting conversions. 
  • Cost reduction → an often overlooked aspect of quality data management is its great potential to optimize every sales and marketing dollar spent by minimizing wasted opportunities, and instead allocating resources to the right customers, messages, timing, and offerings.   
  • Strategizing → enriched data is also the most powerful tool for measuring business performance, accurately forecasting, and making long-term decisions on a strategic level, that in turn affect virtually all business operations. 
  • Retainment → few people talk about this, but data enrichment shouldn’t stop once a prospect becomes a client. Regularly updating your existing customers’ profiles with data regarding how they use the product, what kind of content they interact with, etc. will allow businesses to create more satisfying and longer-lasting experiences. 

Types of data enrichment 

There’s tons of data flowing into each business every single day, and to avoid any complications in the data enrichment process it’s empirical to distinguish the different types of data out there.

#1. Demographic data 

This is the most common data type, and it’s mainly about beefing up your customer profiles with general personal information like age, gender, education, or even income levels. 

Now, in a B2B setting, you’re not exactly targeting someone because they’re 45 years old, but understanding the persona behind the decision-maker can help in account-based sales and marketing. 

For example, knowing you’re dealing with a younger, tech-savvy operations manager might change how you pitch your solution versus an older, more traditional executive. 

#2. Firmographic data 

This one’s a complete game-changer for B2B companies. 

Firmographic data is basically the “who” and “what” of the companies you’re targeting—stuff like industry, company size, revenue, and location. These details are crucial because the way you approach a small startup versus a multinational enterprise is going to be completely different. 

Maybe your solution is scalable, but knowing a prospect’s annual revenue lets you adjust your pitch to highlight the benefits that are most relevant to their current needs.

#3. Technographic data 

Knowing what technology your prospect companies work with can be a goldmine for B2B firms, and this is exactly what technographics is. Enriching customer profiles with technographic data allows businesses to determine which companies are most likely to need and benefit from their solution. 

At the same time, many software types work great together, especially CRMs with sales/marketing automation platforms and lead management tools, so your outreach strategy may revolve around this. 

#4. Behavioral data 

Behavioral data may very well be the most valuable information for sales teams, and it’s all about understanding how your potential and existing customers act throughout their buyer journey. 

In other words, it’s data about how they interact with your brand, for example, what kind of content or lead magnets brought them in, website browsing habits, product usage habits during their free trial, and so on. 

Such data not only helps create more personalized and relevant engagement but also predicts future actions of new potential buyers. 

#5. Psychographic data 

A relatively unpopular data type, psychographic data refers to the more personal customer insights such as interests, values, lifestyle, etc. 

While such data is much more common in B2C, an increasing number of B2B firms have also started enriching their customer profiles with such data to foster more personal and meaningful connections with clients. 

B2B data enrichment in practice  

To put theory into practice, let’s briefly explore a common, generalized example of how B2B businesses leverage data enrichment to significantly improve their sales process.

For starters, a B2B firm will segment its audience for targeted prospecting efforts, leveraging firmographic data to enrich its contact lists and filter out businesses of a certain size in specific industries. This is often the first instance of data enrichment in the sales process, as it sets the foundation for improved conversions and ROI. 

Businesses will probably use a reliable B2B data provider and use their extensive search filters to determine what data points are relevant to their prospecting efforts: 

B2B data enrichment in Reply.io with live data

The next step could be enriching their existing CRM with external data sources, including technographic and behavioral data to add context to their prospects, in order to create an effective lead-scoring system. 

For instance, in our case of a sales automation platform, qualified prospect companies that are growing in size yet still using a simple email tool for their outreach would score highest, considering they are most likely to be interested in adding conditional multichannel outreach and AI to their engagement efforts. 

While on this topic, it’s worth mentioning that AI and data go hand-in-hand, given its amazing ability to process enormous chunks of data and contextualize it in seconds. 

Live data as a part of data enrichment

A great example of such synergy is our AI SDR which leverages existing customer data and enriches it with external insights from sources like LinkedIn to create hyper-personalized outreach campaigns. 

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Enrich my data!

Last but not least, once we’ve created targeted prospect lists, implemented a data-driven lead scoring and qualification system, and integrated external data enrichment software—it’s showtime for outreach. 

After continuously enriching customer profiles with the most relevant data, depending on the nature of your business, sales teams will have a much easier time building effective outreach campaigns that aim to make meaningful connections with potential buyers, rather than push for a sale. 

All in all, there are countless data enrichment examples in business activities, and while companies will differ on what kind of data is important to them and how it will affect their strategy, the ultimate goal is to make better, more profitable decisions along the entire process. 

Step-by-step data enrichment process 

Now that we’ve seen how it works in a real-life scenario, it’s probably a good idea to break down the data enrichment process step-by-step to help you create your own strategy from scratch. 

After all, it’s not just about finding new data and adding it to your CRM—it’s about creating a cohesive and functional system that automates the entire data enrichment process.

Clean your data 

Chances are, your business already has tons of raw data from internal and external sources, from newsletter signups and other lead magnets to prospect lists from LinkedIn’s Sales Navigator and beyond. 

The first step should always be to ‘audit’ your existing data and ensure there is no duplicate, outdated, or wrong information in your CRM. Skipping this team may result in faulty decision-making way further down the line, affecting your conversions in the long run. 

Imagine spending time to enrich a customer profile with advanced technographic and behavioral data just to have the initial email never reach their inbox because the one you have stored is outdated!

Gear up with the right tools

Once you’ve cleaned your data (or found a way to automate this process), it’s time to integrate your preferred data enrichment tool into the tool stack, creating an automated bridge between external data sources and your customer/prospect profile database. 

The type of software you choose entirely depends on the type of data you need and your existing tool stack, because at the end of the day, it’s about creating a cohesive engine rather than simply going for the most marketed solution. We’ll cover the different kinds of data enrichment software in the next section, along with some of the top options in the market.

Create automated workflows 

This is probably the hardest yet most crucial part of ensuring your data enrichment efforts yield the desired results, and it involves integrating and automating your raw datasets and CRMs with your chosen data enrichment software. This may require some technical expertise, but once it’s ready to go—you can forget about manually updating data records. 

A common example in B2B practice could be setting up a system where, once a new lead comes into play and is added to your centralized customer database, your data enrichment tool will automatically enrich their profile with whatever data it has on them, and potentially perform an extra external search for any missing info. 

Last but not least, businesses can also include alerts in their data automation, meaning when a certain event/action occurs, such as a prospect changing jobs or the company growing, sales teams will be notified in real-time, empowering them to make timely and effective decisions.

Transform data into actions 

Now that you’ve created an automated data enrichment process, it’s time to put it to work. We’ve already mentioned the numerous ways that quality data helps businesses make decisions, but here’s a quick recap: 

  • Better customer targeting and segmentation based on similar data points 
  • Data-driven lead qualification and lead-scoring systems 
  • Personalized sales and marketing campaigns to create a better customer experience 
  • Improved strategic decision-making regarding outreach strategy, budget allocation, etc.

Keep the process ongoing 

Something that doesn’t get talked about enough in the context of data enrichment is that it should be an evergreen, ongoing process. You can clean all your data, take a few days to enrich it with external insights, and even smartly leverage it to create your sales and marketing strategies. 

The problem is that, after some time, some of the prospects will change jobs, some companies will rapidly grow and require a more enterprise-centered solution, and a million other things that will affect your efforts. 

Instead, your data enrichment has to become a non-stop engine that both cleans and enriches your data sets in real-time, because now more than ever, timing, relevancy, and personalization have become absolutely critical.

Data enrichment tools

Much like with many other tool categories, data enrichment software has become a somewhat ambiguous term these days. 

The reason I say this is because there are dedicated, standalone data enrichment tools like DropContact and Enrich.so that are solely responsible for finding and validating emails, auditing existing datasets to remove duplicates or errors, and keeping an eye out for any personal or company enrichment opportunities. 

On the other hand, with an increasing number of sales automation platforms like Reply.io becoming more and more consolidated, many of them have built-in lead databases that contain more than enough information to enrich customer profiles and create meaningful, personalized campaigns. 

Data enrichment in Reply

So while standalone data enrichment tools may provide better enrichment in real-time (though, that’s not always the case), with consolidated sales tools containing native databases, users can build their enriched customer profiles and then add them to tailored outreach campaigns in just a few clicks.

Finally, there are more extensive data providers like ZoomInfo that offer tons of prospect and company data in real-time, but unless you’re a large business aiming for aggressive lead generation, their hefty price tag is most probably not worth it, and native databases from sales tools like Reply.io or Apollo could be more than sufficient. 

Making the most of your data 

All business decisions should be data-driven to ensure you’re getting the results you want. And while AI and the countless tools available make this easier and automated, there’s also more data flowing around than ever before, so it’s crucial to break through the noise. 

Creating a mechanism for transforming raw data into actionable insights enables businesses to better target potential buyers, create a more personalized buyer experience, and more effectively convert them into loyal customers. 

Modern sales and marketing teams are entirely data-driven, and the quality and quantity of data at their disposal will have a direct impact on the success of their efforts.

Data enrichment may seem complicated at first glance, but once that engine is created (with Reply’s live data as its fuel)—businesses will much more effectively connect with their customers, and that’s exactly what makes them stand out from the competition.

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