Data is a critical aspect of a B2B lead generation process. It can mean the difference between the success and failure of your outbound efforts. That said, high-quality data is essential to fill your sales pipeline with high-quality potential clients and make the work easier for your sales team. Hence, it is imperative that you have the right people to collect and process your B2B data.

Data collection teams and SDRs must work together to be able to close successful deals. One cannot perform well without the other. It doesn’t matter if you have the best SDRs on your team; if your data is low-quality and your lead list is poor, the efforts of your sales team will bear little to no fruit.

Amazingly, some companies still believe that purchasing a data subscription or a pre-generated lead list will be enough. Let us tell you right now that it’s not. Data must be cleansed, enriched, organized, and made into high-quality targeted data for SDRs to get the best out of it. Even if a pre-generated list consists of solid data with varied options, it won’t be enough for high-performance sales.

What is B2B sales prospecting research?

Sales prospecting research is the process of creating a customized list of contacts that match your buyer persona and ICP. Simply put, this is a list of the people and companies that are likely to buy your products and services.

There are two types of sales prospecting research:

  • Enrichment. This is done to fill in missing data about contacts or update the information that already exists in the database.
  • Generation. This is research done from scratch, which is significantly more difficult than enrichment. Here, researchers must find particular types of data using a variety of tools and sources, but a lot of its processes can be automated.

Tools, databases, and techniques can make both types of research faster and more efficient, helping ensure high-quality leads and fast turnaround. However, the human component is still an important part of both types of sales prospecting research.

Data research vs. data subscriptions

Compared to buying data subscriptions or pre-generated lists, targeted data research has certain advantages, such as:

Higher quality contacts

Perhaps the most difficult part of lead generation is ensuring the accuracy of data. And for many prospecting businesses, generating high-quality leads remains a primary pain point. Nevertheless, there are several ways to get past this problem, and one of them is finding as many quality leads that fit your buyer persona as possible. Unfortunately, this is something that a pre-generated list cannot provide.

Targeted data research, on the other hand, ensures that all contacts are found based on criteria specified in the ICP. This results in customized, accurate, and micro-targeted lead lists that fit your requirements.

  1. Machine-powered yet human-driven

Having human data curators gives you the ability to combine datasets. Data researchers use a variety of tools like Leadbird to almost completely automate the process of client research. However, these tools still need human supervision since a machine simply cannot understand certain things such as context, suppression, and overlap.

  1. Up-to-date data

Another challenge for prospecting businesses is ensuring that their data is up-to-date. Decay rates can be as high as 8% per month. That said, even a perfect lead list generated last year can be completely worthless by the time you buy it.

The older the data is, the more irrelevant the information. And not only can insufficient data be a waste of time, but it can also lead to blacklisting, poor email deliverability, and do-not-call bans, among other negative consequences.

Having a high-performing data research team, on the other hand, can provide you with the latest and most relevant information. Either they generate it from scratch or enrich the existing data set, which both result in accurate, up-to-date data.

  1. Real-time feedback and consulting

Data researchers and analysts can provide valuable advice to clients on search criteria, datasets, industry, and many other aspects of research. With a dedicated data team instead of a purchased list, you can leverage real-time feedback and consulting to make better decisions for your lead generation process.

Moreover, you can incorporate changes to requested data or the buyer persona with weekly list approval. Utilizing machine learning tools can improve this process even further.

  1. Fewer mistakes

A good data team will be able to provide you with an error-free list with very few contacts or companies that have ceased operations, changed domains, or made other major changes to their organization. As a result, you will have fewer email bounces, which, in turn, helps improve your email deliverability (the rate at which your emails actually get to your recipients’ inboxes) and saves a lot of time and effort.

What a typical data team looks like

Every data team is unique, but in general, you’ll find these key players:

Team manager. They are responsible for overseeing the entire team, including data researchers and QA specialists. They also regularly interact with customers.

Data team leads. These are the people that obtain the raw data querying different databases or parsing web sources, which they then send to data researchers for cleansing and enrichment. They are also in charge of validating final data sets to ensure they are of the highest quality.

Data researchers. These team members get the data from team leads, verify it, fill in missing information, input extra points, and ensure that it matches the ICP.

Lead researcher. The main functions of a lead researcher are to prepare two samples of the contact list at the start of the campaign, send the list to the client for assessment, and make necessary changes as needed. They deliver data to a customer weekly or biweekly, and regularly participate in weekly calls with the data team to analyze results and make changes based on the client’s feedback.

A day in the life of a data researcher

To further understand what a data researcher does, let’s take a look into what day in the life of an average data team member looks like. Let’s call our data researcher Anna.

9:00 A.M. The beginning of the workday. Anna goes through her usual routine of starting the computer, opening up all her programs, and making a cup of coffee before starting to look for new prospects for her client.

10:30 A.M. Anna takes her first break of the day. For data researchers who require a high level of focus on details, frequent breaks can help avoid tiring them too quickly and shortening their attention spans. She uses this break to stretch, drink, chat with a neighbor, or get a snack.

10:45 A.M. to 1:00 P.M. Anna is in focus mode. She puts all of her concentration into building the lead list.

1:00 P.M. to 2:00 P.M. Anna takes her lunch break. She uses this time to eat, unwind, and take a break from her computer.

2:00 P.M. to 6:00 P.M. The data research continues until the end of her shift. In between, she may attend meetings, training sessions, or do other tasks related to work. At times, this is also the time managers use for coaching to help data researchers improve their techniques.

Bottom line

Hopefully, this glimpse into the typical life of a data researcher helps you simplify your data research process. Sales prospecting research is not that simple, but it doesn’t have to be overly complex either. What it all boils down to is how the research is done, how data researchers are trained, and how data managers and team leaders manage their teams. By focusing on what’s important, you will be better able to fill your pipeline with high-quality leads in no time.