Lead generation businesses provide the service of building lead databases. Typically, their clients specify their description of an ideal customer by asking to look for a certain company size, with a certain revenue level, from a certain location, and so on. 

For quite some time, I’ve been working with such a startup myself, and I noticed how often the databases contain irrelevant contact information or names of long-retired decision-makers from target companies. So I found some hacks that could help any leadgen company distinguish itself from similar startups and ensure better lead quality. 

In this article, I’ll tell you about two things:

• What common obstacles are shared between most leadgen businesses, and 
• What your team can potentially do to make your startup stand out. 

So yes, I am currently building a leadgen startup. During our market research, we discovered many tools that can generate a lead list. As a rule, these are tech services and platforms able to find leads by specified filters: geography, industry, etc. However, we found significant problems in how these services processed leads.

Since it was likely that both our clients and competitors also used these tools for lead generation, we assumed that they must be experiencing these problems as well. We saw significant quality drops of lead lists that these tools generated, and since it was something our customers already had, we needed to be better. 

In other words, if we want to build more precise databases, our task is to find a workaround and solve the following three challenges.

The first problem was with inconsistency of information. Some tools we were using showed irrelevant contact details, such as emails of people who no longer work in the target company. We also noticed errors with determining the location of the lead.

For example, there was one time when a company turned to us for our services. It segmented its customers (Shopify stores) by industry, location, revenue, and traffic, so it wanted to get a contact list of decision makers in selected industries, in a certain country, and with the right number of visitors.
After researching the market, we found a tool called BuiltWith, and it seemed to be able to solve our task. We bought a trial version and downloaded the first 100 leads. These were supposed to be American stores from Shopify, but upon manual rechecking it turned out that these stores were not located in the United States and came from elsewhere.

Now, the next parameter. Indeed, BuiltWith showed revenue, but it wasn’t clear how the program calculated it. Without knowing the sources of information and methods of calculation, we could not guarantee the correctness of the data.

As for traffic, BuiltWith only showed if a target company had more or less than a million users visiting its webpage. This wasn’t what we were looking for in our segmentation, so we concluded we could not only use this tool. We needed something else for our technology stack, but it turned out that some other tools we tried (SimilarWeb, Semrush, etc.) had similar problems.

That’s how we defined our first challenge: it was unclear how to generate lead lists that only contained correct data. Overall, it’s quite common for data analysis: you always need to know what sources to address, which ones you can trust, and how much you can trust them.