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How Better Data Got a Leading Automation Firm Back on Track

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Overview

There comes a time in every company’s lifecycle when its first spurt of rapid growth levels out. Boxed in by the very forces that once propelled its rise, the company drifts—uncertain, waiting for the next wind to fill its sails.

 

That’s exactly where one of Spain’s top intelligent automation firms found itself. This firm built its name by automating the manual, time-consuming work small businesses deal with every day. 

 

Think of chatbots handling customer queries for small businesses via Instagram DMs, or helping local banks speed up data entry and identity checks—simple tools making a real difference.

 

But to ignite their next phase of growth, they needed fresh customers—and their inbound marketing was falling flat.

 

They desperately needed more leads. 

 

That’s where we came in.

Key points
  • The client is a leading intelligent automation firm in Spain, specializing in affordable, effective RPA (Robotic Process Automation) solutions for small businesses. 
  • After consistently surpassing growth targets, they hit a plateau and needed a fresh stream of customers. 
  • Inbound marketing had run its course. To reach the next phase of growth, they needed a carefully curated list of high-intent leads—without it, even the sharpest messaging would fall flat. 
  • From our data-first perspective, what they really needed was a high-quality dataset of potential customers they could directly engage. 

Challenges

The first challenge was sourcing high-intent leads without blowing through the client’s limited budget.

Big Data might sound glamorous—but it’s messy.

After evaluating multiple sources, we zeroed in on two data-rich platforms for small businesses: Instagram and Google Maps. Both offered publicly visible business information, but Instagram stood out.

Importantly, we ensured full compliance with privacy laws—extracting only publicly available data from open platforms. 

Our approach prioritized transparency and respect for user boundaries. 

Unlike Google Maps, Instagram bios often included not just business names and phone numbers, but also email addresses—making it a stronger channel for lead generation. Plus, the platform is updated more frequently by users themselves, giving us fresher, more accurate data.

That said, extracting clean data from Instagram wasn’t easy. Bios were often messy—phone numbers buried in text, loaded with emojis or odd formatting. Standardization was a real challenge. And unstructured data like that isn’t something a sales team can use out of the box.

Google Maps came with its own quirks. While useful for verifying business locations and categories, it generally lacked email data—and had occasional issues with duplicate listings and missing contact info.

Still, we had a plan—and the right tools to make it all usable.

 

Grepsr is the best value for money and accuracy of data. It’s like flipping on a light switch or answering the telephone. It just works!

Matt S. Computer Software

4 x

increase in outbound call efficiency

60 %

reduction in data collection cost

85 %

leads matched the client's ICP

Solutions

We kicked off the data collection process by building a simple yet effective data extraction workflow.

Our team identified Instagram profiles that matched the client’s target audience—geotargeted to Spain, with particular attention to Barcelona. 

Since Instagram was our primary lead source, we developed a crawler with smart filtering logic—it automatically skipped reels, news, and unrelated posts to stay laser-focused on relevant profiles.

To maintain consistency and improve data quality, we set a few strict standardization rules:

  1. Collect phone numbers and other contact information only from a publicly available profile bio
  2. Limit results to Spanish phone numbers and other contact information.
  3. Remove any separators like dashes or dots
  4. Standardized phone numbers to Spanish format by removing international prefixes.
  5. Resolve any concatenated numbers before including them in the final dataset

Despite automation, manual QA played a crucial role. When the sales team’s opportunity cost is high, it’s worth bringing in human eyes to guarantee precision—and that’s exactly what we did.

To strengthen the dataset even further, we validated leads through Google Maps and added contextual data wherever possible. The outcome was a clear win:

  • 85% of the leads matched the Ideal Customer Profile
  • Outbound call efficiency improved 4x
  • 60% reduction in data collection costs

With the right data in hand, the company regained momentum. What had felt strategic limbo turned into focused movement—toward new markets, new conversations, and measurable growth. 

 

Solutions

Similar challenges faced across the industry:

Lack of technical know-how to automate routine data extractions

Businesses need fresh data to gather the best insights. To that end, one or two data extractions a day does not suffice. They need a system that can easily schedule crawl runs at specific intervals, as well as on demand.

Lack of resources - time, money and manpower - for data sourcing at scale

Data extraction is extremely tedious and highly error-prone. Most businesses lack the infrastructure to perform high volumes of data sourcing, and at a quality that yields the best results.

Overcoming data source restrictions

Most websites place limits on how many requests can be made in a set time period, and regularly block bots from accessing their content.

PROCESS

Getting started with Grepsr

Start with Grepsr in a few easy steps. Leave the data sourcing heavy lifting to us, so you can focus on innovation and growth.

1

Initial project consultation

First, we'll discuss the specifics of your web data needs and the KPIs you would like to have in order to ensure successful project execution.

2

Instrument web crawlers

We'll then set up automated extractions specific to your use-case, and send you a sample dataset before moving on to a full-scale crawl.

3

Begin data collection

Once you've approved the sample data, we will start scaling and performing the full run, and deliver the data in the agreed timeframe.

4

Hassle-free maintenance

Our team will ensure that all subsequent runs are running well, and that your data is delivered as scheduled with the least disruption.

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