Case Study

MailShark Expands Lead Generation Nationwide Using Google Map Scraper

Client Overview

Specializing in the field of direct mail marketing, MailShark is well-known for crafting tailored marketing campaigns for specific industries. For their sophisticated and effective direct mail campaigns, they have been recognized on the INC 5000 list since 2012. Their Annual Mailing Program is a testimony of their powerful campaign strategies. No matter what the industry might be, they provide custom direct mail marketing solutions in a cost-effective and effective way.

Challenge

When Josh Davis tried to expand MailShark’s client network to pizza restaurant owners in the USA, they hit a few roadblocks:

Incomplete Dataset: Their existing data extraction methods did not provide comprehensive data of all pizza restaurant owners in the USA. No business can rely on such an incomplete database.
Irrelevant Data: Since the data extraction was not precise, MailShark got the data of businesses outside the specific category they had in mind, leading to irrelevant bits they did not need.
Lack of Contact Information: Many of the Google Maps business listings did not include their contact details such as email addresses. This causes a problem because without this bit of information, direct mail marketing is not possible. The entire exercise of scraping the Google Maps data becomes futile.

Solution

Unlike other generic web scraping tools and solutions, ProWebScraper designed a targeted and advanced Google Maps scraping solution to cater to the highly specific requirements of MailShark:

Targeted Data Extraction Across the Nation: ProWebScraper’s tool ran an extensive search for pizza restaurants across 40000 zip code in the USA. For example, in the 10001 New York zip code area, we spotted every listed pizza restaurant. In this way, it became a comprehensive data extraction campaign.
Precision Filtering for Relevant Results: To make sure there are no inaccuracies in the data, we applied filters for exact category matches. This ensured that non-pizza restaurant businesses such as burger joints or coffee shops which came up in initial search were filtered out.
Elimination of Redundancies: At times while scraping data, same entries come up multiple times causing the issue of duplicate data. Our tool took care of duplicate data and removed any such repeat entries. For instance, if ‘Joe’s Pizza’ came up multiple times, we made sure that there was only one entry in the name of ‘Joe’s Pizza’ in the final dataset.
Enhanced Contact Information Retrieval: Since some businesses do not list their contact details on Google Maps listing, we ensured that our tool finds the contact information in such cases from elsewhere. In this case, we looked up the website and social media profiles of such pizza restaurants and retrieved the contact information. This ensured that every pizza restaurant listing scrapped by our tool contained the necessary contact information.

Conclusion

Compared to other tools, MailShark found our Google Maps scraping tool to be more tailor-made and comprehensive. It effectively tackled MailShark’s challenges related to data collection. Moreover, it provided MailShark with accurate and reliable dataset that they could use to propel their direct marketing campaign to new heights. This case study shows the way our tool can transform a client’s business and make data scraping for their diverse needs a hassle-free exercise!

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