Identify Companies using SAP by analyzing Job Postings

Introduction

In today's fiercely competitive market, knowing the tools and software that companies are using can significantly elevate your sales strategy. Imagine having precise insights into whether a company is using a tool suites like SAP, Salesforce, or Oracle. This level of understanding allows you to target and prioritize leads, tailor your offerings, increasing your chances of closing deals, and driving growth.

Companies often reveal critical information about their operations through their job postings. These job data provide subtle yet powerful company signals about the tools, software, and SaaS solutions they rely on.

This article will guide you through the process of extracting these company signals from job postings, focusing on identifying tools such as SAP. We’ll use Techmap's free data feed to demonstrate how to analyze these signals to create actionable insights that can enhance your sales strategy.

Understanding Company Signals

Company signals, derived from sources like job postings, press releases, and social media, reveal a company's activities, strategies, and future plans. Analyzing job data, in particular, allows businesses to tailor sales pitches to specific tool needs, prioritize leads based on a company's tool landscape and focus, and gain a competitive edge by understanding the tools competitors use.

These signals can be categorized into several types of company signals:

• Product-related signals offer insights into a company's offerings, aiding in identifying potential customers and differentiating products.
• Operations-related signals reveal how a company organizes itself, both internally and externally, and can uncover opportunities for strategic alliances.
• Work-related signals provide information on the tools and technologies employees use, highlighting opportunities for introducing complementary products.
• Financial-related signals shed light on a company's financial health, informing investment and advisory opportunities.
• Image-related signals indicate how a company is perceived, offering insights for branding and marketing services.
• Growth-related signals reveal a company's current performance and future plans, offering opportunities for business development.

You can read more on company signals in our article A Classification of Company Signals in Sales Intelligence. In this article, we focus on tool signals, a subset of work-related signals, specifically referencing tools like SAP mentioned in job postings. Companies typically mention the tools they are using to inform applicants about the qualifications they need and what the tool environment for their work will look like. Identifying these signals can reveal opportunities for offering complementary products or advanced solutions to companies using SAP.

Extracting Job Data from Postings

Companies distribute their job postings across various platforms, including corporate websites, job boards like Monster, aggregators like Indeed, and social media platforms like LinkedIn. Therefore, we'll use Techmap’s free Luxembourg job data feed available on AWS Data Exchange (ADX). This feed provides historical job data since January 2020. For June 2024, it includes approximately 15k job postings from sources such as LinkedIn, CareerJet, Indeed, and SmartRecruiters.

Job postings are more than just advertisements for open positions—they are treasure troves of job data that reveal insights about a company’s operations, goals, and tool landscape. Typically, a job posting contains:

• Company Information: Details about the industry, company size, locations, and organizational culture.
• Job Information: Descriptions of the role, specific responsibilities, and key objectives tied to the position.
• Requirement Information: Specific skills, tools, and qualifications that applicants must possess.
• Compensation Information: Information on salary ranges, benefits, and additional incentives offered.
• Application Information: Clear instructions on how to apply, including deadlines and required documentation.

Our focus will be on extracting tool-related details, such as mentions of "SAP" and other specific software systems, to identify tool signals.

Implementing the Extraction Process

In order to identify and analyzing tool signals, we now prepare an extraction algorithm to identify tool signals. More specific, we use Linux shell commands to process the data files, and regular expressions (regex) in Java for identifying "SAP". The regex below is designed to detect mentions of SAP in job postings:

(?i)\b(SAP)\b

With the regex prepared, we can now identify tool signals in the job data. This involves downloading the data files from AWS S3, processing them with shell commands, and extracting relevant tool signals.

After subscribing to the data feed, you’ll receive an alias to access the S3 data bucket with job data. The following commands show how to download and decompress the job postings:

List all files from June 2024

aws s3api list-objects-v2 \
  --request-payer requester \
  --bucket <YOUR_BUCKET_ALIAS> \
  --prefix 'lu/techmap_jobs_lu_2024-06-' | grep Key

Download individual files

aws s3 cp \
  --request-payer requester \
  s3://<YOUR_BUCKET_ALIAS>/lu/techmap_jobs_lu_2024-06-01.jsonl.gz .

Download all files from June 2024

aws s3 sync \
    s3://<YOUR_BUCKET_ALIAS>/lu/ . \
    --request-payer requester \
    --exclude "*" \
    --include "techmap_jobs_lu_2024-06-*.jsonl.gz"

Decompress all files

gzip -d *.gz

Filtering the Job Postings

With the job data files decompressed, we can loop through them, apply our regex to identify tool mentions like SAP, and generate a compact JSON output for each match.

#!/bin/bash

# Define the regex for SAP
export REGEX='\b(SAP)\b'

# Output file for results
export OUTPUT_FILE="company_signals.txt"
printf '' > "$OUTPUT_FILE"

# Loop through files and extract tool signals
for file in techmap_jobs_lu_2024-06-*.jsonl; do
    if [[ -e "$file" ]]; then
        cat "$file" | jq -r --arg regex ".{0,20}$REGEX.{0,20}" '
          select(
            . | to_entries[] | select(.value | type == "string" and test($regex; "i"))
          ) | {
            job_name: .name,
            job_url: .url,
            company_name: .company.name,
            location: (.location.orgAddress.addressLine // .location.orgAddress.city),
            matched_text: [
              . | to_entries[] | select(.value | type == "string" and test($regex; "i")) | .value | match($regex; "i").string
            ] | unique | map("..." + . + "...") | join(", ")
          }
        ' >> "$OUTPUT_FILE"
    else
        echo "No files found."
    fi
done

This process took 54 seconds to process around 15k job postings, producing an output file with job postings mentioning SAP. Here’s a snippet of the output:

{
  "job_name": "Accountant",
  "job_url": "https://lu.linkedin.com/jobs/view/accountant-at-altamea-3940540790",
  "company_name": "Altamea",
  "location": "Luxembourg",
  "matched_text": "... experience with SAP is a plus ..."
}
{
  "job_name": "Revenue Controller",
  "job_url": "https://lu.linkedin.com/jobs/view/revenue-controller-at-deutsche-börse-3938667655",
  "company_name": "Deutsche Börse",
  "location": "Luxembourg",
  "matched_text": "... Proficiency in SAP ERP and BW ..."
}

After extracting the tool signals, cross-reference the results with your existing data, such as customer or competitor information. This will help you leverage these new signals for more targeted decision-making.

To extract unique companies from the job postings:

cat company_signals.txt | grep company_name | sort | uniq

This generates a list of companies mentioning SAP in their job postings, which can be used for further analysis or direct outreach.

Conclusion

In our analysis of tool signals for "SAP" in Luxembourg, we identified 544 job postings mentioning "SAP" from 122 unique companies in June 2024. Further analysis for other months and regions, such as the USA, revealed a similar pattern. For instance, in June 2024, we found over 31k job postings from 3.4k companies across the USA, within a dataset of approximately 1 million job postings.

By analyzing job data to identify company signals, particularly tool signals like "SAP", we gain a significant advantage in the marketplace. This process empowers us to:

• Enhance our product offerings by understanding the tools our potential customers use.
• Refine our sales strategy to target companies with specific tool preferences.
• Stay ahead of the competition by anticipating market needs and trends.

The ability to extract actionable insights from job data is a game-changer. With tools like Techmap's data feed and a systematic approach to data extraction, we can unlock valuable company signals that drive smarter business decisions.