Implementation reduced from months to days
Full transparency & visibility through integrated systems
Increased sales and conversions 15%
Name: MyJobNow Employees: 15+ Locations: Athens Industry: Employment SaaS/Tech
Data Warehouse Destination: PostgreSQL
MyJobNow is a two-way employment marketplace using an algorithm to connect people seeking work with companies looking to hire staff. Job candidates can create a professional profile, scan job opportunities, and connect with potential employers.
MyJobNow targets hiring managers who are given limited access to job candidates in which to view, assess, interview, and potentially hire an applicant.
As co-founder of MyJobNow, Agamemnon Papazoglou is focused on what every C-suite executive lies awake at night thinking about: predictive and scalable revenue growth. Overloaded sales reps taking too many calls began manually prioritizing leads when they saw an increase in conversions. When the sales team became overburdened with disorganized leads, Papazoglou knew it was time to streamline the sales call process through automation.
But he lacked the time and resources needed to integrate new technology.
Hiring managers in their trial period are able to post a single job opening for potential candidates to submit applications. Employers have access to communicate with one candidate before upgrading their account. Once a hiring manager’s trial period expires, they become a lead for the sales staff at MyJobNow.
In an attempt to convert the most leads and upgrade their account, the sales team called on the hiring manager based on a set of criteria manually formulated in an effort to streamline their sales process. Initially, two sales reps were all that was needed to call leads in an attempt to convert them.
When the volume became too many to manage, Papazoglou and his sales reps prioritized their efforts by calling leads further down the sales funnel first. However, bottlenecks were created as reps manually researched and evaluated every single lead before making each call.
This became unsustainable so MyJobNow needed a process by which they could prioritize inbound leads based on a formulated probability of which leads would convert to an upgraded account.
In other words, MyJobNow needed an automatic lead prioritization process with the following datasets and sources:
|Customer profile (Location, Industry, etc.)||Pipedrive CRM, PostgreSQL Production database|
|The effort invested by Lead (quality of job posting)||PostgreSQL Production database & Intercom|
|Value received during the trial||PostgreSQL Production database data|
|Timing data||Pipedrive CRM|
|Advertisement data||Facebook Ads, Google Ads|
Using ordinary tools and open-source libraries for similarly advanced data calculations would normally require Papazoglou to build-out custom integrations, create data models, and iterate as needed – all while maintaining the current technology stack in-house. The process can easily span months and demand valuable resources from their main product development.
“Using the database populated by Blendo,” Papazoglou explains, “we were able in less than a couple of days to play around with the data and find a pretty decent model to use.” Blendo’s built-in data models combined with one-click integrations allowed MyJobNow to abandon the existing manual process in favor of an automated process in far less time – from months to days.
Until recently, Extract, Transform, Load (ETL) has been the preferred method for companies who utilize their existing business data in new ways for better decision-making and business insights. ETL is the approach in which raw data is first extracted from a data source, transformed into clean, structured formats before being loaded to its target destination, such as a Business Intelligence (BI) system.
Extract, Load, Transform (ELT), on the other hand, is a comparatively newer approach in which raw data is first extracted and loaded into the data warehouse and the transformation takes place at the final destination system. With ELT, the original data source does little more than delivering raw data which considerably reduces the load time.
The real distinction between ETL vs ELT lies in where the data transformation takes place – instead of transforming the data before it’s written, ELT gathers information from an unlimited number of data sources then lets the more powerful target system to do the transformation process.
“It’s very, very easy to use and powerful.”
-Agamemnon Papazoglou, Co-Founder, MyJobNow
Like many data-driven organizations, MyJobNow collects raw data from various data sources and needed it transformed into actionable Business Intelligence. For MyJobNow, it was decided they take the more robust ELT approach.
Papazoglou was able to manage all MyJobNow’s customer and lead data through Blendo with convenient, ready-to-use features. “It’s very, very easy to use and powerful. We can also trust the data now, it is more reliable, and we have more control. When something is broken or incorrect, I only have to check one or two places,” he said. “We now use the insights to take action immediately, and not second guess our tools.”
|Predicted Probability||Conversion Rate|
|Top 20% of the population (in terms of scores)||42%|
|20–50% of the population||21%|
|Bottom 50% of the population||7%|
“We have better insights and help us optimize where to spend our time and energy.”
Today, MyJobNow has a better picture of what to focus on from the leads that matter to them. With Blendo, they get data-driven insights to help optimize their sales process and spur company growth.
Papazoglou is proud of the results his team has realized. MyJobNow is focused on enhancing the mobile app and website as they continue finding innovative ways to make their data speak to them with Blendo.
“We can trust the data now, it is more reliable and we have more control.”
-Agamemnon Papazoglou, Co-Founder, MyJobNow
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