Case Study

"Helping a Global Staffing Corporation Save Millions"

We worked with a global staffing corporation based in the UK, with offices all over the globe. They had over 20,000 records in HubSpot, and more than 50% of these had bad data. Sales reps weren't generally entering all the data values that were needed for marketing to do their jobs, and didn't even know what marketing needed to begin with. Imagine how much money was being flushed down the toilet when half their customer records didn't even have basic data points needed to provide simple reports and allow marketing to send relevant messaging! HubProsper held cross-departmental meetings and created data policies to be implemented from the top down, to ensure sales and marketing could work with each other, not against each other.  

We also worked diligently to align all data. Missing fields were given their proper values through large data migrations, using automations to be as efficient as possible in cleaning the data as it came in, as well as maintaining clean data across all records in the Contacts database. We normalized everything, so "CEO" was one job title, instead of having "CEO," "C.E.O," "Chief Executive Officer," "Chief Exec Officer," "Chief Executive," .... you get the idea. There was no good way to segment and target CEOs when entering "CEO" couldn't identify everyone in the records.    

Once we had complete and consistent data across all records, we de-duplicated against the multiple data imports from various sources to ensure data was tight, and no one person would get multiple identical emails from a single campaign. Now the data was actually ready to be used. We began a hyper-segmentation project to give marketing as many options as possible for creating messaging that was highly personalized.    

We also created and implemented a lead scoring system that would let the organization see, at a high level, which leads were more valuable and likely to convert and so should be pursued at a higher priority level. Analyzing the data (now that we could get accurate reports), we looked at revenues across geographies, company size, and more, and also built in behavioral factors such as time spent on site, how many emails were being opened, etc."