Industry: HVAC

Mauzy Heating & Air Conditioning

Matt Mauzy decided that to remain competitive and grow his business, he needed to modernize his system for creating and managing sales materials and proposals.

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The Situation

To create business proposals, the sales team at Mauzy were using a 2-part NCR form that they would complete by handwriting the required information. They would then give the prospective client a copy of the mostly handwritten proposal.

The problems and frustrations caused by using this system were:

  • Because they created their proposals in real-time, prospects had to wait an average of 20 minutes while a salesperson created the proposal.
  • The manual proposals were inconsistent and error-prone.
  • Proposals were difficult to track and manage as they were not in an easily accessible, trackable, and manageable digital format.

Mauzy doubled its sales from $5.7 million to $11.4 million using ClientPoint.

The Solution

ClientPoint worked with Matt Mauzy and his team to build a template that matched his business needs. Matt and his team were then trained on how to use the ClientPoint system to create, track, and manage all their business proposals on location with prospects.

Their real-time proposal tracking system is great because it notifies us whenever a prospective client is looking at their proposal. This allows us to know that prospect is available so we can call them to answer any questions they may have. This feature alone has helped us win a higher percentage of new business.

Matt Mauzy, CEO, Mauzy Heating & Air Conditioning

The Results

  • Mauzy Heating, Air, and Solar increased sales from $5.7 million to $11.4 mil using ClientPoint. Matt Mauzy credits much of their rapid growth to their ability to quickly create, track, and manage professional business proposals using ClientPoint.
  • The Mauzy sales team prepares about 450 proposals per month. Thanks to ClientPoint, time spent creating those proposals now averages 37 hours per month, compared to the previous 150.
  • Proposal data errors were reduced by over 95%.

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