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Three Sales Ops Automations, With What They Actually Cost

TL;DR: $6,500 to build all three, $270/month to run, roughly 50 hours a month of manual work replaced. Payback around two months. The numbers are the easy part - the section on what went wrong in each build is the part worth reading before you commission one. I am Ivan, I build these for a living. Rather than another piece on what AI could theoretically do for sales operations, here are three…

Three sales operations automations were analyzed, with their costs and outcomes outlined. The first automation focuses on lead enrichment and scoring for a B2B SaaS client. Building the system cost $3,500, and it currently runs at $180/month. The automation replaced approximately 50 hours of manual work per month, resulting in a payback period of around two months.

The automation proved successful, with a focus on avoiding false positives when scoring leads. The second automation is pipeline reporting for a logistics company. The initial development took three days for $1,800 and the monthly running cost is $60. This automation eliminated three to four hours of manual reporting each Friday, increasing consistency and providing a short narrative explanation of any changes.

A notable mistake was an API token expiration causing a reported data drop, which was later resolved by implementing a guard that prevents the publication of inaccurate zeroes. The third automation addresses stalled deals in the pipeline. Building the alert system took two days for $1,200, and the monthly running cost is $30. The automation detects deals that have been inactive for 30 days or more, notifying the relevant reps and managers.

The system changed from a daily alert to a weekly summary for managers to avoid the surveillance effect and prevent manipulation of the data. Overall, the three automations all had costs ranging from $1,200 to $3,500 for development, with monthly running costs between $30 and $180. The system successfully reduced manual work by around 50 hours per month, providing a payback period of approximately two months.

The key lesson learned is to design systems that avoid overconfidence, especially when dealing with low-judgment tasks, as overconfidence can lead to a lack of trust and potential manipulation of the data.

Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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