Servigistics

Servigistics
Spare Parts Planning
Stochastic Planning and Forecasting

Servigistics was a startup which, like many, had multiple failed attempts at large scale revenue growth, so it could not approach a liquidity event. It was a raw startup only in the sense that it was a “restart.”

Servigistics had a tiny market but one that could be defined with total precision. If a company had a product that had a service parts network, Servigistics had great technology to optimize that network.

Unlike standard forecasting, “stochastic” or intermittent forecasting as done in the parts space uses very different forecasting criteria. One cares about weather events, routes where aircraft fly because these determine when spares are necessary.

Wisely, Servigistics served this target market very well. Unwisely, it brought in early money that destroyed any chance of the employees and founders ever making the payday they had worked to get.

The right strategy would have been to stay small and agile, have a handful of great sales and marketing people, find the early deals with much better technology than the dinosaur against whom they sold, and get bought out at a nice multiple.

Didn’t happen because Servigistics “partnered” with VCs who made the concept of “preferences” something of an art form.

We were brought in to drive new revenue both in the aircraft sector and the central United States. Again, the strategy was clear: find the customer looking for you. That wasn’t too hard for Servigistics as it sold to anyone with an aftermarket parts network.

The complication was that our assigned area had no activity so it was difficult to know where to start. In order to find the customer looking for us, we decided to find the customer who nibbled on our marketing.

First step is to take every “lead” that has come in for the last 3 years. Every response card, every webinar attendee, every trade show “hello” and every touch point were put into a spreadsheet totaling over 7,500 line items.

We took the position that sales cycles were 6-12 months, sometimes 18 months. So anyone in that spreadsheet had some propensity to buy.

And buy they did. We summarized the sheet on line items by company name and guess what we found—-the 3 future customers who made this the top performing territory in the company. One of them closed for $3.7 million plus millions in services, essentially a funding event without giving up more equity.

But the company was not its own master. It had early VCs who forced it to make bad decisions and ultimately the company sold to a bottom feeder VC and the employees got about zero.

A friend of mine had seen their financials and he told me the company had so many “preferences” for the VCs that it would have to sell for several hundred million for an employee’s stock options to buy lunch.

Key takeaway: if the VCs are in first and in deep, if you are not Google, you are not getting enough to buy lunch from even founder stock.

So sell your way to success, do not dilute yourself with VC “partners.”