Digital twin models are powerful, because they match reality - but only if we get them right!

So I always tell new-comers "If the model's behaviour doesn't match reality, there's something wrong, so fix it before moving on" 

But the model may not be wrong.

es there could be a mistake - or the model could be telling you something important that you didn't know.

In a model I built with a friend in a B2B SaaS business, support staff productivity had historically been rising as those staff gained experience, from ~3 customer-tickets fixed per day to ~10 tickets/day. And that's what the model confirmed should be happening. 

But in reality, in spite of low staff turnover, service productivity was actually falling.

What was really happening?

It turned out that customer relationship managers - trying to be helpful - were bypassing  the ticket system and emailing support issues direct to the support staff ... who, of course, were diverted from dealing with the tickets they should have dealt with.

Benefits from this insight

• The knowledge helped the support team leader much better manage the work pressure on his team, escalating the most serious issues within the ticket system, instead of bypassing it.
• Much more important ... product-development, marketing, sales and support teams all realised their product had bigger quality issues than they had thought (the issues from the CRMs were way more serious than the mostly routine ticket-issues)

Adapt the model for a stronger strategy

When we adapted the model for the true service quality trends (more, and more serious quality issues than the ticket system suggested), it was clear that the business was in more serious trouble than the leadership realised. 

This had big implications for their mid-term business development strategy. Should they pause winning new customers while they fixed those quality issues - or keep going and risk annoying both existing and new customers alike?

Well - the model answered that question - and exactly how to implement the answer!

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