16 When a digital-twin model tells you something new

Digital twin business models are incredibly useful for businesses… as long as they’re accurate, with behaviours matching real-world experiences.

I have explained elsewhere just what a "digital-twin business models" actually are, and why they are so useful - but only if they are accurate.

So I often tell new-comers that "If, at any stage in building a model, its behaviour doesn't match data about the real world, there's something wrong, so fix it before moving on".

But I should add that one of two things could be wrong ...

yes, there could be a mistake in the model, or
the model is telling you something you did not know about the real world.

In a model recently built for a friend's B2B SaaS business, service productivity had historically improved as staff gained experience, from ~3 customer-tickets fixed per day to ~10 tickets/day. But in recent times, in spite of low staff turnover, productivity was actually falling - but that's not what the model said should be happening.

... which caused my friend to go investigate.

It turned out that staff were not managing to keep up with a rising rate of customer support tickets. So customer-relationship managers, trying to be helpful, were emailing support issues direct to the support staff, and not going through the ticket system.

So the falling service productivity was only partly true. The service team were actually fixing many more issues than the ticket-system reported - so 'issues fixed per day' was greater than 'tickets fixed per day'. But productivity was indeed being hit by the disruption to the team's work from those emailed issues.

Two big benefits from this discovery:

fixing the issue helped the service team leader much better manage the work pressure on the team
... much more important: product-development, marketing, sales and support teams all realised their SaaS platform and service had bigger quality issues than they had thought (the emailed issues from the CRMs were way more serious than the mostly routine ticket-issues)

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?

And of course, we 'fixed' the model to capture the impact of emailed support issues, to work out how much, of what, to do to progressively deal with the issue that the model had itself alerted us to.

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Building a digital-twin business model is nothing like as complicated as you may think - it's actually easier, faster and less error-prone than building a spreadsheet model for the same purpose. Check out our online business modeling course on how to do it.

Categories: : business models