Bruce Merrfield: Human biases trump analytics
Survey stats: The brave and the fading.
I scan a lot of articles, with one of my research themes being return on investment for analytics, for which there are frustrating results.
One survey finds 70% of companies are not benefitting from their analytics investments. And it gets worse. The percentage of firms thinking themselves as data-driven has declined.
Over the past three annual 2019 Big Data and AI Executive surveys by New Vantage Partners, 37% were date-driven in 2017, compared to only 31% in 2019 (Google this survey for even more stats).
Why the analytical-insight execution gap here?
Be honest: Most individuals and businesses already know what they could do to be better. Who needs even more analytical insights for more improvement opportunities? What’s needed, instead, are more effective change-management strategies, tactics and tools. Most folks need help to be what they want to be.
How can a distributor immediately engage all employees’ minds, hearts, wallets and team spirit to move down a new path of innovation?
Try getting on this logic train
Discuss with all employees:
Who wants more total compensation along with job security, growth and pride? The answer should be 100%, yes.
Then, we must, at least, grow gross-profit dollars per full-time-equivalent employee (GP$s/FTEE) because only GP$s can pay for our wishes!
What controllable input activities can we do immediately to start to move GP$s/FTEE higher by working smarter, not harder?
Order size assumptions: No one can do two customer-related activities at the same time, such as sales calls, order-taking, quotes, picks, deliveries, invoice-paper-matching, etc.
If for each activity the average GP$s involved was magically higher, then GP$/FTEE would rise.
How, then, do we win more large-GP$ orders while consolidating small-GP$ orders?
Let’s invent some new analytics. Why not rank all customers by their average GP$s/invoice along with their total invoices? And, on the side, let’s divide total orders for the year into operating expenses to find out what our average cost per invoice is.
What do we discover? What next level of questions and new invented analytics will arise? At some point, what might be our first, easiest experiment to try?
Scary? Is fine-tuning the status-quo, instead, a viable option?
Help? Have other distributors ever gone down this path that we could learn from?
The answer to No. 10 is YES!
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