Why Shared Services Organisations should benchmark with the entire market (and not JUST the most successful companies)
Posted by email@example.com on October 23, 2017
It’s possibly the best kept secret in the world of benchmarking (or more likely, that most of us happen to sit outside of the mathematician circles where the theory is well recognised). But either way, I confess to having spent most of my career completely oblivious to the issues with data selection bias in respect of benchmarking.
It wasn’t until recent years, when I started spending time with data scientists and statisticians (releasing my inner geek) that the penny dropped. It’s actual a pretty simple concept: If we only ever compare ourselves to top performers, we run into the very real danger of ignoring large (and very relevant) datasets from the wider demographic that, together with the top performers data, offer us a more complete view of the universe.
We all know the traditional premise of why corporate benchmarking is so popular (especially in an industry like Shared Services & Outsourcing that was built on performance measurement and lives and dies by its metrics and SLAs). It’s also basic human nature to aspire to be like those who perform better than we do. And so we religiously study the behaviours and results of the most successful companies in an attempt to join the ranking “Best in Class”/“World Class” or “Top Quadrant” (or whatever the latest marketing label that consultancies and analyst firms attach to the elite) in the hope that some of their genius rubs off.
But actually, the statistical facts are telling quite a different story. One that shows that looking for magic formulas solely amongst the top performers is rather missing the point, because the top performers will only ever be able to tell us part of picture. If we want the full story, then we have to read the whole book (and not just its most exciting chapter).
This isn’t new news. Analytics experts have been saying this since God was a boy, but somehow the message hasn’t filtered through to the mainstream, and by and large most of us still think benchmarking with the top of the quadrant is the only way to go. Don’t get me wrong – it’s definitely ONE way to benchmark, and certainly creates valuable insight into the high bars of excellence being achieved by reputable brands that we could all aim for. The Data Science team here at SSON Analytics fully supports the viability of this route (see crowd-sourced best practice metrics from Top 20 most admired SSOs). But the bigger question is about whether a truer reflection of market benchmarking is to ALSO consider the wider set of data benchmarks that don’t make the selected peer group or elite squad.
If you’re coming at this concept for the first time, then I’d strongly urge that you read some much better rhetoric than my own (admittedly highly unqualified) take on this to help you form your view. My favourite lay explanation of the theory is Jerker Denrell’s HBR article Selection Bias and the Perils of Benchmarking. It’s an oldie but a goodie.
Denrell presents the issue far more eloquently than I have:
“Looking at successful firms can be remarkably misleading….Here’s the problem with learning by good example: We fall into the classic statistical trap of selection bias…. relying on data samples that are not representative of the whole population. The theoretically correct way to discover what makes a business successful is to look at both thriving and floundering companies”
The question now is how to absorb this into our day-to-day benchmarking exercises. SSON Analytics put its mind to examining whole population datasets that equip the user with a more comprehensive perspective of the market. Our Chief Data Scientist feels pretty strongly about this, and as such masterminded the Metric Intelligence Hub™ which includes a series of Finance and HR metric benchmarks across whole datasets across 121 countries and 22 vertical industries. This frontier efficiency approach examines data from all companies in the selected population (both thriving and surviving) eliminating the possibility of data selection bias. It’s a pretty neat idea and you don’t need add your own data to read the country-specific and vertical industry specific results.
We could debate the merits of different benchmarks approaches until the cows come home, and I suspect there will never be a definitive answer (given the highly contentious nature of the topic). There will doubtless be plenty of people who have plenty to say about our suggestion that Top Performer Comparisons only will always give a limited view of the universe.
I’d (politely)suggest those who dispute this theory may have a pretty good commercial reason to disagree that this wider approach to benchmarking has real legs. And I expect they’ll (equally politely) suggest back that we have a commercial MO too. Repeat forever…
- Should You Consider Healthcare Risks When Choosing A Shared Service Location?
- Why Financial Benchmarking Really Matters
- How Philippine Delivery Centers are Deploying Success Levers in the Year Ahead
- ESTABLISHING A CENTER OF EXCELLENCE (COE) IN AUTOMATION – 4 Critical Ingredients
- Dummy Blog
- Dummy Blog
- Data readiness – a precursor to realising the true potential of automation
- 3 Trends Confirmed at SSON’s European Flagship Event
- Top 3 Trends in European Shared Services in 2019: How are scope, outsourcing and automation strategies adapting – and why?
- Higher Education Institutions must look beyond implementation challenges of Shared Services
- What’s driving successful Business Transformation in the Nordics right now?
- What does good really look like? Benchmark your SSO against 2 different benchmarking datasets
- Business Continuity Plan alert: Q3 2018 Earthquakes now at a record high
- Shared Services in Deutschland: Fokus auf Produktivität und Working Capital
- German Shared Services Focused on Productivity and Working Capital
- Open for Business: Consistent figures for 2018 US Shared Services jobs market
- How to succeed with Intelligent Automation: what our surveys tell us
- Is "gender" impacting shared services careers?
- Chief of Payroll, UNICEF Global Shared Services Centre
- Is a profitable business necessarily a prosperous one?
- Accounts Payable: the case for automation and offshoring
"Despite having the highest risk of windstorm and flood as well as a high earthquake threat, Manila has the largest SSC concentration in APAC"
Data from 'Evaluating Risk in Asia Pacific Shared Services Hotspots' - Analytics Workbook - SSON Analytics
"We have found the Enterprise Dashboard and the SSC Metric Checker particularly insightful, as both are developed on factual analysis based on quantitative measures such as cost, cycle time, FTE effort, use of shared services, and application complexity."
Paul Bartley - Director, Global Shared Services - BD
"By utilizing SSON Analytics’ data tools/analytics reports BD has been able to benchmark and assess how the company is positioned against world-class performers. Knowing that what we are reading has been provided by practitioners makes it a very trustworthy source of information."
Paul Bartley - Director, Global Shared Services - BD
"SSON Analytics have been a really useful resource in providing Market Intelligence and insight into the shared services industry, giving me new perspectives into the strategic positioning within cities, countries and beyond. I am also encouraged to see incremental improvement to its offerings over time, and the engagements with the analysts and management have been more than pleasant! Excellent overall!"
SSON Analytics subscriber - Research Manager - leading chemical company
"As we measure our operations, we are tapping into the comprehensive metrics in SSONA’s City Cube to benchmark what our peers in the same region and industry are able to achieve. This helps us to identify opportunities to drive continuous improvements."
Thomas Hung - Vice President, Group Finance Shared Services - Shangri-La Group