ETL Stories of siloed metrics: How to accidently incentivise the wrong behaviour and overlook star performers

By Kim Furman, Synthesis Marketing Manager

If you are not measuring the right data, you are likely rewarding the wrong behaviour. Or you are not rewarding the right behaviour leading to poor business outcomes.

This is what Adam Grant refers to as obvious insights. It seems obvious yet over the years I have come across numerous businesses who are making these same mistakes.

Why? Because measuring the right data is not simple.

You can go to the doctor with chest pain, have an ECG and be told that the data shows you have nothing to be concerned about. A concerned doctor sends you home with a 24-hour monitor and stress tests you during an ECG by making you run on a treadmill and suddenly the data tells a different story.

The first set of data was not lying – it was just siloed.

The aim is to reveal the truth and the truth requires a full picture. Siloes or examining information disparately are the enemy of truth, especially customer moments of truth where we feel our businesses are creating excellent customer service, but our customers are actually experiencing something very different.

The antidote

The antidote – ETL. This stands for extract, transform and load – bring data into one place.

This process is where data is extracted from different sources, transformed into a usable resource, and loaded onto a single system.

“ETL tools break down data silos and make it easier for your data scientists to access, analyse data and turn it into business intelligence. It also enables senior management to have access to accurate, real-time information allowing them to make accurate business decisions based on all the data, not siloes, ” explains Deon Schwabsky, Customer Success Manager at Synthesis Software Technologies.

Let’s make this relatable: “Every single company in this world has exorbitant amounts of data, and, in fact, they probably have more data that they can’t actually utilise. But if that data is transformed where the labels across systems mean the same thing (customer name is customer name, all the customers’ addresses are in the same format and phone numbers are in the same format) and then allocated into a single system – suddenly you have business clarity because every system speaks the same language.”

The stories below illustrate how easy it is to make the above-mentioned mistakes and how a single source of truth with ETL prevents my opening statement.

Story 1: Rewarding the minute maker

Over a decade ago, a Customer Service expert gets called into a call centre to make improvements. The company explains that they define success as solving the call and wrapping up the problem or request in a certain time frame. Let’s call it a minute. As a starting point, our expert decides to secretly listen in on some of the calls beginning with the company’s top performer. This person has managed to wrap up every call in a minute.

He jumps on the call eager to learn what makes the “minute maker” tick. As he listens in and the clock rapidly ticks closer to a minute, he realises that the minute maker is about to lose their track record.

The call is nowhere near being wrapped up. Suddenly the line goes dead. This happened time and time again. The “best performer” was being rewarded for putting the phone down in the designated time, not solving the problem and servicing the customer. The call centre was confusing closing the call with call closure.

Applying ETL

What happens to companies that cannot listen to every call to detect this? They need integrated systems.

The systems that measured time of calls could have been integrated with the system that measures repeat calls – the customers would be calling back to complain of a dropped line. A dashboard with consolidated data would indicate that a significant number of customers allocated to the minute maker call back. This consolidated data could then trigger an investigation.

Story 2: Welcome to the park-in

A fast-food drive-in wanted to get customers in and out as quickly as possible – who doesn’t want fast fries?

They decided that employees would be penalised if customers were parked too long and not out of the drive-in in a certain number of minutes. According to the data, this was working beautifully.

However, the reality was different. Picture the poor employee who gets a person rolling up to the window requesting a veggie burger – no lettuce, extra mayonnaise, double cheese, added onion and no tomato as the driver is allergic.

The employee starts to sweat. They are going to get penalised. What do they do?

They get the idea to tell the driver to park their car in the parking lot and they will come when it is ready. The cars keep moving. Problem solved. Employees catch on that complicated customers just need to simply park elsewhere and wait.

Applying ETL

The drive-in was measuring the wrong metric (a siloed metric) – how long a car is parked for.

They could have been measuring how long the car was parked for as well as order time.

The system could have shown that when those two numbers do not correlate – something has gone wrong. Instead, they were rewarding employees for encouraging a park-in and not creating solutions for complex orders.

Story 3: Covid and the overlooked star performers

When Covid hit a business’ call centre was faced with a problem – they could not access enough 3G cards to keep their over 500 agents connected remotely. How would they keep the centre up and running?

They decided to give 80 of the top salespeople 3G cards and laptops. They would have to hold down the fort or rather hold up the business.

What they did not expect is how well these 80 people would do. These 80 people made 80% of the monthly sales.

Suddenly the Pareto Affect or the 80-20 rule was real with 20% of the workforce making 80% of the sales. This allowed the business to see that it was not rewarding its star performers as well as they should and that other employees needed attention to determine why they were starkly underperforming.

Applying ETL

We understand what certain areas of the brain do because unfortunate individuals over the years had damage to those areas or had to have them removed – this illustrated not only their function was but the function of other areas.

This is not the ideal way to find out a function and neither is the above. In this case, star performers were not being rewarded as needed and there was no retention strategy.

Under performers were not being identified, nor the reason for the problem. In this case, if the data was transformed and brought into a single system, there would have been visibility of which salespeople were generating the most signed deals and under performers could have been managed.

Closing thoughts

Customers experience moments of truth every day – an instance where they come into contact with a company and it leaves an impression, often a lasting one.

However, numerous businesses are missing their moments of truth because their data is siloed, leaving them to measure the wrong data, reward the wrong behaviours and miss problems that need solving. That may be the harshest truth of all but it does not need to be the reality.

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