The people at Vanguard have written a lot about studying demand in services. Why you should do it, how you should do it, etc. They even invented the term 'failure demand' – so I'm not going to repeat what they say here. For a short introduction to studying demand, I recommend this one minute read from Simon Pickthall.
Instead, I'm going to share a few things I've learned that are not helpful to do when you study demand. I've either witnessed everything in the list below, or made the mistakes myself.
1. Do all the studying yourself. As mentioned in a previous post, the main purpose of studying a service isn't for analysis. It's to help people unlearn and relearn. The best way for them to do this is by studying the service from the customer's perspective. And studying demand is a great place to start.
I made this mistake more recently than I would have liked. The demand analysis became just another slide in a PowerPoint presentation. It lost all it's impact, and definitely didn't change anyone's thinking about the service.
2. Guess what the demands are. Get a bunch of managers together in a room. Do some 'brainstorming' and – based on opinions – identify the most common type of failure demand. Put these in spreadsheet, with actions, due dates, etc, against each type of failure demand.
Or, decide the most common types of demand in advance. Make them in to a tick sheet, and give it to the people who receive the demand to fill in.
Studying a service is all about learning and discovery – it's not about trying to have all the answers.
3. Use existing data. Run a report from your CRM, using categories already defined by the consultants who implemented the CRM. This is similar to the previous point, where you shouldn't assume in advance you know what the customer is calling about. The difference here is you've paid someone else to do the guessing for you.
4. Be constrained by the existing rules. I was once with a team listening to demand for council housing repairs. One of the most frequent demands – particularly around midday each day – was "are you still coming to today's appointment?" The appointment was for a tradesperson to arrive at their home and carry out a repair.
The existing appointment slot was 8am to 1pm. For this reason, most of the team initially felt this can't be a failure demand. The resident was calling during their appointment slot, not afterwards, so surely we've done nothing wrong?
They were viewing this demand through the lens of the existing organisational system, and all it's constraints. Yes, no person has done anything wrong. But the system could be improved to stop this demand happening in the first place. For example, you could shorten the appointment slot. Or, as some councils have done, ask the resident when they want you do carry out the repair, and turn up then.
5. Don't involve the people who receive demand. When you're sitting with them listening to demand, don't explain why your there or what you're doing. Don't show them what you're writing down.
It's important to show your findings to the people who receive the demand. It removes some of the mystery about what you and your team are up to. It's also an ideal way to validate your findings – show it to them and ask them if this looks like a 'normal' day. Listen to what they say about it.
6. Fear failure demand. Look for excuses to categorise everything as value demand, for fear it will look bad or demotivate staff if you find too much failure demand. If this happens, it starts to give you some clues about the organisational culture.
7. Ignore the type of service you are studying. I've made the mistake a couple of times of studying demand in a people-centred service in exactly the same way I would in a transactional service. I've learned from this mistake now, and it's something I plan to write about in a future post.
8. Gather too much data. I've had people on my team before who were concerned they needed to collect data on thousands upon thousands of demands. Yes, you need to be somewhat scientific. But not to the extent of randomised controlled trial or anything else that requires similarly high levels or rigor.
When have you studied enough demand? When the people you're working with have learned what they need to learn, and when the demand has become predictable. This means you're no longer seeing types of demand you haven't seen before.
9. Do nothing else. I've seen demand analysis be treated as a one-off exercise, done in isolation. There may sometimes be value in doing this, but you're missing out on a fantastic opportunity to improve the service if you do nothing else.
It's important to next find out how the service responds to value demand, and does what matters for the customer. You'll then want to learn why the service responds in the way it does.
8. Gather too much data. I've had people on my team before who were concerned they needed to collect data on thousands upon thousands of demands. Yes, you need to be somewhat scientific. But not to the extent of randomised controlled trial or anything else that requires similarly high levels or rigor.
When have you studied enough demand? When the people you're working with have learned what they need to learn, and when the demand has become predictable. This means you're no longer seeing types of demand you haven't seen before.
9. Do nothing else. I've seen demand analysis be treated as a one-off exercise, done in isolation. There may sometimes be value in doing this, but you're missing out on a fantastic opportunity to improve the service if you do nothing else.
It's important to next find out how the service responds to value demand, and does what matters for the customer. You'll then want to learn why the service responds in the way it does.
* * *
How about you? Have you done or seen any of these mistakes? Are there any others you've seen that aren't covered here? Please feel free to comment below, or share this with someone who might be curious.
No comments:
Post a Comment