Calum Shepherd     speaking     consultancy     archive     feed

Product Management Blog

Listen, Understand & Nudge

Joining a service when the team have been through discovery, alpha and beta phases together can be challenging.

It’s a team that’ll have bonded through some tough times, and come together to solve problems with their users.

There are no fires to put out, so what do you do in the first 30 days?

Listen

I took the first two weeks to listen as much as possible, with as many people as possible.

It’s amazing what you can learn when you care about what the other person has to say.

Ask questions, and listen - product, people, process, technology and finance are good places to start.

Immerse yourself.

Understand

Listening helps you understand, which in turn helps you make sense of things.

It’ll be an iterative process in the first few weeks. You’ll think you’ve got it, then you will realise you don’t - and that is OK.

You’ll want to know about the organisation, stakeholders, service proposition, user needs and so much more - it’s a lot to make sense of and understand in such a short space of time.

Beginning to understand things is bringing all this together into a coherent whole.

Don’t rush.

Nudge

It’s time to begin to make small interventions, where you feel those interventions will be valuable. You can work with individuals where there is something specific, helping move things in the right direction - nudging things in the right direction ;)

Collaborate with the team as a whole to tackle bigger things - such as new problems to solve or questions to answer.

Canvas Conference - An Emotional Rollercoaster

I popped along to a conference for product people in Birmingham last week. Canvas Conference was brimming with great stories from people working in the product space. It included speakers from companies such as Thriva, Microsoft Research, Starling Bank and Monzo this year.

However, I’m not sure I was prepared for one of the presentations - nor were quite a few others whom attended.

Emotionally, it was a bit of a rollercoaster.

Haiyan Zhang is an Innovation Director at Microsoft Research. Haiyan works with people to identify where technology could improve or enrich their daily lives, where medical conditions put them at a disadvantage.

Haiyan talked about quite a few things, including a new platform for participants, and the need for making solutions financially viable further down the line.

Parkinsons is a disease affecting 10 million people worldwide, and leads to a loss of motor control. There is no cure.

I know first hand the effects that this disease has on a human being. It’s been close to me since I was a young boy. It has the ability to strip back all the things you cherish.

So, it was getting into Project Emma that was the most striking for me. It’s ambition was to help Emma, a women diagnosed with early on-set Parkinsons, write and draw again - giving her back a little of what had been taken away.

Haiyan and her team spent vital time sitting with Emma to better understand the importance drawing plays in her life, and more widely how Parkinsons has affected her daily life. Haiyan and her team worked day and night for months to better understand the problem and explore a solution to help Emma.

Remarkable, right?

It’s helped remind me that we all have a role to play. I guess most of us shape solutions for the majority of people, however people and their needs are heterogeneous - with all the complexity that brings.

We have a responsibility to those who have different levels of ability, or who need support to see the benefits that the things we build bring to people.

It’s why research, analytics and how we build things are so important to do right by our users - whoever they are.

C

Visualising IA with Mind Node

It’s time to break the blogging drought with a hands on post.

When producing an information architecture for a recent website our team decided use mind mapping techniques, as opposed to opting for a more conventional site-tree.

“Information architecture is the practice of deciding how to arrange the parts of something to be understandable” - IA Institute

We decided to use software to help us achieve this. It would have been ideal for us to use a physical space to collaborate. However, one wasn’t available - so we thought this the next best solution.

MindNode2 is a nice little application available from the Mac App Store. It became a our primary method to visualise our early thoughts both within the team, with stakeholders and with users.

You can add nodes as and when new information or topics become available through user research, search demand, conversations with stakeholders and insights from subject matter experts.

Diagram Tips

  • Line Colours - separate different topics, or to highlight different audiences
  • Line Weightings - suggest the volume or the complexity of the information
  • Arrows - highlight related nodes, or vital cross-linking
  • Node Types - suggest the type of content (e.g. video, image etc)
  • Notes - explain where it came from e.g. user research

You can then create a couple of additional main nodes to act as a legend.

It’s all vague enough that people won’t mistake it for a final piece of work. It’s also flexible enough that if you move things around all the nodes will dynamically move and resize.

You can read more about information architecture on Web Designer Depot

So, have a go and let me know how you get on.

Uber should borrow your new driverless car

It’s reported Uber have plans to reduce their dependency on human drivers and their vehicles through introducing driverless cars.

Uber is currently the modern day equivalent of your old school taxi company - just without many of the limitations and drawbacks that these have. However, if it does continue down the route of purchasing driverless cars and operating these directly - it becomes a company managing what could be an extremely large fleet of driverless vehicles.

Given that the rise of driverless cars will also happen within the private car ownership space initially - it strikes me there is actually a better opportunity adopting a hybrid model.

If the current appetite from people for car finance (either ownership or user-ship) continues and there is still a desire to have their own vehicle, would they be willing to offer it up during non-use for companies such as Uber?

When you think about it, people are already willing to fork out money each month for a vehicle which remains unused for the majority of the day, minus the morning and evening commute. So, why wouldn’t this continue with the introduction of driverless cars?

Though this time, your vehicle could have an Uber navigation app installed and be turned into a taxi during it’s downtime - all while you sip on your morning coffee.

Sounds good to me. Uber continues to have a relationship with human drivers, but begins to dip into driverless cars without having a massive upfront investment.

Assumed facts and educated decisions

I love the definition of data provided by Google - data, in philosophy is “things known or assumed as facts, making the basis of reasoning or calculation”.

There is something beautiful about this definition. It creates a relationship between things assumed as facts and their usage as a source for reasoning - ultimately helping us to make educated decisions.

Reasoning is “the action of thinking about something in a logical, sensible way”.

We know data is fundamental to what we consider to be good user centred design. However using the wrong data is sometimes worse than having no data at all, as it can support questionable decisions and go unnoticed for quite some time. When we use the wrong data to create solutions, we can get stuck in a loop of uneducated change - the opposite of what we are attempting to achieve through true iteration.

change is “an act or process through which something becomes different”

whereas;

iteration is “the repetition of a process or utterance as a means of obtaining successively closer approximations to achieve a solution”

So to really improve things we need to iterate - which can only happen if we use the right data at the right time. Our methods should be providing us with data that fuels reasoning and educates decision making - not having us bark up the wrong tree.

Understanding and proposing likely user behaviour can be tough at the best of times. Understanding the available methods, what they will provide and how to converge data seems vital.

For example, pulling some numbers out of Google Analytics is meaningless without context, which may or may not come from other sources. Context could be whether or not a filter is applied to the data. Or, it could be to find out why people are doing something, not just what they are doing.

It is also important to strike the right cadence with any research. Stages like ’research’ and ’measure’ aren’t optional, although can sometimes be seen as such - we just need to dial up or down the intensity to make it practical on a regular basis.

Thus, it feels more important than ever to ensure we get the basics right; collecting the right data, at the right time, with the right people involved to present back a series of assumed facts that help us make more educated decisions.

I have a feeling my phrase of 2016 will be ‘assumed facts’.

Have a great New Year!