Improving data awareness.

Posted in Design thinking

28 Aug 2017

Bill shock is a significant factor in a customers decision to leave their current provider and find someone else to fill their data hunger.

In order to keep them both happy and able to use their mobile for what they want, when they want. We decided to launch the new data usage tool.

This tool gives them live usage information, guidance to help them from going over their bill whilst promoting add-on packs for those who just can't stop tweeting about how much they are LOL-ing right now.

Let's kick this off

To help understand the problem we ran several workshops, involving key stakeholders, call centre staff, technical masterminds and feedback from the customer experience team. From here we noticed a few things:

Digging into the research

Working with the call centre & callminder we were able to get direct access to customer complaints, feedback and insights into how the care team were able to help resolve their issues. Using this information we compiled some simple customer surveys and through a Lean testing approach we were able to survey store employees and customers.

Let's get drawing

Next up we organised several iterative sketching sessions asking attendees to sketch out their ideas, journeys and then share with their group in multiple rounds of 2-3 minutes per round. A facilitator would then write down any limitations, observations & questions for follow up with the Business Analysts and UX teams at a later date.

Most attendees, including us designers, seemed to focus on solving this problem using charts. This became quite interesting later on when we got into the analysis & design phase, but we'll get to that later.

The main callout of the day was that customers wanted to fix the problem before it costs them $$.

Understanding tech

No solution would be possible without the awesome technical teams working tirelessly behind the scenes to make it happen. But before starting any solution we really needed to understand the limitations of the wide ranging systems in play as well privacy, security and anything else that may impede what we would like to put in place.

So to get us on the right track we organised tech/design workshops, daily check-ins & weekly run throughs. Some callouts we had were:

Understanding these limitations helped push maybe's to definitely and add anything that was left behind to the technology roadmap post MVP.

The first cut is the deepest

I'll be honest, in the first rounds of design I was transfixed on creating a chart that could help convey not only how much they had used, but when they had used it. Locked in the war room I drew every type of chart I could, line charts, bar charts, you name it I drew it.

Something just didn't feel quite right, so I created a quick Marvel prototype and headed into the user testing lab for the first round of in-house testing. My gut was right, there was just too much information. Data used, data accumulated, overage amounts and date ranges.

But it wasn't all bad, after wiping the tears from my eyes and bidding farewell to my old friend the data chart, we found users were focusing on two pieces of information we had tucked away:

Gauges are so hot right now

If I had emoticons available to me I would say I (heart) gauges, they offer simplistic views of data in a compact form and to top it all off they look pretty cool, especially on mobile. Keeping this in mind we started the design for the data and days left elements.

The data chart was cut back to a much simpler "daily breakdown", in this bar charts showed data used on each day and was moved to a secondary tab. Moving this kept the initial screens simple, yet gave more detailed information to those who needed it.

In subsequent rounds of testing users were found to be able to understand not just how much data they had left but when they used it. Plus with contextual usage CTA's they were able to purchase more data before it became a problem.

This little piggy went to market

Customers who had, gone over their data allowance, called care and were then provided with more information about where their data was used had a higher satisfaction rate than those who had none. Although they may still have to pay the full amount, they were now able to either change their data habits or purchase more data so they could keep up to date with the latest Meme's on their morning commute.

The downside to this was that the care agents would have to compile a report from pages of tabular data and then calculate how much data certain data types had used. For example, if I had used 4GB on dropbox this could be anything from 40 > 1000's of records.

When you factor in a call centre cost of around $6/min, this is not only painful but quite costly too.

If only there was a nicer way to bubble up this information in a clear concise way, allowing the user to see if any of the data was hogging up their allowance.

The data hogs were born, or as they came to be known outside of the design team, the data consumers.

Marvel-ous prototyping

There are many prototyping tools in the market, from Axure to Invision, Flinto to Indigo. But my tool of choice has come to be Marvel, not only is it so simple to use but the amazing team are always there to implement feedback and add features we need to make our tests easier to run.

The main difference was it's Dropbox integration, due to the rapid iteration of design you don't want to have to spend hours updating each screen whenever an element is updated. So by linking the prototype to a dropbox folder you can simply update the PNG in that folder and all prototypes using that screen are updated automatically. Magic.

Without delving too much into it's awesomeness, the prototype phase helped test basic mobile interactions and gave a better understanding of how the screens flowed.

Coding, it's not just for the cool kids

Being a coder has its benefits, it allowed me to get my hands dirty in the code, prepare transitions and timings to help bring the flat designs to life. But more importantly it helps collaborate better with the team, to understand how a design is broken up into it's smaller parts and help nurture the product towards a lighter MVP. This helped save countless hours on having to design for each iteration of development.


As soon as the data usage tool went live Vodafone noticed a 29% reduction in data related calls and a 300% increase in online data add-on purchases. Overall data related NPS also increased as well as a great first run in the App Store.