Data: does anyone really know what they’re doing?

Mastering Customer Journey Analytics and Big Data, published in Netzwoche magazine 

I’ve recently moved to Switzerland, which means I’ve had the fun (expensive) job of furnishing a new apartment. One of the most important purchase I made was the sofa. A very important purchase. Not only because of the financial aspect, but also this is something you have to live with day in and day out. And when I say ‘I’, I mean ‘We’. Ultimately, purchase decisions are rarely made individually.

After some initial research online - getting ideas, narrowing down brands - I visited a store, or shall I say  a ‘Sofa Experience Centre’. No salespeople, no arrows on the floor directing me around, just sofas and some friendly staff who were more interior designers and sofa geniuses than sales people.

After a cup of coffee, and having given each sofa a good test, I left without a purchase or any next steps.

Two weeks later, as part of my Sunday evening routine I was randomly browsing through Instagram. Due to my recent past search history, a post from the sofa company appeared on my feed. I explored a bit further and ended up visiting the website of this company.

Whilst on the website I was able compare models and use tools to help me narrow down my choice. I then was able to fully configure my sofa, choose my size, choose the fabric type and the colour all from my tablet.

I then made the purchase. I hadn’t seen this sofa in person, I didn’t have to revisit a store, and I didn’t have to talk to any sales people. Everything was easy. Three weeks later my sofa arrived.

The reason I’m talking about my sofa purchase is because of the Customer Journey.

Think about the traditional method of purchasing a sofa -  it’s very different!


The Customer Journey

There has been a monumental shift in customer experience. We’ve moved from individual interactions to customer journeys. A customer journey is a unique set of interactions a customer has with a brand to accomplish a task.

A few years ago it was about individual channels, web, email, social etc, and how we optimise those channels for their specific function. Now, it really is about a 360 Customer Journey Experience.

Understanding and focusing on customer journeys delivers a lot more value than the fixing individual interactions.

However in today’s times, the complexity of a customer journey can be mind-boggling. Customers move back and forth between different channels. Identifying which paths are working and which are getting in the way of success and growth, is a big data and analytics challenge.

Most companies have the data, they just don’t know what to do with it or how to extract its value.

And it is this that prohibits a lot of people from ever starting.

The good news is that you can start very small, and still deliver huge value.


Data and Reporting vs Analytics

This is by far the biggest challenge! The majority of organisations are still focusing on reporting. Reporting just tells you what has happened. Analytics though is not only about what has happened, but why and what to do about it. It becomes less about fixing gaps and issues but more about predicting and understanding your audience.

Analytics is about having sophisticated data sets that are ready to be used, that feeds and drives an actionable insight programme.

Analytics should be perpetual, reporting is scheduled. Analytics should evolve, reporting is static. Reporting translates raw data into information. Analysis transforms data and information into insights. 

Think push vs pull. Reporting is a push approach, reports are pushed to users who are then expected to understand them for themselves and most of the time just sit in someone’s inbox. Whereas analysis follows a pull approach, where particular data is pulled by an analyst or data scientist in order to answer specific questions.

The most important difference: Reporting = Individual Interactions. Analytics = Customer Journey.

Now I’m not saying reporting isn’t needed, it is. It just wont help you deliver a meaningful understanding of your users and evolve your business.

So where to start…


Big Data

Big Data has been a big term the past few years. And a lot of people are scared of it because of the word BIG. The term big data rarely reflects the size of the data. It more refers to the use of user behavioural analytics, predictive analytics, or other advanced data analytics methods. Instead of having all of your data in separate places, data is all stored in one place so can be easily handled. Of course, the quantities of data are large, but it’s not the main characteristic.

If I were to give only one piece of advice to people, it would be to “just get started”. I’ve seen so many data projects become stuck for months, even years. Data can be a very big investment, and the best way to approach this is by starting small. Start delivering value quickly and the business case builds itself.


Your organisation

At the beginning the most cost effective and efficient way will be to outsource the majority of work. Longer term you need to think about your organisation though. It really is about the classic People, Process, and Technology mix. You do need specialist people, you will need to adopt new processes (and maybe a new mindset), and new technology is needed.

For technology, you’ll need to think about three main areas:

·      Big Data infrastructure such as cloud database and business intelligence tools

·      Analysis tools for example A/B testing and machine learning

·      Visualisation tools to display data into formats that can be easily analysed


There are a number of open-source off the shelf tools now, with flexible and cost effective pricing. You can have a workable solution within a couple of days.

For people, I would think of it as you will need specialists for each technology area. Of course at the beginning you can have hybrid roles, or agencies supporting certain functions.

For process, there are going to be some major changes to current ways of doing things. This is one of the most important parts. You could have the best data team and world class technology, but if the processes aren’t in place across the organisation to support it you’re not going to get very far.


Putting it into practice

We work with many businesses of all levels of maturity. For some clients we’re implementing advance machine learning and AI techniques, and for others who are just starting out on their journey with ad hoc analysis.

I was recently working with a very large global business. They had a very complex customer journey. The majority of initial customer research was done on third party websites. The evaluation and decision phase was done mainly on their website. And final sale was always done in person. It was also a heavily above the line media focused brand. Connecting these dots was a challenge.

Their biggest challenge though was the inability to move quickly. Red tape everywhere. Teams that are used to building 12 month project plans, instead of actually delivering the work.

In order to overcome these challenges we had to shake things up a little.  We chose a hypothesis (to correlate online actions with offline sales), and set ourselves a three week deadline to completion.

Of course, we didn’t do this at a global level, we chose a specific product in a specific market. This allowed up to prove out a theory, which then was the foundation for our business plan.


Our Data Principles

  1. Don’t waste time. No matter how incomplete the data is, it’s better to get moving and start to create value.
  2. Create actionable insights. Data and analysis is nothing with execution.
  3. Put your users and business goals at the centre. Don’t cast your net too wide, you have to be hypothesis driven. So start with hypotheses around your users or business challenges
  4. It’s not about technology. It’s about people. The technology is just the enabler.
  5. Change management. Don’t underestimate the importance of internal communications and upskilling your employees.

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About the author

Luke Watkins-Lewis

Senior Digital Strategist, blue-infinity Linked by Isobar