Find insight from the user journey
You interact with your users, customers and potential customers in so many ways, through many channels and at all stages in their journey. So, how do you use data to create insights which improve their experience and grow your business?
There are three broad approaches to this problem out there. These are:
- the digital path approach
- the statistical modelling approach
- the user experience approach
Let’s explore these and think about how each can lead to new insight: what else we might be able to do with the user journey.
1. Digital path analysis of user journeys
At Google in 2008, a small team in London started looking at custom DoubleClick ad server platform data tracing the ad exposure and click path across digital channels to conversion on an advertiser web site (the team was headed up by yours truly). At around the same time others at Google were looking at measuring the assisting role of generic search clicks to brand search conversions. These analyses became the underpinnings of user path analysis, search funnels and attribution modelling, now built into Google Analytics.
The first task of the attribution pioneers was challenging the built-in assumption that ‘last click wins’ when measuring digital conversion journeys. Today most companies still rely on this flawed conversion tracking logic inherited from the earliest days of internet marketing. The issue is that when the customer sees an ad or searches around to research a product, they often decide what to buy first and then only after that do they make the final last click to the sale: last click does not in fact ‘win’.
More recently user path analysis based on behavioural data has become widespread and is now serviced by small army of marketing attribution technology companies. Data on marketing and web analytics has the potential to provide a complete view of the user path both off and on the website. Still many companies are only just waking up to these new ways of measuring marketing impact and a recent eConsultancy survey found that only 39% of marketers believe they understand customer journeys and adapt the channel mix accordingly.
The analytical challenge today is finding the most robust method for data driven attribution, ensuring that data on the non-converting paths are used to infer the contribution of each channel, campaign and ad. Several companies claim to do this, and clients of Google Analytics Premium can leverage Google’s own data driven modelling approach to optimise their digital marketing spend.
If you want to know what role a digital click typically plays in user journeys for your industry in your part of the world, you can even access a free data tool by Google to do this at a very high level.
In this view user journey analysis would seem to be a solved problem at least for marketing ROI, but of course it is not that simple as there are huge challenges remaining. Foremost, the data is generally incomplete: users are rarely tracked across part of the journey, even if the tracking is set up right, they use different devices and browsers which miss out the whole picture. People also have a habit of being influenced by channels you cannot so easily track in the same data set as your trackable analytics, like offline ads, call centres and not forgetting, competitor offers. In addition the data you get is usually anonymous, with limited data on who exactly you are influencing, which matters a lot because different types of people are influenced in very different ways.
So much for method one: it’s a great data story when it can tell you how to better allocate your online spend, and optimise your web site, but it is only as good as the incomplete data that goes into it.
2. Statistical modelling for user journeys
While digital path analysis takes the problem from the bottom up, an older method looks from the top down, tracking sales and channel influences over time, and using regression modelling to identify the role of each channel. The same techniques can be used to compare steps in a user journey on a site, tracking views and clicks over time and modelling how they influence each other in aggregate.
A great advantage of this approach is that it can look at relationships beyond what can be tracked at user level. Notably the role of offline channels can be included. The statistical techniques are well understood and applied methods like market mix modelling are taught at many business schools and universities. Given the potential to get more of a complete picture, some are advocating this type of approach (for example this post).
The downside is that while you gain in non-tracked data, you lose granularity in the tracked data. The data about each step a user takes is aggregated with the steps of all other users, leaving out the important path information. As a result the technique has somewhat fallen out of fashion, despite the advantages in linking all online and offline channels that it potentially offers. Also, if you use statistical modelling beware: a non-expert cannot tell the difference between a spurious regression model and a robust one, but the former kind is all too commonly peddled even in large organisations. So, make sure you have plenty of data and a statistician who knows what they are doing.
3. User experience for customer journey mapping
The third approach which many companies follow is to storyboard the customer journey across all touchpoints. The idea is to create a visual map of how different types of customer interact at all stages of the journey through to sale and ongoing engagement, across all touchpoints. Each type of customer is identified by a customer persona. Developed by UX design professionals, the map then becomes a useful way to exploring the blocking steps or friction points in the customer journeys, and for making sure that all channels are working together in synch.
The main upside of a customer journey map is the ability to summarise and communicate the user experience to key folks in product management, marketing and senior management. A good map can create empathy for the customer and focus minds on the central problem of improving their experience.
Because mapping is a communication tool, it makes for a great data story. However, of the three approaches outlined here it runs the obvious risk of being strongest on the story, but weakest on the data to back it up. To be data driven customer journey maps can work provided they are grounded in research derived customer personas and each stage in the journey is linked to metrics and KPIs that can be tracked to understand how the journey is building new and repeat business.
The true actionability of a customer map lies it the way it highlights the bottlenecks in the customer journey, so that these can be given due attention. Using market research personas, metrics and KPIs grounds the focus on the steps which matter, and guides data driven priorities. Not a bad approach, however unlike the other two, it is unlikely to yield information about the true incremental impact of any changes you make.
The best of all worlds
So, is it possible to get the best of all worlds? The simple but expensive answer is that you could do all the above and explore areas of agreement and inconsistency. A more manageable answer would be that if you are mainly a digital play, path analysis and attribution make sense. If you have significant offline to online interactions, and your business is high volume, you should explore statistical modelling. And whatever your business model, a high-level customer journey map can help you to understand the overall picture.
Also, if you do make changes to how you interact with your customers, do not forget to apply experimentation and AB testing to validate and optimise the journey.
Finally, if you really want to understand your customer journey, you must try going through the process yourself. After that, try the journey again but this time imagine you know nothing about your product, you are on your iPad and you are in a big hurry. You will soon find room to improve.
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