Customer journey analytics
Analyzing customer journeys is now more challenging than ever, with complex conversion paths, greater privacy restrictions, and more customer touchpoints. There is a reason companies undertake this challenge, however; those that better understand a customer’s journey – both through pre- and post-conversion – are able to outperform their competition and drive increasing demand.
What is customer journey analytics?
Getting a 360° view of your customers means looking at every touchpoint, from their first ad or blog post view to their sales conversations and total lifetime value.
Obtaining a holistic view of this journey means businesses can focus their efforts on the parts of the customer journey that need improvement, or those that are really moving the needle. This can create the famous flywheel effect – a virtuous cycle that sees more customers and more referrals.
Getting the data to describe the customer experience in its entirety requires effective data unification. This is the ability to join multiple data sources and aggregate this data to the same level in the same location – generally using data modeling.
If data sources are not connected to a central source of truth, information becomes siloed, and this often leads teams to work at cross purposes, rather than focusing their efforts.
While some companies use all-in-one, or ‘packaged’, data tools to achieve this, leading companies are using the data warehouse as a central source of truth, allowing all teams to generate data models from a singular data set.
Once the full view of the customer journey is achieved, you can create the conditions for “hyper-relevance” as a key differentiator in a crowded market. A data-centric attitude has allowed newer ‘disruptor’ brands to compete against large names by being more customer-centric.
How to analyze the customer journey using behavioral analytics
While demographic and transactional data (e.g. age, income bracket, purchases made) help create a picture of the customer journey, these data points are like the nouns in your story, the verbs come from behavioral event data.
Behavioral data is comprised of the second-by-second actions taken as a user moves through your marketing funnel or product, such as ‘click’, ‘view’, ‘scroll’ etc.
Behavioral data is by far the most predictive type of data, as what we have previously done is the best indicator of what we will go on to do.
In order to create a customer journey with behavioral data, the metrics captured need to be captured and then validated. Data can be validated before or after capture (Snowplow advocates pre-validated data) and then stored in a centralized location.
At this stage, however, the data is still hard to use, as every single event from every customer may be stored in the order the events occurred, creating a lot of noise. Data modeling reduces this noise to permit decisions to be made. First, the data can be aggregated by sessions and then by users, so an identified user’s actions are listed in the order they occurred. Averages can then be taken to identify common journey paths – e.g. blog reads, demo views, sales contacts, etc.
What is customer journey mapping?
Mapping out every touchpoint in a visual way is known as customer journey mapping.
This can be seen as a 5-step process:
1. Define core personas and their aims – different personas often have different journeys and different goals
2. Identify every touchpoint for your different personas
3. Collect data for each touchpoint
4. Discover where your users are experiencing pain, friction or satisfaction
5. Create hypotheses to improve the customer journey and experiment
The customer journey map allows you to paint a fuller picture, eventually meaning you can tailor dynamic personal experiences for consumers.
Which use cases can be built with customer journey analytics?
Recommendation engines. Suggest the best product or content to the right user at the right time based on previous interactions.
Targeted emails based on web or mobile browsing behavior
Personalized customer experiences based on information from various sources, e.g. CRM and marketing data
Product analytics that combines in-app behavioral data with other sources
Effective marketing attribution combining behavioral data from all channels and platforms to optimize ROI
What is customer journey analytics orchestration?
This is a method used to improve the customer journey by using real-time analytics to constantly analyze customer interactions. Within a given context, the most significant interactions can then be identified and used to tailor the customer’s experience.
Behavioral data is the ideal ingredient for customer journey analytics orchestration, as it provides a record of how users are interacting with your assets or products, allowing for cutting-edge personalization. This is not just based on what they are doing at any given moment, but on everything they’ve done up to that point.
The challenges with customer journey analytics
1. Multiple devices, touchpoints, sessions, and conversion paths
There are many different paths a customer can take on their journey to making a purchasing decision or gaining value. Many tools struggle to create data of a sufficient quality to map the many potential routes to the same goal.
An example might be a user changing devices halfway through a check-out funnel, creating the appearance of a brand-new user spontaneously making a purchase.
Packaged tools such as Google Analytics also restrict the length of a session to 30 minutes, meaning that longer checkout journeys are counted as multiple sessions – even if they only lasted 31 minutes.
Customers are also likely to interact with various systems and channels, from websites, storefronts, call centers, and chatbots, to marketing emails and TV ads. This means data has to be integrated data from different sources and systems, leaving room for error.
2. The need for speed – real-time latency
To compound these issues, latency often presents a huge challenge. Once the customer journey is better understood, companies often start to automate actions based on predictive behavioral models. Many tools do not guarantee a minimum latency, which means information can be out of date by the time you’re ready to act, reducing relevance.
3. Privacy restrictions
ITP restrictions mean many platforms can now only track Safari users for days. Previously, this was multiple years. This creates an uneven view of customer activity, as this group of users will appear to be massively inflated, compared to Android users, for example.
Other browsers, such as Firefox, have now followed suit, and the result is that businesses cannot always create an accurate customer 360° and may be making decisions based on inaccurate data.
4. Data quality and a single source of truth
As we’ve mentioned, creating effective customer journey analytics requires taking data from multiple sources and storing it centrally.
This is a challenge, as data can often arrive in different formats and at different levels of quality. When mismanaged, the result is data siloes and tribal knowledge as engineers hack their way around data quality issues without proper documentation.
In this scenario, it is impossible to be a data-led and customer-centric organization, as you don’t know how customers are responding when you make decisions.
What is the value of customer journey analytics for your business?
Customers increasingly demand excellent user experiences on digital platforms, expecting personalization as well as highly-relevant marketing.
The value of successful customer journey analytics is huge; you can start to answer questions like:
– What’s the best time to engage a particular customer?
– What channels are best for engaging with a certain customer segment – or even individual customers?
– Which types of customers are most likely to take a given path to purchase?
Customer journey analytics in the travel industry – case study
Given the unique challenges of the travel industry, such as market fragmentation and fierce competition, identifying the single customer view (SCV) is a huge competitive advantage.
Travel start-up Tripaneer has created effective customer journey analytics in order to get a singular view of user interactions. They enabled a cross-device, chronological event log per customer that helped them to develop a deep understanding of the end-to-end customer journey for building customer relationships.
– Marketing attribution
– Customer lifetime value
– Key data for creating hyper-personalized experiences
– Insight into customer intent and other predictive measures
“If you want to do multi-channel marketing well you have to understand how these different channels interact with the customer in their research and purchase journey.”
Using Snowplow for customer journey analytics
Snowplow offers all the relevant pieces to help you deliver hyper-relevance to your users by fully understanding the customer journey.
What makes Snowplow ideal for helping you visualize your entire customer journey is:
- Capturing the full story: first-party and server-side tracking circumvent ITP restrictions to offer a complete view of user activity (at the time of writing for up to 400 days).
- Cross-device user stitching: capture the journey even when users change between phones, tablets, laptops, and IoT.
- Real-time data: get the data to your warehouse in seconds, allowing you to automate actions and achieve hyper-relevance
- Complete ownership: we never store your data and Snowplow’s infrastructure is deployed within your own cloud environment (“private SaaS”). This allows you to operate in a compliant and fully private way.
- Ultra granular and accurate data: with a range of user identifiers, access to your raw, event-level data, and the ability to ingest third-party data from multiple sources, a complete, end-to-end customer journey can be constructed.
- Flexibility – freedom to customize tracking to capture exactly what makes sense in your context, allowing for more experimentation and personalization.
To test this for yourself, try Snowplow for free