You might find it shocking that most consumers don’t make purchases after an initial site visit.
As a consumer and a seller, you probably already understand how customer engagement works and some of the metrics needed to boost happiness and turn potential customers into actual buyers.
But what you probably don’t realize is that even knowing these things won’t prepare you for this: a staggering 98 percent of all consumers never make a purchase after just one visit. That’s probably more than you thought, right?
What’s more daunting is that even when visiting a brand’s website or mobile app with the intent of purchasing, 32 percent of consumers rarely or never make a purchase.
Purchases and transactions don’t happen instantly nor in a silo. They’re always preceded and likely followed by multiple interactions with the brand or the company.
All of these interactions are a part of a customer journey that happen across different platforms and dimensions. From real life influences to online research, from store visit to online shopping, user journeys are rarely simple and straightforward.
Today the great majority of these interactions can be measured and analyzed in detail. This is where customer journey analytics come into play. They are utilized to better understand, and ultimately optimize all those touchpoints, and consequently the entire customer journey.
To fully appreciate the complexity and importance of customer journey analytics, we first need to go over the core concepts of a customer journey.
What is a customer journey?
Simply put, the customer journey is a snapshot of the entire customer experience starting from the customer’s first interaction with your company and brand and everything interaction thereafter.
Per Salesforce, these as a part of a core marketing funnel divided into 2 main phases: pre purchase and post purchase with 10 steps among them.
The main stages of the customer journey are: awareness, consideration, decision, retention, and advocacy. As you can imagine, all of these stages are intertwined with multiple interactions.
These customer interactions are usually referred to as touchpoints. A touchpoint represent any time a potential customer or customer comes in contact with your brand–before, during, or after they purchase something from you. These touchpoints can be grouped to create a map of customer journey steps, which are a part of the aforementioned customer journey stages.
All these different layers can be neatly represented as follows:
And measuring these specific touchpoints while retaining a perspective on the general customer journey is in the core of customer journey analytics.
Customer journey analytics
Simply put, customer journey analytics connects all the data from all the touchpoints across all channels over time.
G2 defines customer journey analytics software, also known as customer journey orchestration software, as software that facilitates the management and automation of the customer experience across all possible channels.
“Journey” is the operative word as the software tracks, weaves together, and analyzes customer interactions across all channels so that businesses can react in real time by executing behavior-driven strategies.
And that’s complete customer journeys. Not just single touch analytics and not just a snapshot of a user visit, but a comprehensive overview of the entire user experience (UX) with your brand.
Related: Your customers should always be front-of-mind. Study up on 101 customer service statistics to see how UX as well as the customer journey shape the customer experience overall.
In a 2017 Dimension Data report, customer analytics was rated as the number one leading trend that would reshape customer experience capability by respondents at their respective companies.
As you can see, using customer journey analytics businesses are able to get to the key insights that have profound implication on business decision making and the business altogether.
Applications of customer journey analytics
These are the six most frequent, impactful applications of customer journey analytics, according to Steve Offsey from Pointillist.
|Boost customer acquisition by identifying the high-impact journeys|
|Increase retention by predicting customer behavior and understanding customer preferences|
|Grow revenue by identifying cross-sell and up-sell opportunities and triggering personalized communication at the right moment and through the right channel|
|Improve customer experience by discovering bottlenecks|
|Maximize customer lifetime value by revealing which factors underlying customer lifetime value are most significant|
|Increase marketing ROI by measuring and improving cross-channel efforts|
Still, implementing the above applications isn’t free of hurdles. A study found that 30 percent of respondents identified UX and customer journey analytics as the biggest challenge to their organization – higher than any other category.
Quadient notes three specific challenges when it comes to customer journey analytics:
- The lack of the required internal skills and capabilities.
- The cost of implementing these programs. Although this came is as the second most rated reason for lack of adoption in the BlueVenn survey, it is by far the #1 difficulty that CMOs have referenced.
- The journey mapping don’t “exist in practical terms”. Many customer experience and marketing professionals find it challenging to implement journey maps that are actionable.
So how can you overcome these challenges and start with customer journey analytics?
4 steps for getting started with customer journey analytics
There are four things you can do immediately to start with customer journey analytics.
First: Map out your customer journey touchpoints.
Go crazy here without a need to be super precise. Think about what customer facing activities you, as a business, are doing. Additionally, map out all the ways and means how would your ideal user interact with your company, product or a service.
This includes reviews. You can find out what your customers are saying and who is listening by taking charge of your G2 profile.
Second: Review all analytics tools and platforms you’re using at the moment.
And I mean all of them – all platforms collecting any kind of data: email clients, social media tools, website analytics, physical tolls, website analytics, ticketing systems… Whatever is capable of sending you feedback and data.
Now, having the data origins in one place helps you grasp the amount and type of data your receiving on your user interactions.
Once you have the first and second steps wrapped up. a simple overlap of your pre-defined touchpoints with analytical tools (and data origins) should clearly show you what are you missing and what you have covered.
Third: Use all your existing analytics platforms with a clear goal of answering customer journey questions.
It is paramount to go beyond user sessions and simple visits. Ask the big questions, like how many touchpoints our best user has with our brand? How and when did they learn about or product? What is the fastest most converting customer journey path we have, and so on.
To get to the answers, you’ll need to combine the usage of all data points available; set strategic goals with tangible (observable) and achievable customer touchpoints; and attach goal-focused touchpoints and journey-specific metrics (OKRs).
Fourth: Find and hire help. buy, subscribe to, and hire help with.
G2 offers a good overview of the market leading customer journey analytics solutions out there. But of course they come with a price. On top of that, if you’re not introduced to the core analytics concepts and without strategic approach to them, you will see no value and get no valuable insights from them.
Thus, it’s important to embark on the customer journey analytics discovery yourself, within the existing processes and platforms you have. With some minimal investment, you can get to insights that will stay with you for a long time onward.
Going beyond sessions or user-based statistics
There isn’t a customer journey analytics solution that can work if all departments or channels work in silos or share data with incompatible formats.
Synchronization and integration is the key here. With an organization-wide defined direction to utilize the data from all your platforms, even Google Analytics can be super powerful.
Without diving deep into property settings, custom dimensions and plugins, I’d emphasize that properly set conversions (goals), a UTM strategy and attribution models are a great start into enhancing your Google Analytics closer to customer journey analytics.
Obviously this is not applicable to all businesses and business models, but for those that rely heavily on digital marketing and digital properties, it’s a good start.
UTM stands for Urchin Tracking Module parameters which are parameters attached to URLs with a goal of tracking the effectiveness of online marketing campaigns across traffic sources and publishing media.
Attribution models are the set of rules that determine how credit for conversions (goals) is assigned to touchpoints in conversion paths. For example, the Last Interaction model in Analytics assigns 100% credit to the final touchpoints (i.e. clicks) that immediately precede sales or conversions. In contrast, the First Interaction model assigns 100% credit to touchpoints that initiate conversion paths.
Properly defined and utilized, these tools can be used as a core in your customer journey analytics. The majority of tools pick up UTMs by default and process them in various representations.
Some tools allow you to visualize the entire journey on your website or a mobile app from the first visit to the last, regardless of the time and number of visits. Filtering journeys with UTMs enables you to observe how users from other sources or at different stages in the buying cycle behave. Visitors from display ads campaigns may behave differently in contrast to those coming from that social media influencer campaign.
Other tools specialize in gathering data from various sources and attribution modelling. This allows users to compare different models and understand what really is driving business and maximizing ROI.
You can even see the most successful channels with Google Analytics. There they are referred to as conversion paths. Here’s an example of how conversion paths (with filters applied for an example) are presented in Google Analytics.
Graphic courtesy of Search Engine Journal
This report alone will give you most successful digital channels that lead to conversion on your website. From that level, you are no longer looking into in average session duration or pages per session, but into what groups of channels and paths are most important for your business.
UTMs and conversions attributions alone can help you in identifying the most successful campaigns and channels throughout time. Connecting these would provide you an abundance of information about your customers and their journey through your digital properties.
Again, when it comes to customer journey analytics, conversion tracking, UTMs, and attribution modelling is as basic as it gets. It is in the core of everything else you’d embark with full-blown customer journey analytics. This is why education is noted as a first thing in this entire process. Understanding the basics would be of great help and crucial for future successes.
Customer journey analytics are a super powerful tool that have multiple benefits for all businesses. The truth is they’re also challenging to properly implement and use. This is simply because customer journeys are never simple either.
Additionally, customer journey analytics shouldn’t be viewed as just a tool. For it to fully work and contribute to business, it requires a strategic approach and integrated focus. Organizations with significant departmental differences and omnichannel efforts face additional challenges to make it work.
Still, every organization can get to customer journey analytics rather easily. Starting with education, common direction, and some basic setup, businesses can build a solid core that will allow them to embark into customer journey analytics without major hurdles.
Any business that can collect any type of data and send it over to a data warehouse or analytics solutions can use these basics to embark on customer journey analytics.
Data for the purpose of understanding customer journeys should at least include:
- A unique and discernable identification of a source (UTMs; QR code on a leaflet or inside a shop)
- Identification of successful positive action (goal, conversion; purchase; sign-up)
- A way to identify different channels and attribute conversions to them (conversion attribution models so no channel gets ignored and success is distributed properly)
When these basics are satisfied, it is really easy to build on them to a full grown customer journey analytics solution.
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