What is Customer Journey Analytics?
Customer Journey Analytics is the process of analyzing the customer experience across every touchpoint in the customer journey. Often, machine learning, python, and various software tools like Adobe or Woopra are employed to fully measure customer interaction. There are many use cases and examples for applying customer journey analytics successfully.
Why Do Companies Use Customer Journey Analytics?
Companies use customer journey analytics because it is one of the single most effective ways to increase customer lifetime value, improve customer loyalty, and drive revenue growth. Customer journey analytics gives teams a window into customer behavior which provides valuable insight they can then use to inform their decisions.
With customer journey analytics, teams gain visibility into every important customer interaction. They no longer need to rely solely on customer feedback, which, while important, rarely tells the whole story. For example, while some users might provide customer feedback via a customer satisfaction survey, most will not, and without customer journey analytics, the company may never know why those customers churned.
Customer journey analytics encompasses advanced analytics methods such as predictive analytics, real time analytics, customer segmentation, and more, in order to provide companies with actionable insight that can directly impact their bottom line.
Customer Journey Stages
The first concept to understand when getting started with customer journey analysis is the three main stages of the customer journey: Acquisition, Activation, and Adoption.
1. Acquisition Stage
During the acquisition phase, a buyer is exploring a solution for their problem. They discover the company through an acquisition channel such as advertising, organic search, billboard, tv, review sites, or word of mouth referrals.
Once the prospect reaches the website, they’ll continue the discovery process by exploring content, searching the website, or watching videos. They may even initiate a chat conversation or contact the company to further investigate its offerings.
A B2B company may engage the prospect at this stage by email or phone. First, a Sales Development Rep (SDR) will reach out to ensure the prospect has the qualifications to purchase using a process called BANT (Budget, Authority, Need, and Timing). Once qualified, an account executive will further develop this prospect into a sale. Companies manage and log this process using a Customer Relationship Management (CRM) platform such as Salesforce or Pipedrive.
At this stage, a user may engage in a trial, sign a contract, or sign up for the service.
2. Activation Stage
Following the acquisition stage, a prospect moves to the activation stage. At this point, the prospect is now referred to as a user or customer.
During this stage, the user goes through an onboarding process. This process can be a combination of an automated product onboarding experience as well as training managed by a customer success team. The former is critical for B2C companies, and the latter is more common for higher-touch B2B companies.
For transactional services, like e-commerce or on-demand services, the activation stage is about making that first purchase. It starts with the user selecting a product they’re interested in and adding it to their cart, and ends with the user providing a payment method and completing their first order.
The activation stage is the most critical moment in the customer journey. It’s during this stage when customers start to develop a strong opinion about the business. Companies often overlook this stage since it’s a transitional stage and underestimate the customers’ lack of familiarity with their products.
By the end of this stage, the customer will either become a promoter or a detractor, so companies must run an NPS survey to analyze their activation strategy’s performance.
3. Adoption Stage
Once activated, customers move to the adoption stage. In this stage, users start to incorporate the product into their work or life habits. The longer they stick around, the less likely they are to churn. For B2B products, users will invite their colleagues to join and collaborate with them. For B2C companies, users will start forming habits and building dependence on the product in their day to day lives.
During the adoption stage, users will also become promoters. They will recommend the product and defend it as if it’s their own. This scenario is more common for modern companies that follow the bottom-up customer acquisition approach, where the buyer is the actual user of the product.
At this stage, companies generally limit their communication with the customer to product updates, best practices, and new offerings.
For SaaS businesses, companies will occasionally check in with the customer to get a sense of their satisfaction, and they will sometimes send them reminders before their subscription renewal.
For transactional B2C businesses, companies will at this stage have a comprehensive profile for each of their users and their preferences. They will personalize their product offerings to maximize repeat purchases.
Businesses must not only be aware of these three fundamental stages, but they should also design a coherent end-to-end journey experience for their users.
An average company engages its customers using 15 different channels throughout their journey. Every channel consists of multiple touchpoints serving a specific purpose in advancing the user in their journey. Below is a list of touchpoints broken down by the customer journey stages:
- Click on advertisement campaigns
- Click on links from other websites
- Land on the website through organic searches
- View website pages
- Initiate chat conversation
- Submit forms to access gated content
- Watch explainer videos
- Read blog posts
- Search website
- Download whitepapers
- Receive email nurturing campaigns
- Engage with sales activities (emails and phone calls)
- View demos
- Create an account
- Set up account and profile
- Complete in-app onboarding guides
- Receive and engage onboarding emails
- Watch training videos
- Read documentation
- Submit support tickets
- Place first order
- Respond to an NPS survey
- Use product
- Invite users
- Use advanced features
- Submit support tickets
- Refer friends
- Upgrade to higher tiers
- Make payment
These touchpoints are only a few of many possible ways the user will engage with the company. The list will grow longer with time as the company matures, deploys more tools, and brings new expertise onboard.
Most tools the company uses to engage customers will provide analytics reporting to evaluate customer engagement trends for the touchpoints managed by that tool.
For example, a marketing automation software that is primarily used for email drip campaigns will provide an in-depth analysis of the performance of every email, such as the number of people who received the email, those who opened it, and took action.
Similarly, an NPS survey software will provide an in-depth analysis of the survey results. That software can go as deep as analyzing the sentiment of the user using sophisticated Artificial Intelligence techniques.
This type of siloed analytics will fail when a company needs to analyze the effect of those touchpoints on the grand scheme of things. To that effect, data-driven companies have been investing in analytics tools that can address this limitation.
Many people wonder what the difference is between Business Intelligence, or BI, and customer journey analytics. BI has become a popular technology to help companies consolidate their dashboards. With Business Intelligence, leaders can now go to one place and visualize the performance of every aspect of the business.
Business intelligence software like Looker, SiSense, or Tableau, coupled with Data Warehouse technologies such as Amazon Redshift, Google BigQuery, or Snowflake, can deliver a lot of flexibility when it comes to analyzing trends. But when it comes to customer journeys, this technology has its limitations and challenges, including:
- Data Services: Business intelligence services on their own are useless unless coupled with a centralized data store. Companies must invest in a separate Data Warehouse technology to get the most of their business intelligence tools.
- Data Management: Companies must hire Data Warehouse developers responsible for designing an analytics architecture and developing the data warehouse to meet the reporting needs.
- Identity Management: While data warehouses enable companies to bring all their data into one place, one of the most redundant tasks is cross-referencing that information and tying it to the right user profiles.
- SQL Limitations: Business Intelligence companies use SQL to interface with data stores. SQL is inherently slow for more massive data sets and is severely limited when it comes to user flow data. Companies can implement simple aggregations using SQL, but to answer basic attribution or funnel questions, SQL becomes counterproductive.
- Complexity: Only data experts can develop reports to answer even some of the most basic questions. Depending on the complexity of the questions, those reports can take weeks or months to build. This fundamentally limits data access within the company, meaning most employees cannot answer questions critical to their work.
Suppose the goal is to establish data democratization and enable teams to analyze their work’s effectiveness on the customer’s success. In that case, companies must adopt a different approach to data. The good news is that Customer Journey Analytics has emerged as a new category of data analytics which solves all the problems mentioned above.
Customer Journey Analytics
Customer Journey Analytics is a new breed of analytics software designed to centralize and analyze customer behavioral data across all touchpoints. The customer data collected consists of front-end interactions such as website page view and product usage, coupled with back-end data imported from various tools the business is using to engage the customer.
Unifying user actions
To have a robust customer journey analytics platform, companies collect user actions cohesively in one central database. Similar to product analytics tools, every collected action must adhere to the following rules:
- When: Must have a timestamp – we must know when the user did that particular action
- What: What is the user doing in this action, and optionally what metadata is associated with this action? (e.g., view page X, create a ticket, use feature X)
- Who: Must have a unique identifier so we can tie that activity to the right user
This process will allow the company to quickly analyze user behavior across all touchpoints in the customer experience without generating complex SQL queries.
Every tool a company uses to engage the customer will have its own set of user unique identifiers. This is a nightmare for data scientists. They have to spend long hours identifying how to bring together data from all these tools. Customer Journey Analytics tools will alleviate this burden using an ID graph system.
For example, a website visitor may initially be tracked anonymously using cookies. They can visit a company’s website from multiple devices but the company won’t yet know they are the same person since they are only being tracked with cookies. Each time the user visits from a new device, the system will treat them as a new user and create a new profile.
Once the user identifies themself through an action like submitting a form, their email or phone number can then be used as a superior unique identifier, and all the cookies will become associated with the user’s email address or phone number. Once they create an account, an internal ID will be created. At this point, the user’s email address or phone number and the cookies will all be tied to the highest priority internal ID. All of that user’s customer interactions will be centralized under this internal ID.
Centralizing this ID graph system allows companies to effortlessly analyze the end-to-end user journey – from the first moment the user visits the website anonymously (acquisition stage), all the way to when they become identified (activation stage) and then a repeat customer (adoption stage).
Specialized Analytics Processor
Analyzing the user journey requires aggregating action sequences. Although it’s possible to build SQL queries to analyze action sequences, the most basic analyses can be intimidating to everyone, including SQL experts. They will fail as questions get more complicated. SQL was never designed for this type of analytics.
For that reason, companies must use a specialized data processing system designed from the ground up to analyze action sequences. This will allow companies to answer many more complex user journey questions using a visual interface to answer all these questions without learning a query language.
Customer Journey Analytics Examples
Now that we’ve covered the building blocks of a customer journey analytics platform, let’s go over the types of analysis it can perform:
1. Journey Reports
The journey report allows companies to visualize customer touchpoints across multiple channels. With this report, companies can:
- Pinpoint potential areas of friction where users are dropping off
- Analyze the effect of initiatives on the conversion process in every step of the user journey
- Discover which paths users are taking before converting
2. Attribution Reports
The attribution report allows companies to zoom in on successful customers and estimate specific touchpoints’ attribution to their success.
For example, product marketing teams can break down their revenues by the features that led users to upgrade. By attributing dollar amounts to different features, companies can fine tune their product roadmap to boost conversions and reduce churn.
3. Cohort Reports
The cohort report allows companies to break down their growth by the periods a user performed a task. For example, a company can break down overall product usage growth by cohorts based on the time they signed up or used a feature for the first time.
4. Retention Reports
The retention report allows companies to analyze how long users continue to engage with the business from the moment they perform a task. For example, companies can analyze the percentage of users who subscribe to an email or push notifications and then come back and use a specific feature.
Customer Journey Analytics vs. Customer Journey Mapping
While customer journey analytics helps companies measure and optimize the effectiveness of their customer experience, customer journey mapping is a related, but different, exercise where companies create a visual story of the customer experience. A customer journey map looks like a series of steps that the customer takes beginning with the first interaction with the brand.
Customer journey analytics both informs and validates customer journey mapping.
For example, customer journey analytics can tell marketing teams if a step in the customer journey is resulting in low conversions. This information can then be used to refine the steps laid out in customer journey mapping, such as adjusting the low performing step.
Similarly, customer journey analytics may confirm that a new initiative defined in the customer journey map is helping to retain customers as intended.
Join The Movement
Most companies today are failing to make customer data accessible to the vast majority of their team, meaning employees are often making decisions based on guesses. Customer journey analytics is one of the most impactful ways companies can democratize customer data. It allows every employee to answer questions critical to their work so that they can make better decisions.