How ClickFox Uses Journey Science to Create a Deep Understanding of Customers

Product Mission

A customer visits the website, sends an email, and tries the mobile app. The customer finally gives up and dials the call center. Multiply that by tens of thousands of customers, with many interactions each. Few companies can bring all of that data together in a coherent form so that they understand the experiences of their customers.

The mission of ClickFox is to create a data and analytics infrastructure so that those responsible for improving the customer experience and the performance of a business can answer all-important questions about the many faceted journeys customers are taking as they interact with a company. ClickFox calls this domain of knowledge “journey science.”

To do this, ClickFox gathers data that tracks all journeys in all available channels, cleans it, organizes it, enhances it, and then connects it all together. The next step is to make the database of journeys available through a powerful and user-friendly analytics environment that enables everyone interested to explore those journey datasets. Then, these analytics are packaged up to meet the needs of different roles involved in various key processes.

With such data and analytics capabilities, the process of understanding what customers are going through is dramatically accelerated. That’s what ClickFox gives you: a way to assess and quickly improve customer experience across channels.

Origin story

ClickFox is a Denver-based company founded in 2000 to analyze web interactions. In 2004, CEO Marco Pacelli shifted the company focus to cross-channel analysis because he could see that current levels of analytics and data management systems were not well suited to delivering on the promise of journey science. Pacelli saw that measuring customer satisfaction using after the fact mechanisms like Net Promoter Score and surveys was valuable, but represented only the start of an analysis process that involves questions such as:

  • What happened to the customer that had a positive or negative score?
  • How can we find customers who are behaving in ways that indicate unreported dissatisfaction?
  • Why are customers apparently refusing to use digital channels?

Pacelli views journey science as the practice of aggregating data, applying analytics, and understanding the behavior of customers as they interact with a company.

Pacelli’s vision is that by using ClickFox solutions, a company can apply journey science methods to understand the fine-grained details of the customer experience.

Pain points and intended users

ClickFox is focused on helping reduce the frustration involved with attempting to understand how to deliver a better customer experience in an omnichannel world.

As the number of channels for and the depth of the customer interactions has increased in recent years, so too have customer expectations. Customers now believe that regardless of how they interact with a business, the business should be able to provide them with top-quality service that incorporates all of their previous histories. Customers want to be understood and do not want to have to start over every time they reach out to a company. The truth is they want a relationship.

Companies cannot see what’s happening with their customers without a consolidated model that combines activity across all of their channels. Without this model, the following problems occur:


Pain Point

Customer Facing Staff

Can’t understand complete history of a customer and deliver service based on what has already occurred.

Product Development

Can’t see where customers are getting stuck so that effective decisions can be made to optimize, debug or improve the user experience.

Line of Business Managers/Senior Executives

Can’t see the overall flow of customers through channels.

Can’t perform proper attribution. For example, may mistakenly blame low CSAT score on the previous channel (often the call center), rather than looking at the entire customer journey.

Analysts/Data Scientists

Can’t drill down to detailed interaction information to see how customer segments behave across all channels. Can’t explore and analyze all the journeys customers are taking.

IT Staff

Cannot quickly identify specific problems across channels that are affecting key business metrics.

More than customers

While ClickFox today is most commonly used to gain insight into customer journeys, it is important to remember ClickFox is built in such a way that it could be used to analyze the journey of anyone or anything, from the journey of a disease in epidemiology to the journey of a product from the drawing board to individual units in the field. It is also possible to apply journey science to product development, manufacturing, or the IoT. It is therefore possible to abstract the journey science model from customer service to other fields.

Central dogmas

Central dogmas are fundamental assumptions that guide the creation of a product and help inform business and product strategy, product management, and engineering decisions.

Here are the central dogmas at the foundation of ClickFox:

  1. ClickFox believes that journeys are stories, and they have a narrative flow. You must be able to find and discover and defend your stories.
  2. ClickFox believes that businesses need journeys to address the problems they are facing. Experiences must be perceived from the customer’s point of view. To make meaningful change, the business must understand all of the customer’s interactions that make up the broader story.
  3. ClickFox believes Journey Science requires integration of relevant data. Data integration techniques must be able to accept data from all sources tracking journeys in any form, even those not directly triggered by the customer.
  4. ClickFox believes Journey Science can be applied to more than customers. The main character of the story can be any entity — person, place or thing — in your business.
  5. ClickFox believes the analytics environment for Journey Science must serve needs of both advanced and casual users. Both business users and data users come in advanced and casual skill sets.
  6. ClickFox believes that journey analytics needs creative visualization for human understanding. Charts and graphs tell just part of the story. People need net new visualizations to understand the complex stories being told.
  7. ClickFox believes that journeys are complex objects that require a combination of tools to properly analyze. Journey analytics requires more than what SQL, statistics, graph analytics, and other data science and machine learning techniques can offer independently.
  8. ClickFox believes an iterative process is the right way to give events relevant context. The story told by raw system data is too detailed and noisy for comprehension and analysis. ClickFox lands the data and then improves it by creating successive layers of cleaner, simpler, more integrated, and enhanced data through a process it calls recursive contextualization.
  9. ClickFox believes the business audience plays a central role in the contextualization of the stories. The contextualization can be assisted by automation, machine learning, and AI, but the business audience must actively participate to guide the decision making process.
  10. ClickFox believes that journeys should be told and retold automatically as new data is created. This automated refresh allows the business to continuously monitor, test, and create hypotheses as each business decision is implemented.

Product Capabilities

Here’s what ClickFox does in a nutshell:

  • ClickFox assembles and integrates all relevant data to track an omnichannel customer experience.
  • The model created allows interaction data to be enhanced with various forms of metadata.
  • The model then becomes useful through an analytics layer built to allow analysis of all of the interactions.
  • These analytics can be crafted into various forms of dashboards and applications to support deeper understanding of the customer experience.

At Evolved Media, we think of products within our Productized Analytics framework. ClickFox is a productized analytics platform for journey science.

The latest release of ClickFox is simply called Fox. It features an elegant and intuitive user interface that invites users to explore journeys and helps them become productive very quickly.

Bringing data in

The first step is bringing the data in: landing it, cleansing it, integrating it, and, in concert with the business, deciding what is important in it using a feature called Fox Build. ClickFox calls this process recursive contextualization. It involves adding layer after layer of metadata to provide additional context for each event in a journey. Here’s how it works:

  • Raw data is landed on the platform.
  • All data is stored as sessions. Sessions include events from the customer’s journey, and then each event has defining attributes attached to it. A session could be a single phone call with a customer, or a months-long interaction, depending on how the company wants to define it.
  • The data is cleaned and normalized and then session objects are created that represent key events within the customer journey.
  • Summarization and contextualization occur, from which new layers of session objects are created that can collapse detailed activity into large-scale events.
  • These session objects are combined with specific customer information, as well as metadata and demographic data, that adds context. Even the products involved in a customer interaction can be included.
  • From there, this data can be summarized further within lines of business.

Eventually, with Fox, you have layers of session objects, with the highest level layers providing overview summaries, cascading all the way down to the bottom, where you find the raw, landed data. The raw data is never overwritten, so it can always be contextualized in new ways over time.

This type of structure allows companies to immediately gain value from the platform, as they can learn about customer journeys from the start, even as they’re building up journey science profiles over time. The platform also does some analysis internally, as we’ll see next.

Exploring data

The first step is to explore the data and see what journeys customers are taking. The Explore feature enables you to see the most common events at a glance, shown in the center and in a darker shade of blue.

You can zoom in to see what each event is and mouse over it for details.

You can then see how these events connect into journeys.

A dynamic journey map shows all the various journeys customers take.

From there, you can look at the events along a particular journey and explore that journey step by step.

ClickFox also allows you to explore top traversals either visually, or ranked.

Here’s a visualization of top traversals that include a web login:

And a ranked list of these top traversals

Visualizing journeys

After exploration, perhaps you decide you want to see all of the journeys that end with the customer giving their experience a low customer satisfaction (CSAT) score. You can load those journeys into Journey Trace, a ClickFox visualization tool that enables you to see those journeys in greater detail.

Companies that don’t take into account the full journey often wrongly attribute low CSAT scores to the channel right before the survey is taken, in this case the call center agent. But digging into the journey data shows that customers who arrive at low CSAT are getting there through a variety of journeys.

The number 1 path to a low CSAT score in this dataset involved a customer with a web payment failure, followed by an IVR payment failure, followed by a successful payment through an agent. The second most common path showed a mobile payment failure as the first step. The real problem was not the website, the mobile app, or the IVR system. It was a backend payment system that all of these three relied on. Two failed attempts at payment led customers to call the contact center, and fueled the low CSAT score even though the agent helped them through the payment process. This is the type of cross-channel issue that ClickFox can uncover.

Analyzing metrics

Executives and LOB managers often want to monitor particular metrics. Hundreds of metrics can be monitored using the Watch feature. This is the equivalent of Evolved Media’s “custom kitchen” productized analytics that can be turned into a “value meal” for particular stakeholders.

Those who want to know more can then drill into a particular metric, examining a scatterplot related to a given metric, such as the number of customers who go from website to agent within 24 hours:

Product Architecture

ClickFox delivers its capabilities with a technology stack that has the following layers:

  • Data ingestion: The data ingestion layer allows data to be landed in ClickFox and cleaned up using ClickFox’s Journey Transformation Language (JTL).
  • Recursive contextualization: ClickFox’s JTL is then used to create session objects out of the raw journey data. Session objects can be created in various layers, each layer usually parses the data, enhances it with information from external sources such as customer or product metadata, and then mapped to common IDs so that journeys can be identified.
  • Visualization and analysis: The session objects can be queried and displayed with a purpose-built journey analytics engine that allows exploration of various levels of session objects.
  • Dashboard and application development: Dashboards and applications can be developed to provide a context for analyzing journeys and to add related functionality.
  • Computing platform: ClickFox runs on a Hadoop cluster, storing data in HDFS and using the YARN interface to create distributed computing infrastructure.
  • API integration: API access to the session objects, the query language, and the analytics capabilities allow for embedding journey analytics in other applications.
  • Integration with other platforms: In addition to its in-platform exploration and visualization capabilities, ClickFox journey datasets can be consumed by other systems,  including BI tools such as Tableau, operations or marketing decision systems, and predictive models.

Use Cases

  • ClickFox supports a wide range of use cases. Here are five example use cases to illustrate how it works.
  • CSAT improvement: The examples described so far show how you can use ClickFox to analyze the characteristics of journeys that lead to low CSAT. Once the characteristics of such journeys are understood, you can use ClickFox to find customers who have taken the same journey but did not complete a survey (silent low CSAT customers). Using this information, you can then improve customer experience, and measure that improvement. 
  • Digital containment: Those striving to keep customers interacting with a business through more cost-effective channels like the Web, rather than calls, can trace journeys to determine why customers leave the website and reach out to the call center and make changes to increase use of digital channels.
  • Churn modeling: Customer churn is expensive. When you take a journey science approach, you’ll find it’s also predictable in many cases. By determining the characteristics of customers who churn, you can proactively reach out to those likeliest to churn and intervene with targeted offers to improve customer loyalty.
  • Operations optimization: Companies can use the ClickFox platform to see what is working and what is not within their operations, and then implement changes based on this. This could be improvements to a customer service call-in center or the clarity of customer service channels on a website.
  • Repeat activity reduction: A cable provider examined why it took more than one visit, referred to as a truck roll, to fix customer issues. By examining the journeys of the customers and the technicians, the provider was able to reduce expensive truck rolls and get problems solved on the first visit.

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