Ken Ong, Chief Technical Officer & Journey Data Expert, BryterCX
Chris Clarke, Director of Artificial Intelligence AI, Data Scientist, BryterCX
Great customer experiences result in happy and more engaged customers. Understanding customer journeys enables businesses to optimize customer experiences, improve customer satisfaction, generate revenue and increase operating efficiency.
Businesses are increasingly personalizing their customer experiences across more channels and more touch points than ever before. This has made it more important to understand in detail how customers are responding to their experiences. While challenging, the increased availability of customer activity data has made it possible for businesses to analyze these customer journeys. To respond to these challenges, BryterCX’s customer journey management platforms leverage data science to help businesses organize customer data into journeys and to optimize these customer journeys.
As pioneers in customer journey analytics, BryterCX business consultants have extensive experience analyzing activity data to truly comprehend customer journeys. We are using customer journey data science to enable our experts and our customers to analyze data at scale.
In this article, we present a few of the techniques our customer journey data science teams are working on. They are using data science to help our data architects filter and organize customer activity events to form customer journeys. By using data science, architects are able to recommend changes to customer journey experiences for improved outcomes.
Transforming Events into Customer Journeys
The exercise of building an omnichannel customer journey dataset involves transforming hundreds or thousands of physical events into behavioral paths of interest. Next, the paths are stitched together from multiple channels into coherent omnichannel customer journeys.
User activity data invariably comes in the form of physical events, such as web analytics clickstreams or phone and branch activity logs. But journeys are about customers doing things, such as applying for a loan or paying bills. Our customer journey experts take analytical data of web page views and logs of phone button presses or branch transactions and turn them into behavioral paths of the customer journeys, e.g., loan application or bill payment steps. This can be a tedious and labor-intensive exercise, even for BryterCX. That’s why we leverage data science algorithms to expedite the process.
Focusing on Customer Journeys that Matter
BryterCX’s data science team is continuously working to design features that can improve the customer experience. One research area for the team is noise reduction: recommending events which are less relevant to the customer journeys to better enable our analysts and our clients to focus on the events that matter. Our team is applying patent-pending data science recommendations to help our data architects to “drop” less relevant events. This dropping or permanent removal of events from a client dataset is a crucial, value-additive step in the surfacing of insights and strengthening of meaningful signals in the data.The dropping of low-value events helps add significance to the data and reduce or eliminate costly computation. Another critical step in the journey mapping process is grouping similar events for a journey. For example, users could pay bills via the website, on the mobile application or in person. This grouping is accomplished by our Event Similarity tool. With this advanced set of Machine Learning algorithms, events in the dataset with similar functions or closely related meanings can be combined into an event with less noise and higher significance.
These algorithms enable process automation and therefore scaling of cumbersome, ad hoc processes that rely on subjective and expensive subject matter expertise. Applying this process results in a beneficial reduction in the events and journeys that Machine Learning and data analysts can tag, map or use to identify surface insights.
Analyzing Behavioral Patterns to Predict Outcomes
As you know, customer journeys are intricate and complex. While there is a clear endpoint representing the customer’s goal, there are endless permutations on how they arrived there. Very often we approach a business problem without a concrete hypothesis. According to Gartner, 85% of analytics initiatives do not have clear objectives. Analysts need a tool that tells them where to start their investigation.
BryterCX’s Data Science team is developing patented software to algorithmically sift through event combinations and sequences in a Journey Dataset™. The goal is to bring back the most significant drivers of an outcome and to visually present the most important combinations and drivers. This provides analysts with the tools they need to identify interesting behavioral patterns within minutes. Analysts are able to see how interesting customer journeys are against the average population and, ultimately, where to look next. All of these easily identified opportunities can then be explored deeper using the BryterCX Journey Management Suite.
Data science enables relationship-based businesses to better understand and personalize their customer experiences to make personalized recommendations.
BryterCX is working to realize the enormous potential of leveraging data science to automate journey analytics and to generate recommendations to optimize customer journeys and experiences. In the end, these Machine Learning ML tools help bring scalability, reliability and automated insights to the crucial first stages of mapping journey data. In turn, we are helping our clients decrease the effort and time to value for identifying meaningful behavior signals in their data. If you’re in the crucial first stages of considering or selecting a CX-optimization partner, request a demo to learn more about what makes BryterCX shine.