Tracking the Customer Journey Is Critical for Engagement

Competition for customers is more intense today than ever before, and companies struggle to differentiate themselves from the competition. Our research repeatedly finds that customer experience is a key differentiator. Our research into next-generation customer engagement said the impetus for improving vr_NGCE_Research_01_impetus_for_improving_engagementengagement is to improve the customer experience in almost three quarters (74%) of participants. One increasingly popular way to do this is to use customer journey maps, which show how companies plan to engage with customers: at what times, through which channels, at which touch points and with which business units or using which self-service technologies. Our benchmark research into customer relationship maturity shows that two-thirds (67%) of very customer-focused companies use customer journey maps. The top four uses are to develop more customer-focused employee training (by 78%), personalize customer experiences (76%), enhance customer experience processes (73%) and drill down on customer experience processes to the customer segment level (73%). Typically producing these maps has been a manual process, perhaps using process mapping tools; in these cases few companies were able to capture and visualize actual journeys. However, as more business units engage with customers and companies deploy multiple channels of engagement – including self-service – improving the customer experience and mapping the customer journey become more complex, and to keep up companies have to invest in processes and tools that help them automate the process of producing maps and capture data about and visualize actual customer journeys.

I covered details of this complexity in my review of lessons learned during 2014. I recommend thinking of the customer journey in four dimensions:

  1. Customer business journey. Customers go through a series of steps in evaluating and using new products and services. It begins with learning about them (often by perusing marketing campaigns, searching the Internet and getting word-of-mouth recommendations), then purchasing those they select, beginning to use them, which might require some initial support, and accessing ongoing customer service. Satisfied customers are likely to repeat the process and provide opportunities for upselling and further purchases. Our benchmark research into recurring revenue shows that this journey has become more complex as more companies move from one-off sales to providing ongoing, Internet-based services – for example, software vendors providing cloud-based rental of applications rather than licensing on-premises products. The customer business journey thus complicates the relationship between marketing, sales, customer service, finance and HR departments and requires processes that flow across business unit boundaries, sharing of customer data, information and metrics, and collaboration between everyone involved.
  2. Customer engagement journey. This often is what is called “the customer journey”; it seeks to map the channels and touch points that prospects and customers use to engage with companies. To do this companies need to understand every point of engagement, how people travel across channels to achieve their objectives, and the outcomes of interactions. To understand them requires capturing every interaction on every channel, business outcomes (Did the customer make a purchase?) and the customer’s emotional state during and after each interaction.
  3. Internal business journey. Most companies are organized into separate business units. These often have their own processes, systems and metrics, and typically each deals with prospects and customers at different points in the customer business journey – in isolation from the other business units. To ensure consistency of the customer experience, companies should develop and share a single view of customers and ensure that decisions and actions are based on that common view; they also should take into account the likely impacts of those decisions and actions on other points in the business journey.
  4. Product and service journey. Most companies have multiple products and services; some are simple and some complex. Prospects and customers therefore engage with companies in different ways and channels, depending on the product or service. Companies therefore have to consider all the above journeys for each product or service.

Managing the complexity of the customer journey in all its facets vr_Customer_Analytics_02_drivers_for_new_customer_analyticsrequires specialist tools. Our research into next-generation customer analytics shows that to manage this complexity a majority are turning to customer analytics to improve the customer experience (63%), their customer service strategy (57%) and the outcomes of interactions (51%). Analytics requires data, and the research finds difficulty here; nearly two-thirds (63%) said that the data they require is not readily available, and almost half spend most of their time preparing data (47%) and  reviewing data for quality (43%). This is a serious impediment to mapping the customer engagement journey, which requires capturing data from multiple communication systems (including the telephone, email and Web servers, mobile phones and social media), having processes and systems that can link transactions from one communication system to the others, and visualizing the outputs in graphic forms that are easy to understand. The outputs should clearly show the business outcomes of journeys, such as whether a customer renewed a contract. Indeed outcomes enable organizations to identify weaknesses in existing journey processes and guide them to improve future interactions.

Data also plays a key role in the customer and internal business journeys. Typically it is captured and stored in a variety of business applications such as CRM, ERP, customer feedback, billing and others. To produce a complete view of the customer, including the individual’s emotional state and likely next actions requires the use of systems that can extract data from all these systems, rationalize it and produce analysis and dashboards in forms suitable for different users.

The ultimate goal should be to combine all these sets of data into a single view of the customer. Where possible the systems should work in real or near real time so all users make decisions based on the most up-to-date information as when vr_Customer_Analytics_03_key_benefits_of_customer_analyticsa contact center agent is asked for the status of a promised delivery. Furthermore the systems should support access to information on mobile devices to enable employees away from their desks to be notified of issues needing immediate action. Our research shows that getting it right can deliver real benefits; chief among them are improved customer experiences (55%), better analysis of the business (52%) and better alignment across the organization (51%). Companies long have talked about having a “360-degree of the customer,” and new systems that can process structured and unstructured data now make it possible to produce such a view. Some of these tools use speech and text analytics to better understand the customers’ emotional states and anticipate their next actions. New systems that can capture all interaction data and combine this with business data make it possible to map actual rather than hypothetical customer journeys and provide analysis that guides companies to improve processes and training and through it future experiences. Tools that manage customer experience and journey maps are available from a variety of vendors. I recommend comparing these systems and choosing the one that best enables your organization to start mapping its customers’ journeys.


Richard J. Snow

VP & Research Director

Alpine Chorus Brings Collaboration and Predictive Analytics to Big Data

In many organizations, advanced analytics groups and IT are separate, and there often is a chasm of understanding between them, as I have noted. A key finding in our benchmark research on big data analytics is that communication and knowledge sharing is a top benefit of big data analytics initiatives,vr_Big_Data_Analytics_06_benefits_realized_from_big_data_analytics but often it is a latent benefit. That is, prior to deployment, communication and knowledge sharing is deemed a marginal benefit, but once the program is deployed it is deemed a top benefit. From a tactical viewpoint, organizations may not spend enough time defining a common vocabulary for big data analytics prior to starting the program; our research shows that fewer than half of organizations have agreement on the definition of big data analytics. It makes sense therefore that, along with a technical infrastructure and management processes, explicit communication processes at the beginning of a big data analytics program can increase the chance of success. We found these qualities in the Chorus platform of Alpine Data Labs, which received the Ventana Research Technology Innovation Award for Predictive Analytics in September 2014.

VR2014_TechInnovation_AwardWinnerAlpine Chorus 5.0, the company’s flagship product, addresses the big data analytics communication challenge by providing a user-friendly platform for multiple roles in an organization to build and collaborate on analytic projects. Chorus helps organizations manage the analytic life cycle from discovery and data preparation through model development and model deployment. It brings together analytics professionals via activity streams for rapid collaboration and workspaces that encourage projects to be managed in a uniform manner. While activity streams enable group communication via short messages and file sharing, workspaces allow each analytic project to be managed separately with capabilities for project summary, tracking and data source mapping. These functions are particularly valuable as organizations embark on multiple analytic initiatives and need to track and share information about models as well as the multitude of data sources feeding the models.

The Alpine platform addresses the challenge of processing big data by parallelizing algorithms to run across big data platforms such as Hadoop and making it accessible by a wide audience of users. The platform supports most analytic databases and all major Hadoop distributions. Alpine was vr_Big_Data_Analytics_13_advanced_analytics_on_big_dataan early adopter of Apache Spark, an open source in-memory data processing framework that one day may replace the original map-reduce processing paradigm of Hadoop. Alpine Data Labs has been certified by Databricks, the primary contributor to the Spark project, which is responsible for 75 percent of the code added in the past year. With Spark, Alpine’s analytic models such as logistic regression run in a fraction of the time previously possible and new approaches, such as one the company calls Sequoia Forest, a machine learning approach that is a more robust version of random forest analysis. Our big data analytics research shows that predictive analytics is a top priority for about two-thirds (64%) of organizations, but they often lack the skills to deploy a fully customized approach. This is likely a reason that companies now are looking for more packaged approaches to implementing big data analytics (44%) than custom approaches (36%), according to our research. Alpine taps into this trend by delivering advanced analytics directly in Hadoop and the HDFS file system with its in-cluster analytic capabilities that address the complex parallel processing tasks needed to run in distributed environments such as Hadoop.

A key differentiator for Alpine is usability. Its graphical user interface provides a visual analytic workflow experience built on popular algorithms to deliver transformation capabilities and predictive analytics on big data. The platform supports scripts in the R language, which can be cut and pasted into the workflow development studio; custom operators for more advanced users; and Predictive Model Markup Language (PMML), which enables extensible model sharing and scoring across different systems. The complexities of the underlying data stores and databases as well as the orchestration of the analytic workflow are abstracted from the user. Using it an analyst or statistician does not need to know programming languages or the intricacies of the database technology to build analytic models and workflows.

It will be interesting to see what direction Alpine will take as the big data industry continues to evolve; currently there are many point tools, each strong in a specific area of the analytic process. For many of the analytic tools currently available in the market, co-opetition among vendors prevails in which partner ecosystems compete with stack-oriented approaches. The decisions vendors make in terms of partnering as well as research and development are often a function of these market dynamics, and buyers should be keenly aware of who aligns with whom.  For example, Alpine currently partners with Qlik and Tableau for data visualization but also offers its own data visualization tool. Similarly, it offers data transformation capabilities, but its toolbox could be complimented by data preparation and master data solutions. This emerging area of self-service data preparation is important to line-of-business analysts, as my colleague Mark Smith recently discussed.

Alpine Labs is one of many companies that have been gaining traction in the booming analytics market. With a cadre of large clients and venture capital backing of US$23 million in series A and B, Alpine competes in an increasingly crowded and diverse big data analytics market. The management team includes industry veterans Joe Otto and Steve Hillion. Alpine seems to be particularly well suited for customers that have a clear understanding of the challenges of advanced analytics vr_predanalytics_benefits_of_predictive_analytics_updatedand are committed to using it with big data to gain a competitive advantage. This benefit is what organizations find most in over two thirds (68%) of organizations according to our predictive analytics benchmark research. A key differentiator for Alpine Labs is the collaboration platform, which helps companies clear the communication hurdle discussed above and address the advanced analytics skills gap at the same time. The collaboration assets embedded into the application and the usability of the visual workflow process enable the product to meet a host of needs in predictive analytics. This platform approach to analytics is often missing in organizations grounded in individual processes and spreadsheet approaches. Companies seeking to use big data with advanced analytics tools should include Alpine Labs in their consideration.


Ventana Research