There are lots of definitions and interpretations available for the phrase “Customer Service” and “Customer Experience”. However, a simplified way of putting things in perspective is as follows:
Customer Service is designing capabilities inside-out to serve your customers and Customer Experience (CX) is designing customer journeys outside-in looking through the eyes of the customer. Traditional pre-internet companies were wired to design everything around the products i.e. inside out. In the past decade or so most successful companies have been built around customer experience i.e. outside in. Please don’t get me wrong, you need both to keep your customers happy. Customer service capabilities and customer journeys complement each other to provide a superior CX. Fig 1 is a 2×2 matrix which further explains the case in point and elaborates on a CX transformation journey leveraging data-driven capabilities.
Fig 1 plots customer engagement capabilities on Y-axis i.e. “How to engage with your customer” with four incremental capabilities and similarly customer data-driven capabilities on the X-axis which is indicative of “How well you know your customer”.
How to Engage with your customer? – Most companies initially started engaging with their customers through a voice channel in addition to face to face interactions. This was done by setting up call centers where a group of agents were attending to customer support calls made to a 1-800 number. As time progressed, new capabilities like web, mobile, social media were added over a period of time. These additional channels were independent of each other as they were owned and operated by separate functions sitting in their organizational silos. However, as customers started expecting more, channels became more coordinated and provided a seamless experience across channels as shown in Figure 1 along the Y-axis.
How well you Know Your Customer? – Customer Data driven capabilities have become widely popular with advent of data technologies like Hadoop, AI, machine learning, deep learning etc. Incremental data driven (analytical) capabilities range from descriptive, diagnostic and predictive to prescriptive. In the context of a “customer” these capabilities define your knowledge about the customer i.e. how well you know your customer by using the data captured on how they purchase and consume your products. Descriptive and Diagnostic are basic reporting capabilities about your customer i.e. What did your customer purchase and how many times did he call the call center when something was broken while consuming your product? Predictive and prescriptive capabilities on the other hand are more transformational as companies can personalize the CX by predicting customer needs and wants.
Traditional pre-internet companies focused on building capabilities along the Y-axis with very basic X-axis capabilities. With advancements in data technologies data-first companies like Google, Amazon, Uber, Apple started focusing on building capabilities along the X-axis as well. This resulted in these companies offering superior customers experience journeys. However, pre-internet companies remained as digital laggards in terms of not leveraging customer data to know more about customers and were unable to offer a better CX. Data first companies became increasingly customer centric and kept customers at the center of everything they did.
Quadrant 1 and 2 of the Figure 1 2×2 matrix roughly corresponds to reactive care where companies work in a break-fix mode responding to customer events. If something breaks customer will call into the call center and the agent will solve his/her problem. The descriptive and diagnostic capability is available to provide standard customer reporting and the diagnostic ability to provide a basic root cause analysis of what happened. Most pre-internet companies were unable to leverage X-axis capabilities thus providing reactive customer service support through call centers would fall in these quadrants.
Quadrant 3 & 4 is where predictive and prescriptive data-driven capabilities come in to play. The data-first companies mentioned previously have built these capabilities to predict and shape customer experience journeys. This is where companies provide proactive care by predicting things using customer data before they happen and subsequently provide adequate interventions.
The CX transformation journey shown in Fig 1 which goes from Q1 to Q3 requires data infrastructure and expertise that will provide these incremental data-driven capabilities shown on X-axis. Customers in today’s world provide a lot of solicited and unsolicited feedback about company products/services through digital and physical channels like call center, retail stores, surveys, social media etc. Also, customers leave a digital trail of their needs and wants when they interact with company digital fronts (website, mobile app, kiosks etc..). Additionally, there is also data about the customer stored in CRMs and other data stores sitting in data silos. All this results in a ton of data being generated and stored which can provide valuable customer 360 insights of how they purchase and consume products or services. These insights can be leveraged to predict and shape CX journeys and thus transform from traditional reactive customer service to more proactive personalized customer experiences.
A data infrastructure which meets the above requirements needs to address a few inherent data technology related challenges that are present in any organization as given below.
- Aggregation of data sitting in silos within an organization
- Common storage of structured and unstructured data
- Easy Data management with shorter time to insight.
- Self Service Analytics and exploration of data.
In future posts we look into implementation aspects of a platform which provides the underlying data infrastructure to support CX transformation capabilities.