Customer experience transformation has been around for more than a decade. It was widely popular in retail industry which was quick to recognize Customer experience as a competitive differentiator. The concept gained traction across consumer facing domains
notably consumer banking, E-commerce, Hi-Tech. Companies have spent millions of dollars in transformation programs focusing on customer experience. Many of these initiatives failed to deliver the results in the expected order of magnitude. Most of these failures
can be attributed to the shifting of goal posts. Technology driven transformation programs got outdated and companies realized that such transformations cannot be a one time exercise rather a continuous improvement program that can leverage the best practices
along with the latest technologies. Today through a combination of big data technologies and analytics models, organizations are able to deliver superior customer experience. The next wave of customer experience transformation will be led by Artificial Intelligence(AI).
Tera-bytes of customer data is collected and stored across organizations. Customer transactions, inquires, purchase behaviors, grievances, interactions etc.,, gets mined, analyzed for actionable insights. These insights may lead to creation of new product/offering
or redesign an existing process or introducing newer touch-points. With respect to utilizing these newer data points organizations are increasingly investing on analytics competencies. There is a mismatch between exponential growth of customer data versus
the ability to tap such a gold mine in a timely manner. Consider an example of an angry customer who is annoyed by a service. There is small time window to retain this customer through corrective measures. If an organization fails to take any action then it
will lead to growing resentment eventually leading to attrition. The pertinent question here is “Does an organization have resources to research all these data with human judgement alone and take timely decisions?”. This is the gap that will slowly but steadily
filled by AI bots. These bots will be able to mine the data and recommend the decisions to business analysts. Thereby cutting the time to course correct or avoid an opportunity miss. Eventually AI bots will not only recommend actions but implement these and
measure the success of these decisions, close loop by self learning on the end results.
In this blog series I will cover the role of AI bots in CX transformation and how some technology trends and business needs will converge in this space.
The purpose of this AI bot is to do the following.
1. Measure customer experience in the form of effort scoring methodology
2. Integrate this across channels and transactions to get a unified view of customer
3. Identify instances that are not addressed sufficiently by touch points /customer service agents(E.g., product inquiry or dissatisfaction over the service)
4. Recommend next best action for these identified instances
Below is a typical Customer Journey in a Multi-channel environment
Customer can choose any of the traditional or digital touch-points to complete a transaction.
For e.g., Bill Payments
If the payment fails then there is an exception handling journey the customer initiates. Again this can follow any of the available touch-points for additional support. At each point a significant amount of data gets generated that can throw insights on
customer’s intent and end result. If the issue doesn’t get resolved in a particular channel then customer is forced to move to a different channel for effectively handling the problem. This channel cross-overs and corresponding wait time along with the over
all handle time to get the issue fixed contributes to customer effort. In addition the conversation quality with the agent (Subject matter expertise, communication skills and professionalism etc..) in deftly resolving the issue also need to be factored in
to capture the overall customer experience. This end to end aspects of a customer journey gets captured as KPIs at different levels and combined together as a single score that is a proxy of overall customer experience. This score although directionally resonate
with the CSAT/VOC surveys but will be fundamentally different in terms of the computational algorithm used and the level at which it is measured. The granularity of the process allows us to pin point areas that caused the increased customer effort in the over
all journey, thereby providing the pain areas that would need a change in process or the operational parameters to smoothen the journey.
The above example captures the measurement and attribution process at a high level. The foundation of this implementation is built on a strong technology and data layer.
1. NLP algorithms that can understand customer interaction in real-time
2. Big data technology to process the unstructured data and integrate it with structured data
3. AI engine that can leverage this data and recommend next-best action
4. BI layer that can act as command center for the business to identify anomalies and monitor performance thresholds
In the next blog I will cover how we can use real-time speech recognition and Conversation AI to drive this transformation.
External | what does this mean?