Order Management & Fulfillment

Edition 3: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases

By Jagadesh Hulugundi posted 4 days ago

  

With advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) widely adopted by businesses, there is a large push towards building solutions with modern tools and technical frameworks.. However, most of the time, technical advancements are perceived as growth in the technical maturity ladder for individuals, teams, IT or business organizations. Such situations often downplay the value proposition of embracing new transformations.

As a result, businesses tend to deboard such solutions which can cost precious dollars to the business. In several occasions, businesses have not understood the AI engine making decision on behalf of an experienced personnel who does it manually or overrides AI engine outcomes eventually pushing the developed solution to obsolete path. The trust factor of such AI algorithms is at stake with explainability being the centre of a hidden rationale.

This is the third blog in this series which attempts to discover the potential real-word use cases for AI infusion into our IBM Sterling Order Management suite of applications from a business perspective in our customer organisations.

Read the previous blogs here:

Edition 1: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases | Persona: Call Center Agent

Edition 2: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases | Persona: Fulfillment Manager



Edition 3

Persona : Customer Relationship Manager

Problem statement

  • Customers hate waiting in the call queue and keep selecting IVR options before a live call centre agent finally talks to them. Businesses are aware of such situations but most of the time call volumes outstrip personnel capacity.
  • Traditionally call centres operate on first come first served mechanism, treating:
    • All customers as equally important
    • All calls as equal priority regardless of repeated /follow-up calls which are about to get escalated
    • All calls as equal value for an enterprise
    • All centre agents as equally trained and therefore able to understand all situations well enough to handle customer queries
    • All business days/hours the same


As a result, in many situations customers experience long hold times and are bounced from one call centre agent to another.

  • Enterprises need to avoid making their customers wait if customer experience is their primary focus. But with limited resources, minimizing the negative impact on customers by prioritizing and responding to the most important calls first becomes a big challenge.
  • One might argue that modern Live Chats or Live Bots provide instant resolution, but these are limited to offer help on traditional queries and updates for customers. When customers have issues that they need to explain to human, context becomes very critical but is often the most neglected aspect in the traditional call routing mechanism.


Solution outline

  • AI algorithms analyze and prescribe call routing solutions to help decide the best treatment for calls coming from customers. A call should be queued based understanding the context of customer call and then prioritized accordingly.
  • The prioritization determination will help size and order the call queues based on importance and predicted business value size. If a call is predicted to be very important, the system will route such calls to the appropriate agents for resolution. Other customer calls will be prioritized and placed in the right sequence in the queue for IVR or agent facilitation.
  • Factors for predicting such call priority is based on array of factors, including:
    • Customer life-time value score which is an indication of customer reaction, volatility, risk of abandoning or any profile/trait demographic characteristics.
    • Current open order context such as fulfillment delays, transaction value, and customer digital activity including the presence of browsing and social media activity of the given customer along with historical patterns of similar situations. Customer order data features are leveraged by AI algorithms for co-relation derivation and predicting the best treatment for calls.
    • In effect, AI infusion here helps determine the best treatment for the call before an agent answers and avoids waiting time for valuable customers or customers who are calling again for the same issue.


Sample Use Case

  • A software company has an operations department that offers a wide range of services for their employees in specific region for their laptop, desk landlines, software, hardware, and network issues.
  • Company has enabled Live Chat Bots for employees to self-help for queries or employees can reach out directly via a 1-800 number.
  • Without the implementation of the solution:
    • Most of the times, employees reach out to 1-800 number and hold times are more than 30 minutes on any given business day.
    • Live Chat Bots look up their laptop configuration details and record a ticket with their operations team and assign a default priority.
    • This is not sufficient when an employee needs to explain the gravity of the situation and the impact on his deliverables, or a planned customer meeting in next few hours that require a fast resolution.
    • Eventually, valuable labor hours are lost and unproductive causing negative impact on stake holders impacted by a situation.
  • With the implementation of the solution:
    • The solution understands the gravity of the situation and the impact on deliverables, or a customer meeting happening in next few hours that requires fast resolution or enhanced SLA.
    • The solution decreases the call hold times drastically based on this derived context of the employee call when the employee dials the 1-800 number for help.
    • The best treatment for the call is determined and routed to a special team immediately who are enabled and empowered to take quick decisions for facilitating quick resolution.
    • In instances where the same employee reports a similar issue but there is no impact to customer meetings, the issue is addressed on a normal SLA/protocol mode.
    • In instances where the same issue is reported but there is no direct impact on deliverables in the next 4 hours (as derived by solution) the call is queued up based on a determined ranking.
    • Live Chat Bots are purely triggered for ticketing, audits and notes perspective and do not add any burden or overhead to anybody working on resolution.


Read the previous editions of this blog series that details the use case for a Call Centre Agent:
 

Edition 1: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases | Call Center Agent

Edition 2: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases | Persona: Fulfillment Manager

Stay tuned for the last Edition of the use case!


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