Telecom PRM Pioneer Delivers 25% Partner Engagement Surge
· Free Press Journal
Channel performance in telecom has long been settled by judgement rather than measurement. One architect built a scoring model to change what a channel organisation can see about its own partners.
Most of what a large telecom operator sells, it does not sell itself. The work moves through resellers, distributors, systems integrators and field engineering firms, sometimes hundreds of them, spread across regions with different regulations, different margins and very different levels of competence. Revenue depends on how well that network performs. Partner performance was, for a long time, something nobody could measure with any precision.
Visit michezonews.co.za for more information.
The evaluation of a partner’s performance was largely subjective. It existed within quarterly Business Reviews (QBR), spreadsheets managed by Regional Managers and the collective knowledge of individuals who have been working with the same partners for over ten years. While that knowledge-base was generally correct, it was also non-auditable, varied greatly from Region to Region and difficult to act upon quickly.
Most of Brahmandanda Naidu Dabbara’s career has involved creating the systems to eliminate subjectivity. As a Salesforce Technical Lead with over eighteen years in Enterprise CRM Architecture experience, Mr. Dabbara creates Partner Relationship Management Platforms. The layer of software used to support the recruitment, equipping, tracking and payment of the companies selling on behalf of an operator. His largest contribution to his work in this area was not a platform but a scoring model.
Mr. Dabbara created the Partner Value Index as a component of an Enterprise PRM build. The Partner Value Index evaluates partner performance based on quantifiable metrics rather than assessments. This may seem like a minor change; however, when an organization can evaluate its partners based on tangible evidence, the way a Channel Organization approaches its decision making begins to shift. Which
partners have early access to product launches? Which require training rather than pressure? How many relationships exist solely because of trust?
Mr. Dabbara architected the platform supporting the index using Salesforce Experience Cloud. The platform utilized Apex, Lightning Web Components and CRM Analytics. Dashboards were added to provide visibility into performance, automated workflows were established to streamline repetitive tasks and secure integration services provided a mechanism for integrating disparate systems. In his words, “the platform supports a large global partner ecosystem” and was designed to handle “high volumes of partner transactions.” Mr. Dabbara stated that he was responsible for all aspects of the platform's technical design, integration strategy and governance during multiple Enterprise Releases.
What makes this body of work relevant outside the telecommunications industry is that Mr. Dabbara repeatedly encountered the same issue in industries that share very little in common. More than twenty Enterprise Transformation Projects across Telecom, Healthcare and High-Tech resulted in similar failures. The failure was not typically due to a lack of software. Rather, it was that every Business Unit created its own software at different times with varying assumptions. None of the units were consistent.
In response to this challenge, Mr. Dabbara stopped designing engagements as custom builds. Instead, he developed reusable frameworks and standardized development patterns utilizing Apex, Lightning Web Components, Screen Flows and REST Integration. According to him, "by investing in reusable frameworks, standardized integration patterns and governance we significantly reduce long-term maintenance costs and accelerate innovation." Mr. Dabbara states that his approach reduced development time by approximately 35% and redesigned execution patterns and asynchronous processing improved transaction speed on high volume operations by 30-50%.
The other half of the work is data. Software operating in isolation produces inconsistent records, duplicated processing, and integration bottlenecks. Those issues don’t remain technical for long. A predictive model utilizing inconsistent data doesn't alert you to the inconsistency. It provides accurate predictions based on incorrect assumptions and executives take action based on those inaccurate predictions.
To address this issue, Mr. Dabbara standardized the interfaces instead of trying to patch each interface individually. He created REST-based integration architectures with OAuth Authentication, Automatic Synchronization and Formalized API Governance and migrated Legacy Processes to Cloud-Native Foundations. Mr. Dabbara states that as a result, data accuracy increased to greater than 99%, resulting in a decrease in Manual Reconciliation.
As would be expected, much of the remainder of Mr. Dabbara’s reported success stems from establishing a solid base of data quality. Workflow Automation, Intelligent Routing and Process Standardization resulted in a reduction of manual business processes by more than 60%. Reporting and CRM Dashboards reduced Report Preparation Time by more than 70%. Continuous Integration Practices and Automated Deployment reduced deployment time by approximately 40%. Collectively these represent improvements in efficiency. However, together they illustrate what might be described as reducing the amount of time between when an event occurs and when someone can act upon it.
He rejects characterizing any of this as completed. "Digital Transformation is not a one-time project; it is an ongoing capability that requires continuous modernization, measurement and improvement," he said.
He has documented his thoughts on this topic as well as his experiences in doing so. Much of his published works cover traditional topics related to this space including cloud-native partner management for Next Generation Telecommunications Infrastructure and the convergence of partner and customer data into a single view. Other works explore concepts further down the line. Examples include deployments of partner management at the Edge for Field Operations where connectivity cannot be assured and work continues regardless, and Federated Learning for Customer Intelligence across telecommunications networks where regulatory and organizational barriers prevent sharing data in the first place.
This latter concept ties to what he believes future enterprise systems will look like. His more recent research addresses Autonomous Agents inside the Software Delivery Lifecycle itself as well as Frameworks for Agent-to-Agent Collaboration where Independent Systems Exchange Context and Coordinate Enterprise Workflows without a Human Relaying Between Them, and Agent-to-User Interaction which defines how such systems communicate Recommendations to People Who Must Act Upon Them. He envisions these components coming together to form Multi-
Agent Ecosystems Coordinating Complex Workflows while Governance, Security & Compliance Hold. Alongside them he anticipates an AI-Specific Delivery Lifecycle which addresses how such Applications Are Built, Deployed Monitored & Governed will become Normal Practice.
The link between AI and partner management is closer than it initially seems. "AI Is Not a Replacement for Enterprise Architecture" according to Mr. Dabbara. An autonomous system allocating inventory identifying an underperforming reseller recommending where to apply Marketing Funds Requires Something Defensible to Reason From. Point it at an ecosystem that is still controlled by regional instincts and Quarterly Spreadsheets and it merely replicates the Judgment Problem rather than Solving it; then executes it faster than Anyone Can Review it.
That is essentially the premise behind much of Mr. Dabbara’s work – both within telecom and external to it. "The next generation of Enterprise Platforms Will Not Simply Automate Business Processes; They Will Collaborate, Reason And Make Intelligent Decisions Alongside Humans," he stated. Such systems are only as good as the measures supporting them; therefore, creating those measures is inherently slower, less visible work than creating automation atop them. A Channel Organization that CANNOT Identify Which Partners are Performing Does NOT Have an AI Problem Yet...it has an Instrumentation Problem — one that MUST Be Addressed First