Oracle enables revenue transformation with Fusion CX Analytics

Today, Oracle announced Oracle Fusion CX Analytics as part of the launch of the next generation of Oracle Fusion Sales. Fusion CX Analytics is a unified solution that combines data from the front, mid-, and back offices to provide actionable intelligence to help users learn from revenue to grow revenue.

In this blog, I will walk you through the journey of how and why we conceptualized this product and what specific business problems it addresses.

Key pain points

Fusion CX Analytics was conceptualized after extensive user research to understand the analytical needs of people in different roles in the Sales, Marketing, Service, and Revenue Operations departments, from leadership positions to individual contributors, across various organizations. Four key pain points emerged:

 

◉ Difficulty and delays in analyses of the entire customer lifecycle from lead to contract through renewal

◉ Overly complex processes required to blend data from multiple sources to perform analyses

◉ Limited ability to answer key business questions, especially for detecting anomalies or exceptions

◉ Inability to quickly predict future outcomes from all available data as an end-user capabilty and not as a request-based process to data science and data engineering teams.

Revenue funnel

To build a comprehensive, cross-departmental solution that could address these needs, we needed to understand and define the revenue landscape these personas sought to navigate.

Many B2B or B2B2C buying processes take a complex journey involving multiple systems before revenue is recognized, with siloed transaction or event reporting only within each system. The journey typically starts with the Marketing Automation Platform (MAP), using multiple channels to process and track how marketing teams engage and convert prospects to leads; these leads get handed to sales teams once qualified. The leads are processed and converted to opportunities by sales, using a Sales Force Automation (SFA) system to track progress within the sales cycle. Sales will generate a quote if successful, typically following the process within a Configure-Pricing-Quote (CPQ) system. Once the pricing is negotiated, an order gets generated in an Order Management system (usually part of the Supply Chain system). If the company’s business model involves a Subscription model, then the Subscription Billing system comes into the picture. Generated invoices lead to Accounts Receivable entries in the ERP system.

The users we talked to wanted to perform analyses across the revenue cycle, using data from all the disparate systems mentioned above and in one place. E.g.,

◉ Where are we leaking revenue in this entire cycle?

◉ Are we acquiring profitable customers with high lifetime value?

◉ What is the impact on our margins with our current discounting strategy?

◉ Did we give away more than optimal discounts?

◉ Which marketing campaigns or channels lead to more qualified leads?

◉ Are there certain touchpoints that have an asymmetrical impact on our revenue?

◉ Given the pipeline for the next four quarters, do we have enough capacity in our production line?

◉ Which pricing model (recurring vs.one-time usage vs. some combination) drives more revenue?

◉ What product configurations are selling more? Which region?

◉ What activities are leading to higher customer satisfaction?

People were looking for ways to understand what the data from Sales, Marketing, Service, CPQ, Subscription, and Finance was showing and in one system. In other words, they wanted to access the data from the front-office, mid-office, and back-office within one system to see the interdependent patterns and assess what could happen next, looking at the business as one block instead of several disjointed units. Plus, they wanted to derive a direct line from all activities to topline revenue, so they could optimize the company's resources and activities through analytics.

Technology struggle

A few companies have embarked on the journey to build systems to address the problems above centered around a data-warehouse strategy. From our research, we found they have faced several roadblocks/pain points:

◉ How do I connect to the source system? Which APIs to use? Which ETL tool is the best? Often, the simplest way was to extract files and ingest them into the warehouse, leading to brittle connections with a high probability of inaccurate data showing up in end-user reports.

◉ How do I create a data model to blend data from these disparate systems? What are the right join keys? What do particular tables or columns mean in the source system? Where do I find experts who understand the source data model?

◉ How do I enforce governance so that accurate interpretations of KPIs are used in my organization?

◉ How do I deal with different vendors for ETL, DW, and BI layers? How do I find experts in each of these systems and manage them?

◉ How do I re-create the same security model that I built in the transactional system?

Evidently, it’s been a struggle, marked by complexity and confusion. Hence, many organizations wouldn’t even consider starting projects on this path.

Alignment and collaboration using KPIs 

Within the context of the holistic revenue process outlined previously, the community of users wanted to define KPIs, get alignment with different business leaders from various departments, monitor these KPIs, and get notified proactively if there were exceptions. Most of them were getting lost with multiple isolated reports and dashboards, which required IT involvement to create. Often they would forget their own initial question by the time that particular dashboard or report was delivered. The technology struggles detailed above make it a massive hurdle to blend reporting and create those cross-departmental KPIs.

Introducing Oracle Fusion CX Analytics

Oracle Fusion CX Analytics was designed to address these needs laid out by our users:

◉ KPI-driven with the ability to drill down into any level of detail with proactive notifications

◉ The ability to analyze the entire contact-to-cash or lead-to-order process to optimize revenue

◉ Cross-departmental alignment and collaboration using shared KPIs

◉ Less complexity around data acquisition & management, and increased trust in shared data

◉ Simplified security by propagating security configurations from the source system

As a foundation, we created new cloud-native data pipelines that simplified the entire data acquisition process through wizards. No need to worry about APIs, connectors, ETL, etc. Just provide the URL to your Oracle Cloud Applications, configure a few parameters, and the data from the source systems gets transferred on a regular cadence. Not only that, but we validate the data and also handle all the complexity related to incremental updates and source schema changes.

Next, leveraging our deep knowledge of source system data models, we crafted a very analytically efficient data model to ensure all kinds of joins perform correctly. On top of that, we layered a cross-departmental semantic model that enables a unified interpretation of all KPIs and metrics across the entire revenue cycle. In other words, we do the heavy lifting to conform dimensions across Sales, Marketing, Finance, and operations systems to enable cross-departmental analysis for business users.

And to further reduce time-to-value, our product contains hundreds of prebuilt KPIs and curated drill-thru reports that enable anyone to increase productivity as soon as the system is provisioned — on day one. 

Finally, Oracle Fusion CX Analytics includes prebuilt mechanisms to easily extend the data pipelines and models with their customizations, whether in Oracle cloud apps, on-premises, or third-party systems.

Source: oracle.com