Welcome sales pro!
Using Oracle Autonomous Data Warehouse and Oracle Analytics you will complete 4 objectives:
- Load your Autonomous Database and prepare data for analysis
- Review high-level quota attainment data
- Dive deeper to inform action + win probability
- Use Oracle Day by Day to prepare for customer meeting
You are now autonomous. Let’s go!
Using machine learning, models created in Autonomous Data Warehouse Cloud can uncover opportunities that will tell us how likely we are to close a deal.
Testing different models and algorithms, results look good!
Next, let’s see how joining this model with other sales information can become a great base upon which we can perform in-depth analysis in Oracle Analytics Cloud.
To gain all the insights we need, combining multiple data sources in one place for fast and easy analysis is key. Let’s start by loading all appropriate data that might help in our analysis.
First, let’s load our machine learning data model.
Objective 1 Complete
Nice work! You have successfully uploaded a new dataset to Autonomous Data Warehouse and joined it with two others to create a new multi-dimensional dataset for your analysis.
As a sales manager, you need to keep a close eye on your team’s performance. Thanks to Oracle Cloud, you have everything you need to track the status! Let’s review the numbers on your sales manager cockpit.
Halfway through the year, quota attainment is looking good at 49%, but we want to focus on the current quarter to make sure.
Select the current quarter to take a look
Now that we’re focused on the current quarter, there’s a troubling discovery: we’re near quarter end, but quota attainment is still below 50%.
However, pipeline and forecast metrics look promising! So, what now?
Objective 2 Complete
Nice work! Now that we have a high-level understanding of the quota attainment data, it’s time to dive deeper and allow focused data to reveal how we can take action.
With current quarter quota attainment below 50% but pipeline and forecast metrics looking promising, let’s review the forecast to help your team identify best potential deals to close quickly and improve current numbers.
Let’s drill in to Lisa’s profile to learn more about her pipeline details and account statuses.
With Lisa’s “Open Revenue” selected, now we can keep the selection to focus the visualization.
Objective 3 Complete
Nice work! Now that we know Lisa is our best chance to improve numbers, let’s jump to her perspective as a sales rep to see how she can take action.
Lisa is in a taxi on the way to visit one of her top customers—a critical deal to close to make the quarter quota. En route to the meeting, Oracle Day by Day notifies her about crucial information: open service requests.
Lisa remembers there was an issue, and the customer won’t want to talk about anything else before solving it. Having complete information about her customer will help her drive the conversation and avoid potential roadblocks.
Lisa can quickly find exactly what she’s looking for, and get the current status of any critical service requests.
Great news—a product patch is in progress to resolve the issue. The customer will be happy, and Lisa can focus on open deals.
To make further progress with her customer, Lisa can tap into machine learning models for more ideas.
“Propensity to Buy by Product” reveals valuable insights Lisa can use to upsell additional products and services.
Now she’s in great shape for her meeting!
Well done! Oracle Analytics Cloud has enabled us to understand the problem from all angles, and work to solve it. By loading and combining multiple data sources, the sales manager has diagnosed the root cause and ensured her rockstar sales rep will make big improvements to the sales quarter.