Welcome sales pro!

We’ll demonstrate how two roles—a sales manager looking to ensure his team is on track to reach quota, and a sales rep looking to close the biggest opportunities and build strong customer relationships—both can get the information they need to focus on the most important tasks and drive business.

Start Quick Tour

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!

Start Objective 1

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.

Scroll down to see machine learning in action

Scroll down to see machine learning in action

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.

Next

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.

Click Create button

Click Data Flow

First, let’s load our machine learning data model.

Select DT_Model
external file

Click the + symbol to add sales manager open opportunities data to the mix.

Click Add Data

Select Sales Manager—Open Opportunity Lines

Click the + symbol to save this new combined data set.

Click Save Data

The new combined data set has now been saved

Next

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.

Start Objective 2

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.

Next

Halfway through the year, quota attainment is looking good at 49%, but we want to focus on the current quarter to make sure.

Click Close Quarter Name

Select the current quarter to take a look

Click Current Quarter

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%.

Next

However, pipeline and forecast metrics look promising! So, what now?

Finish Objective 2

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.

Start Objective 3

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.

Click Forecast Review tab

Here we see detailed insight by each sales rep from your team. Lisa Jones may be a prime candidate to push to close open deals, as she has the highest “open revenue”—but, by itself that’s no guaranteed indicator.

Next

Let’s drill in to Lisa’s profile to learn more about her pipeline details and account statuses.

Click Lisa’s Open Revenue blue bar

With Lisa’s “Open Revenue” selected, now we can keep the selection to focus the visualization.

Click Keep Selected

Good news - looks like Lisa’s current deals are in later stages of the sales cycle.

Next

Even better, machine learning algorithms predict these deals are likely to be won and closed.

Next

This is exactly what we needed to take action towards a strong improvement in current quarter numbers. The conclusion: call Lisa to form a close plan for these deals and help her achieve her target.

Finish Objective 3

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.

Start Objective 4

As a sales rep, Lisa needs to keep track of current deals and have all the latest information at her fingertips, even when traveling. Enter: Oracle Day by Day. Let’s see it in action!

Next

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.

Click notification

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.

Click Search tab

She can even ask it aloud in natural language.

Next
Show me the status and delivery date for integration issue

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.

Click For You tab

“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!

Finish Objective 4

Mission accomplished!

Sales

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.

Next step:

This simulator is only the beginning. Uncover the full potential of Oracle Autonomous Data Warehouse by signing up for a free trial.

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