Converting opportunities in the pipeline into successful deals is the primary focus of the sales organization and is critical to any company!
In this example I’m going to play the role of a sales executive at an automotive supply wholesaler and I’m trying to investigate a sales execution issue. We have not been converting enough opportunities lately. I want to better understand our sales pipeline and which deals our sales team can expect to win or lose based on data that I’ve pulled out of our pipeline database.
Ask your question right here on the Watson Analytics main page:
I begin by typing my question in natural language right here on the Watson Analytics main page. The built-in cognitive ‘smarts’ looks across all datasets to fetch the results and then ranks those insights in order of their relevance.
I can immediately see that Midwest and Pacific are my leading regions in terms of opportunity amount. I drag and drop ‘opportunity result’ from the data tray on top of this visualization and format it accordingly to understand the deal conversion efficiency across these regions. There is surely a huge room for improving our sales execution efficiency across all these regions!
I continue my analysis with a new discovery tab and while I can use one of the dynamic and ‘unbiased’ starting points, I type another question and choose one of the more relevant visualizations. Here I take a look at the spread of opportunity amount across my top 5 route to markets and regions. While Field Sales & Resellers are significant, I can further fine-tune this visualization to focus on each of my route to markets and their performance across regions or say across Opportunity Result.
What really drives whether you’ll win or lose a sales deal?
While it’s great to have a handle on what’s happening, I now want to understand the WHY behind what’s happening. With that intent, I ask my next question as though I were talking to an expert – “I want to understand Opportunity Result” and choose the recommended spiral visualization.
This easy to understand spiral visualization shows me the factors that drive opportunity result and ranks them in order of their predictive strengths. Looks like the combination of ‘revenue from client since the past two years’ and ‘total days identified through qualified’ has the most significant impact on whether we win or lose deals.
I click on the detailed insight for the top combination driver to dig deeper!
Guidance based self-service predictive analytics brings out the latent insights buried deep within your data:
This detailed insight essentially shows ‘revenue from client past two years’ along the y-axis and ‘total days identified through qualified’ along the x-axis. In general, looking from left to right I realize that my chances of winning a deal decreases as it stays longer in the pipeline.
I also see that an opportunity is more likely to result in a loss if the client didn’t buy anything from us within the last 2 years. Biggest chance of winning a deal is when a client did business with us in the last two years – esp. to the tune of 0-25K USD (blue area in the tooltip). However, although the deal will likely result in a win if they have bought within the last 2 years, that chance of a win decreases as the sales deal rises (look at the red areas from top to down).
That was great insight and more importantly, in such short time! Now I want to look at the predictive model for opportunity result.
I take a look at these statistically derived decision rules. The simplest rule tells me that if a client’s purchase history with us in the last two years is less than 25000 dollars, there is an 83% chance of successfully closing that opportunity. Similarly, we can look at the more complex deal profiles below. These profiles are extremely valuable in the hands of my sellers and sales managers when reviewing their deal pipeline, anticipating pipeline gaps and course correct our sales strategy accordingly.
Wrapping it up in a Display:
I then use the Display option to open an existing Sales Win Loss dashboard. Looking from left to right, this Scatter chart shows that irrespective of opportunity amounts, we start losing deals as they stay longer in the pipeline. This could help formulate threshold levels for each supplier based on how many days a deal is in the pipeline and create alert mechanisms to expedite its progression.
I click to edit this dashboard so that I can add more content to it. I drag and drop two visualizations from the Discovery Set I just created along with two filters viz. ‘competitor type’ and ‘client size by revenue’. Based on the insights I gleaned so far, I know we need to take a second look at our pipeline strategy so that we focus on the right deals and then work to maximize their progression through the pipeline.
So to recap, I started with just a question and within minutes I was able to understand more about our opportunities, sales pipeline and more importantly, what drives our wins and losses.