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Ok – so it’s Data Science. But it’s real and SAP is using it in some very real ways.  This is the first of a two part blog on how SAP is bringing Predictive Analytics together with Machine learning to create tools that can help you to accurately forecast Sales and Deliveries to master Order-to-Cash end to end scenarios and to learn how to squeeze more information out of your data.

At SAP, we have long been pioneers in the field of predictive analytics.  From the Predictive Workbench to the acquisition of KXEN to today, SAP has been buying and building predictive tools – not solely for the purpose of building a tool-set – but with the goal of embedding these analytics into our customers’ core business processes.

That is, a forecast does no one any good if it’s just numbers that say “Hey. Look at me!”.  Rather if the forecast can be provided in the context of an end-to-end business process, then it helps to lead you into more time critical decision making that can transform your business. In this blog, let’s focus on one specific Sales use case –  predicting Sales Quotation conversion rates.  In a later blog, we’ll also look at how we use predictive analytics to predict Sales Performance,  and then how we can smartly predict delays in the delivery of Sales orders.  Some of these use cases targeted for release later this year are fine tuned to provide  sales managers and the sales representatives improved sales predictive analytics that can help them achieve better results!

Embedding Predictive Analytics into the Sales Line of Business

SAP S/4HANA Cloud reimagines businesses for the digital economy.  The entire value chain is becoming more digitized and additional intelligence is being injected into the ERP business processes with SAP S/4HANA. As a Sales manager or an internal Sales representative, I need to be able to better predict sales volumes and revenue to stay ahead of the competition.

SAP Quotation Conversion Probability rate in Sales

One of the toughest analysis for a sales manager is to predict the probability that a sales quotationwill be converted into a sales orderthat eventually become sales revenue. Currently a lot of manual work is involved to estimate and calculate this probability. With embedded Predictive Analytics, SAP has created an app to calculate the probability of converting a Sales quotation into a Sales order. The app utilizes core classification and regression modeling techniques that are embedded into the sales quotation process to predict the conversion probability expressed as a percentage of the net value of the Sales quotation.

Armed with a probability based on actual historical results, the sales manager can dramatically improve his or her Sales forecast and then manage a more realistic revenue forecast based on achievable Sales volumes.  In the example below,   low Quotation conversion rates will surely result in a less achievable forecast.  As they say “You Can’t manage what you can’t measure”, sales management is left only with the hopeof beating a historically low conversion rate. If you can forecast this ahead of time, other actions can be taken in the marketing mix (remember the 4 P’s- Product, Price, Place and Promotion?) to improve conversion rates and actually start to manage for higher revenues.

If you dive into the “Quotation Conversion Rates” app and run through some scenarios of alternative Quotations, you will see a higher probability of a particular quotation being converted into a Sales order

So you can see from these examples how we are using predictive analytics – and data science todigitize thesales process and enable customers to accelerate and improve their decision making in the Sales line of business.

Be sure to bring your questions, comments and concerns about how you can use predictive analytics to Tech Ed – which is just around the corner (be sure to register for TechEd 2018 (Bangalore and Barcelona).  There will be lots of experts on hand to give you a deeper dive into the technologies behind what we have just scratched the surface of in today’s blog as well as to show you other interesting ways that SAP is using these predictive tools to help you in your own journey to the Digital Enterprise!

 

Links to more information:

 

Follow us via @SAP and #S4HANA, or myself via @SDenecken

 

Part2: Predictive Analytics is not a Crystal Ball – It’s SCIENCE!:  Part 2

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1 Comment

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  1. nishant singh

    Hi,

    Really appreciate the use case u mentioned and developed in SAP environment.

    One question arises along with predictive analytics results is the question of why or what are the features that are contributing the most in the results apart from the accuracy of the results. Do u have such functionality?

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