Machine Learning in SAP Sales Cloud – What Does it Mean?
We all know that data is the key to designing successful, personalized engagements with customers, and many of us have already spent time setting up systems to collect and track customer data that we know will be useful. With machine learning, it may finally be possible to easily and effectively make use of all that valuable information in ways that humans alone cannot.
Having said that, a comment I hear frequently from customers is, “I don’t think my business is ready yet for artificial intelligence or machine learning.” But at the rate that solutions are becoming more sophisticated, yet easier to adopt and use, you might want to rethink that impression – especially when considering your sales organization.
What is Machine Learning?
Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to ‘learn’ from data, without being explicitly programmed. Put simply, it uses past examples in data to find patterns. In the case of a sales organization, an example would be using historical won and lost opportunities to predict the probability of a win.
Although the term ‘machine learning’ was coined in 1959 by Arthur Samuel, it has come to the forefront today because we can now create large data sets across applications and make use of them like never before through massive improvements in hardware and computing power. In addition, we now have access to a wide variety of best-in-class deep learning algorithms to help the system learn.
What Machine Learning Can Do for Sales
One of the things that surprised me with the latest version of SAP Sales Cloud was opportunity scoring – what SAP calls Opportunity (Deal) Intelligence. The system is using artificial intelligence (AI) to actually build the scoring criteria on its own. It goes through the data, begins to identify patterns and makes calculations based on criteria it identifies that are associated with won deals. It then applies those patterns and criteria to deals in flight and scores them on the probability of a win.
Opportunity scoring collects all closed opportunities and removes any examples with no transaction history to ensure the data being scored is balanced. The overall scoring model uses the combined weight of over 75 features derived from ‘feature buckets’ (see diagram below for more detail), using both SAP and non-SAP datasets. The model is based on at least one year of historical data, and is updated with a data refresh every quarter. The insights gained from historical data are then applied to newly created opportunities, with a prediction phase of 3-12 months, depending on the size of the company.
When you consider that 70% of all B2B generated leads are not sales ready, and your team is wasting time targeting wrong-fit customers and prospects, it’s easy to understand why having the ability to ‘sanity check’ your pipeline is important.
Lead scoring helps the sales and marketing teams to focus on leads with the highest propensity to convert and become customers. It facilitates alignment between your sales and marketing teams and helps you to better prioritize leads and build a cleaner pipeline with predictable forecasting.
But lead contact scoring alone does not give you the full picture. Many sales organizations find themselves struggling with account data that is siloed across multiple applications, with no real-time intent and engagement metrics at an account level.
Account insights help the sales organization target B2B accounts with the highest propensity to buy/close while maximizing lifetime value. It helps your team to understand the health of accounts and use insights to engage in account-based selling and nurturing.
Looking Forward …
Even more capabilities are coming to SAP Sales Cloud in the future: product recommendations and activity intelligence – and even further out into the future, predictive sales forecasting and predictive ordering.
Does this make the sales team obsolete?
Absolutely not. As machine learning takes over crunching the numbers for forecasting and scoring deals, it frees up your sales team to focus on building and nurturing those all-important relationships. Machine learning will ultimately provide the insight that makes it easier for sales reps to reach their numbers and for sales teams to meet and exceed revenue goals.
Want to learn how SAP Sales Cloud can help your organization? We’d love to chat with you. Request a meeting.
Want to learn more about SAP C/4HANA? Watch for our next blog article, Trends in Field Service Management.