This post is a summary of a webinar we recently hosted, “Boosting Sales Operations Efficiency with Machine Learning”. The 30 minute on-demand version can be viewed (no form fill required) on our Vimeo channel.
Artificial intelligence has been posed as a solution to everything from self-driving cars to the Internet of Things—and now SpringML has launched an AI assistant for sales. If you’re a salesperson or a business development manager, you probably know that running a sales bullpen is a lot more difficult than driving a car. How can machine learning help with sales?
Today, we’re going to learn a few things:
- How salespeople can use machine learning tools to help grow the business
- How machine learning tools use sales data in order to become smarter
- Why you can trust predictions that are made by software
How can machine learning tools boost sales?
Research from Aberdeen Group puts machine learning under the heading of Sales Performance Management Technology, defined as any technologies that bolster “the end-to end data collection, analysis, workflow creation, and overall management of sales operations.” Their research suggests that companies who use these tools achieve a 7.83% year-over-year improvement in profit margin, as opposed to just 3.93% for non-users. Here, then, is how machine learning can be used in a sales context.
Forecasting is a common thread that ties together nearly every company that does business today. Everyone needs to forecast their sales. Machine learning lets businesses cut out the errors, manual processes, and spreadsheets from the forecasting equation. An unbiased, data-driven approach will more accurate forecasts more quickly.
Deal prioritization is important from a cost-benefit standpoint. Why opportunities are most likely to close, and therefore deserve most attention? More importantly, what are the factors that make this deal likely to succeed? Once this is understood, it becomes easier to replicate those factors.
Outlier detection can help forestall disasters or identify unusual opportunities. Two examples would be a deal that’s taking too long to close, or a deal that’s projected for more than a usual amount of money. The machine learning engine can be set up with rules that flag these characteristics and automatically notify people like the sales manager, account representative, and so on.
When applied to sales, machine learning can augment a traditional CRM. It will also trounce a spreadsheet—up to 90% of CRM spreadsheets contain errors. A machine learning platform will automatically distinguish the factors that separate closed-won and closed-lost opportunities, and learn. It will tell you which kinds of deals at which kinds of companies will be more likely to close-won, and allow your representative to spend more time in that sweet spot.
Although human representatives can certainly analyze deals and refine targeting on their own, machine learning software has an ability that people can sometimes lack. This is the ability to derive relationships between sets of data that may not appear congruent. For example, the software might point out that putting more money into a marketing budget only increases revenue up to $75k, and only delivers diminishing returns thereafter.
How does data make machine learning smarter?
Machine learning software is only as smart as the data it uses. If the data going in is bad, the prediction going out will be less than accurate. Notably, however, the amount of data isn’t necessarily what’s important. If you’re using the correct statistical model, it’s possible to get accurate predictions from a small dataset. The model is what’s important.
In the sales context, a machine learning model can look at the changes an opportunity goes through on its journey from creation to close. It can then use those changes to make predictions about opportunities that are currently open.
Why can you trust sales predictions?
SpringML offers an advanced predictive model that learns as it goes. It offers four separate pipeline views in order to expand its usefulness and offer more ways for users to check its accuracy. In addition to a simple forecast, SpringML constantly keeps tabs on changes in your pipeline, highlights high-probability opportunities, and tracks the flow of opportunities as they evolve over time.
In addition, SpringML is agnostic, allowing sales leadership to compare predictions from other platforms either built into the CRM or derived from Excel. Over time, SpringML will adjust its forecast to become more accurate as it matches its predictions against a company’s actual attainment.
For more information, as well as a detailed overview of how SpringML can work for sales, check out our 30 minutes webinar on how machine learning improves sales ops efficiencies.