- 30th September 2019
- Posted by: Manolis
- Category: Artificial intelligence, machine learning
How automated artificial-intelligence driven sales are having a positive impact on CRM systems.
What comes to mind when you think of artificial intelligence (AI)? A self-learning system that will supersede human intelligence, a far-off technology that will give us human-like machines with the potential for taking over society e.g. Skynet or just another nebulous industry buzzword?
Simply put, artificial intelligence is based on algorithms and applied to data buckets to create additional intelligence using machine-based pattern recognition – and this approach can now be implemented in sales to create an AI-driven automated sales platform that addresses common issues faced by the sales teams, by saving salespeople north of 20 percent of their time each week and providing unerringly accurate data to better inform sales processes and drive more deals.
How does it work? Let’s look at CRM first. CRM is essential to sales; we all understand this. This is all well and good but on the ground many salespeople simply don’t have the time or inclination, to feed data into their CRM applications, and thus help build the data moat for their organizations.
HubSpot nailed this with a survey that showed less than half of salespeople stored leads and customer data in a CRM system vs their paper notebooks, even now, in 2019! Their biggest challenge was manual data entry. Salespeople are driven to hit targets so despite the importance of CRM data often it’s not treated as a priority.
With AI, all of a salesperson’s new contacts and logging activities such as emails, phone calls, meetings, calendar events and so on are automatically logged into the CRM system. This captured data is matched with the correct account and opportunity, not only saving endless time but ensuring more accurate reporting and smarter decision making.
Of course, this has a positive boost to sales. The sales team is no longer sinking under the weight of administrative burdens, so they have more time to do what they are good at, which is selling and closing deals. In short, productivity is increased.
Forecasting and pipeline analysis
When it’s fuelled by the correct data sales forecasting and pipeline analysis is a science with predictable and verifiable outcomes. The problem, as outlined, is that appropriate data is often not entered into the CRM system such as new contact records, meetings, and new opportunities. This lack of, or inaccurate data, can skew the numbers which in turn leads to other reactions such as increasing marketing spend to boost the sales pipeline to solve a problem that doesn’t actually exist.
Sales reps can be working out of Gmail, Outlook, calendars, phones, or other applications that enable them to contact their prospects. They could also be “sandbagging” by hiding deals or pushing out close dates.
Identifying buying group dynamics
AI-driven sales ensure data is clean and accurate. It provides real-time pipeline analysis and tracking of deal trends, both real-time and historical. This enables insights into the central factors that led to successful deals such as the number of people in a buying group, the number of times they were contacted and correlations between the number in the buying group and the size of the deal.
This latter point is most important. Increasingly, buying groups are seen as the secret to real success. The People.ai platform provides clear insight into who has been spoken to at each stage of an opportunity. This is vital information for sales leaders who can work with their team to identify which buying group roles are moving deals forward. The data can also be used to create deal models and set benchmarks for future sales.
Sales team leaders
This also dovetails with the role of sales team leaders. Their experience is vital. They’ve fought the battles, won tough accounts, understand why customers are lost, know the best tactics and have an arsenal of advice to pass on to their teams. The trick, however, is to identify the best moments to step in and provide coaching. Should they wait until performance is dropping or ride in on the crest of a wave when sales are positive?
Since AI is a great tool for sifting through data, making sense of it and providing actionable insight to drive intelligent decision making it can be used to identify when interventions are needed for each individual salesperson. By taking all the relevant sales data and presenting it an easy-to-use dashboard sales coaches have an at-a-glance view of all activities for each salesperson.
They can see the number and types of activities, accounts that are engaged with, prospect response times and so on. Having this information on tap enables sales leaders to establish direct correlations between the type and volume of activities and quotas, and to identify coaching areas for each individual.
The sales contact database is also a foundational tool to start, enhance and drive sales. But research has shown that one in four contacts in the typical database contains critical errors. On the ground salespeople don’t need research to tell them this; they use it and discover the errors. It’s no surprise then that they find other ways to track prospects and deals. Of course, this is one more reason why CRM adoption is low.
Through applying AI to CRM data, enterprises can monitor the health of their CRM data, measure data gaps and cleaning opportunities in the CRM contact, account, and opportunity records. It ensures accuracy, creates new contacts in real-time and matches and assigns existing contacts to the right opportunities. One user discovered their team was working with over 3,000 contacts that weren’t even in their CRM system!
As can be seen, AI in sales isn’t some nebulous concept from the pages of science fiction or a far-off unattainable technology. It’s a practical and down-to-earth tool that gives salespeople the time and space to nurture contacts, identify new opportunities, dramatically improve efficiency, enable scientifically driven forecasting and ultimately increase time to revenue.