Artificial intelligence (AI) is now part of our daily lives thanks to smart assistants, self-driving cars, recommended products on e-commerce sites, and the rapid adoption of ChatGPT.
Within business circles, interest is growing in predicting and personalizing marketing to garner better sales forecasting and competitive intelligence.
Predictive AI is one of the newest CRM tools to help sales agents improve their selling game.
However, potential business adopters also worry about properly engaging with the new sales technology. Some of their concerns fall on the potential negative impacts of AI and human job retention.
The potential for predictive sales AI does not replace sales staff by automating the selling process, according to Zac Sprackett, chief product officer at SugarCRM. Rather, it provides sales reps with insights to help them do their selling better.
He suggested that humans often only know about potential sales leads when they interact with limited information on traditional CRM platforms. A lot more information exists in the wild, but it takes humans far too long to find, compile, and analyze it.
“Predictive AI can help to institutionalize that knowledge and parse all of that information very quickly and make recommendations that even somebody brand new to the business can take into account,” Sprackett told CRM Buyer.
Hiring a Top-Notch Leads Prioritizer
Marketing questions about adopting AI often focus on the best way to start using it in sales. While AI technology is becoming ever-present, shop owners with limited IT support may not know how to harness it or what tangible business benefits it unlocks.
Predictive AI enables organizations to leverage more data-driven strategies to sell smarter. Predictive sales AI uses historical data, patterns, and even external sources to forecast and enable businesses to make better sales decisions, offered Sprackett.
“By applying predictive algorithms to a company’s CRM or ERP data, sales teams can cut through noise and automate much of the sales process themselves. This allows sales reps to focus on nurturing and closing deals,” he explained about the predictive data process.
Predictive AI is not only a smarter and more accurate way of selling. It is the sales strategy of the future, he remarked.
Businesses adopting predictive AI can expect to reach the next level of business performance. It forms a direct pathway to fueling sales growth.
Predictive AI pulls together all of the historical information in the company’s various systems. It helps analyze how frequently people churn, plus so much more.
How Predictive Lead Scoring Works
How this technology works varies by CRM vendor.
SugarCRM, which was founded as open source but later transitioned to proprietary releases, takes all the historical information within the marketing, sales, and service platforms.
That data includes deals completed successfully and those that sales departments failed to close for whatever reasons. The platform also looks at the retention and support load of those deals, according to Sprackett.
“We basically look at all of those factors together, and then we take all of the information available inside of the CRM and bolster up with relevant firmographic information. So, things like the company’s industry, revenue, number of employees, all of that kind of information that also exists in third-party data sources are analyzed,” he explained.
SugarCRM takes all of this information and divides it into known outcomes, such as things that were successful or less successful. Then, the software assigns all the resulting data into two categories.
The first becomes the training set for AI to build models to predict whether a particular lead or opportunity will have a positive outcome. That model is exposed to the second data category, which is data kept from the training set but also has known results.
The predictor software checks the accuracy achieved in making predictions with data that the model has never seen before, Sprackett said about SugarCRM’s secret sauce.
“We are able to get really high levels of accuracy for our customers in terms of being able to make those predictions. Then we apply that to new data on a daily basis to guide sellers in terms of which opportunities are more likely to have these positive outcomes,” he detailed.
The process further provides customers with information about what they can do to advance an opportunity to make it more likely to be successful.
Flexible, Platform-Specific Design
Sprackett discounts the notion that predictive AI is something to fear. Marketers for years have been tweaking CRM platforms to analyze better what works and what does not in successful selling. Adding AI into the mix just makes the process faster and more accurate.
“It came down to gut feelings for a lot of people. You would have different people who had different interpretations after spending a ton of time making them,” he observed about pre-AI CRM solutions.
The goal of predictive AI is to level the playing field and make institutional knowledge accessible to anyone, regardless of experience. The end game is getting sales reps to raise the bar so that everybody can play at a higher knowledge level without investing countless hours analyzing research. Instead, they can spend more time interacting with customers.
One of SugarCRM’s advantages for new users is its modular capabilities. The company tries to configure the platform to tackle one specific area of a customer’s business, show success in that area, and then expand from there, noted Sprackett.
The ultimate goal is to see every user running the entirety of the SugarCRM platform. But the reality is that some businesses only want to engage with a specific aspect or function.
“We can integrate into other parts of your stack. And then when we show success, you know, hopefully, we will be able to expand from there,” he offered.
Importance of Data Quality
Regardless of the desired platform configuration, the quality of the data entered matters. SugarCRM’s ability to gather accurate and timely information globally maintains ideal sale predictability.
People have used CRM to monitor sales activity for years. Fortifying those efforts with AI makes the predictability results faster and more accurate.
SugarCRM is not limited to the information that is in the platform itself. Data quality is always something that causes organizations to struggle. Data decays the older it is.
So, SugarCRM regularly bolsters the information inside its platform with third-party data like firmographics, relevant news, and other details from external systems to help marketers see around corners in their data and make better predictions.
Designing Factors
Sprackett sees those design and reliability issues as key reasons for not distributing separate AI modules. SugarCRM’s modular configurations provide a flexible platform rather than bolting on a user’s existing CRM product with different AI components to decipher a company’s acquired data.
“So the preferable path to take is not latching this new element with AI onto an existing CRM. We actually rolled out a new CRM that has it built in,” said Sprackett.
The company went through all of the different parts of the platform that lacked AI and figured out how to connect it there. That design approach was better than bolting it on from a third party to an existing CRM, he suggested.
“I think you want the vendor to be the one who is crafting the solution. It is going to be better to scale and easier to maintain in the longer term,” he added.
AI and the Future of Sales Projections
Sprackett is laser-focused on what lies ahead with predictive AI. The technology has been around for a while but is fast moving off the ground floor.
“It is still kind of nascent and emerging, and in some cases, where I get really excited is being able to combine it with some of the crazy things you are seeing on the generative AI side of things,” he said.
For instance, being able to take these predictions and output them in plain English as to why a particular projection is being made is a marvel to ponder. You can imagine things like completely automated systems that help to prevent churn.
“You start to notice that maybe behavior is declining. Or access is declining in your application. And you automatically start sending people information that nurtures them and helps them to understand how to better use your systems, your processes, or your products,” he said about CRM capabilities just around the corner.
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