Software-as-s-Service has its lures and challenges for providers in the B2B realm. Recurring revenue is great, of course, but the ever-present challenges are how to gain customers and, once they’re there, to blunt churn. Machine learning and data analytics can help sales efforts pay off for smaller B2B firms, says Cup of Data chief product officer Greg Werner.
In a famous scene from Glengarry Glenn Ross, a character played by Alec Baldwin states a mantra of sales: Always be Closing.
In the B2B world, a productive sales team translates into top-line growth and a steady conversion pipeline.
Certain verticals, however, pose specific challenges in getting deals and having strong leads in place … and even keeping the customer in place once they sign on. It’s churn, of course, the bane of subscription models.
As always, bringing some automation to the process at hand can help alleviate some of those issues.
To that end, Cup of Data, which offers marketing science solutions for software-as-a-service (SaaS) B2B firms, said last week that it had forged an OEM partnership with InsideView.
The two firms said they are linking account-based marketing (ABM) with smart data and predictive analytics, which in turn can foster strong levels of engagement with those same accounts.
In terms of mechanics, Cup of Data says its marketing science API integrates with firms’ CRM tools, with an eye on spotting what are known as “warm leads” – identified through a variety of data sources that help pinpoint behavioral signals that point to a propensity to buy.
Some larger trends within B2B are afoot, illuminated by the pacts. In an interview with PYMNTS, Greg Werner, chief product officer at Cup of Data, said that in general, technologies such as machine learning and AI are used to train models that address specific problems. Everyday examples can be seen though consumer-based interactions with chatbots and IVR.
B2B firms are also gaining leverage with machine-based and deep learning models, which can illuminate patterns and potential gleaned from behavioral, account and contact-centric data gain.
“At the end of the day, we are all trying to save time and money so that businesses are more competitive,” said Werner.
If one must always be closing, it’s key in the B2B realm to be clear on just where to focus efforts.
“When selling SaaS, you need to make sure that multiple personas are satisfied with the product.” Werner added that APIs are growing in importance as integration with existing software tools remains paramount. Frustrating integration issues can lead firms to jettison software, or the vendors they’ve enlisted.
But knowing just who to contact in the first place – identifying and following up on leads that are crucial for smaller B2B firms that might be shut out of the process by bigger players – can be eased with marketing science.
Said Werner, an average of 6.8 people are involved in making decisions to purchase a SaaS tool for their firm’s own use.
Far-flung data sources, spanning advertising and social media and offering up “firmographic” data, can be leveraged to improve outreach and ROI on sales efforts. Werner termed it “account-based marketing,” where marketing and sales work together to identify ideal customers.
“We rank them using a propensity score by combining both fit and behavior information. The behavior information is based on things such as news mentions,” he told PYMNTS, as well as whether data shows customers are spending more time on certain sites than others or conducting research for some technologies more than others – indicating a propensity to buy a certain feature or service.
Churn rates, said Werner – where subscribers simply stop, well, subscribing – are crucial to the financial health of SaaS companies within B2B.
The aforementioned integration efforts can lead to churn, he said, but other issues can hurt as well, including “lack of a clear roadmap, not complying with service level agreements – when you open up a support ticket, it takes too long to get a response.”
Other considerations: a coherent, firm-wide message, where machine learning and chatbots can help proffer a homogenous message to a SaaS firm’s clients, which in turn can shorten training times for customer service representatives.
Here, then, machine learning can give SaaS firms an action plan that directly addresses churn. If models show an 80 percent or higher likelihood of churning, then, Werner posited, the systems can send an alert to the customer service manager and have them engage directly with the client who may be getting ready to churn and mitigate the risk. “That’s where artificial intelligence can really hook into existing engagement tools and help a company move from predictive to prescriptive” efforts, he said.
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