Stephen Hurrell's Analyst Perspectives

Ten years have passed since artificial intelligence (AI) first appeared in sales technology, and the results are mixed. Early tools applied rudimentary machine learning (ML) models to customer relationship management (CRM) exports, assigning win probability scores or advising on the “ideal” time to call. The mathematics was sound, the demos impressive, yet adoption faltered because little thought was given as to how sellers should use this information.

I was recently a guest of SugarCRM at their 2025 Analyst summit with an opportunity to meet with the executive team and new CEO, David Roberts.

In a previous role I was actively involved with identifying and implementing dashboards and reports tracking key sales department metrics and KPIs for enterprise-size customers as well as high-tech growth companies. These included recognizable examples such as opportunity conversion rate, average deal size and sales velocity (length of time to close, won or lost). Wind the clock forward and in my role as an industry analyst I now talk to customers and have a wider view of the market.

Much development and marketing resource is being directed at technology that supports tracking and managing the customer experience (CX) and identifying prospects. All of this is good for business and highlights some of the exciting potential for artificial intelligence (AI) and generative AI (GenAI) to really accelerate the basic need of matching buyers and sellers to create more efficient markets for the benefit of all. But part of the B2B customer experience also revolves around quality and...

Sales and revenue operations teams have for the most part traditionally relied on CRMs for information relating to leads and opportunities. For inside sales (sometimes referred to as high-velocity sales) as well as field sales, relevant product, pricing and customer have been loaded or entered into the CRM to be accessed via reports, dashboards or spreadsheets. For inside sales teams, dialers with prioritized seek lists were an additional need. Yet traditional CRM software providers were...

Ventana Research recently announced its 2024 research agenda for the Office of Revenue, continuing the guidance we have offered for nearly two decades to help enterprises across industries derive optimal value and improved outcomes from business technology. Chief Sales and Revenue Officers face an imperative to manage their sales and revenue organizations, but they do not always have the guidance they need to embrace technology. As we look forward to 2024, we are focusing on the entire selling...

There are stark reminders that all is not well in business-to-business sales and marketing land. Many organizations struggle to hit revenue targets, and individuals consistently fail to hit their quotas. Various reasons are put forward, but the more convincing argument is that buyer behavior has transformed over the years, requiring changes to the traditional way of engaging with B2B prospects and customers. Even Jon Miller, co-founder of Marketo, has acknowledged that the process on which he...

Customer relationship management systems have been around since Tom Siebel created one while working at Oracle in the late ‘80s and then monetized it founding Siebel Systems that eventually was bought by Oracle. When Salesforce launched its “No Software” slogan in the early 2000s, CRM moved to the cloud, but the concept was still based on a object model centered around an opportunity that represented a potential sale. The user interface allowed entry of data to the opportunity, along with...

Having worked at a start-up during the first wave of artificial intelligence (AI) that was predicted to revolutionize sales in the mid-2010s, I guess I am a natural skeptic when it comes to new claims about how AI will change sales forever. In the mid-2010s, vendors were using machine learning (ML) models trained on sets of historical opportunity data that represented closed won and closed lost deals. The ML models were looking for correlation and patterns to see whether they could predict...

PROS has been in business since 1985, but over the past 10 years, the company has transformed itself from being primarily a vendor of on-premises price optimization software supporting the airline industry to providing a cloud-based set of applications that use data and artificial intelligence (AI) to aid in price determination. PROS also offers a scalable configure, price, quote (CPQ) application.