Although historically there has been a hard divide between what are colloquially called “Inside and Field Sales,” changes over the last 10 years have narrowed the distinction. The pandemic has only accelerated the path to unifying sales activities commonly performed to engage buyers and customers. Characterized by a very disciplined and controlled endeavor, inside sales teams have been heavier users of technology. This has enabled more productive engagement including emails and calls, as well as provided techniques such as gamification to set competitive internal dynamics that help motivate sales professionals.
Topics: Sales, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Sales Performance Management (SPM), natural language processing, AI and Machine Learning, intelligent sales, sales enablement
The annual Ventana Research Digital Innovation Awards showcases advances in the productivity and potential of business applications, as well as technology that contributes significantly to improved efficiency and productivity in the processes and the performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations that advance business and IT.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing
Ventana Research has been evaluating analytics and business intelligence (BI) software for a long time—almost 20 years. Our methodology for these assessments is referred to as a Value Index. We use weightings derived from our benchmark research about how you, as buyers of these technologies, value and evaluate vendors. You can view our 2019 Value Index results here. I am in the process of completing the 2020 evaluation now.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing
The last decade has seen exponential growth amongst subscription-based business models. Pioneered in the B2C market with cloud-based SaaS offerings, the last decade has seen exponential growth in the share of the economy that is now subscription based. Increasingly, this modern business model is permeating throughout more traditional style industries and companies. But regardless of whether a company is natively subscription based, or is transitioning, maintaining this growth requires organizations to foster long-term relationships with customers and deliver products and services that get better over time.
Topics: Sales, Customer Experience, Office of Finance, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Contact Center, Product Information Management, Price and Revenue Management, Digital Commerce, Enterprise Resource Planning, ERP and Continuous Accounting, natural language processing, robotic finance, AI and Machine Learning, revenue and lease accounting, subscription management, agent management, intelligent sales, sales enablement
An important recent development in software designed for the Office of Finance is the addition of what we’re calling a data aggregation device (DAD) for analytical applications. A DAD automates the collection of data from disparate sources using, for example, application programming interfaces (APIs) and robotic process automation (RPA). With a DAD, users of the analytical application have immediate access to a much broader data set; one that incorporates operational as well as financial data from internal and external sources. The larger data set enables a much more expansive set of analyses than has been feasible in the past because the process of acquiring the data is automated, and the data aggregation is handled in a controlled manner. This control means that data in the system is authoritative, accurate, consistent, complete and secure. The difference between a DAD and a finance data mart is that the former is prebuilt for the specific application, and therefore eliminates this source of implementation costs and offers faster time to value.