Longview’s recent Dialog user group meeting highlighted the company’s continued commitment to providing much needed automation tools for improving tax department performance – tools that enable the tax function to play a more strategic role in the management of a company. The sessions also covered the capabilities contained in the company’s latest release, Longview 7.2 Update 2 and gave customers a detailed product evolution roadmap following their merger with arcplan.
Recently my colleague Tony Cosentino wrote an analyst perspective asserting that big data analytics will displace net promoter score (NPS) for more effectively measuring the entire customer experience. This prompted a response from Maxie Schmidt-Subramanian, asserting that big data and NPS aren’t the only ways to measure customer experience success. The main point of Tony’s piece, as I interpret it, is that NPS is just a number, but big data analytics can reveal much more about customer behavior and intentions, and it can link these to business outcomes. On the other hand Maxie argues that whether or not companies use NPS, when it comes to measuring the customer experience, they rely too much on surveys and no one metric does the entire job. While to a large extent I agree with both arguments, from a business perspective I don’t think either addresses three very important questions. The first is what actually is the customer experience? Second, how should it be measured? And third, what is the best use of big data in relation to customer experience?
The Performance Index analysis we performed as part of our next-generation predictive analytics benchmark research shows that only one in four organizations, those functioning at the highest Innovative level of performance, can use predictive analytics to compete effectively against others that use this technology less well. We analyze performance in detail in four dimensions (People, Process, Information and Technology), and for predictive analytics we find that organizations perform best in the Technology dimension, with 38 percent reaching the top Innovative level. This is often the case in our analyses, as organizations initially perform better in the details of selecting and managing new tools than in the other dimensions. Predictive analytics is not a new technology per se, but the difference is that it is becoming more common in business units, as I have written.
Topics: Big Data, Microsoft, Predictive Analytics, alteryx, Operational Performance Management (OPM), Customer Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, Location Intelligence, Oracle, Information Optimization
To impact business success, Ventana Research recommends viewing predictive analytics as a business investment rather than an IT investment. Our recent benchmark research into next-generation predictive analytics reveals that since our previous research on the topic in 2012, funding has shifted from general business budgets (previously 44%) to line of business IT budgets (previously 19%). Now more than half of organizations fund such projects from business budgets: 29 percent from general business budgets and 27 percent from a line of business IT budget. This shift in buying reflects the mainstreaming of predictive analytics in organizations, which I recently wrote about .
Topics: Big Data, Microsoft, Predictive Analytics, alteryx, Customer Performance, Analytics, Business Analytics, Business Intelligence, Operational Intelligence, Oracle, Business Performance Management (BPM), Rapidminer
Our recently released benchmark research into next-generation predictive analytics shows that in this increasingly important area many organizations are moving forward in the dimensions of information and technology, but most are challenged to find people with the right skills and to align organizational processes to derive business value from predictive analytics.
Topics: Big Data, Predictive Analytics, alteryx, Customer Performance, organizational transformation, Analytics, Business Analytics, Business Intelligence, Business Performance, Operational Intelligence
My research and experience show that contact center agents and others handling customer interactions face the continuing challenge of meeting customer expectations while keeping down the cost of handling interactions. Our benchmark research into the agent desktop and customer service finds that one obstacle to meeting these dual objectives is that users have to access multiple systems – typically four or five – to resolve a customer interaction. The research shows that this impacts efficiency (by increasing average handling time and reducing first-contact resolution rates) and effectiveness (by degrading the customer experience, introducing data entry errors and undermining agent satisfaction). This situation is compounded as companies support more channels of communication, often making it necessary for agents to access even more systems.
Our benchmark research on next-generation business planning finds that a large majority of companies rely on spreadsheets to manage planning processes. For example, four out of five use them for supply chain planning, and about two-thirds for budgeting and sales forecasting. Spreadsheets are the default choice for modeling and planning because they are flexible. They adapt to the needs of different parts of any type of business. Unfortunately, they have inherent defects that make them problematic when used in collaborative, repetitive enterprise processes such as planning and budgeting. While it’s easy to create a model, it can quickly become a barrier to more integrated planning across the business units in an enterprise. As I’ve noted before, software vendors and IT departments have been trying – mainly in vain – to get users to switch from spreadsheets to a variety of dedicated applications. They’ve failed to make much of a dent because although these applications have substantial advantages over spreadsheets when used in repetitive, collaborative enterprise tasks, these advantages are mainly realized after the model, process or report is put to use in the “production” phase (to borrow an IT term).
Topics: Planning, Predictive Analytics, Reporting, Sales Forecasting, Budgeting, Customer Performance, Operational Performance, Analytics, Business Analytics, Business Collaboration, Business Performance, Financial Performance, Business Planning, Demand Planning, headcount planning marketing, Integrated Business Planning, project plannin
Our benchmark research into next-generation customer engagement finds that the top priorities in customer service for companies are to improve the customer experience (said 74%) and their customer service performance (70%). To do this, the technological steps most companies expect to improve customer engagement are to deploy collaboration systems, redesign the customer portal, deploy internal mobile applications, deploy mobile customer service apps and use social media for customer service. All of these we regard as potentially innovative and required digital technologies. Deeper analysis of the results finds key primary drivers for these priorities. Employees across the organization are handling customer interactions, but customers expect consistent responses no matter who they engage with. Customers are using more electronic channels of engagement, but here, too, they expect consistent responses. People on both sides are engaging more while they are on the move, so mobile support for employees and customers has become essential. Let’s consider how each of these five technologies can help companies meet these challenges and improve customer engagement.