ImageThe market for customer analytics continues to grow as organizations realize the current competencies and technology are not aligned to the priority of providing the best possible customer experience through supporting business processes. At the same time those organizations that have invested and continue to improve in this area are taking advantage of why I call a new generation of customer analytics. As I research into technology to support customer analytics, I had a chance to assess the work done by a business analytics software provider called Alteryx. My colleague Tony Cosentino who is the research director of our business analytics efforts recently wrote an analysis of Alteryx, but I wasn’t familiar with the company until my own briefing about its customer analytics focus. For the technical aspects of the product, you can consult Tony’s analysis, but I want to discuss several key points that came up during my briefing.

The company has a rich history since the late 90s in the business of data management and location analytics, and this heritage shows in its approach today. I know that customer data management is no easy task because there are so many sources of customer data that come in various sizes and formats. In fact, our benchmark research on customer information management shows that participants with IT titles on average identified 21 different sources of customer data while business users on average identified only 2.5 sources. Alteryx provides support for these two types of users, which it calls data artisans and business decision-makers, respectively. Data artisans or who we would call analysts do the nitty-gritty work to access, clean, rationalize and integrate the data, and then define and build analytic applications. The decision-makers are business users who define the information they need, provide parameters to make the apps work in the way they want, and then consume the results. This is critical as the iterative effort to add and augment data to customer information happens continuously by these analysts.

Analysts use Alteryx’s Designer Desktop to create apps using a three-stepvr_bti_br_whats_important_in_choosing_technology approach. First, the tool set provides drag-and-drop capabilities that allow users to integrate data from any source, be it transactional data from internal business systems, data collected from cloud-based systems, or unstructured data collected from electronic devices or social media. The tools help users define how the data should be cleansed and enrich it with geocoding data or data collected from third-party sources such as financial data providers. Critically the software helps align data to the customer identification data to enrich it for analytics. Second, the tool set enables users to create applications such as behavioral, spatial or predictive analytics, which typically are built so business users can input specific parameters to define the profile of the information they want to see that could be customer profile or segments. In the third step, the apps produce the results business users are looking for to analyze and interact with dynamically. The outputs can be published to the cloud and in different visual forms to suit the user. Analysts can also define rules to create action plans to address conditions identified in the outputs. Our research on business technology innovation shows that usability is highly important when purchasers evaluate new systems. In this context Alteryx’s three-step approach, using a common tool set, is appealing because it makes it easy for both analysts and business users to support the end-to-end process of accessing data and delivering results. Most importantly it can follow along an analytic process and at each step a range of analytics to rules can be applied to provide more context or depth to the analytics.

The same technology innovation research shows that analytics is the top vr_bti_br_technology_innovation_prioritiespriority among new technologies for companies, something that is borne out in my recent research into customer service and the agent desktop and next-generation workforce optimization. These results show that many companies lack a complete view of their customers, largely because of the increasing number and types of customer data sources. My latest research into the next generation of customer analytics is already identifying the changing landscape of processes and technology while identifying the divide between organizations that perform this responsibility well compared to those that do not. Alteryx has analytics technology that companies need to build these views on a range of customer information and metrics, and with more depth than many of the application providers in CRM and contact centers, so I recommend that those looking to advance to a new generation of customer analytics evaluate them.