Alteryx Analytics Brings Power of Predictive and Big Data to Market

Alteryx has released version 9.0 of Alteryx Analytics that provides a range of data to predictive analytics in advance of its annual user conference called Inspire 2014. I have covered the company for several years as it has emerged as a key player in providing a range of business analytics from predictive to big data analytics. The importance of this category of analytics is revealed by our latest benchmark research on big data analytics, which finds that predictive analytics is the most important type of big data analytics, ranked first by nearly half (47%) of research participants. The new version 9 includes new capabilities and integration with a range of new information sources including read and write capability to IBM SPSS and SAS for range of analytic needs.

vr_Big_Data_Analytics_08_top_capabilities_of_big_data_analyticsAfter attending Inspire 2013 last year, I wrote about capabilities that are enabling an emerging business role, that which Alteryx calls the data artisan. The label refers to analysts who combines both art and science in using analytics to help direct business outcomes. Alteryx uses an innovative and intuitive approach to analytic tasks, using workflow and linking various data sources through in-memory computation and processing. It takes a “no code” drag and drop approach to integrate data from files and databases, prepare data for analysis, and build and score predictive models to yield relevant results. Other vendors in the advanced analytics market are also applying this approach, but few mature tools are currently available. The output of the Alteryx analytic processes can be shared automatically in numerous data formats including direct export into visualization tools such as those from Qlik (new support) and Tableau. This can help users improve their predictive analytics capabilities and take action on the outcomes of analytics, which are the two capabilities most-often cited in our research as needed to improve big data analytics.

vr_Big_Data_Analytics_09_use_cases_for_big_data_analyticsAlteryx now works with Revolution Analytics to increase the scalability of its system to work with large data sets. The open source language R continues to gain popularity and is being embedded in many business intelligence tools, but it runs only on data that can be loaded into memory. Running only in memory does not address analytics on datasets that run into Terabytes and hundreds of millions of values, and potentially requires use of a sub-sampling approach to advanced analytics. With its RevoScaleR, Revolution Analytics rewrites parts of the R algorithm so that the processing tasks can be parallelized and run in big data architectures such as Hadoop. Such capability is important for analytic problems including recommendation engines, unsupervised anomaly detection, some classification and regression problems, and some clustering problems. These analytic techniques are appropriate for some of the top business uses of big data analytics, which according to our research are cross-selling and up-selling (important to 38%), better understanding of individual customers (32%), analyzing all data rather than a sample (30%) and price optimization (28%). Alteryx Analytics automatically detects whether to use RevoScaleR or open source R algorithms. This approach simplifies the technical complexities of scaling R by providing a layer of abstraction for the analytic professional.

Scoring – the ability to input a data record and receive the probability of a particular outcome – is an important if not well understood aspect of predictive analytics. Our research shows that companies that score models on a timely basis according to their needs get better organizational results than those that score all models the same way. Working with Revolution Analytics, Alteryx has enhanced scoring scalability for R algorithms with new capabilities that chunk data in a parallelized fashion. This approach bypasses the memory-only approach to enable a theoretically unlimited number of scores to be processed. For large-scale implementations and consumer applications in industries such as retail, an important target market for Alteryx, and these capabilities are becoming important.

Alteryx 9.0 also improves on open source R’s default approach to scoring, which is “all or nothing.” That is, if data is missing (a null value) or a new level for a categorical variable is not included in the original model, R will not score the model until the issue is addressed. This process is a particular problem for analysts who want to score data in small batches or individually. In contrast, Alteryx’s new “best effort” approach scores the records that can be run without incident, and those that cannot be run are returned with an error message. This adjustment is particularly important as companies start to deploy predictive analytics into areas such as call centers or within Web applications such as automatic quotes for insurance.

vr_Big_Data_Analytics_02_defining_big_data_analyticsAlteryx 9.0 also has new predictive modeling tools and functionality. A spline model helps address regression and classification problems such as data reduction and nonlinear relationships and their interactions. It uses a clear box way to serve users with differing objectives and skill levels. The approach exposes the underpinnings of the model so that advanced users can modify a model, but at the same time less sophisticated users can use the model without necessarily understanding all of the intricacies of the model itself. Other capabilities include a Gamma regression tool allows data matching to model the Gamma family of distributions using the generalized linear modeling (GLM) framework. Heat plot tools for visualizing joint probability distributions, such as between customer income level and customer advocacy, and more robust A/B testing tools, which are particularly important in digital marketing analytics, are also part of the release.

At the same time, Alteryx has expanded its base of information sources. According to our research, working with all sources of data, not just one, is the most common definition for big data analytics, as stated by three-quarters (76%) of organizations. While structured data from transaction systems and so-called systems of record is still the most important, new data sources including those coming from external sources are becoming important. Our research shows that the most widely used external data sources are cloud applications (54%) and social media data (46%); five additional data sources, including Internet, consumer, market and government sources, are virtually tied in third position (with 39% to 42% each). Alteryx will need to be mindful of best practices in big data analytics as I have outlined to ensure it can stay on top of a growing set of requirements to blend big data but also apply a range of advanced analytics.

New connectors to the social media data provider Gnip give access to social media websites through a single API, and a DataSift ( connector helps make social media more accessible and easier to analyze for any business need. Other new connectors in 9.0 include those for Foursquare, Google Analytics, Marketo, and Twitter. New data warehouse connectors include those for Amazon Redshift, HP Vertica, Microsoft SQL Server and Pivotal Greenplum. Access to SPSS and SAS data files also is introduced in this version; Alteryx hopes to break down the barriers to entry in accounts dominated by these advanced analytic stalwarts. With already existing connectors to major cloud and on-premises data sources, the company provides a robust integration platform for analytics.

Alteryx is on a solid growth curve as evidenced by the increasing number of inquiries and my conversations with company vr_Customer_Analytics_08_time_spent_in_customer_analyticsexecutives. It’s not surprising given the disruptive potential of the technology itself and its unique analytic workflow technology for data blending and advanced analytics. This data blending and workflow technology that Alteryx provides is not highlighted enough as it is one of the largest differentiators of its software and reduces the data related tasks like preparing (47%) and reviewing (43%) data that our customer analytics research finds gets in the way of analysts performing analytics. Additionally Alteryx ability to apply location analytics within its product is a key differentiation that our research found delivers exponential value from analytics than just viewing traditional visualization and tables of data. Also location analytics like Alteryx provides helps rapidly identify areas where customer experience and satisfaction can be improved and is the top benefit found in our research. The flexible platform resonates particularly well with line-of-business and especially in fast-moving, lightly regulated industries such as travel, retail and consumer goods where speed of analytics are critical to be performed. The work the company is doing with Revolution Analytics and the ability to scale is important for advanced analytic that operate on big data. The ability to seamlessly connect and blend information sources is a critical capability for Alteryx and it’s a wise move to invest further in this area but Alteryx will need to examine where collaborative technology could be used to help business work together on analytics within the software. Alteryx will need to continue to adapt to the market demand for analytics and keep focused on varying line of business areas so it can continue its growth. Just about any company involved in analytics today should evaluate Alteryx and see how it can streamline analytics in a very unique approach.


Ventana Research

InContact Advances Workforce Optimization for Contact Centers

InContact has cloud-based products that cover multichannel communications infrastructure (sometimes referred to as a “contact center in the cloud”) and workforce optimization. The channel management products were developed by inContact and through a partnership with Verint. InContact has been working to make Verint’s workforce optimization products available in the cloud while integrating the two sets of products. I met Kristyn Emenecker, inContact’s VP of workforce optimization, at the recent ICMI Contact Center Expo to find out how the recent announcement that it has acquired Uptivity, which also provides workforce optimization products in the cloud, will impact that partnership and the future direction for the products.

vr_CCC_actions_to_improve_customer_interaction_updatedShe explained that one reason for the acquisition was that many contact centers around the world have relatively few seats and that Verint’s product is best suited to larger centers with several hundred or thousands of seats, but Uptivity’s is a better fit for smaller centers. My contact center benchmark research projects confirm the prevalence of smaller centers. For example, in my recent research into next-generation workforce optimization more than two-thirds of participating organizations’ contact centers have fewer than 250 seats. My research also shows that smaller centers invest in contact center systems only half as often  as larger ones, relying instead on their agents’ initiative to deliver good experiences and leaving managers to use spreadsheets as their main analysis and reporting tool.

Another one of my research projects, into the contact center in the cloud, finds that to improve interaction-handling many companies are planning to invest in contact center applications such as workforce optimization in the cloud and, to a lesser extent, communication technologies in the cloud. More detailed examination of the results shows that smaller centers are more likely to make these investments if cloud-based systems are available. This hasn’t gone unnoticed by inContact, hence its focus on cloud-based systems and the investment in Uptivity.

vr_NGWO2_06_use_of_agent_workforce_applicationsI have covered Uptivity (formally known as CallCopy) for several years and recently wrote that it had added analytics and gamification to its workforce optimization suite. That suite consists of call recording, quality management, workforce management, coaching and training, and performance management, which aligns with the top five systems companies in my next-generation workforce optimization research most commonly said they use. In addition inContact has products that support compliance, desktop analytics, desktop recording, speech analytics and survey management. These are widely used products that enable companies to understand customer sentiment during and after interactions and to manage the people who are charged with improving those experiences. In combination these applications provide an integrated set of products that allow companies to manage their contact centers better. My research shows that most companies want products that meet these fundamental needs and they want them integrated so they are easy to use and manage. Availability in the cloud also enables companies, especially those with smaller centers, to manage costs and requires fewer skilled resources to operate.

vr_NGCE_Research_01_impetus_for_improving_engagementMy research into next-generation customer engagement shows that customer experience management requires a combination of integrated multichannel interaction management, business applications and analytics. Integrated multichannel interaction management provides customers with a choice of channels but ensures that the experience is the same on whichever channel the customer uses. Our research finds the benefits to be clear on improving the customer experience which is top benefit in almost three quarters (74%) of organizations. Workforce optimization addresses the people side of interaction-handling, and analytics provides an understanding of what is going on, which helps companies optimize the use of agents and channels. Kristyn explained that after the Uptivity acquisition, inContact has two options to meet these needs, which are similar but aimed at different sizes of centers. When I asked, where the boundary is between them, she said that there is no hard and fast rule based on the number of seats and that consultation with individual customers will determine which is more suited to their needs. Furthermore, customers need not upgrade from one option to the other if a center grows beyond a certain size. In the short term this situation is complicated further because although inContact has a roadmap to produce a fully integrated, cloud-based based option based on the Uptivity product, the final version won’t be ready soon. For companies considering inContact,  I recommend they be very specific about which product and which version of it they select to meet their needs. I also expect some tension between inContact and Verint until they agree how to handle these situations.

In my experience acquisitions always create issues. They impact current and future customers in product support and roadmaps. Product functionality often overlaps, and integration can be problematic between two what are probably very different products sets. The acquiring company has to integrate two workforces, and in cases like this, the purchase can impact existing partnerships. I was assured that inContact is aware of and working on all of these issues, but it will be some time before the situation begins to resolve and customers can assess the level of success.

Few vendors offer the combination of channel management, workforce optimization and analytics that inContact has, so if it succeeds in handling the challenges, it should be in a strong position to support companies seeking to improve the customer experience. I will keep tracking developments, but in the meantime inContact is one of the vendors I recommend evaluating, while keeping the issues discussed here in mind.


Richard J. Snow

VP & Research Director – Customer Engagement