ResponseTek is a software vendor whose platform and services help companies collect and act on feedback from their customers. It supports a closed-loop process that collects feedback, analyzes it, provides customizable reports and analysis dependent on the user, and most importantly enables taking action based on the information. This allows companies to understand product and service issues, customer sentiment, intentions, and likely behaviors, and where necessary ensures the most appropriate actions are taken.

The platform consists of six modules. One is (ex)pressRT, which ResponseTek calls as listening module. It enables users to create surveys and render them to customers through any channel. Users can apply existing analyses of customers to personalize surveys, with the aim of making them more relevant and thus more likely to be completed. Users can apply text analytics to information gathered through surveys and social media posts to extract insights such as root causes of interactions and customer sentiment. This allows companies to gather customer feedback through the customer’s channel of choice and ensures users gain insight into a variety of customer-related tasks.

Another module, (ex)ploreRT , analyzes data captured by (ex)pressRT and combines it with other customer data to provide real-time reports, dashboards and scorecards showing a comprehensive view of customers and their relationships with the company. All the analysis can be customized down to the individual user level so all users see the information relevant to them and the tasks they are carrying out. Each user also has the ability to drill down from higher-level analysis to the underlying data. This allows user to focus on information important to them and drill down to underlying, supportive information.

A third module, (ex)ceedRT focuses on employee performance and the impact employees have on the outcome of interactions. From that analysis it identifies areas in which employees need to improve. It includes capabilities to set actions based on the analysis and to track that such actions are carried out, for example, specific training or coaching the employee should receive and that they take identified training and coaching. This allows companies to personalize training and coaching to address specific employee needs.

In contrast (ex)changeRT focuses on the customer. It uses analysis to initiate proactive engagement with customers and allows users to personalize interactions. This allows companies to proactively reach out to and build better relationships with them by showing their voice is being heard and acted upon.

The administrative module is (ex)ecuteRT, which provides tools for system administrators to set up, modify and run the system to deliver results required by business users. All five of these modules run on (ex)celeratorRT, a cloud-based platform that provides the environment, logic and common tools to support them. It provides a scalable, configurable, reliable and secure environment so that the system can be set up to suit individual company requirements.

Our research into customer feedback management finds that vr_cfm_benefits_of_capturing_customer_feedbackcompanies face people, process and technology issues with their customer feedback management and voice of the customer programs. From a people perspective, most don’t have sufficient resources to respond to the results of feedback programs (19% said this is their top people issue), 18 percent don’t have processes in place to make best use of the findings, and from a technology perspective, companies neither have the tools to identify the reasons behind customer interactions nor to collect feedback through all channels (35% each). The ResponseTek platform addresses all these issues, and being available in the cloud allows companies of all sizes to take advantage of it. It also allows companies to close the loop and assure customers that their feedback is being listened to and action is being taken based on it; currently only one-third (34%) of companies in our research can always do. Companies that have advanced feedback software and processes said that they have received a variety of benefits, most often improved customer satisfaction and loyalty (65%), improvement in their products and services (45%) and better focused employee training and coaching (44%).

It is obvious that there is no point in analyzing data if you don’t take action on the insights from it. The same is equally true of customer feedback management; in fact poorly managed programs encourage customers to stop giving feedback because they feel it is a waste their time; this likely undermines customer satisfaction as well. Regarding employee performance effective analysis of feedback can help guide responses to customers and identify areas in which employees need to improve in handling interactions. Companies that want to gain deep insights into their customers, their feelings and their intentions, and seek to establish excellent customer relationships would do well to put in place a closed-loop voice of the customer program. Those thinking about this should evaluate how ResponseTek can support those efforts.


Richard J. Snow

VP & Research Director

As I discussed in the state of data and analytics in the cloud recently, usability is a top evaluation criterion for organizations in selecting cloud-based analytics software. Data access of cloud and on-premises systems are essential antecedents of usability. They can help business people perform analytic tasks themselves without having to rely on IT. Some tools allow data integration by business users on an ad hoc basis, but to provide an enterprise integration process and a governed information platform, IT involvement is often necessary. Once that is done, though, using cloud-based data for analytics can help, empowering business users and improving communication and process .

vr_DAC_16_dealing_with_multiple_data_sourcesTo be able to make the best decisions, organizations need access to multiple integrated data sources. The research finds that the most common data sources are predictable: business applications (51%), business intelligence applications (51%), data warehouses or operational data stores (50%), relational databases (41%) and flat files (33%). Increasingly, though, organizations also are including less structured sources such as semistructured documents (33%), social media (27%) and nonrelational database systems (19%). In addition there are important external data sources, including business applications (for 61%), social media data (48%), Internet information (42%), government sources (33%) and market data (29%). Whether stored in the cloud or locally, data must be normalized and combined into a single data set so that analytics can be performed.

Given the distributed nature of data sources as well as the diversity of data types, information platforms and integration approaches are changing. While more than three in five companies (61%) still do integration primarily between on-premises systems, significant percentages are now doing integration from the cloud to on-premises (47%) and from on-premises to the cloud (39%). In the future, this trend will become more pronounced. According to our research, 85 percent of companies eventually will integrate cloud data with on-premises sources, and 84 percent will do the reverse. We expect that hybrid architectures, a mix of on-premises and cloud data infrastructures, will prevail in enterprise information architectures for years to come while slowly evolving to equality of bidirectional data transfer between the two types.

Further analysis shows that a focus on integrating data for cloud analytics can give organizations competitive advantage. Those who said it is very important to integrate data for cloud-based analytics (42% of participants) also said they are very confident in their ability to use the cloud for analytics (35%); that’s three times more often than those who said integrating data is important (10%) or somewhat important (9%). Those saying that integration is very important also said more often that cloud-based analytics helps their customers, partners and employees in an array of ways, including improved presentation of data and analytics (62% vs. 43% of those who said integration is important or somewhat important), gaining access to many different data sources (57% vs. 49%) and improved data quality and data management (59% vs. 53%). These numbers indicate that organizations that neglect the integration aspects of cloud analytics are likely to be at a disadvantage compared to their peers that make it a priority.

Integration for cloud analytics is typically a manual task. In particular, almost half (49%) of organizations in the research use spreadsheets to manage the integration and preparation of cloud-based data. Yet doing so poses serious challenges: 58 percent of those using spreadsheets said it hampers their ability to manage processes efficiently. While traditional methods may suffice for integrating relatively small and well-defined data sets in an on-premises environment, they have limits when dealing with the scale and complexity of cloud-based data. vr_DAC_02_satisfaction_with_data_integration_toolsThe research also finds that organizations utilizing newer integration tools are satisfied with them more often than those using older tools. More than three-fourths (78%) of those using tools provided by a cloud applications  provider said they are satisfied or somewhat satisfied with them, as are even more (86%) of those using data integration tools designed for cloud computing; by comparison, fewer of those using spreadsheets (56%) or traditional enterprise data integration tools (71%) are satisfied.

This is not surprising. Modern cloud connectors are designed to connect via loosely coupled interfaces that allow cloud systems to share data in a flexible manner. The research thus suggests that for organizations needing to integrate data from cloud-based data sources, switching to modern integration tools can streamline the process.

Overall three-quarters of companies in our research said that it is important or very important to access data from cloud-based sources for analysis. Cloud-based analytics isn’t useful unless the right data can be fed into the analytic process. But without capable tools this is not easy to do. A substantial impediment is that analysts spend the majority of their time in accessing and preparing the data rather than in actual analysis. Complicating the task, each data source can represent a different, possibly complex, data model. Furthermore, the data sets may have varying data formats and interface requirements, which are not easily addressed with legacy integration tools.

Such complexity is the new reality, and new tools and approaches have come to market to address these complexities. For organizations looking to integrate their data for cloud-based analytics, we recommend exploring these new integration processes and technologies.


Tony Cosentino

Vice President and Research Director

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