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Our recently released research into next-generation customer analytics shows that the most participants (52%) use spreadsheets as a customer analytics tool. I recently wrote that while these popular tools are adequate for some tasks, they are not suitable for analyzing large volumes and many types of customer data. So I think it is appropriate that one in four (26%) participants have adopted a dedicated customer analytics tool and a further 29 percent are planning to invest in such a tool in the next 24 months.

There are good reasons to use a capable tool for this critical areavr_Customer_Analytics_08_time_spent_in_customer_analytics of analytics. The research shows that the biggest issue for companies in producing customer analysis is data; users spend most of their time preparing (47%) or reviewing (43%) the data before they can perform any analysis. If companies don’t take action to correct this, the situation is only going to get worse. In my various research I have identified 23 sources of customer data; they include transactional data in business applications such as CRM, ERP and knowledge management, call recordings, text-based interaction data such as letters, forms, text messages, chat and Web scripts, event data such as agent desktop clicks as they try to resolve interactions, and social media posts. So not only do users have very large volumes of data to deal with, but the data comes in many different formats, several of which are unstructured. To produce as complete an analysis as possible, companies need systems that can handle almost all of these sources of data, that can automate the process of extracting the data from them, and that can standardize the data to ensure it is of the highest quality and all data relating to a single customer can be integrated. I believe that making the right product choice for customer analytics depends first on what and how much data it can process.

That said, the research reveals some other factors that impact the choice of customer analytics including real-time (21%), advanced vr_Customer_Analytics_06_most_important_customer_analytics(19%), statistics (14%), predictive (12%) and visual (10%) as first ranked priorities. Many customer-related tasks require information that is as up-to-date as possible; for example, a contact center agent needs to know what a customer attempted to do before calling the contact center so the response can be put in the context of previous interactions as well as the customer’s profile. Product evaluations thus should look for systems that not only process all forms of data but that can collect the data in real time or near real time and produce the analysis likewise. Another factor is that in dealing with customers, it is increasingly important to have predictive capabilities. To keep up organizations must move from relying on historical analysis to predicting likely future action; for example, an unusually high volume of complaints might lead to customer defections, and real-time capabilities could, for example, indicate when a negative post on social media is likely because of what the customer is saying during a phone call. I suspect that the majority of users who rely on spreadsheets do not have high expectations about the way the results are presented. But I believe that as engaging with customers becomes more complex, users will need information presented in more visual ways that help them quickly see areas that need addressing or present the data in more useful forms, such as showing the customer’s location on a map to help find the nearest service engineer to deal with an emergency.

Ventana Research tracks six technologies that are changing the vr_Customer_Analytics_07_new_technologies_for_customer_analyticsways users access and consume technology that our research finds important beyond analytics itself: big data (60%), cloud computing (44%), collaboration 62%), mobility (38%) and social media (35%). My research into next-generation customer engagement shows that companies expect analytics to have the greatest impact on the way they engage with customers in the future; more recognize that without a complete view of customers it is hard to develop a focused customer service strategy, enhance customer-related process, provide personalized responses to interactions or understand how their company is performing from the customer’s perspective. Each of the other five next-generation technologies is also having a direct impact on customer analytics. By whatever definition you use, customer data is “big” – it comes in large volumes and in multiple forms, has to be processed in real time and requires predictive capabilities. Increasingly more of it resides in the cloud and must be integrated with on-premises data, and many companies are looking to cloud-based services for customer analytics. Because many business units engage with customers they should share a single set of customer reports and analysis so that all actions and decisions are based on the same information. To do this, more companies are looking at collaborative capabilities that allow users to share customer information and work together on actions such as resolving customer issues. In addition many employees need access to customer data while away from their desks; nearly two-fifths (38%) of participants in the customer analytics research said that mobile access to their customer analytics systems is important. And finally, there is no doubt many consumers use social media, and more are doing so all the time; many of these users are also employees, and they want their work systems to be socially enabled. Add to this that companies need to understand what their customers are “saying” about them on social media, so at the very least a customer analytics system should be able to processes social media data feeds.

One of the latest buzz phrases is the Internet of things, which will serve the connected customer on more devices than ever. People now engage with companies increasingly electronically, often using smart mobile devices – they are more connected and can do things much faster than ever before, including look elsewhere if they are not satisfied with a company. Knowing your customers therefore has never been so important. Ventana Research recommends that you evaluate the options now available in customer analytics tools to help improve customer service and the outcomes of customer engagement.


Richard J. Snow

VP & Research Director

The last time I reviewed Confirmit it had just acquired CustomerSat and was re-engineering its products to support a broader approach to voice of the customer (VOC), which Ventana Research defines as a complete view of customer interactions, customer sentiments after interactions and the outcomes of those interactions. During my latest briefing, I found out that the new architecture will be available in version 18 of the product, which Confirmit recently announced as generally available. Confirmit also recently announced the acquisition of Integrasco for social and text analytics and says it intends to have those products at least partly integrated into the core product during the second quarter of this year.

The core of the new product is called Confirmit SmartHub. The vr_Customer_Analytics_10_data_types_included_in_customer_analyticsconcept behind it is simple but the product takes a lot of development to create: Combine market, business, employee and customer engagement data, rationalize it and apply analytics to derive insights based on a wider range of data than is typical. The challenge is that the data comes from many sources and is increasingly in unstructured formats, especially text. This complexity is highlighted in our forthcoming next-generation customer analytics benchmark research, which finds that companies have to process data from seven systems on average, including text-based sources such as CRM notes, mobile text messages, instant messaging and Web scripts and social media posts. The research also shows that because of this complexity, companies spend by far the most effort on tasks such as accessing data and ensuring its quality. Confirmit SmartHub addresses many of these issues and enables users to focus on the key tasks of identifying the information they are looking for, interpreting the outputs and deciding on appropriate action.

Confirmit’s ability to process text-based data currently comes through its OEM arrangement with Clarabridge. Eventually the new acquisition of Integrasco will provide an in-house option that provides very similar capabilities. Both technologies extract text-based data from Smarthub and use text analysis capabilities to first categorize the transaction and then derive a customer sentiment score from the content. It passes the results back into the hub where they can be blended in with other customer insights and analysis, producing a more complete voice of the customer.

In my experience and research into customer feedback management,vr_NGCE_Research_01_impetus_for_improving_engagement I’ve found that there are many interpretations of VOC. Some take it literally, restricting it to analysis of customer conversations, while to others it means analysis of structured customer feedback. But the most advanced companies appreciate that VOC requires analysis of all forms of customer interaction and transactions, regardless of whether they are solicited by the company or come by way of unsolicited activities (such as a social media post), or whether it is explicit feedback or insights derived from less explicit activities (such as use of a loyalty card). Indeed, my research into the next-generation customer experience shows that the top priorities for companies are to improve the customer experience and customer service, both of which require analysis of all customer-related data, regardless of its nature.

There has been an explosion in the use of social media by consumers and companies, although my research into next-generation customer engagement shows that consumer use is far more extensive, as companies mainly use social media for marketing and struggle to see where it fits with customer service and VOC programs. For Confirmit, the acquisition of Integrasco empowers it to offer companies an option that puts social media at the heart of VOC in four complementary ways. Users can identify hot issues being posted on social media and explore these in more detail through a more formal VOC process. Second, in a similar way they can identify early warnings of brand, product or service issues and explore them in more detail. Third, they can compare VOC findings with insights gained from social media, thus adding weight to the findings. And finally, they can make social media another channel of interaction to enrich the insights already gained by a VOC program.

Confirmit has built a strong presence in VOC, through a combination of acquisitions of and continues through new in-house developments. Acquisitions bring both benefits and challenges, not the least of which is integrating products into a harmonious whole. Ventana Research’s benchmark research shows that usability is the top determinant for companies as they evaluate software, so it will be key for Confirmit to continue to invest in its user interface and fit the Integrasco technology into it. In my view, understanding customers and their interactions with the company are paramount to business success, so I recommend that companies continue to invest in the broadest VOC program possible and Confirmit should be one of the companies they evaluate.


Richard J. Snow

VP & Research Director

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