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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

Recently my colleague Mark Smith wrote about Splunk and its latest technology supporting analytics for IT on machine data and providing operational intelligence. I wasn’t familiar with the company, which has focused on IT users and improving the performance of a company’s networks and IT systems. From a customer management perspective, these are of little interest unless they impact the customer experience; for example, if the website is down or the online banking system is unavailable. But in a follow-up briefing I learned that Splunk is increasingly playing in the business analytics market and has several user cases that relate directly to customers.

I was intrigued by Splunk’s interesting use of the term “machine data.” I am a self-confessed technophobe, so I thought all data was machine-generated. Not so, according to Splunk, which can process data into three areas including:

  • Business application data, such as transactional records, invoices and service case records
  • Human-generated data, which includes interaction records such as email messages, IM logs, Web scripts, voice recordings and video
  • Machine data, which is data generated by hardware or software systems on its operations.

Splunk focuses on processing the last two types and integrates with the first to extract contextual data; for example, it can use an email address to look up additional customer data in a CRM system. When I heard this bells began ringing in my head, because I have written that a 360-degree view of the customer should be more extensive than most people think. The common perception is that it should include all available transaction data concerning customers (in Splunk’s terminology, business application data), but I think it should also include all available information about customer interactions (human-generated data) and any event data (machine data) that might impact the customer experience – for example, whether a cell within a mobile network is down, preventing customers from making calls, which in turn is likely to result in lots of calls to the contact center. I also have observed that while some vendors are getting close to producing a 360-degree view, no one is there yet.

Splunk has an advantage here, because machine data is structured and often unstructured, and it has developed techniques that allow it to identify specific locations within a record as a defined field; for example, starting at location 100 in the script captured as a customer completes an online purchase, the next 20 characters represent the code of the product purchased. These definitions can be created automatically by analyzing multiple records, or users can define fields using tools within the product. These fields can be used to index records and thus to search for specific records to include in the analysis. Splunk’s product can also use fields as indexes to information from records in a business application; for example, a cell phone number could be used to look up the corresponding record in a CRM system to obtain additional customer information that can be included in the analysis. Analysis can also include data from multiple types of transactions, such as email, call recordings and Web scripts, allowing users to see, for example, all the different types of interactions a customer has had with a company – a technique that is often called cross-channel analytics. The analysis occurs in real time, and so could be used, for example, to show a contact center agent all the previous interactions a customer has made just prior to the current call.

Splunk has user cases showing use of its product to track customer downloads from a website, the use of mobile messaging and the steps customers take to complete an online purchase. Although Splunk is not a well-recognized vendor in the customer analytics space, such applications show that it has the ability to combine business, interaction and machine (event) data to provide operational intelligence. In this context, that allows companies to better understand customer behavior and thus to proactively improve the customer experience.


Richard Snow – VP & Research Director

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