You are currently browsing Richard Snow’s articles.

Much is written about omnichannel customer experience, and various software vendors now claim to focus on the customer experience. With various degrees of credibility they range from providers of communication channel management to workforce optimization, voice of the customer, self-service, analytics and even CRM. This bandwagon raisesvr_NGCE_Research_12_all_current_channels_for_customer_engagement the question of what  omnichannel customer experience really is and how companies can achieve it. Our benchmark research into next-generation customer engagement shows that consumers now engage with companies through as many as 17 channels of engagement though companies on average support six. The research also shows that every business group, with the exception of IT, engages with prospects and customers at different times during the customer life cycle. Customers today, we know, are more demanding than ever. They want to choose the channel and time of engagement. They want the process to be easy, and they want to be recognized so responses can be personal to them. They expect consistent responses regardless of channel and not to have to repeat actions if they change channels. They want agents empowered to resolve an issue at the first try. Finally, at the end of the interaction they want to feel good about how it went and the outcome.

All this goes into the customer experience and providing all aspects of it in a multichannel and multitouch-point environment is no easy task. vr_NGCE_15_supporting_multiple_channelsAnalysis in our research reveals three related issues for companies:

  • They have multiple systems containing customer data, and it is not easy to integrate them or to share data between systems so that, for example, if a customer’s address changes it is reflected in every system containing the
  • They have implemented multiple channels of engagement, but typically they are stand-alone systems managed by different business groups. This fragmentation impedes sharing data collected through one channel with any subsequent channel the customer uses and even makes it hard to see that the same customer is using different channels.
  • Because business groups tend to have their own processes and systems, it is difficult to ensure that customers always receive consistent information. This causes downstream issues; for example, customers may receive marketing information that doesn’t match what they receive at the point of purchase, which can cost the company sales.

Faced with these challenges, I recommend that companies begin their journey toward providing omnichannel customer experience by adopting three types of systems: analytics, an advanced desktop and collaboration.

Analytics – First, most companies need better understanding of how their current interaction-handling processes are working. They need to know which processes deliver the desired outcomes, which employees are performing best, which channels prospects and customers use for particular actions, and what actions they take during and after interactions. From this they can gauge the overall business success of interaction handling. To manage the volume and types of data required to produce such a comprehensive view and then gain insights from it all, companies should deploy advanced analytics systems for data, speech and text that can ingest data from all sources and produce analysis specific to particular users and uses. Such analysis can be used to identify areas in need of improvement and to create action plans.

Advanced Desktop – To meet all of the customer expectations outlined above, employees need access to all the systems that contain data about the customer. The most practical way of achieving this is to deploy an advanced agent desktop system. In general such a system brings everything together in one place to make it easier to handle interactions and mitigate the need to integrate systems. It should make it easy to sign into and use any system, see what interactions need handling in the different channels, and to access current and historical information about the customer. The system should enable changing channels to deliver responses if need be and automate updating of multiple systems with the same latest data. It also should help automate the process of creating responses, for example, by using templated email responses. The most advanced systems include rules-based processing that can guide the employee’s response, indicating other information to collect and which is most relevant to resolving the interaction.

Collaboration – Even when using an advanced desktop system, it is rare that every employee will have the knowledge, skills and authority to resolve all customer interactions. To meet customer expectations of resolving issues at the first attempt, it is vital that employees be able to collaborate with others who can help them. The latest collaboration systems enable this in a seamless way and ensure that all parties are using the same information.

These three tools are not all it takes to deliver experiences that fully meet customer expectations. As for that, I recently wrote about all that is required to provide EPIC customer experiences. However, for companies not in a position to replace several systems or having limited budgets to invest in new systems, these three types of software present a practical way of achieving that goal.


Richard J. Snow

VP & Research Director

During recent IBM analyst big data event, I learned about a new product, IBM Predictive Customer Intelligence. It extracts and processes customer-related data from multiple sources to analyze customer-related activities and has capabilities to predict customer behavior and actions. Predictive Customer Intelligence is built on IBM’s big data platform and supports extraction and integration of data from multiple sources, internal and external, and from structured and unstructured data. It can process data created by third-party products, such as text-based files of data created by converting speech to text. The product can capture and analyze customer interactions from multiple communication channels such as voice, email, text messages, chat and Web usage scripts and social media posts.

Predictive Customer Intelligence has four primary modules, for predictive modeling, reporting, real-time scoring and a real-time analytics data repository, which are connected by the IBM Integration Bus. These modules support a predefined process in which users build models from customer data stored in analytics real-time customer database and use them or predefined models to run real-time analysis against the customer data and produce scores, recommendations, reports and dashboards related to customer activities. The outputs can be delivered through a variety of channels such as outbound email, direct mail or text message. This can help contact center agents provide personalized and contextualized responses to customers’ questions. Other outputs can be used to produce targeted marketing campaigns or to respond to customer interactions through other communications channels.

vr_NGCE_Research_08_all_channels_for_customer_engagementMy benchmark research into next-generation customer analytics shows a need for such a product because companies have up to 21 potential sources of customer data. These include transactional business applications such as CRM and ERP, customer data warehouses, spreadsheets, call recordings and text-based files containing content from email, forms, letters, text messages, chat scripts, Web scripts and social media posts. All of these not only contain valuable customer information but also interaction data from which companies need to derive insights into customers’ feelings about products and services and other aspects of the business. The research shows companies have difficulty in extracting value from this data, partially because on average they use only six sources of customer data in their customer analytics. Interaction data is especially problematic because most of it is unstructured and requires tools that can automatically access and extract insights from them; few companies have such tools. This situation also is becoming more complex, as my benchmark research into next-generation customer engagement shows: Companies are supporting more channels of interaction and expect volumes of interactions to grow in every channel as our research shows up to 17 channels in play.

IBM Predictive Customer Intelligence has capabilities that can help companies meet these challenges. However, a close look reveals that it is not one but 10 individual products (not including three connectors) packaged together. Organizations therefore need to understand the cost and operational impact of managing and use these products.

At the big data analytics event, Frank Theisen (IBM VP of front-office transformation for Europe) summed up the information challenges companies face; they need to know:

  • What happened?
  • Why did it happen?
  • What can be learned?
  • What action should be taken?
  • What could happen in the future?

Ventana Research believes that big data analytics can answer these questions. vr_Customer_Analytics_03_key_benefits_of_customer_analyticsFor example, my benchmark into next-generation customer analytics shows that one-quarter (26%) of companies have deployed a dedicated customer analytics product and have found it has helped them improve the customer experience and their analysis of business performance. More generally my colleague Tony Cosentino wrote about three Ws that are key: What data you have, what information you want to derive from data, and what action should be taken as a result of insights gained from it. Once you can answer these questions you can decide which analytics product best fits your requirements.

IBM focuses intensively on its technology sometimes to the extent of obscuring the business applications of those systems. One prime example is that more and more IBM big data products are moving to the direction of IBM Watson and methods of cognitive computing. Basically Watson is a platform that can search very large volumes of information to deliver insights from the data by use of natural language, and it is smart in that it learns as it searches, so that future answers are more refined and targeted to the questions asked. Such capabilities are particularly useful for analyzing the very large volumes of customer interaction data companies accumulate; they help identify trends, hot issues and focused information to help personalize responses and put them in the context of an overall customer relationship.

Our next-generation customer analytics benchmark research shows usability is the top priority for selecting analytics software: 64 percent of companies said it is very important. To provide it vendors should support point-and-click access to information on mobile devices and visual ways of showing the results of analytics. One case study IBM used during the day illustrated this; the user collects a vast array of data, integrates it and delivers analysis in visual formats on Apple iPads. This is well-suited for assisting customer-related activities that happen in real time (such as phone calls) where users need instant access to up-to-date information in forms they can understand immediately.

Companies already have huge amounts of customer-related data, and if you factor in the increasing volume of electronic communications, social media and the coming Internet of Things, this need will grow more acute. IBM has a variety of analytic products and is developing more. The challenge is to figure out which IBM products can best process what data and produce the required information and insights to drive decisions and action. Predictive Customer Intelligence and IBM’s other big data analytics are worth considering in organizations’ efforts to improve understanding of customers and their experiences.


Richard J. Snow

VP & Research Director

Twitter Updates

Ventana Research


  • 59,685 hits

Get every new post delivered to your Inbox.

Join 75 other followers

%d bloggers like this: