How to transform customer experiences with cognitive call centers

Customer data and insights can help steer companies to new levels of innovation, engagement and profit. And, most organizations are sitting on a gold mine of customer data. But, it’s how customer data is used that matters. How is your organization collecting and using customer insights? Are you using it to create the most engaging customer interactions? And, are you creating a cognitive conversation with your customers? These are critical questions that not all companies are yet considering, but should.

Understanding the importance of cognitive conversations

A cognitive conversation takes advantage of data from external, internal, structured and unstructured voice and multichannel sources to deliver a customer response that is more conversational, relevant and personal. Organizations can meet changing customer preferences and behaviors by learning from every interaction. All parts of the organization can take advantage of data collected from different areas of the company to improve customer loyalty.

Businesses are recognizing that while customer interactions often begin on one channel, valuable insight and feedback is also being gathered from customers on other channels across the business. Unifying customer information across channels gives businesses a more complete context to resolve customer issues more fully and more quickly.

With the speed to which customer expectations are changing, it is conceivable that customers will prefer to manage their relationship with businesses without interacting with a live agent. Customers are using more channels to interact with businesses and are doing more research through websites and referrals before ever engaging.

A more conversational, personal, seamless and device-independent approach to customer engagement is critical. Customers are likely to commit to a brand or product after a satisfactory experience, so it is time the whole company focused on adopting capabilities to enable a more cognitive experience for clients and potentially surpass competitors.

Taking full advantage of the gold mine of customer data

Every department in your organization, from marketing to sales to customer service, has data on customer behavior. That collective data can be used to meet and even exceed the demands of customers. An organization’s brand, website, notifications and certain self-service channels, whether voice or chat, demonstrate commitment to the customer.

It may seem overwhelming to gather all this information across your organization at every entry point of interaction and also deliver a seamless, consistent and cognitive experience. But, it doesn’t need to be.

If you can tap into cognitive capabilities, they will turbocharge interactions across channels. This combination can transform traditional self-service brand engagement into a more relevant and relational experience for customers. Customers can use both traditional interactive voice response (IVR) features with cognitive capabilities, which can be integrated with an existing contact center environment, as well as other third-party applications such as computer-telephone integration (CTI). Combining these capabilities is a unique approach that transforms the customer experience to a more conversational and cognitive interaction. As a result, resolutions can be achieved more quickly than interactions handled by traditional IVR systems alone.

Transforming the customer experience

Changes in the customer experience journey are happening fast. There is no slowing down or stopping the convergence of technology and increasing demand for quick, relevant and personalized customer interactions. The stakes are high when it comes to the customer experience, and it’s not just the contact center that needs to take notice. The customer experience is a whole-company issue.

Customer experience is at a crossroads of change and transformation, adding a new level of engagement across the organization. Taking advantage of the forces pressuring organizations to evaluate their strategy, technology and general understanding of customer behavior will set the pace for the cognitive revolution.

Four key factors are making transforming the customer experience a hot topic:

1. Increased value of customer experience as a market differentiator
2. Speed of changing customer demands
3. Cognitive capabilities make collecting, learning and understanding data in near real time a reality
4. Market leaders figuring out how to combine knowledge with technology to magnify the customer experience

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6 common DevOps myths

There are a lot of DevOps myths floating around the IT world. That’s not surprising, given how much hype the term — a combination of “development” and “operations” —  has built up in the past few decades.

DevOps is more than worthy of the hype. When done properly, the DevOps approach can deliver massive positive impact for businesses. It can reduce costs, improve performance and break down silos between teams.

To understand the power of this approach, however, it’s important to know what DevOps is and what it is not. Let’s start by correcting six common DevOps myths.

1. DevOps is only for shops born on the web.

It’s true that DevOps mostly started at companies that were born on the web. Maybe that’s why people get the idea that this methodology will only work at internet firms such as Netflix or Etsy. That idea turns out to be a myth.

Large enterprises have been successfully using DevOps principles to deliver software for decades.

2. DevOps only matters to engineering and operations.

The name DevOps clearly reveals the origin of the approach. DevOps started as a better way for operations and development teams to work together.

Today, the approach can empower the entire organization. Everyone involved in the delivery of software has a stake in this methodology.

3. DevOps can’t work for regulated industries.

Regulated industries have an overarching need for checks and balances, as well as approvals from stakeholders. This doesn’t mean DevOps is a problem, however.

Adopting DevOps actually improves compliance, if it’s done properly. Automating process flows and using tools that have built-in capability to capture audit trails can help.

Of course, organizations in regulated industries will always have manual checkpoints or gates, but these elements can be compatible with DevOps.

4. You can’t have DevOps without cloud.

When many people think of DevOps they think of cloud. There is a good reason for this. Cloud technology provides the ability to dynamically provision infrastructure resources for developers and testers to rapidly obtain test environments without waiting for a manual request to be fulfilled.

That doesn’t mean cloud is necessary to adopt DevOps practices, though. As long as an organization has efficient processes for obtaining resources to deploy and test application changes, it can adopt a DevOps approach.

Virtualization itself is optional.

5. DevOps means operations learning to code.

Operations teams have a long history of writing scripts to manage environments and repetitive tasks. With the evolution of infrastructure as code, operations teams saw a need to manage these large amounts of code with software engineering practices such as versioning code, check-in/check-out, branching and merging.

Today, operations teams can create a new version of an environment by creating a new version of the code that defines it. This doesn’t mean, however, that operations teams ,must learn how to code in Java or C#. Most infrastructure-as-code technologies use languages such as Ruby, which is relatively easy to pick up for people who have scripting experience.

6. DevOps doesn’t work for large, complex systems.

This myth is totally off-base. The opposite is actually true: complex systems often require the discipline and collaboration that DevOps provides. Large systems typically have multiple software or hardware components, each of which has its own delivery cycles and timelines. DevOps facilitates better coordination of these delivery cycles and system-level release planning.

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Social Learning in Practice at IBM

What is social learning and how can it help drive engagement and develop a culture of learning?

The social learning theory of Bandura emphasizes the importance of observing and modeling the behaviors, attitudes, and emotional reactions of others. Bandura (1977) states: “Learning would be exceedingly laborious, not to mention hazardous, if people had to rely solely on the effects of their own actions to inform them what to do. Fortunately, most human behavior is learned observationally through modeling: from observing others one forms an idea of how new behaviors are performed, and on later occasions this coded information serves as a guide for action.” Basically, Bandura’s theory is that human beings can learn by example.

Why does social learning matter?

Research states that most people only recall 10% of information learned within just 72 hours in typical training environments. Social learning can reverse this curve. In fact, research shows retention rates as high as 70% when social learning approaches are employed. Rather than relying on typical training environments with low recollection rates, social learning allows learning to happen in the working environment. Learners can pull knowledge from experts within the organization rather than have it pushed on them. Learning becomes a part of the organization culture.

An example of social learning at IBM

The Data Analytics Center Of Excellence (COE) at IBM continuously provides Data Science training for our employees and decided to pilot the use of the recently IBM Data Science Professional Certificate on Coursera. They identified 2 different controlled study groups 1) A group of individuals who would have otherwise gone through a 5-day full time face to face bootcamp and 2) A group of instructors who would typically teach this bootcamp. One of the biggest problems of using MOOCs for enablement is the high dropout rate, research shows that approx ONLY 5% of the total learners complete a course. Here are a few ways in which we are keeping this group of learners engaged:

FAQs and Forum

A dedicated SLACK channel has been established with the pilot participants in which employees can pose questions and receive answers from within the group. This promotes collaborative learning as individuals can learn from their peers and also learn from questions posed by others. Apart from the pilot, there is also a large IBM Data Science Community  that hosts events on a regular basis and has plenty of enriching forums with discussions.

Organization Wikis

The participants are encouraged to blog about their experience. Bernard Freund, STSM – Data Analytics CoE writes a blog post at the end of each week as he completes a course. This post not only provides user with a thorough review of the course, but also highlights some issues along with workarounds which has been extremely useful for other learners attempting the course later.

Utilize expert knowledge

Besides the SLACK channel, we have also instituted check-point calls with the Coursera and course development team. Not everyone attends these calls, but it does give the participants an opportunity to get some 1:1 time with the SMEs to overcome any obstacles that may be preventing them from completing the program.

Gamification and rewards

You can’t force people to learn but you can give them the right tools and incentives to make sure they don’t waste opportunities. IBM does this through the Open Badge program. The program awards badges upon the completion of each of the 9 courses and a certificate upon program completion. These badges provide a way for the administrators and users to track their learning progress.

Currently, we are 1 month into the 3 month pilot and the learners seem very engaged and vested in their progress. On an average most participants have completed at least 2 of the 9 courses which does put them on track for completing the certificate within the pilot timeline. Stay tuned as we report further results in the coming months.

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