Redefine digital transformation

Organizations are changing their approach to digital transformation, seeing it as an ongoing series of small steps rather than a giant leap with a finite landing place. In a similar way, they’re taking a more long-term view of customer experience, too, as they seek to connect the dots in each customer transaction as well as over the customer’s lifetime journey with the organization. Data is central to everything and needs robust systems to ensure its potential is not overshadowed by the challenges of managing it.


Big thinking, small steps: redefining digital transformation

Digital transformation is now understood as being an ongoing way of working, not a linear progression towards a definite – and finite – strategic goal. Organizations are adopting an opportunistic mindset and an agile approach.

The definition and focus of digital transformation are shifting dramatically. Organizations recognize it’s not about extreme changes and restructuring the entire business through a once-off project. Instead, digital transformation is seen as a behaviour: a way of working that’s rooted in a culture where people are encouraged to think and act differently.

Start somewhere and keep moving

To get results, faster, abandon grand, sweeping plans that are holding back progress in favour of smaller projects that tackle burning issues around operational efficiency and customer experience. Pay attention to the employee experience. How can you better support cross-functional teams to be more agile?

Take incremental, tactical and repeatable steps. This way, digital transformation becomes an integrated, organization-wide way of doing business, and continuous learning becomes business as usual.

83% of organizations say digital transformation should focus on driving a change in culture and behaviour to operate as a more agile and responsive organization.

'Being responsive to markets … involves an ever-greater dependence on instant response in a world where the client engages through technology and not people.'

Minoo Dastur, President & CEO, Nihilent, an NTT company.

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Data-driven dynamics: where CRM meets ERP

Data becomes central to digital transformation as the information collected across the enterprise is used to reengineer the organization and position it for success.
The move from mass service to mass personalization has created a new dynamic: customer relationship management (CRM) is informing enterprise resource planning (ERP). Businesses are leveraging what they know about their customers to improve business processes and the customer experience, and innovate with new products, services, channels and delivery models.
Chief digital officers and chief marketing officers are taking the lead in IT spend and driving innovation as organizations look to bring together data points well beyond the ERP system.

Unlock the value of data

Automation, augmented analytics and artificial intelligence are the keys that will help organizations unlock the value that lies in data collected in systems and applications across the enterprise.

Automation must be a priority, as there is simply no other way to process these massive volumes of data. Data strategies should incorporate plans for analysing the data, as that analysis will inform next steps in growing customer engagement, loyalty and spend.

Organizations surveyed say analytics and revised operating models are the top contributors to improving workforce optimisation; 58.9% have some form of knowledge management technology.

Dimension Data 2019 Global Customer Experience Benchmarking Report


Lifetime relationships: the cloud-enabled customer journey

Customer experience extends from managing channels and interactions to enabling lifelong customer relationships, powered by the cloud.

The buzz around omnichannel saw many businesses rushing to integrate various channels to improve the customer experience (CX) at every touchpoint of a transaction. But long-term customer loyalty isn’t built on a single successful transaction – it’s the sum of many individual interactions that an individual customer has with an organization.

Customer journey management, the next version of omnichannel, focuses on building and managing long-term customer relationships through relevant offers, exceptional service and a personalized experience from the very first engagement.

Optimize your multicloud environment

Data analytics, AI, machine learning and robotic process automation will play a key role in helping you manage these journeys and further augment CX through sentiment analysis, assisted-service and self-service capabilities.

As our physical and digital worlds converge, you’ll need a strong multicloud environment to support the infrastructure and workflows behind customer journey management.

24.3% of organizations say CX delivery is consistent across channels. Silo management is the main challenge in implementing an omnichannel strategy.

'To evolve to become an effective data-driven organization, you must place data at the center; making sure it's not only the heart of the business, but its lifeblood.'

Matt Drayson, Practices, Partners & Alliances, Australia, NTT Ltd.

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Real-time insight: sentiment analysis gets more sophisticated

Organizations are investing in several technologies to get real-time insight into customers’ moods and preferences in an effort to adapt their offerings and improve the overall customer experience.
It’s not only the methods used to measure customer sentiment that are becoming increasingly sophisticated; the way organizations respond to the feedback is also changing, from a primarily reactive to more proactive approach.

An organization that understands how people are feeling at a point in time – positive, negative or neutral – will be in a stronger position to forecast their next steps and meet them there with the right product, service and experience. It will be able to proactively position products, services and visual merchandising based on real-time insights.

Biometric technology has already found several uses outside the realm of cybersecurity, where it’s been used primarily for authentication. The next level of predictive intelligence will see this technology being used in addition to tools such as text analysis, conversation intelligence and natural language processing. Qualitative ‘mood’ data will be layered on top of quantitative ‘action’ data to get insight into customers’ emotional dispositions and likely behaviour.

Enable innovation on demand

Taking this one step further, if you know where customers are going, you can commercialize the data you have to target smaller audiences and develop innovative products and services to meet diverse needs. As the Internet of Everything and behavioural sensors come into play on a bigger scale, you’ll need the right infrastructure, processes and tools to both capture and analyse the data.

72.7% of organizations are using analytics intelligence to inform product and service transformation; 23.9% will validate their proposition strategy against external benchmarks, including emerging CX innovation.

Dimension Data 2019 Global Customer Experience Benchmarking Report


Data lakes and digital twins: enabling new analytics models

Data lakes that contain qualitative and quantitative data will enable new models of predictive analytics and unlock the potential of digital twins.

Data lakes can hold massive data sets from all sources in the enterprise – data that can be aggregated and configured in numerous ways to enable deep analysis that yields rich insights.

Organizations can tap into any number of data points to create a ‘digital twin’ of each customer that’s the sum of all their data parts: demographic data, browsing behaviour, purchasing patterns, interests and payment preferences. They can then build machine learning models that predict what the real customer wants, and when, so the business can respond with a relevant offer.

Get the right skills

The ability to measure qualitative data, such as customer sentiment, and combine it with ERP and CRM data to create richer insights and new analytics models will increase the demand for robust ERP systems and AI-driven automation.

Organizations will need skills to set up, manage and secure their data lakes, and build data models that will extract the insights they need for ongoing innovation.

42.9% of organizations say analytics systems aren’t meeting existing requirements.

Dimension Data 2019 Global Customer Experience Benchmarking Report

'Now that AI has evolved, we can move from being purely transactional to having a more relational engagement with customers, applying rules that bring empathy to the interaction and establish trust with the customer.'

Marc Alba Otero, Senior Vice President, NTT, Inc, and Head of NTT Disruption


Disruptive technologies to watch

Building digital trust through customer conversations with social chatbots and social robots.

Trust is extremely important in any business. Currently, most of the interactions customers have with front-end interfaces such as apps, websites, digital signage and chatbots are designed to help them complete a transaction.

Now that AI has evolved, we can move from being purely transactional to having a more relational engagement with customers, applying rules that bring empathy to the interaction and establish trust with the customer. We’re seeing very positive results from these conversations in two domains: chatbots, for example on websites and social media, and physical robots that can be deployed in any environment, such as hospitals or banks.

Physical robots can demonstrate empathy and build digital trust through conversations they have with the people that engage with them.

The approaches we’re working on will apply to any industry and could lead to a complete disruption in the way we serve our clients, enabling greater engagement and retention.

Nemo Verbist

Nemo Verbist

Senior Vice President, Intelligent Business and Intelligent Workplace, NTT Ltd.

Nemo Verbist is the Senior Vice President for Intelligent Businessand Intelligent Workplace. In this role he’s responsible for delivering the vision, strategy and go-to-market execution for these two practices.  

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