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Are your customers really happy? What if they’re not? What is the role of technology? How can you connect your brand goals and your customer moments of truth and align with the journeys your customers want to take?
Customer-centricity is about focusing on the customer; designing a compelling and engaging omni-channel experience by designing connected customer journeys as well as an extraordinary user experience across channels and touchpoints.
Are your customers really happy customers? And what can you do if they’re not? What is the role of technology? How can you connect your brand goals and your customer moments of truth? How can you align the journeys your customers want to take with the brand?
The answer? Customer centricity.
Customer centricity is about focusing on the customer; designing a compelling and engaging omni-channel experience by designing connected customer journeys as well as an extraordinary user experiences across channels and touchpoints. But it’s about technology; connecting user front-ends, customer data and management solutions, and linking the business processes orchestrating customer interactions with the enterprise.
I believe there are five uncomplicated rules to achieve success:
The more satisfied your customers or clients are, the more they’re likely to spend with you. A recent Capgemini study reinforced that Customer Experience is valuable and valued—81% of consumers are willing to pay for a better experience. Consumers are ready to reward better experiences with increased spending.
This is not only true for the Business-to-Consumer (B2C) market, but more and more for the Business-to-Business (B2B) market. After all, one business buying from another, is just as much a customer as those in consumer-facing sectors.
We already know that new technologies are driving new behaviors in both the B2C and B2B. The efficient, effective, easy to use, my way, anytime, anywhere’ demands of today’s B2C customer have been apparent and accepted for a while now. But more and more the B2B buyer is changing. Analysts are telling us that there is a paradigm shift of how professional consumers (B2B) are navigating their buyer journey—for example there is a growing propensity towards self-service. Forrester’s The B2B eCommerce Playbook for 2017 advises that in 2016 68% of B2B buyers prefer to research online on their own, up from 53% in 2015. Equally, in 2016 B2B buyers who said that they do not want to interact with a sales representative as their primary source of research grew from 59% to 60%.
So, as a business selling to other businesses, it’s important to understand not only what makes your customer (the buyer) happy but also influence the action they take as a result of interacting with your brand. In my opinion, the “The Segment of One” is a game changer. Be clear about what the individual needs of a customer are and deliver the experience according these individual needs. This is completely different than customer segmentation based on generic profiles, and is far more effective.
Digitization in our society has led to reduced personal contact; face-to-face is only one channel available to your buyers, and is not always the preferred path. This has implications on how your brand is represented. It is therefore key that you build a relationship with your client that goes past being transactional and into something that is unequivocally positive and personal.
The aforementioned report also tells us that organizations tightly linking their business operations with the customer experience reap greater rewards in terms of NPS® and positive customer perceptions. So whether you closely link (monitor your NPS® or customer experience performance on a daily basis and share the information with managers to create a better alignment between business operations and NPS® performance) or loosely link (monitor NPS® or customer experience on a regular basis/fixed intervals basis) it is a key action in keeping (or making) your customers happy.
Do all this well, and you will create experiences that deliver rapid and sustainable value for your customers and your company.
Chat applications are becoming a mainstream trend and our preferred way of interacting with colleagues, friends and family. Learn how bots differ from mobile applications in important, market-changing ways.
Chat applications are becoming a mainstream trend and our preferred way of interacting with colleagues, friends and family. From the early days of SMS to the favorite snaps of our children, real-time online conversations are everywhere and here to stay.
The acquisition of WhatsAapp by Facebook in 2014 for a hefty $19 billion looks more and more like a brilliant move and not a foolish one, as TechCrunch noted one year later. The acquisition of Instagram in 2012 for a mere $1 billion now seems even smarter, not only to grow the audience and the revenues but also to be a magnet for digital talents and ambitious entrepreneurs.
But although TechCrunch saw messaging apps as the future of mobile portal, they remained more or less aligned with the existing lines of forces internet, without a direct impact, except their increasing audience.
The growing interest in bots and AI is changing the game and we’ll be witnessing the second major fragmentation of the internet.
The rise of mobile apps and the growing usage of smartphones generated a completely new user experience: instead of searching and browsing content from one page to the other using hyperlinks, we were siloed in different apps, each brand driving its own experience, sometimes sharing basic data between each other like pictures.
This was the first fragmentation of the internet and we accepted it, mainly because we gained mobility and ubiquity at the same time thanks to smartphones.
Fragmentation always generates a symmetrical need for order and the app stores of the two mobile OS giants became our gateway to the prolific apps world and the kings of this new world.
Bots are a very different kind of animal than Apps for several reasons:
All these characteristics will lead to a more profound fragmentation of the internet, impacting both the user experience on the front-end and the business models at the back-end: all the ingredients of a new disruption are there.
Apps fragmentation generated a need for order, trusted third parties, and distribution channels. Bots will not be an exception and the competition is already heating up.
A Super App takes services that its users would naturally want and integrates them, EVEN if these services are unrelated to core product.
Amazon, with its Echo product line, powered by its Alexa Business + Technology Architecture, is leading the pack on conversational commerce embedded in IoT devices, growing its ecosystem at a rapid pace to reach platform leadership. The whole Alexa Business Platform can be seen as another example of Super App, aiming at consolidating Amazon’s strong foothold in the e-commerce arena in order to fight against disrupters like WeChat.
Facebook Messenger is already an undisputed gatekeeper for last-mile access to customers, fuelled by its nearly two billion MAUs. Facebook’s strategy in payment and e-commerce services is not yet crystal clear, but we can reasonably expect a stronger foray into that space, at least to avoid being disrupted by actors like WeChat.
Google Home has now become a fast follower to Amazon Alexa and Echo. Interestingly enough, it lacks the e-commerce focus of Alexa but it strongly leverages its search DNA and a clear connection to Android through Google Assistant. Both hardware and software pieces can be seen as a two-sided line of defense against Amazon and WeChat.
Siri seems now to be a more strategic piece of Apple’s roadmap, with its own homebrew technology replacing Nuance and the Apple HomePod coming to the market. The software nature of voice recognition, bots, AI, and software probably suffered from Apple’s focus on designing beautiful hardware. It sounds as though Apple has finally understood that voice won’t kill its devices but will sustain their USP.
There are of course many other niche players, either specialized in the B2B market or in technology enablers, which will be covered in another post.
Let’s wrap up the new field of forces that brands are now facing online:
There are always opportunities accompanying these risks and the best way to cope with it is to be in motion, in a test-and-learn approach, just like when you first learned how to ride a bike as a kid. Lean startup frameworks are now well documented and popular, they offer the right mix of process and freedom to sustain IRL (in real life) applied innovation.
So, will your competitors, incumbents like new entrants, so you better start now this great journey.
How much ‘human’ work can machines really get done for us? Can we ever make computers that will ‘think like us’?
I’m lucky enough to be able to grapple with these problems as part of my day job here at Capgemini, exploring how automation and Artificial Intelligence (AI) can be applied to the challenges of Business Process Outsourcing.
We’re already used to seeing robotics in many core areas of industry (just think of a modern car production line – they’ve been using robotics en masse since the 80s) and it’s now gained a foothold in non-core back-office processes too (think along the lines of Accounts Payable processing). But just what ‘human stuff’ will machines be able to do for us in the near future, and how should we approach this fascinating challenge?
Back in the early years of computing, Alan Turing foresaw some of the fundamental questions around AI that still preoccupy us now. The Imitation Game, the recent film about his work and life, takes its name from a test he devised to explore the problem.
Today’s well-publicised ‘Turing Tests’ still attract plenty of attention, as computers attempt to masquerade as humans and outwit a jury of real people. Although there’s plenty of debate about how useful these are as a true test of intelligence, they’re a good starting point here.
Even in Turing’s time it was clear that computers would struggle to imitate humans in a couple of key areas:
These days, we can create computers that are better and better at mimicking us but they still don’t really work or learn like us.
To think practically about AI, we need to think about the way we humans solve problems at work.
When it’s something simple, we can follow basic rules to fix it. Think of inputting and processing details like those in supplier invoices (tax, due dates, matching the right documentation and approvals). This is already a relatively easy area to automate.
What about handling the exceptions, when there’s not an obvious next step?
When something unexpected happens, as humans, we tend to ask our boss or a colleague with more experience for help. Most of the time they’ll use that experience to solve the problem. In some cases they may also be flummoxed and then ask someone else. If the answer still eludes them, they will then get round the table with others and try to ‘work out’ a novel solution for this novel challenge.
This collaboration applies a complex mix of human imagination and experience, and applies it to a new context. I suppose this is what we call ’thinking outside the box‘.
As humans, we’re good at this. Our brains are pattern hunters – but we also enjoy looking for and making unexpected connections and building new concepts in this way. It’s what makes us human.
But crucially, we’re not always right. Experimenting and getting things a bit wrong (while judging how much risk to take) is all part of the process. This is a world of grey areas; the solutions we arrive at in these cases are essentially subjective – we could argue for and against them, but we eventually agree to get on with it and give it a try.
AI systems get just as flummoxed as people, and they also need to be taught how to deal with new circumstances. Perhaps by allowing them a little more ‘Artificial Stupidity’ and letting them act a little more randomly, we can give them the opportunity to learn from success and (controlled) failures as we do. Compare that to today’s Robotic Process Automation that simply repeats the same tasks to set rules, which leaves little opportunity to learn and evolve.
I don’t think we’re close to a time when computers can replicate the human intelligence needed for more nuanced business analysis and recommendations. This is probably a bit of a red herring anyway – even if it does raise fascinating technical and philosophical questions.
However, we are already seeing computers encroaching further on the ‘subjective layer’ of business process decision making. Almost all high-value work performed by humans today is augmented by increasingly complex tools. The technology is making more and more intelligent ‘suggestions’ which we then take a measured final decision on, based on all of our human experience and intuition.
In that case, perhaps we should think of AI in terms of a dual-pronged evolution – with humans a key component in the system. This will soon be far more common for medical diagnoses – and has already been done in chess, led by Garry Kasparov.
These are exciting times to work with AI. It’s rarely far from the news or the big screen these days, while predictions for the long-term future range from the utopian to the apocalyptic. The reality may turn out to be a little less dramatic (hopefully!) but the critical point for business is that any approach must be intrinsically linked to value creation – whether that’s reflected in speed or quantity or other metrics.
I believe there is the potential for a really natural fit with modern BPO. Just how we and our customers go about getting the best from it must wait for another blog!
Companies are swimming in a sea of data—more than they could have imagined, and the potential is ripe for turning that data into insights. But are organizations using data and applying analytics in the right way? Many are not.
Organizations have always used data to guide business decisions, but with the arrival of the internet, increasingly powerful analytics tools have provided an opportunity to understand more about the sectors in which they operate. Today, companies are sitting on terabytes of data that cover a wide spectrum of important factors, ranging from customer behavior and market trends to raw information about future developments.
The potential value of this data is so great that analytics itself has become big business. It has created a new industry for number crunching and caused a major shift in IT-related jobs. But even in this data-fueled environment, a big question remains: are organizations making the most of the information at their fingertips?
Dr. Catarina Sismeiro is an associate professor at Imperial College Business School where she teaches the Executive MBA program as well as the Business Analytics and Strategic Marketing master programs. Dr. Sismeiro argues that many businesses have adopted a twin-speed approach to analytics. Complex algorithms are used to boost operational efficiency, cut costs, and track customer behavior, but only a select few are using what they learn to drive strategic direction.
“The main issues are a lack of data-centric culture, not enough willingness to rely on algorithms or data analytics for strategic insights, and the absence of a strategic plan for data-driven insights, especially at the top level,” she explains. “Although the evolution for a data-centric approach at operational levels started long ago, pushed by the need to improve efficiency due to fierce competition, changes at the top strategic level have been slower.”
Is the Board on board?
There is a long list of brilliant data implementations, such as yield management systems pioneered by American Airlines, which Dr. Sismeiro says led to an immediate uptick in profitability for the company. All major car rental companies, hotels, and airlines now use similar systems to cut waste and create efficiencies.
While this is taking place on the ground, C-suite executives are perhaps proving less adept at making the change. The key is for senior staff to be able to derive action points. It’s a process that moves from raw data to information, to insight, to action, and ultimately to business impact.
The transition to data is also happening organically, as a growing number of digital natives are promoted up the chain of command. It is telling that large dotcoms jam-packed with millennial talent—like Facebook, Amazon, and Google—are leading the charge. But it will also take an organizational reshuffle at the top, with greater coordination between CMOs, CIOs, and CTOs. And with new rules affecting data gathering about to hit the EU, there will also be a greater collaborative role for Chief Privacy Officers (CPOs), and Chief Data Officers (CDOs).
David Morgan, human resources director, EMEA at Kronos agrees: “The more transparency you have, the more likely the business is able to drive better planning, forecasting and employee utilization and engagement. Good decision-making based on what is happening in marketing and data supports improved prioritization based on demand, need, and where to spend.”
Data mining means delving deeper
For growing businesses lacking the large IT budgets of global corporations, the challenge is to deliver relevant insights from the data at hand. Often, this means filtering out the information and refining it to a granular level.
“Data cleanse is crucial before acting on a poor metric, particularly if you’re looking to spend a fortune on A/B testing or CRO audits. Simple things like filtering data for your target region can go a long way to making KPIs healthier,” says Marc Swann, search director at the digital agency Glass Digital. “There’s a danger in acting on top-level metrics. Dig deeper to see if you can get more insight into exactly what’s going on, and you’re more likely to make the smartest decision. Segmenting data by page type might show that your product pages are doing well, but your help guides are dragging the average down because users visiting these pages don’t have purchase intent. This context will direct your efforts into retargeting campaigns and better calls to action on blog posts, rather than funneling unnecessary investment into product pages.”
Making data work harder
Growing businesses should consider sweating existing data before splashing out on new insights. According to Dr. Sismeiro, internal data is often surprisingly rich and it’s free. In many cases, companies just need to organize, integrate, and store it for easy access.
For organizations wanting to get more from their data, Dr. Sismeiro has further advice—don’t underestimate the important contribution employees can make. She recommends empowering all employees, not just analysts and data scientists, to use data and extract strategic insights.
Organizations should remove “data silos” and work harder to integrate online and offline data to make it easier to draw out insights that are relevant to the business as a whole. This can be done quickly in small projects that when combined will have a big impact. It’s better to act this way than to spend a lot of time bringing everything together all at once.
Investing in systems is, of course, an important part of updating a data strategy. Legacy systems might not be capable of creating the new holistic approach. But this need not be a big up-front investment, due in large part to the affordability of cloud applications and Software-as-a-Service (SaaS).
Finally, it’s important to share data successes. If teams understand the power of data and the results it can generate, they are more likely to respond and buy in to organizational goals. Bigger companies should consider creating a dedicated team to help instill an “insights culture” and promote the progress being made in the business as a whole.
With the right approach, all organizations can benefit from analytics. According to Dr. Sismeiro, businesses everywhere are waking up to data’s full potential. “We can now find many examples of businesses that use analytics to discover and assess new business opportunities, find what customers say about the firm or products, unveil market segments and make product recommendations, aid decision making, improve logistics and increase efficiency, influence voters, and even control manufacturing and protect crops.”
Organizations that embrace the power of analytics will drive ahead of their rivals in the data-driven business environment. Those that fail to do so will be forced to take a back seat and watch as their rivals pass them by.
Too often we gather the data, doing the mathematics—the analytics, and then assume that actions will happen. But generally that’s not the case. Our approach needs to fundamentally change so that we carry through to the point of action. Hear what Steve Jones, Global VP- Big Data, Capgemini has to say.
It began with Bitcoin, but now it has spread. Blockchain has the potential to radically transform the way industries do business. Here are some examples of success and challenges as blockchain inserts itself into transactions and process.
Defined as “an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value” by Blockchain Revolution authors Don and Alex Tapscott, Blockchain technology holds massive promise for multiple industries, particularly in regards to securing data.
Information held on a Blockchain exists as a shared and continually reconciled database. Because the data isn’t stored in any single location, no centralized version of this information exists for hackers to corrupt.
Hosted by millions of computers simultaneously, the data is accessible to anyone on the internet, making it public and verifiable. Blockchain can only be updated with the consent of the majority of its participants. Once entered, information can never be erased, allowing for the creation of a definite and verifiable record of “digital events.”
Industries like financial services, cybersecurity, education, healthcare, supply chain, and governments have begun to explore the application of Blockchain for a variety of uses; however, most uses remain experimental.
We look at how these sectors are implementing this technology. Read the highlights below, and download our audience insights on the opportunities and challenges of Blockchain for a more in-depth look.
With Blockchain, healthcare organizations can capture an individual’s lifetime medical history. Privacy concerns can be managed via permissionless Blockchains, where all parties can view all records, or permissioned Blockchains, where privacy can be maintained following an agreement on which parties can view what transactions and when it’s necessary to mask the identity of the party.
Governments are turning to Blockchain as a potential means to better serve their citizens and improve processes for public administrative functions. The ability to record transactions on distributed ledgers offers new approaches for governments to improve transparency, prevent fraud, and establish trust.
Half of these trailblazers have already invested in three primary areas: asset management, identity management, and regulatory compliance.
According to the Harvard Business Review, the coming shift in the industry will have both utilities and consumers producing and selling electricity provided the technology proves to be both reliable and scalable. If so, it will drive the transition to what the energy industry calls a “distributed world,” in which both large and smaller power generation systems exist for homes, businesses, and communities.
Blockchain is poised to create major cost- and time-saving opportunities for the supply chain, logistics, and transportation sectors.
It can be seen as a new method of tracking any kind of shipment or transaction, in any kind of supply chain. With each participant in the supply chain or transaction keeping their own live record of all their information, the potential to tamper with the data decreases.
The opportunities for implementation of Blockchain are limitless, as are the challenges. Download our insights report for an in-depth look at these.
Does your organization fully understand what “digital skills” means? Is your recruitment team equipped to meet your needs? The digital talent gap report offers some well thought-out ideas, around attract, develop, and retain.
A Capgemini and LinkedIn joint research report shows that the digital gap is widening.
The challenge of the digital talent gap is no longer just an HR issue; it is an organization-wide phenomenon that affects all areas of the business.
Join Frank Wammes, CTO, Continental Europe and Will Peachey, Group Supply Chain Officer as they share insights on how to bridge the gap with the right digital skills and talent.
Read the complete report here.
The Renaissance was a historic period that saw the development of new tools and skills. If you want to leverage the power of SAP Leonardo, you need to master the new skillsets, tools, and methods required for cognitive computing.
If you want to leverage the power of SAP Leonardo, you need to master the new skillsets, tools and methods required for cognitive computing.
In my previous blog, I posed the question whether SAP is ushering in Renaissance 2.0 or if we are going to experience the second Cognitive revolution. As a good consultant, rather than providing a straightforward answer, I provided a perspective with which you can build your own view of the situation.
However in this blog, I will attempt to provide some direction. My personal feeling is that the Cognitive Revolution will come, though not just yet, as the technology needs to develop further before the use cases can follow. With respect to SAP Leonardo and its ability to create business value in the organization, SAP’s clients will certainly enter Renaissance 2.0 as they mix new technologies to create new value propositions.
The medieval Renaissance was more than just the convergence of different professions; it was also the period which saw the development of new tools and skills. In order to paint differently, artists started to experiment with new methods and materials which helped them create masterpieces. While these developments yielded many insights, many artists applied lean start-up principles such as ‘Fail Fast, Fail Forward’ – most notably Leonardo Da Vinci when he decided to use wet plaster when working on (perhaps the biggest masterpiece of all) The Last Supper.
If SAP clients want to leverage the power of SAP Leonardo, they need to master the new skillsets, tools and methods required for cognitive computing. SAP itself has provided good examples by being one of the first large European companies to introduce “Design Thinking”. But it requires more. We need experts on Agile development, data modelling and advanced architecture, especially in the large traditional “SAP houses” where such skills were neither required nor developed.
When I was leading Capgemini’s SAP business in 2007 we introduced the Agile methodology to our SAP Community through one of the leaders in the field, Sander Hoogendoorn; but was met with scepticism and it took a few more years before people started to see the value. The development with the HANA Cloud Platform definitely accelerated adoption of this method.
So how do we accelerate adoption of new skills, tools and methods for the more traditional organizations? One of the tools we use is the Applied Innovation Exchange which is perfectly suited to test the possibilities that SAP Leonardo opens up. In the Discover phase of the AIE, clients can identify where and how SAP Leonardo will bring short term impact. Perhaps even more important, in the “Devise” phase, the client can build real value scenarios in short sprints (4 to 6 weeks) and prove to the business why the investment makes sense. Not only a good way to experience SAP Leonardo but also to have a quick build-up of the new skills without having to go into massive reskilling of the workforce!
As with the Renaissance, the acceleration led to an enlightened society. Let us help accelerate and enlighten your organization. We look forward to having you in our AIEs around the world.
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