Why 5G security?

5G is the fifth generation of cellular technology, offering faster speeds and lower latency compared to 4G. It makes the connected era and Internet of Things (IoT) possible, and whether it’s smart cities, steelmaking, or healthcare, few industries will be untouched by its capabilities.

There are two types of 5G networks: public and private –

• Public 5G networks are primarily used by retail customers for smartphones and other day-to-day devices connected to the internet. Owned and operated by mobile carriers, public networks are available to anyone who subscribes to their service. As a network established by telco providers, the security rests with them for the most part.
• Private 5G networks are not accessible to the public. They are owned and operated by a single entity, such as a company or government agency, and are used to connect devices within a specific location or facility. For example, a factory might set up a private 5G network to connect its machines and other equipment to streamline operations and improve efficiency.

Most companies using 5G for manufacturing and operations will need to build a private network or employ a hybrid model of public and private, fitted to the requirements. Whichever model a company uses must be underpinned by robust security frameworks.

5G security is complex because, unlike 4G, it operates outside the perimeter of dedicated equipment, servers, and protocols. Instead, a highly vulnerable software ecosystem of virtualized RAN and cloud-forward services constitutes its core network. The concept of 5G security is new and evolving, which is why it’s essential to be alert to the challenges and develop and deploy new security measures in response.

5G security challenges

The introduction of new use cases, new business models, and new deployment architectures makes securing 5G networks more challenging. But without a cohesive approach to mitigating the security risks, it can be difficult to ensure that all potential vulnerabilities are identified and addressed.

These are the key security challenges for 5G as we see them:

• Increased attack surface: Millions of new connected devices are entering the digital ecosystem, which increases the attack surface exponentially. Many IoT devices are vulnerable and unprotected and typically operate with lower processing power, making them easy targets for attackers. This makes implementing zero-trust frameworks with true end-to-end coverage critical for protection against threats.

• New paradigms for telco: With 5G, the telco ecosystem is essentially inheriting IT challenges requiring a software security mindset. Whether public or private, 5G’s virtualized network architecture creates a new supply chain for software, hardware, and services, and this “virtualization” of traditional single-vendor hardware is a major security challenge. It’s time for professionals to acquaint themselves with network function virtualization (NFV), virtualized network functions (VNFs), service-based architectures (SBAs), software-defined networks (SDNs), network slicing, and edge computing.

• Operational challenges: The requirements or the capabilities needed to monitor a 5G network are different to IT and OT. This means that the tools used for monitoring the IT and the OT networks cannot be retrofitted or scaled for the cellular world, so 5G requires new tools and new capabilities. This involves training new people to understand the protocols and use cases.

• The complexity of implementation: There is no one way to build 5G architecture. It depends on the requirement of the organization and, as a result, the specification range can be extensive. Trying to bring these models together and manage them is one part of the challenge; the other is finding skilled professionals who know how to do it. Consequently, the margin for human error is another factor to bear in mind.

• Increased number of stakeholders: Finally, the industry recognizes that the success of building 5G networks is dependent on the entire ecosystem of hardware and software vendors spanning multiple suppliers, from chip vendors to cloud providers. Coordinating new stakeholders and their security efforts while ensuring that all potential vulnerabilities are covered is likely to be challenging. Note that different stakeholders may have different levels of knowledge and expertise when it comes to security.

Introducing 5G risk assessment

5G security is extensive and there are multiple parts to be cognizant of to understand where the risks and vulnerabilities are when running a network. You’ll see this mapped out into horizontal and vertical layers in the diagram. To conduct a comprehensive risk assessment of 5G, both axes need to be secured. Knowing where to start involves understanding what constitutes each layer:

• 5G horizontal security is the sum of five parts: user equipment, radio access, edge/multi-access edge computing, core network, and the cloud. Due diligence is necessary in every area to ensure assets are protected from confidentiality, integrity, and availability attacks.
• 5G vertical security is the sum of four layers: the product, the network, the applications, and the security operation layer on top. This is generally referred to “chip to cloud” security, particularly in the context of IoT devices.

A risk assessment, therefore, has to be holistic in nature, covering every aspect of the horizontal and vertical layers with due consideration of the threats, vulnerabilities, and assets that touch each of the specific components in the architecture. Such a risk assessment must also address any regional and industrial compliance requirements, and we will discuss this later in the series.

At Capgemini, we know that building and securing a 5G network is complex. We also know that everything must be protected end-to-end and in unison for it to work effectively. With deep technology, business, and engineering expertise, Capgemini has the unique capability to guide you on the 5G security journey end-to-end.

Security today adds value to a business tomorrow, and realizing the possibilities of a new, truly Intelligent era relies on it. Our experts can help you maximize the benefits.

The next blog in the series will consider how to conduct a robust risk assessment and monitoring in more detail. 

Great basketball teams and great personalization teams have a lot in common.

Imagine a shopping experience that’s completely generic. Worse than generic, it goes out of its way to recommend things you don’t want. It recommends actions that are the opposite of what you’re looking to do. It’s perfectly frustrating. How long will a business based on that sort of experience last?

Now imagine a personalization experience that knows you so well it’s constantly providing you with serendipitously delightful experiences. You’re discovering things you never knew you wanted. But you’re never allowed to use it because the experience never sees the light of day. The MVP never becomes an available product.

Both scenarios are terrible. Unfortunately, a variation of the second is more common. 77% of AI and analytics projects struggle to gain adoption. Fewer than 10 percent of analytics and AI projects make an impact financially because 87 percent of these fail to make it into production. What if we could flip the odds? What if rather than most recommendation projects failing, most of them succeeded? Cross-functional, product-centric, teams can do just that. It’s how innovators like Amazon and Netflix were able to succeed so quickly and so often in their personalization programs. It’s also been critical for the dozens of successful personalization programs we’ve delivered at Capgemini.

Recommendation experiences

Everything is a recommendation. That insight came from Netflix: “the Starbucks secret is a smile when you get your latte, ours is that the website adapts to the individual’s taste,” said Reed Hastings, co-founder of Netflix. Recommendations weren’t features or algorithms. They were the experience; the means to delight, surprise, frustrate, or anger customers. At Amazon, Jeff Bezos’ original goal was a store for every customer. This wasn’t AI for the sake of AI. Both companies made personalization central to their experiences, and personalization enabled Amazon and Netflix’s visions for more innovative, delightful, and serendipitous experiences. Recommendation experiences (RX) were critical to customer experiences (CX). Experiences were the product. Building products is hard. Josh Peterson co-founded the P13N (personalization) team at Amazon. He described the early days of Amazon as challenging because the company was siloed. Design, editorial, and software engineering were fragmented. “It was really hard to ever get anything all the way out to the site without begging and borrowing people from silos. The one time it was always different was when we did a product launch… So, if there was a big enough effort like launching music or auctions then you had permission to borrow everyone to put together your team.” In the early days of Amazon, there were many engineering efforts around personalization. Even though these efforts were led by brilliant engineers, they saw limited success. It wasn’t until after the launch of Amazon Auctions that personalization made a real impact.

After Amazon Auctions, Peterson and Greg Linden looked to make Bezos’ vision for a personalized store for every customer a reality. The goal was a team that could “own its whole space,” to break silos to create a cross-functional team to rapidly experiment and deliver. This was the first team, outside of the design organization, to have designers in their team embedded with web developers and technical project managers. This enabled a higher number of launches compared to other teams. The impact of their model was so successful that it became the basis of Amazon’s famous “Two Pizza Team” approach – essentially a team small enough that they could be fed with two pizzas. Small teams that were decentralized, autonomous, and were “owners” of the business could move faster and launch more experiments. More experiments would enable them to have more successful innovations.

Experimentation

Successful personalization teams foster a culture of experimentation. Creating a culture of experimentation requires diverse, multi-disciplinary teams. Below we show the various skillsets and domains that are required for modern personalization teams. The circles don’t represent people, they represent skills. Great basketball teams and great personalization teams have a lot in common. In basketball, you need defense. You need offense, both close to the rim and from afar. You need diversity in skillsets. You could get lucky and find a unicorn but fielding multiple teams of unicorns is not practical. Creating a team of all-stars sounds good on paper, but there are plenty of examples where those super teams fail to live up to expectations. A team without a diverse set of skills is unlikely to be very successful, and almost certainly not great.

“Experimentation requires blending creativity and data. Practically, this becomes a blend of statistics, behavioral economics, psychology, marketing, and expertise in experience design.”

Small teams with most of the skills above are more likely to do end-to-end personalization well. No one person will have all the skills needed, but together they’ll bring more experiments to the table. Early Amazon teams were engineering and data-science heavy. It wasn’t until the addition of design, business expertise, and a product-centric approach that they were able to execute end-to-end and achieve Bezos’ vision.

Velocity is a leading indicator. Successful personalization teams test many ideas. They break experiments into small chunks so no one failure is large enough to disrupt the business. They test and learn quickly. Testing a dozen ideas and refining them will be more efficient than trying to make one idea “perfect.” Our intuition on what is going to work is often wrong. Testing many ideas allows the data and results to guide us, rather than intuition. This requires personalization teams to develop many ideas end-to-end quickly.

What does the future hold? Cross-functional, product-centric teams are the beginning, not the end. Experimentation requires blending creativity and data. Practically, this becomes a blend of statistics, behavioral economics, psychology, marketing, and expertise in experience design.

These teams need to track which features drive results to understand what is working and what is not. The goal is to achieve consistent and reliable serendipity from personalization efforts. The obvious is not serendipitous. Experimentation is needed to discover that which is not obvious and that which drives business outcomes. Without that, we can’t scale serendipity.

INNOVATION TAKEAWAYS

DIVERSITY LEADS TO SPEED

Speed leads to innovation. Diversity leads to innovation. End-to-end cross-functional teams with dedicated resources are more likely to successfully implement personalization programs and innovate faster than their peers

A CULTURE OF EXPERIMENTATION IS CRITICAL

Velocity, variety, and volume of experiments are leading indicators of innovation. “Our success at Amazon is a function of how many experiments we do per year, per month, per week, per day.” – Jeff Bezos

SPEED IS A COMPETITIVE ADVANTAGE

Testing and learning iteratively as well as being able to deploy quickly contribute to faster speed to market. “Companies rarely die from moving too fast, they frequently die from moving too slowly.” – Reed Hastings

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 5 features 19 such articles crafted by leading Capgemini and partner experts, about looking beyond the usual surroundings and be inspired by new ways to elevate data & AI. Explore the articles on serendipity, data like poker, circular economy, or data mesh. In addition, several articles are in collaboration with key technology partners such AWS, Denodo, Databricks and Dataiku. Find all previous Waves here.

However, nothing compares to what might be about to happen – the convergence of artificial intelligence (AI), blockchain, and 6G/satellite connectivity combined with concepts like the metaverse will change the way we design, develop, and deploy software. For the purpose of this short blog, I will focus on the metaverse and the effect it might have on software developers. 

What is the metaverse? 

The metaverse is a virtual reality that allows us to interact with a fully virtual (and immersive) environment just as we do in real life, doing the same things we would in real life. According to Wikipedia, the metaverse “is a hypothesized iteration of the internet, supporting persistent online 3-D virtual environments through conventional personal computing, as well as virtual and augmented reality headsets.” A Capgemini publication focusing on metaverse in healthcare defines it as “a container of 2D or 3D virtual spaces, a persistent place parallel to the physical world, aiming to combine online digital and real-time interactions with the sense of presence.” 

For years, games like Roblox and Fortnite, but also older games like World of Warcraft, Minecraft, or Second Life, have developed a parallel virtual world where players can engage and connect with others in a mostly fantasy-like landscape. To illustrate the concept, one could also draw parallels with the film The Matrix; in the film, the main character “moves” between two reality-like parallel worlds.  

Many consider the metaverse as the internet V3, with V1 back in the 1990s, and the emergence of social media at the start of the 2000s as V2. Several use cases for the metaverse exist: for example, in the smart city space and in healthcare. However, there are also some who are more skeptical, who believe that the metaverse is already part of the past. The truth might lie somewhere in between. What seems clear, however, is that either the metaverse or part of the various metaverse concepts will impact the way we develop software: 

1. Moving away from mouse and keyboard 

When apple unveiled their iPhone in 2007, it heralded the start of the end of mobile phone keyboards. With the emergence of the metaverse, we might see the same happening to our PCs. The mouse, invented by IBM back in 1964, still the de facto PC input device next to the keyboard, might be slowly replaced by gesture, speech, and movement for end users (some state that using mind control devices might also become more mainstream). Of course, VR has been around since the mid-1990s after its invention in 1968, but due to various factors has not quite hit the mainstream. This might change now that developments in the metaverse have started, with more vendors announcing they are developing MR devices – Apple has started production in March 2023. 

For developers, the shift away from using text for coding is still a big unknown. If the shift occurs, then text input devices will slowly disappear. If it does not, then developers will have to deal with both traditional physical and new virtual ways of working. In any case, designing and developing software that supports different data input devices will require different skills, techniques, and tools compared to relying on mouse and keyboard only. It seems most likely that we will see a convergence in which developers use a mixture of traditional physical and new virtual ways of working.  

2. The move from 2D monitor screen interactions to full 3D with the use of VR, AR, MR, and XR 

It is not just our traditional user input devices that might change. We might also see our traditional user interaction devices change. Over the past 30 years, the PC monitor only changed in terms of resolution and size, but not really in concept: a screen that displays data in visual and text form, projected on a two-dimensional screen. The touch screen tried to allow for a better experience but failed to really take off. Driven by the metaverse, we might see a shift from today’s PC-based fixed and two-dimensional monitors to the use of mixed physical and virtual reality devices. Using virtual reality (VR) headsets or mixed reality (MR) glasses for user interaction combined further with either smartphones, gesture, or even mind reading might fundamentally change the way we design and develop applications. It is very likely that the shift will be a gradual process. The emergence of MR for both end users interacting with applications and for developers designing, developing, and delivering code might still be a way off. However, software developers must master the new (and currently various) software development kits (SDKs) to ensure that they can establish fully seamless and fluid interactions.    

3. New development platforms  

With the advent of the metaverse, organizations and communities are also starting to develop new programming languages. For instance, in December 2022, Epic Games launched the Metaverse programming language Verse. Verse is focused on making it possible to create social interactions in a shared three-dimensional (3D) world in real time. The web3 programming language family now includes Verse along with others like Clarity, Solidity, Curry, Mercury, and Rust. Verse also aims to support interoperable content by utilizing operational standards from several game engines, such as Unity, and live upgrades of active code. Another example is solidity. Created by Ethereum, solidity is a statically typed programming language designed for developing smart contracts that run on Ethereum. Solidity is used on the Ethereum blockchain, an object-oriented programming language, for building and developing smart contracts on blockchain systems. The question with all new programming languages is whether they will become mode dominant or widespread. Clearly only time will tell.  

4. Testing  

The quality of applications will be as important as in today’s applications. However, with MR as well as digital twin type environments, testing the use of both physical as well as virtual devices will be different as new testing facilities are needed to avoid manual interventions that might read “put the headset on, run the app, and see if it works.” The integration of MR and/or different VR devices as well as the use of different platforms might require different testing regimes.  

5. Being more aware of non-functional aspects like latency, security, and safety  

Walking around with Google Glass or any other VR or MR devices could pose various risk profiles, and developers must consider this when designing and developing metaverse-based solutions. In addition, latency – the time it takes for a service to respond (also sometimes referred to as “lag”) – is another aspect developers will have to consider more than in our current “traditional” 2D environment. User experience will be a key critical aspect in the metaverse, and a fully immersive experience can only be achieved if the rendering is fully fluid and seamless. With the end user being mobile or stationary with various data transfer opportunities (currently 5G, but soon 6G or even via low orbit satellites) it is important to ensure the developed metaverse solution fully considers that. With these requirements, more “traditional” aspects, like writing efficient netcode (referring to synchronization issues between clients and servers) and 3D engines, will become even more important.  

6. The move from two dominant mobile platforms (Android and Apple) to multiple platforms 

The metaverse will require massive 3D content to engage users, and 3D is expensive to make, to understand, to store, and to transport. Developing a metaverse application involves creating a virtual experience for platforms such as HTC Hive, Oculus Quest, and other VR or MR systems. Popular developer tools for metaverse focused on 3D creation include Epic’s Unreal Engine, Unity, Amazon Sumerian, Autodesk’s Maya, and Blender. And then there are the various (at the time of writing) development platforms that cover metaverse-related tools and accelerators like Webaverse, Ethereal Engine, JanusWeb, WebXR, Open Metaverse, Nvidia’s Omniverse, Hadea’s metaverse infrastructure, and the Microsoft metaverse stack.   

7. Increased importance of application programming interfaces (APIs)  

Interoperability (getting systems to talk to each other) will be one of the main challenges for developers writing metaverse applications. As with the advent of the internet in the mid-1990s, where multiple vendors as well as open communities developed and released new standards, the metaverse is also triggering numerous, and sometimes conflicting, standards. How it will all pan out is still open. However, what is clear is that software developers must have an excellent appreciation of data integration, particularly as data is being exchanged in real time between different platforms.   

8. Greater emphasis on real-time collaboration 

As applications in the metaverse will be used in an interactive and real-time manner, applications written for the metaverse will have to be able to respond to unpredictable events in a real-time manner, providing a seamless user experience. This means that software developers will have to use statistical techniques like deep learning on provided data and real-time user interactions to predict a response or next step, without the software having been specifically programmed for that task. 

9. Security and trust will be critical elements  

The success of the metaverse will also depend on users trusting the virtual counterparts; this means active and passive security will be a critical element. As the metaverse will evolve around the real-time exchange of virtual assets, new ways of securing and controlling virtual assets and interactions in real time will be needed. This will include authentication and access control, data privacy, securing interactions and transactions, and protecting virtual assets. In addition, passive security-related aspects, like strong network security protecting from cyberattacks, hacking, and other security threats, will be needed.  

10. The further use of tech like blockchain and NFTs 

One of the main use cases in the metaverse is the trading of goods and services. Therefore, it is likely that technologies like blockchain and non-fungible tokens (NFTs) will be supporting the exchange of virtual assets. And this means that software developers should have an understanding of how to manage NFTs as well as distributed ledgers like blockchain.  

11. AI will impact software development  

Another technology that will be part of the metaverse is AI. AI will be a key element in supporting the metaverse as it will help with end-user personalization, content creation to create more immersive and engaging virtual environments, as well as analysis of user behavior to help to identify trends and patterns, enabling developers to optimize the virtual world and provide a better user experience.  

Even without the emergence of the metaverse, AI will impact software development significantly. AI is positively impacting the way we design and develop software in these areas:  

1. Generating code: several AI tools can generate code, including DeepCoder developed by Microsoft, Kite, TabNine, GitHub Copilot, etc. 

2. Automation: AI can automate repetitive and time-consuming tasks in software development, such as testing, debugging, and code optimization. 

3. Quality: AI can improve the accuracy of software development by identifying potential bugs and vulnerabilities in code before it is deployed. 

4. Efficient resource utilization: AI can help software developers optimize resource utilization, such as server capacity and memory usage, to ensure that applications are running efficiently. 

5. Increasing immersion: by, for instance, making aspects more dynamic and immersive in the environment 

6. Creating virtual worlds: through, for instance, “text-to-environment” or “text-to-world.” Instead of placing assets using a mouse and keyboard, a developer could describe the environment instead. 

Today, many use cases exist where AI is aiding the entire software development process. The possible advent of the metaverse, or aspects of it, will further impact and change the way software developers work.  

Summary 

It is anyone’s guess as to whether the metaverse will indeed be the next incarnation of the internet. I remember an interview with David Bowie in 1999 in which he accurately predicted the impact the internet will have. He might have said the same about the metaverse today. In any case, technologies like VR, AR, MR, and AI will drive more and more user interactions into the virtual world, and software developers must deal with the shift in technology and the change in user experience. 

Special thanks to: Stuart Williams, Simon Spielmann and some support from ChatGPT 

Just as professional rally drivers rely on a navigator to get them from A to B, so the sales function depends on strong sales operations support.

It’s the role of the sales operations team to generate, track, and progress sales leads; to capture, validate, and track opportunities as part of sales forecasting; to move those offers forward to the offer stage with a configured and competitive quote; and when the sale is made, to convert the purchase order into a valid sales order for fulfilment.

These responsibilities are beset by all kinds of challenges. Sales operations teams frequently find they have insufficiently accurate, easy-to-access data and insight-driven forecasting; that their sales technology is outdated; and that they have inadequate resources and roles that are not clearly defined. At the same time, teams constantly need both to recruit and retain talent, and to adapt to changing business models.

All these challenges often mean that sales operations teams spend much of their time dealing with day-to-day tactical issues when they would rather be thinking and acting strategically – looking ahead, developing plans, testing them, and then putting them to work.

Design, build – and transform

What’s needed is a smart, seamless sales operations model (think of this as a sales operations-as-a-service concept) that can be tailored to the culture, practices, and needs of the individual organization – and that empowers the people who use it.

It’s the bespoke nature of the model that makes the design stage so important. If a service provider is involved, it’s our view that the best approach is for that provider to work closely with its client organization, designing and mapping processes based on lived experience within the sales operations function, and also on relevant personas.

What should emerge from this deep dive into future aspirations and current practices is a target operating and service model. The organization and its service partner work together to design and set up services including policies, process rules, a control framework, and new ways of supporting sales operations team members.

The final stage in the transition is to move from current processes to a more streamlined and coherent smart digital model. Technology collapses processes and creates a tremendous opportunity to eliminate drag in a process and improve how an internal or external user experiences it. Focusing on customer experience not only delivers hard gains (ROI, margins etc.), but also qualitative benefits such as CSAT/NPS that translates to stickiness, repurchase, loyalty, and “mind-share.”

What does success look like?

At Capgemini, our digital sales solutions take advantage of innovative technologies and sales systems to integrate, streamline, and optimize sales touchpoints and processes across the lead-to-order lifecycle – delivering accurate, easy-to-access data, enhanced sales support, and data-driven sales analytics.

The aim is to enrich our clients’ digital sales strategy with relevant insights and data that drive operational excellence and efficiency across the sales function. And we’ve seen some truly transformative business outcomes, including 15–25% reductions in turnaround time, 3–5% improvements in win-rate, 15–25% increases in time returned to sales, and 10–20% improvements in net promoter score.

Everybody wins

Intelligent, integrated sales operations of this kind not only address those organizational challenges I outlined earlier in this article – they also provide increased value for a company’s customers and business partners.

When sales processes are efficient and cost-effective, and when sales operations teams are well informed and in control, everyone is happy.

To learn how Capgemini’s Empowered Sales Operations solution delivers frictionless, digitally augmented, data-driven sales operations that drives competitive advantage across your business, contact: deepak.bhootra@capgemini.com

Deepak Bhootra is an established executive with two decades of global leadership experience. He delivers process excellence and sales growth for clients by optimizing processes and delivering seamless business transformation.

Based on the above, it’s perhaps easy to ask “Well, why can’t my ERP do all that?”, and to a certain extent it is possible for an ERP, or the combination of an ERP and eCom platform, to cover some of the functions described. But the challenge should not be “can my ERP do this?”; it should be “is my ERP the right system to do this both today and in the future?” If all your experience is with an ERP, then it’s tempting to see it as the solution to all types of problems; in the words of Abraham Maslow, “If the only tool you have is a hammer, you tend to see every problem as a nail.” But an OMS should be seen as another tool in your belt – perhaps not right for every task, but certainly an option to be considered in the right circumstances.

So, what could be some of the reasons for using an OMS rather than an ERP or a commerce platform?

• In many businesses, the ERP which manages back-end functions around supply chain or finance is not the same solution managing customer-facing or in-store functions. A customer offer doesn’t care about these boundaries (e.g. click and collect needs to know inventory, which may be in the ERP, but the store systems are critical in enabling the pick-up process), so strategy should be driven firstly by customer experience, customer-focused use cases and value drivers, and only then should existing organizational or systemic constraints be considered. A modern OMS connects one or many customer-facing front ends via APIs or built-in apps to back-end supply chain & finance processes, acting as a reliable bridge between channels, stores, warehouses, ERPs, and more.
• Customer service – like above, the ERP is usually not the system which enables customer services’ call center tools. An OMS can easily integrate through enterprise APIs to whichever systems the call centers are using to reduce complexity and connect the customer journey.
• Returns – returns are a huge cost driver for virtually all D2C businesses, and many resort to implementing a specific returns solution separate to their channels and/or ERP. Whilst this can enable more customer functionality, it is often at the expense of being able to tie the returns flow directly in with the outward fulfillment, often making the customer journey disjointed. An OMS enables retailers to automate and coordinate the return process to decrease cycle times and handling costs – all while simplifying the customer journey.
• ERP order fulfillment flows are typically more aligned to an order to cash process, driven by a limited number of customers ordering large quantities of products frequently via Electronic Data Interchange (EDI) or even through dedicated account managers, and making limited changes to those orders, rather than a consumer-focused fulfillment flow where large numbers of customers will order small baskets of products infrequently across a large number of self-service channels, and will often want real-time changes to those orders during or after that fulfilment – the differences in these two approaches are considerable, almost like speaking two different languages, and thus having an OMS in the middle can be the translator.
• As fulfillment networks grow and become more complex, with the options to fulfill both from owned warehouses, but also stores, 3^rd party logistics providers, retail partners, drop ship vendors, and other locations, it’s likely an ERP simply cannot master all inventory or location details (or perhaps would not want to, given that to do so may impact financial calculations), which then makes presentation of a consolidated supply view difficult, let alone accurate for a customer promise.
• ERPs are typically designed to mandate best practice flows to business processes, and deviation from those flows is often costly or not possible, so implementing new logic or processes to handle the ever-changing world of customer fulfillment can be disruptive and costly. By contrast, an OMS is set up to expect ongoing change in logic, new workflows, new offers, and new capabilities.
• Lastly, a modern OMS is highly modular and designed for agility – Fluent Commerce order management for example, is event-based, so inputs trigger outputs and operations are all in real time. A MACH (short for Microservice, API-drive, Composable & Headless) architected application like this means updates can be made often and changes are much easier to test, deploy, assess, and iterate, generally without continual needs to test end-to-end functionality.

Where could an ERP be suitable for enabling OMS capabilities?

All of the above is intended to point out some of the difficulties of trying to manage OMS functionality inside an ERP, but it isn’t the case that it’s always the wrong decision. Modern ERPs do allow more flexibility in their operations, and if you’re a D2C business which has grown up with the capabilities to deliver a customer-centric offer via digital channels, then many of the considerations above have probably already been considered and factored in. In such examples, the key consideration should perhaps not be whether an ERP is right for my business today, but whether there is enough agility in my operations that I could adapt ways of working to handle new channels, new offers, new products, etc. without impacting the wider ways of working each time.

So, in summary, what are some of the initial questions I need to consider when looking at my existing systems vs. a new OMS?

• What are the customer features/functions/offers which are going to add value to my business in the near term and long term?
• How feasible is it to adapt my current systems to handle these new customer offers/requirements? And how feasible is it given whatever else is already in the pipeline for these systems?
• If my requirements change, can my current system keep up with frequent changes? Is it future-proof? Scalable?
• Can I add additional value by decoupling my customer offer from my core processes through use of an OMS?
• How much would implementing an OMS cost vs. adapting my current ERP?

These aren’t easy questions and there will always be good arguments on both sides, so please reach out to the experts at Capgemini.

It’s no secret that, in today’s competitive landscape, customer experience has become one of the most important factors in a company’s success.

However, providing the best customer experience requires employees that are engaged and motivated to deliver excellent service. This is particularly important in contact centers where customer interactions are the primary touchpoint for many businesses.

So, how do you ensure your employees continue to have positive experiences in often demanding roles?

Engaged employees – increased performance, reduced turnover

Employee engagement is the emotional commitment an employee has to their organization and its goals. Fostering this within your company helps your people feel more connected to their work, their colleagues, and your company’s mission, driving a variety of benefits including better performance, productivity, and retention rates. Furthermore, engaged employees are more likely to go above and beyond to help customers, resulting in higher customer satisfaction and loyalty.

The way you engage your people plays a crucial role in building a strong brand for your company – particularly in client-facing roles. This means how they interact with customers can make or break the perception of your brand.

Engaged employees act as brand ambassadors, representing your values, culture, and brand image. They understand the importance of providing excellent customer service – and strive to deliver it in everything they do.

When engagement is high, your people are more likely to stay with your company, reducing employee turnover rates and the negative impact turnover can have on customer experience. New employees may not be as familiar with your products, services, or procedures – leading to longer wait times, errors, and increased customer frustration.

Improve NPS quickly and easily

Engaging employees can also improve a company’s Net Promoter Score (NPS) – a measurement of customer loyalty and satisfaction. Studies have shown that companies who focus on engaging their people have higher NPS scores than those with disengaged employees. This is because engaged employees are more likely to provide a positive customer experience, leading to higher customer loyalty and advocacy.

A study by Temkin Group found that companies with highly engaged employees have a NPS that is 2.5 times higher than companies with low employee engagement. The study also found that engaged employees are more likely to provide a better customer experience, resulting in a 20% increase in customer satisfaction ratings. Conversely, disengaged employees are often responsible for a 15% decrease in customer satisfaction ratings.

Investment is key

In conclusion, employee engagement is critical for contact centers to build a stronger brand, reduce customer churn, and improve NPS.

It’s crucial then to invest in people development, recognition programs, and the creation of a positive work culture if you want engaged employees working in your call centers that can generate better customer experiences, which lead to increased customer loyalty and revenue growth.

To learn how Capgemini’s Intelligent Customer Interactions solution delivers a next-generation digital contact center service to drive a more meaningful, emotive, and frictionless relationship with your customers, contact: tim.szymanski@capgemini.com

Tim Szymanski focuses on orchestrating and streamlining customer experience operations to improve profitability, quality, efficiency, and brand loyalty for Capgemini’s clients.

“The pace of change has never been this fast, yet it will never be this slow again.”

I recently came across this fantastic quote by Justin Trudeau from the 2018 World Economic Forum, which really encapsulates the speed at which change is increasing, especially post the global pandemic. This got me thinking on how technology has impacted supply chains in recent few years, and how the evolution of technology will undoubtedly disrupt our market like never before.

The value of supply chain innovation

Some noteworthy innovations that have impacted the supply chain sector over the last few past years include the Internet of Things (IoT), predictive analysis, artificial intelligence (AI), 3D-printing, and blockchain technology – which each bring their own innovations to the table.

IoT collects real-time data from your supply chain, providing increased visibility into inventory levels, shipment status, and environmental conditions. While predictive analytics processes leverage data, statistical algorithms, and machine learning techniques to identify the likelihood of future business outcomes. They also leverage targeting to identify potential disruptions, optimize inventory levels, and improve delivery cycles.

AI, meanwhile, doesn’t just automate repetitive tasks within your supply chain. It typically analyzes data and identifies patterns – enabling your team to make better decisions across the board. While 3D printing enables products to be manufactured on demand, leading to reduced lead and transportation times, improved flexibility, and significant waste reduction.

Finally, blockchain provides a secure and transparent platform that drives increased visibility and product and transaction traceability to enhance trust and reduces fraud across your operations.

While these innovations will continue to have the potential to make supply chain management and operations more efficient, transparent, and responsive to changing business needs, their potential is far from being fully exploited.

Technologies and trends to watch

However, if Mr. Trudeau is correct, we can expect more emerging technologies and trends to drive profound change to supply chains both now and in the future. These might include:

• Digital twins – virtual replicas of physical assets or processes that model and optimize logistics operations, predict maintenance needs, and simulate scenarios to identify potential issues in an isolated digital environment
• Augmented reality – which enhances supply chain operations by providing real-time information and visual guidance to workers who may be working on complex assembly processes, or helping locate items more efficiently
• The sustainability and circular economy – which places greater emphasis on sustainability, as supply chains are expected to become more focused on environmental and social responsibility
• Quantum computing – which has the potential to improve optimization and decision-making processes, while providing holistic risk simulations, better insights and visibility, and higher levels of cybersecurity

With potential, comes challenge

Although all of the technologies above have shown the potential to revolutionize supply chain operations, they also present new challenges such as the increasing threat of cybersecurity, and highlight the need for an upskilled workforce.

Indeed, moving towards an intelligent supply chain requires significant and consistent investment. Not only in streamlining processes and implementing new technologies, but also supporting emerging roles and skillsets to respond to and stay ahead of the evolving nature of work within the supply chain.

To discover how Capgemini’s Intelligent Supply Chain Operations delivers cognitive, touchless operations, and data-driven decision-making to your organization, contact: joerg.junghanns@capgemini.com

Jörg Junghanns leverages innovation and a strategic and service mindset to help clients transform their supply chain operations into a growth enabler.

These strategies and reports tend to focus on innovation as a system, or an ecosystem – something where external forces and organizations drive change. But far too little attention is paid to how unequal innovation is at the individual level, at every stage: from those who participate in innovation and entrepreneurship; to those who can access and benefit from innovation. I argue that without this lens, innovation risks entrenching pre-existing inequalities, failing to benefit from the full breadth of diverse perspectives, and will suffer from patchy adoption.

Who are the innovators?

Research into the characteristics of innovators has identified certain personality traits that seem associated with the kinds of people who like to self-identify as innovative: “creative people tend to be better at identifying (rather than solving) problems, they are passionate and sensitive, and, above all, they tend to have a hungry mind: they are open to new experiences, nonconformist, and curious.” The idea that innovation is linked to intelligence, or genius, has been pretty roundly disproved – or the evidence is inconclusive, at best. Anyone can have a great idea. And now, more than ever before, globalization and the internet have lowered the barriers to innovation: pretty much anyone can access the tools to build a new piece of software, an app, find a like-minded community, and apply for seed funding to get an idea off the ground. The UK is one of the best places in the world to launch a start up. Levels of UK Venture Capital investment is third in the world, hitting a record high of £29.4bn in 2021 mainly in ICT, biotech, and healthcare.

So, why is the typical profile of a UK entrepreneur white, male, degree educated, in their forties, and living in the South East? Evidence shows that women, people from a minority ethnic background and those from poorer socioeconomic backgrounds face systematic disadvantage in founding businesses, raising investment and finding success – and intersectionality is important, with female entrepreneurs from ethnic minority backgrounds experiencing the biggest disadvantage. Between 2009 – 2019 in the UK, all-ethnic teams received an average of 1.7% of VC investment (despite being 14% of the population), while only 2.87% went to all-female teams – however 42.72% of VC cash went to founding teams with at least one member from an ‘elite educational background’ (Oxford, Cambridge, Harvard, Stanford). These patterns of unequal access to investment may be partly explained by the findings that roughly 3% of VC investors in the UK are Black, and 5% are of Mixed Heritage, with very few being partners or having decision-making seniority in their firms.

What’s preventing us from benefitting from more diverse innovators?

It seems undeniable that factors such as lack of access to finance, deprivation, poor education, lack of time and support, and being under-represented in senior decision-making roles limits the ability of some people to become innovators, even if they have a great idea. Socioeconomic inequalities can make aspiring entrepreneurs less able to access funding from friends and family, with no personal wealth to fall back on when times get tough, and less access to capital assets – factors compounded by traditional caring gender expectations, which may prevent some women from putting in the 24/7 hours entrepreneurship tends to demand. Having less of a financial safety net might ensure that underprivileged entrepreneurs are first to go bust in turbulent financial waters, whilst, as we have seen, they are less able to access private equity investment.

And in supposedly innovative organizations, too, the people who are granted the permission to innovate, and invited to lead and join internal innovation teams, may be disproportionately white, male, educated, and middle aged – in the image of their start up counterparts. Partly, this could be down to unconscious stereotypical role models of successful entrepreneurs – Bill Gates, Steve Jobs, Elon Musk – and a kind of ‘innovation theater’ that has become common currency for a certain type of confident, articulate maverick who benefits from the prestige of being an influential thought-leader. This style over substance has become a lazy stand-in for truly innovative ideas and people, who may be neurodiverse or otherwise seen as ‘difficult’ or non-conformist in ways less easily forgiven by corporate hierarchies, and who are given less of a voice.

Some evidence suggests that while women are equally as innovative in generating new ideas as men, their ideas are less frequently implemented within their organizations. Organizational innovators may fail to receive promotion for being ‘disruptive’, leading to them becoming frustrated and leaving, or feeling they are not listened to or supported. It’s a rare line manager who can see the individual’s potential and be willing to put their reputation on the line to protect a disruptive, perhaps pushy, innovator.

Why do we need to tackle systematic disadvantages?

For the individual themselves, the unique lived experiences that have shaped their personality and attitude may make them more, or less, inclined to be innovative – characteristics such as self-belief, confidence, skills, and knowledge.  It is possible that people wired a certain way who crave novelty and change are more likely to spot and go after opportunities (which may account for supposedly higher rates of attention deficit hyperactivity disorder (ADHD) among business founders) – but may be less adept at the follow-through and consistent hard graft over the years needed to deliver and scale the idea. Those who missed out on formal education may have less expertise to draw upon and lack some of the skills to research, analyze, and judge good ideas. People with fewer social and communication skills and without family connections may struggle to build and maintain the networks needed to land a good idea and find first customers. People who repeatedly experience pushback and failure are less inclined to keep pushing. Women may tend to be more risk-averse – or, perhaps, more alert to consequences – and might be perceived as less effective in leading innovation teams, while nonetheless having many key characteristics that are crucial for successful innovation, such as resilience and different viewpoints, which can help an idea find traction.

The notion that successful innovators are simply more driven, more persistent, more resilient, and more energetic than their less successful counterparts (and, by implication, others will succeed if they just try harder) is a harmful narrative that fails to consider the different starting points people have, and the differential opportunities available. That is the very essence of privilege: failing to recognize and tackle these systematic disadvantages means entrenching inequality and can ultimately lead to groupthink, which may prevent the innovation from becoming generally adopted – so not good for the bottom line!

Who benefits from innovation?

Apart from the unequal access to participating in innovating, the diffusion and adoption of innovation is also greatly impacted by a lack of consideration of diversity, and systematic inequality. Early adopters of innovative ideas and technologies are those most like the entrepreneurs themselves – usually with greater access to wealth than average, degree educated, younger, and more risk-taking. But many innovations fail to bridge ‘the chasm’ between ‘early adopters’ and the ‘early majority’, and never become adopted by the majority of the potential customer base. Many reasons have been cited for the uptake, or not, of innovation (which is undoubtedly a complex and not always logical process) – ranging from supply chain problems/ failure to scale, high initial costs, too complex, lack of awareness, failing to solve a real-world problem, lack of resource, lack of skills, organizational culture, and active resistance by some parts of the population. What has been less well understood is how sociological and individual factors in the modern world can prevent innovation adoption.

It is natural that innovations being developed for profit will focus on the needs and problems of affluent potential consumers – excluding almost by definition sectors of the community who will not afford the innovation. The profit driver underlies most of the technology transfer, market research, and VC investment activity that makes up a large proportion of ‘visible’ innovation, such as new products.

What about innovation for the public good?

However, much public sector innovation has less well-defined goals, such as making people’s lives better, healthier, or safer, creating desirable social change, sustainability, and more efficient and effective public services – these more invisible innovations should absolutely concern themselves more with accessibility, disadvantage and ensuring that vulnerable members of society can benefit in the same way as everyone else. Arguably, any public investment which supports profit-driven innovation with the aim of creating economic prosperity and jobs should also ensure that principles of inclusivity and accessibility are designed in from the outset (as has been the case with Government Digital Services).

Even for profit-driven product innovation, considering the factors that may encourage someone to use or not use their product will benefit from having diverse perspectives – receiving feedback from someone who experiences the product differently in their lives could alter the design, for example, or even change the end use of the idea by applying it in ways not initially intended. Having a wider range of people who see how an innovation will benefit them and spread the word to their peers – given that personal recommendation from a trusted source is the most effective way to spread new ways of doing things – means it is more likely an innovation will become widespread. It is important as well to be aware early on of cultural, social norms, or individual barriers that might inhibit adoption. Indeed, failing to understand a cultural nuance can sink a product launch in its tracks, close off entire market segments, or expose the company to public ridicule.

Innovation usually means doing things differently, taking a risk, or trying something new, which can be psychologically challenging for people who are resistant to change. Most leaders will have experienced resistance from those who seem deeply opposed to proposed change – most viscerally when it is felt to impact on their status, identity, or beliefs. People may distrust the product, or the maker, or are transferring poor previous experiences, or are loyal to another way of doing things out of habit, or inertia (‘that’s not how things are done here’). That is why – despite change management encouraging people to see change as something done ‘with’ people rather than ‘to’ them – most change initiatives fail, something often written off by optimistic innovators as the idea being ‘ahead of its time’ where the conditions were just not right, even though the benefits were (to them) obvious.

Failing to understand how privilege, inequality and difference plays out in the diffusion and adoption of innovation leads to deepening inequalities in society – in access to digital tools and skills that can make lives easier, to jobs and career progression, in the disadvantaging of vulnerable people and to poorer life chances for too many.

So, what should we do?

Equality, diversity, and inclusion should be seen as fundamental to innovation policy at every level – from Government to organization to start up. Diversity is key to successful innovation. It makes it more likely that the right products and services are developed that meet the needs of the many, not the few, and with fewer barriers to adoption. Historically, innovation and technology change has disproportionately benefitted the wealthy elite, which are more able to take advantage of it, over generations: this needs to change if innovation is to become a driver of social change and economic prosperity.

As a society, we need to find ways to support more people to become engaged in and benefit from innovation – as creators, participants, and users – and find better ways to recognize and reward innovative individuals, whatever their background or style. We need to widen the view of who and what innovators are, and value a wider range of innovation skillsets than merely those of having the idea: implementing innovation is a team sport which benefits from having people who can complement and challenge one another.

We need to consciously create a multidisciplinary approach to innovation: one that incorporates social, behavioral science and psychological insights and theory to view innovation through the social and individual lenses, as well as the economic and technical lenses more usually applied. If we get this right, the UK can benefit not only from a world-leading R&D base but also from the diverse, multicultural society we have, to truly become a global world leader in innovation.

Managing your finance operations is challenging at the best of times. It’s a challenge to formulate, develop, and implement a forward-looking strategy, when your day-to-day business is demanding so much of your time and attention.

In addition, international economic downturns, geopolitical upheavals, and the legacy of lockdown are creating unprecedented disruption. On top of this, there’s the rise of digital.

Digital technology changes everything. With data expanding at unprecedented rates, growing customer expectations, and continuous change of regulatory landscapes, there’s a need for organizations and their CFOs to create a data-driven, agile, and frictionless enterprise.

Finance transformation with minimal effort

As part of this digital revolution, Capgemini’s AI.Receivables solution – part of our Frictionless Finance offer – integrates with your corporate systems, infusing AI into your cash and collections processes to deliver next-generation, frictionless order-to-cash (O2C).

Our solution is enabled by HighRadius’ next-generation Autonomous Finance platform – an AI-powered platform trained on vast amounts of receivables transaction data to drive frictionless finance processing. This augments your finance teams with AI to eliminate exceptions and friction across the O2C life cycle to drive a range of business outcomes, including:

• Improved credit – with proactive credit reviews, customized credit scoring, AI-powered blocked order prediction, and faster customer onboarding to drive enhanced customer experiences and reduced credit risk
• Enhanced cash applications – zero-touch, straight-through remittance capture, payment posting, AI-powered invoice matching, and exception handling to help your people apply payments without delays
• Improved collections – with AI driven decision making process, prioritized worklist, auto communication and real time results visibility
• Enhanced deductions – with AI research and resolution of trade and non-trade deductions that require minimal human intervention
• Enhanced EIPP – with frictionless electronic billing and global payments enabled through auto-invoice delivery and self-serve payment portals

Humans and machines working together effectively

Capgemini and HighRadius’ partnership is based on our shared belief in the power of humans and machines to drive real-world capital impact, enhanced efficiency, and top-line growth to your business.

Enabled by HighRadius, Capgemini’s AI.Receivables solution gives you the power of data driven insights, machine learning, human and machine interaction – taking your O2C to the next level.

To learn more about how Capgemini’s AI.Receivables solution delivers frictionless O2C processing, taking you one step closer to – what we call – the Frictionless Enterprise, contact amiya.chand@capgemini.com

Amiya Chand is a strategic advisor with key expertise in guiding clients with their digital transformation journeys. He leverages his finance domain knowledge and expertise with data, analytics, ERP, and cloud to help clients unlock the Frictionless Enterprise.

Vikram Gollakota has over 20 years of experience in consulting and implementation of finance solutions globally. He has worked for corporations in various industry verticals including Consumer Goods, Pharmaceutical, Banking, Agriculture, Retail, Oil and Gas, Manufacturing and Food Processing. He currently leads the Global Go To Market with Alliances and Partners for HighRadius.

In times of uncertainty, companies may be tempted to curtail investments – and foregoing technology upgrades can seem like a prudent strategy. But for marketing teams, it’s essential to prioritize data-driven marketing investments now – helping them better understand their current and potential customers.

For many brands, 2023 has certainly started off on an uncertain footing. Inflation and higher energy bills have eroded consumer confidence – resulting in lower revenues and tighter margins for many organizations. In this environment, marketing executives are looking for flexibility in how they spend their budgets. They’re not necessarily slashing budgets, but rather are deferring spending to make sure they’re investing marketing dollars where they will have the greatest impact.

Determining that is only possible with insights derived from high-quality data. In an era defined by consumer demand for personalized engagements with companies, it’s more imperative than ever for organizations to invest in how they collect, manage, and use data about their consumer relationships.

At the same time, growing consumer awareness of privacy issues has placed increasing requirements upon enterprises to ensure their data-driven activities do not run afoul of regulations or betray customer trust.

Changing purchasing patterns present opportunities

There’s no question that consumers today are anxious. In the 2023 edition of its annual research series, What matters to today’s consumer, the Capgemini Research Institute noted that 61 percent of those questioned in late 2022 were “extremely concerned” about their personal financial situation. Many respondents were worried about the cost of feeding their families and buying other essential items.

The rise in their cost of living has prompted many consumers to change their purchasing patterns – with 69 percent of those asked saying they’re cutting back on non-essential items, 73 percent making fewer impulse purchases, and 65 percent switching to private label and other lower-cost brands.

Meanwhile, 58 percent of respondents reported spending more time searching online to find deals or discounts while 57 percent said when shopping, they now regularly visit multiple stores in a quest for the best pricing.

These are significant challenges for brands – but there are also opportunities. Capgemini’s report notes that as inflation bites into their buying power and disposable income, 78 percent of those surveyed would be more loyal to companies that help them through this difficult time. Meanwhile, 74 percent said they would remember those companies that help, and would buy more products and services from them in the future.

That indicates there is a clear upside for companies that are perceived to be helpful. Brands that assist customers with their current cost-of-living challenges can build loyalty and deliver higher customer lifetime value. But before they can offer that help, companies need to gain the best possible understanding of their current and potential customers.

The era of real-time marketing insights

Real-time insights enable brands to connect and engage with their audiences at the right moment with contextual and personalized experiences, while building relationships that last. A properly designed and implemented data-driven marketing strategy enables marketing teams to positively influence all stages of the customer journey – from awareness and consideration, to purchase and advocacy. What’s more, this approach can provide the insights needed to improve product innovation and development, pricing, and promotions.

In its September 2021 report, A new playbook for chief marketing officers: Why CMOs should enable real-time marketing to drive sustained growth, the Capgemini Research Institute outlined how the rapid growth of ecommerce has increased the need for real-time insights. These help companies effectively capitalize on fast-changing consumer behavior by enhancing digital-commerce campaigns across both their own online assets – such as websites, apps, and email campaigns – as well as via paid placements on platforms owned by others.

Done right, data-driven marketing outcomes include increased brand awareness, higher conversion rates, more customer satisfaction, and better customer retention. In short – a positive and memorable customer experience. Here are a couple of examples of what that might look like.

• A customer is shopping for food from a company with several brands. The company knows the customer has a peanut allergy, and so uses targeted communication to highlight products from across its portfolio that are made in nut-free facilities. It can also alert the customer if they’ve inadvertently added a product containing nuts to their shopping cart. The customer’s takeaway is: “This company cares about my health.”
• An apparel company knows a customer wears several of its brands – from high-end to discount. Based on past purchases, the company can develop a style and color profile for the customer. Whenever the customer visits one of its brand’s websites, it highlights new arrivals that complement what the customer already owns – including what they’ve purchased from the company’s other brands. The customer’s takeaway is: “This company helps by making it easy for me to look good.”

Success strategies

In Capgemini’s experience, organizations looking to better understand their customers should answer some key questions, including:

What is the opportunity? Identifying customer problems the company can solve is a great place to start.

What should the customer experience look like? Identifying the desired customer experience is important, so everyone in the organization understands the goal.

How does the company know what the customers need or want? The research and data points should be clearly defined, so gaps can also be identified and addressed.

The Connected Marketing Engine

While some organizations have built their business around their own, proprietary marketing analytics platform, in-house data science and engineering teams are expensive and most companies cannot commit the human or financial resources they require.

To fill that need, Capgemini created its Connected Marketing offering. It includes solutions for brand management, content marketing, customer activation, marketing organization, and marketing technology to deliver real-time data-driven marketing insights into customer expectations. Capgemini enhanced this offering in 2022 with the launch of the Connected Marketing Engine. Built using Adobe technology – including Adobe Journey Orchestration, Adobe Journey Optimizer, Adobe Analytics, and Adobe Launch – this end-to-end portfolio of capabilities and services helps organizations master the complexities of marketing in the digital landscape. It’s flexible enough to make a difference for a single department or brand, and completely scalable so it can be deployed across the enterprise.

The Connected Marketing Engine empowers marketing teams to deliver real-time, seamless, contextual, omnichannel engagements with customers. These connections help build trust and loyalty even as they enable brands to improve the return on investment in their marketing campaigns. The payoff is that the relationships fostered during these lean times will be poised to flourish as inflation recedes and consumer confidence returns.

Join us at Adobe Summit for a live Connected Marketing Engine demo.

To learn more about the Connected Marketing Engine and how Capgemini’s Connected Marketing can transform your company’s customer-engagement experience, get in touch with Neerav by clicking on the email button below.