Digital transformation is built upon Business Use cases. Selecting and prioritizing them to drive real business impact which deliver a solve for all stakeholders and begin an era of a lasting legacy is a critical first step.
From the outset, it’s essential for business KPIs to lead IIoT transformations, and for each potential use case to be tested against the business value that it is trying to create. In some cases, the best solution may not be a technology play at all.
Design thinking IIOT inspired digital transformation does not sideline the excitement of advances in quantum computing, utilizing digital-twins versus legacy simulation with real-time – huge technology WOW factors. This future gravitas would render itself surmountable value if we included real customers, marketing and sales stakeholders in the product innovation and audience discovery sessions amplified by design thinking.
It is easy to get excited about the role a digital-twin can play and being at the advent of an impending 5G rollout. It is prudent for manufacturers to steer away from technology in mind and then try to build a business case around it, and this sets them up to fail. For marketing and sales to deliver the messaging and articulate each use cases to a potential customer, it’s essential for business KPIs to lead IIoT transformations, and for each potential use case to be tested against the business value that it is trying to create. In some cases, the best solution may not be a technology play at all.
Selecting Design thinking driven Use Cases with KPI driven business impact are critical before the handoff to Marketing and Sales
The following image, one of my personal favorite creations when I switch to my marketing outfit serves as a constant reminder and nudges to never forget the customer. After all, the audience segments which IIOT serves and the beneficiaries and recipients of the advances brought about by digital transformation we owe a responsible stewardship of the message delivered by the marketing and sales team to put a shine on the north star.
The COVID-19 pandemic has highlighted, steered and illuminated how the Industrial IoT (IIoT), or Industry 4.0, can enhance organizational resilience in a state of crisis. Digital management tools and connectivity, for example, have enabled organizations to react to market changes faster and more efficiently.
Industrial IoT, or the Industrial Internet of Things (IIoT), is a vital element. IIoT harnesses the power of smart machines and real-time analysis to make better use of the data that industrial machines have been churning out for years. The principal driver of IIoT is smart machines, for two reasons. The first is that smart machines capture and analyze data in real-time, which humans cannot. The second is that smart machines communicate their findings in a manner that is simple and fast, enabling faster and more accurate business decisions.
Specifically, the market has seen the convergence of information technology (IT) and operational technology (OT) due to advances and synergies between the respective areas. This has resulted in the Industrial Internet of Things (IIoT), which is a solution that collects and centralizes mass amounts of machine data gathered from industrial environments. Applications built on these IoT platforms collect, analyze, and enable you to quickly act on the data to fundamentally boost operational efficiency and production.
Thanks to continuous streams of real-time data, it’s now possible to create a digital twin of virtually any product or process, enabling manufacturers to detect physical issues sooner, predict outcomes more accurately, and build better products.
While its output is a physical object, manufacturing inevitably begins with data during the design phase. That data is communicated to machines that execute designs—the point of transition between the digital and physical worlds. Increasingly, additional data is captured during manufacturing and eventual use of the final product. This data, in turn, can be extremely valuable for informing future designs and modifications, creating a virtuous cycle of innovation and improvement.
Put all those pieces together, and it’s clear that a digital “thread” of data now flows continuously. Aggregated and integrated in real time, it can be used to stitch together the physical and digital worlds, creating a virtual replica of a product or process that can reveal significant new insight. This digital thread can enable the digital twin by providing the data it needs to function.
The digital twin of a complex product such as a jet engine or large mining truck, for example, can monitor and evaluate wear and tear as the equipment is used in the field, potentially leading to design changes over time and informing predictive maintenance. The digital twin of a process can replicate what is happening on the factory floor (Figure 1). Sensors distributed throughout can capture data along a wide array of dimensions, from behavioral characteristics of the production machinery to characteristics of works in progress (thickness, color qualities, hardness, torque, and so on) and environmental conditions within the factory itself. Analyzed over time, these incoming data streams can uncover performance trends, potentially triggering changes to some aspect of the manufacturing process in the physical world.
Technologies enabling digital twins include sensors that measure critical inputs from the physical process or product and its surroundings. Signals from these sensors may be augmented with process-based information from systems such as manufacturing execution systems, ERP systems, CAD models, and supply chain systems. Those data streams are then securely delivered for aggregation and ingestion into a modern data repository, followed by processing and preparation for analytics. Artificial intelligence and other techniques can be used for analysis; the resulting insights can then be fed back to the physical world through decoders and actuators for implementation via additive manufacturing, robotics, or other tools.
A Real-World Example
An industrial manufacturer was facing numerous quality issues in the field, resulting in costly maintenance and high warranty liability. To address these problems, its engineering and supply network organizations pursued a digital twin approach. First, they combined the as-designed bill of materials (BOM) with all the analogous information produced during manufacturing (also known as the as-manufactured BOM), including procured parts details and assembly details. That step allowed them to run analytics and glean insights into production variations affecting quality. As a result, the team was able to improve the assembly process, reducing rework by 15 to 20 percent.
The manufacturer’s after-sales department is now planning to apply the digital twin process to information from products in the field (the as-maintained BOM) as well to better understand how process variation in field maintenance affects performance and to identify further potential improvements. All in all, capturing a variety of live measurements from the as-designed, as-manufactured, and as-maintained BOMs amounts to a cradle-to-grave digital journey, creating opportunities for better asset availability management, spare parts inventory optimization, predictive maintenance, and services.
“As a result, the team was able to improve the assembly process, reducing rework by 15 to 20 percent.”
The IIoT has already gained traction within countless industries, including manufacturing, food and beverage, oil and gas, healthcare, automotive, and more. For machine builders, it is quickly becoming a business imperative. According to an IDG and Siemens IoT survey, 53 percent of companies have started an IoT initiative. To keep pace with leaders in the industry, you need to start acting now.
Yesterday, Google announced an updated timeline for its Privacy Sandbox milestones; in its blog post are two major announcements that marketers should focus on:
Google says it’s planning to develop a more rigorous process to test and deploy Privacy Sandbox proposals across various use cases, like ad measurement, targeting, and fraud detection. The goal is to deploy these by late 2022, help scale adoption, and only then start to deprecate third-party cookies. Under this plan, 3P cookies will be phased out over a three-month period in 2023.
Google is concluding the first (current) trial for Federated Learning of Cohorts (FLoC). It received feedback on the first implementation of FLoC and intends to incorporate that into future testing. (The FLoC test has faced some challenges, from its use by advertising technology vendors to build persistent profiles to its inability to be used by marketers in regulated industries.)
Google also indicated that these changes will allow for “…public discussion on the right solutions, continued engagement with regulators, and for publishers and the advertising industry to migrate their services.”
Marketers should not take this announcement as a signal to ease up on their preparations for a future without third-party cookies. Google continues to evolve its plans, and this likely won’t be the last time the company does this.
So, don’t let this distract you from the larger context of the moment: As an industry, we are transitioning away from opaque consumer data collection and usage and toward a choice-driven, transparent, and privacy-friendly future. Marketers must:
Continue to future-proof current targeting, digital media buying, and measurement strategies. Keep testing contextual advertising, first-party-based targeting, and cleanly sourced second-party audience segments using Forrester’s “The Future Of Audience Targeting” research as your guide.
Talk to your technology and services partners about how they are preparing for a data-deprecated future. Why do they believe their proposed approach(es) is sustainable, and what steps do they suggest you take on your own data deprecation journey?
Keep investing in your first- and zero-party data assets. Identify moments in the customer journey where you can collect behaviors, preferences, context, and intentions. Then, ensure that you’re optimizing opportunities to use that data with an eye toward delivering a valuable consumer experience.
Google said the delay would give it more time to get publishers, advertisers and regulators comfortable with the new technologies it is developing to enable targeted ads after cookies are phased out.
“While there’s considerable progress with this initiative, it’s become clear that more time is needed across the ecosystem to get this right,” Google said.
Google’s decision reflects the challenges tech giants face as they try to address demands for stronger user-privacy protections without rattling the $455 billion online-ad ecosystem or inviting complaints that they are giving themselves special advantages. Apple Inc. has rolled out several major privacy updates for its devices this year, including a requirement that all apps get users’ permission to track them. Google and Apple have each faced complaints from the ad industry that the changes they’re making will strengthen their own ad businesses.
Earlier this week, the European Union said it is investigating Google’s plan to remove cookies as part of a wide-ranging inquiry into allegations that Google has abused its prominent role in advertising technology.
Google has separately pledged to give the U.K.’s competition watchdog at least 60 days’ notice before removing cookies to review and potentially impose changes to its plan, as part of an offer to settle a similar investigation. That probe stemmed from complaints that Chrome’s removal of cookies would give an advantage to ads on Google’s own products, like YouTube or Search, where Google will still be able to do individual-level targeting.
In the U.S., Google’s cookie-replacement plan was raised in a December antitrust lawsuit against the company brought by Texas and nine other U.S. states.
Google plays a central role in the online advertising ecosystem. It owns the dominant tools used to broker the sale of ads across the web. Cookies, small bits of code stored in web browsers to track users across the web, are widely used in the industry, including in Google’s Chrome browser, which has 65% of the market globally, according to Statcounter.
Google’s delay was met with relief by advertisers and publishers, who will have more time to test and adapt to the technology that replaces cookies. Ellie Bamford, global head of media at RGA, a digital ad firm owned by Interpublic Group of Cos., said Google “underestimated the fear that marketers had about what this would mean and the level of preparedness marketers need to have.”
Paul Bannister, chief strategy officer at blog network CafeMedia, said that since the vast majority of digital advertising is powered by cookies, “it’s critical that the replacement technologies get things right. It’s also critical to make sure that even more money doesn’t go to the tech giants in the process.”
Google has been testing several new tools to replace various functions of third-party cookies, as part of what it calls a privacy sandbox. The first such replacement technology, dubbed federated learning of cohorts, or Floc, is intended to allow advertisers to target cohorts of users with similar interests, rather than individuals, in order to protect their privacy.
But early technical testing of Floc, which began in April, has been slow. Initially, Google indicated it would allow advertisers to purchase ads for Floc in the second quarter as part of Google’s tests. Google later shifted that time frame to the third quarter, ad executives said.
Ad-industry players have also expressed skepticism about Google’s claims that targeting ads with Floc is at least 95% as effective as cookie-based targeting. Google has “struggled to build confidence in Floc,” said Jayna Kothary, global chief technology officer at MRM, a marketing agency that is part of Interpublic Group of Cos. Most advertisers don’t believe Floc is 95% as effective as cookies and “the early experiments haven’t proven this yet,” she said.
Google engineer Michael Kleber said at a developer conference in mid-May that the company is working out answers to how Floc should eventually work.
“We don’t have that ready yet because we don’t know what the answers are,” Mr. Kleber said. He added that everything “about how Floc works is very much subject to change.” The acronym Floc was chosen to reflect a flock of birds, Mr. Kleber said.
Google said Thursday it has “received substantial feedback from the web community” during the initial testing of Floc.
The company also said it plans to complete testing of all of its new cookie-replacement technologies, and integrate them into Chrome before late 2022. Then digital publishers and the digital advertising industry will have a nine-month period to migrate their services, during which time Google will monitor adoption and feedback.
The final phaseout of cookies will happen over three months in late 2023, the company said, adding that it will publish a more detailed timeline.
Two rival web browsers that promote privacy, Mozilla’s Firefox and Brave, have said they aren’t supporting Floc. Some prominent websites have debated whether to opt out of using the system. And the Electronic Frontier Foundation, a digital rights group, says Floc could be misused to help with device fingerprinting, a technique to identify specific web browsers without relying on cookies. That could potentially reveal sensitive information gleaned from web browsing, despite safeguards Google says it’s building, the rights group says.
On Thursday, Google said it is making progress in its work on technologies to hinder device fingerprinting via Chrome, including by reducing how much technical information a Chrome browser provides to websites it visits.
A Google spokesman declined to comment further.
Brian Lesser, chief executive of InfoSum Ltd., a data services company, and former chief executive of AT&T Inc.’s digital-ad company Xandr, said Google’s “intentions are noble in the sense that they want to protect consumer data. Floc is one idea and I think it needs to exist within a range of different alternatives to cookies.”
Medallia Experience Cloud is a customer feedback management (CFM) solution that
consolidates real-time data into a single platform that can be customized and scaled for each unique business unit. To better understand the benefits, costs, and risk associated with Medallia Experience Cloud, Forrester Consulting conducted a Total Economic Impact™ (TEI) study based on interviews with six customers with experience using Medallia Experience Cloud as their customer feedback management platform. This summary is based on a full TEI study, which can be downloaded here.
Based on the TEI analysis, a representative organization deploying Medallia Experience Cloud has experienced a quick payback period with the following three-year financial impact: $35.6 million in benefits and $5.1 million in costs, a net present value (NPV) of $30.4 million and an ROI of 591%. Readers can use this representative organization to understand the economic impact of deploying Medallia Experience Cloud and apply or adapt it to their own situation and experience.
Quantified Benefits
The following risk-adjusted quantified benefits are representative of those experienced by the companies interviewed:
› Improved customer experience leading to an increase in net income of $20.1 million. Organizations were able to meet the needs of today’s customers by making product and channel improvements informed by real-time customer feedback data from the Medallia Experience Cloud. These improvements drove growth through an increase in both customer retention and average basket size.
› Operational efficiencies resulting in a savings of $13.8 million. Organizations were able to improve organizational operations by aligning business and strategic initiatives with the insights gained from the Medallia Experience Cloud. Additionally, call volume to service desks was significantly reduced due to its enabling of organizations to systematically identify and reduce customer pain points.
› Cost avoidance of the previous solution. Organizations avoided the cost of running and maintaining legacy solutions by moving to Medallia’s cloud-based platform.
Unquantified Benefits
Examples of additional benefits that the interviewed organizations mentioned as significant but were not quantified for this study:
› Faster closed loop cycle. Organizations were able to more quickly close the loop with customers through preferred channels and recognized a positive impact on both customer churn and employee morale. › A shift in overall organizational culture towards CX. Employee access to real-time customer feedback information brings the customer experience to life and helps keep the entire organization focused on meeting customer needs and expectations
Key Investment Drivers And Results
S. Ernest Paul
Organizations shared the following challenges prior to Medallia Experience Cloud:
› Inability to provide meaningful CX insights. Surveys and other solutions provide data points but do not facilitate the insights or analytics necessary to understand and meet the needs of today’s customers.
› Inability to effectively handle organizational scale. Legacy vendors and solutions could not reliably process the necessary volume of data quickly or effectively, preventing organizations from moving quickly to reduce pain points and incorporating customer feedback into strategic initiatives.
Organizations achieved key results with Medallia Experience Cloud:
S. Ernest Paul
› Develop actionable insights to improve CX and drive product improvements. A VP of customer insights in the telecommunications industry stated: “We were able to reduce the number of calls to our service desk, because we’ve addressed and eliminated many of the reasons, the root causes, of why people were calling us: technical reliability, product functionality, billing issues. This has categorically changed the game for us.”
› Effective scaling and flexibility. Medallia’s experience with global deployments and large enterprises made the implementation and scaling an easy, collaborative process.
› Drive a shift in organizational culture. The ability for a single platform to collect all CFM data, its accessibility to anyone in the organization, and the direct connection to actions for closed loop feedback drives a shift in organizational culture towards meeting and exceeding customer expectations.
Composite Organization
Based on the interviews, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected, covered in greater detail in the full study. The composite organization has the following characteristics:
Description of composite. The composite is a global conglomerate with $9 billion in annual revenue, growing at a rate of 2% year-over-year (YOY), prior to its investment in Medallia. Its main revenue streams include membership fees and 750 retail stores, along with other B2B and B2C lines of business (LOBs). It has 2 million total customers and 18,000 employees, which includes 750 key accounts with 150 account managers, along with 4,500 contact center agents.
Deployment characteristics. The global conglomerate composite organization has deployed the Medallia Experience Cloud for transactional and relationship surveys, both internally-facing (employees) and externally-facing (customers). The composite organization initially rolled out Medallia’s Best Practices Package for Retail to its 750 stores. For Years 2 and 3, the program expanded to cover the full enterprise, including the 4,500 contact center agents and 150 key accounts.
Economic Impact
› Increased income due to improved customer experience. Using customer feedback obtained through the Medallia platform, organizations were able to make product improvements leading to additional sales, and website improvements to remove pain points and barriers to purchases. Organizations were able to improve overall NPS, realized an increase in customer retention rate as well as increased average basket size per customer leading to an increase in income of $20.1 million.
› Operational efficiencies represent $13.8 million in savings. Unification of data across the organization and throughout the customer lifecycle allowed organizations to better define strategic initiatives, better focus resources on those initiatives and reduce overall service desk tickets by analyzing customer feedback to reduce pain points.
› Cost avoidance of previous solution. Organizations noted a total of $1.7 million in cost savings related to licensing and management of previous CFM solution.
Unquantified benefits. These are some of the benefits not quantified in the financial analysis but were mentioned as significant.
› Faster closed loop cycle. A senior director of customer insights in the retail industry told Forrester: “Medallia’s closed loop feedback mechanism has proven to be effective, along with the simplicity of the dashboards.”
› A shift in organizational culture towards CX. Exposing employees to real-time customer feedback promotes a deeper understanding of customer needs and expectations, and enables organizations to introduce solutions that have a meaningful impact on Customer Experience.
Medallia does not come cheap
S. Ernest Paul
Costs
The composite organization experienced two cost categories associated with the Medallia Experience Cloud investment. Over three years, the composite organization expects risk adjusted total costs to have a PV of $5.1 million.
› Medallia Costs of $2.6 million. The organization paid Medallia for an annual software subscription, ongoing managed services, and implementation services for initial and Year 1 costs.
› Internal costs of $2.6 million. These include day-to-day management, implementation costs, and training associated with the new platform
The costs could go up id the add-ons are utilized
The value of flexibility is clearly unique to each customer, and the measure of its value varies from organization to organization. There are multiple scenarios in which a customer might choose to implement Medallia Experience Cloud and later realize additional uses and business opportunities, for example:
› Ability to adapt the CFM solution to fit evolving needs. Medallia is willing to work with clients to find solutions or benchmarks to unique business challenges.
› A/B testing. The Medallia Experience Cloud allows customers to quickly A/B test customer experience solutions or pilot business process changes before rollout.
› Text analytics search. Changes in customer sentiment can occur quickly, and the text analytics feature allows organizations to identify and adapt to unexpected developments.
Financial Summary
Flexibility, as defined by TEI, represents an investment in additional capacity or capability that could be turned into business benefit for a future additional investment. This provides an organization with the “right” or the ability to engage in future initiatives but not the obligation to do so.
SUMMMARY
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment in Medallia Experience Cloud. Forrester assumes a yearly discount rate of 10% for this analysis.
The role of the product manager is expanding due to the growing importance of data in decision making, an increased customer and design focus, and the evolution of software-development methodologies.
Product managers are the glue that bind the many functions that touch a product—engineering, design, customer success, sales, marketing, operations, finance, legal, and more. They not only own the decisions about what gets built but also influence every aspect of how it gets built and launched.
Unlike product managers of the past, who were primarily focused on execution and were measured by the on-time delivery of engineering projects, the product manager of today is increasingly the mini-CEO of the product. They wear many hats, using a broad knowledge base to make trade-off decisions, and bring together cross-functional teams, ensuring alignment between diverse functions. What’s more, product management is emerging as the new training ground for future tech CEOs.
As more companies outside of the technology sector set out to build software capabilities for success in the digital era, it’s critical that they get the product-management role right.1
Why you need a product manager who thinks and acts like a CEO
The emergence of the mini-CEO product manager is driven by a number of changes in technology, development methodologies, and the ways in which consumers make purchases. Together, they make a strong case for a well-rounded product manager who is more externally oriented and spends less time overseeing day-to-day engineering execution, while still commanding the respect of engineering.
Data dominates everything
Companies today have treasure troves of internal and external data and use these to make every product decision. It is natural for product managers—who are closest to the data—to take on a broader role. Product success can also be clearly measured across a broader set of metrics (engagement, retention, conversion, and so on) at a more granular level, and product managers can be given widespread influence to affect those metrics.
Products are built differently
Product managers now function on two speeds: they plan the daily or weekly feature releases, as well as the product road map for the next six to 24 months. Product managers spend much less time writing long requirements up front; instead, they must work closely with different teams to gather feedback and iterate frequently.
Products and their ecosystems are becoming more complex
While software-as-a-service products are becoming simpler for customers, with modular features rather than a single monolithic release, they are increasingly complex for product managers. Managers must now oversee multiple bundles, pricing tiers, dynamic pricing, up-sell paths, and pricing strategy. Life cycles are also becoming more complex, with expectations of new features, frequent improvements, and upgrades after purchase. At the same time, the value of the surrounding ecosystem is growing: modern products are increasingly just one element in an ecosystem of related services and businesses. This has led to a shift in responsibilities from business development and marketing to product managers. New responsibilities for product managers include overseeing the application programming interface (API) as a product, identifying and owning key partnerships, managing the developer ecosystem, and more.
Changes in the ‘execution pod’
In addition to developers and testers, product-development teams include operations, analytics, design, and product marketers that work closely together in “execution pods” to increase the speed and quality of software development. In many software organizations, the DevOps model is removing organizational silos and enabling product managers to gain broader cross-functional insights and arrive at robust product solutions more effectively.
Consumerization of IT and the elevated role of design
As seamless, user-friendly consumer software permeates our lives, business users increasingly expect a better experience for enterprise software. The modern product manager needs to know the customer intimately. This means being obsessed with usage metrics andbuilding customer empathy through online channels, one-on-one interviews, and shadowing exercises to observe, listen, and learn how people actually use and experience products.
Three archetypes of the mini-CEO product manager
There are three common profiles of the mini-CEO archetype: technologists, generalists, and business-oriented. These three profiles represent the primary, but not the only, focus of the mini-CEO product manager; like any CEO, they work across multiple areas (for instance, a technologist product manager will be expected to be on top of key business metrics). Most technology companies today have a mix of technologists and generalists (Exhibit 1).
Exhibit 1
As these three archetypes emerge, the project manager is a fading archetype and seen mainly at legacy product companies. The day-to-day engineering execution role is now typically owned by an engineering manager, program manager, or scrum master. This enables greater leverage, with one product manager to eight to 12 engineers, versus the ratio of one product manager to four or five engineers that has been common in the past.
Common themes across the three archetypes
An intense focus on the customer is prominent among all product managers. For example, product managers at Amazon are tasked with writing press releases from the customer’s perspective to crystalize what they believe customers will think about a product, even before the product is developed. This press release then serves as the approval mechanism for the product itself.
There are, however, differences in how product managers connect with the users. While a technologist may spend time at industry conferences talking to other developers or reading Hacker News, the generalist will typically spend that time interviewing customers, talking to the sales team, or reviewing usage metrics.
A new training ground for CEOs
Modern product managers are increasingly filling the new CEO pipeline for tech companies. Before becoming the CEOs of Google, Microsoft, and Yahoo, Sundar Pichai, Satya Nadella, and Marissa Mayer were product managers, and they learned how to influence and lead teams by shepherding products from planning to development to launch and beyond. Such experience is also valuable beyond tech: PepsiCo CEO Indra Nooyi started her career in product management–like roles at Johnson & Johnson and Mettur Beardsell, a textile firm.
While today such a background remains rare among CEOs, product-management rotational programs are the new leadership-development programs for many technology companies (for example, see the Facebook Rotational Product Manager Program, the Google Associate Product Manager Program, and the Dropbox Rotation Program). Any critic of the analogy between product managers and CEOs will point out that product managers lack direct profit-and-loss responsibilities and armies of direct reports, so it is critical for product managers with ambitions for the C-suite to move into general management to broaden their experience.
The product manager of the future
Over the next three to five years, we see the product-management role continuing to evolve toward a deeper focus on data (without losing empathy for users) and a greater influence on non-product decisions.
Product managers of the future will be analytics gurus and less reliant on analysts for basic questions. They will be able to quickly spin up a Hadoop cluster on Amazon Web Services, pull usage data, analyze them, and draw insights. They will be adept at applying machine-learning concepts and tools that are specifically designed to augment the product manager’s decision making.
We anticipate that most modern product managers will spend at least 30 percent of their time on external activities like engaging with customers and the partner ecosystem. Such engagement will not be limited to consumer products—as the consumerization of IT continues, B2B product managers will directly connect with end users rather than extracting feedback through multiple layers of sales and intermediaries.
