Turning consumer and retail companies into software-driven innovators

 

This article was written by Aman Dhingra, Chandra Gnanasambandam, Rahul Mangla, Hannah Mayer, and Roger Roberts. The original article was published by McKinsey & Company. You can find the article here.

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Software is disrupting and transforming every industry, and the impact is particularly pronounced in consumer-facing organizations. With the rise of the direct-to-consumer model, revenue increasingly comes from online rather than traditional channels. More than 500 million people interact with the Nike brand across its apps.1 The Starbucks app is the second-most-popular mobile payment platform in the United States for point-of-sale transactions, trailing only Apple.2 As digital experiences carry the weight of revenue, consumer-facing organizations have to make effective digital investments.

While technology has already revolutionized this sector, not least with the advent and mass adoption of e-commerce, the next wave of transformation is imminent. Customers increasingly expect experiences powered by software and on par with those offered or enabled by the most successful software and tech players. Building a shopping app, for instance, no longer suffices; the experience needs to be as engaging and seamless as it would be if app delivery were the organization’s core competency.

Investing wisely in software across the entire value chain, from initial customer interactions to internal corporate functions, can help consumer packaged goods (CPG) and retail companies meet these rising expectations. And that investment can pay off in the long run. With technology increasingly a competitive differentiator, companies that make software a core part of their organization and harness emerging technologies—such as AI (including generative AI), mixed reality, and robotics—can lay a strong foundation for sustainable growth.

Many retail and consumer players recognize this reality and have already made decisive software and technology investments. For example, Starbucks developed Deep Brew, a tool to leverage AI for various applications. Lego partnered with Epic Games to create a metaverse for kids to connect, playing between digital and physical worlds seamlessly. And L’Oréal invested in Digital Village—a virtual world-building platform and nonfungible token (NFT) marketplace—to bet on opportunities within the metaverse and Web3 for virtual store creation.

Our research shows that consumer and retail companies investing heavily in software outperform their peers: digital leaders have created outsize value to shareholders—three times the returns over the past five years, compared with nondigital leaders.3 Our analysis of more than 120 public consumer and retail companies also reveals that those with a mature technology operating model outperform those that operate more traditionally. Markers of strong DevOps and developer tooling, modern engineering practices, best-in-class product development life cycles, and structural and strategic alignment toward products are directly tied to strong business results: organizations with high maturity across these dimensions boast, on average, 2.2 times greater return to shareholders, as well as 40 to 45 percent higher customer engagement and brand awareness, compared with those that have little or no technology operating culture.

In addition, the advent of generative AI, which helps to accelerate, automate, and augment human tasks, can potentially drive the transition of traditional consumer companies into software entities. Consumer and retail organizations are anchored on many functions where generative AI’s impact is projected to be felt most heavily, including marketing, sales, and customer operations. As a result, the annual productivity impact of generative AI on the sector is projected to be $400 billion to $660 billion, among the highest of all industries. This expectation only raises the already-high stakes of staying ahead of the technology curve for consumer and retail players.

But what exactly does it take to keep up and make that level of technology innovation part of a consumer or retail organization’s DNA? According to our research and experience, six principles are critical for consumer and retail organizations to leverage tech effectively and perform more like software companies. Those principles align broadly with cross-sector trends examined in McKinsey’s recent software transformation research.

Build a software- (and customer-) centric culture

For most legacy companies, cultural change is the biggest challenge in digital transformations. Not only should organizations clearly articulate software’s role in their existing culture, but they also need to ensure that the culture drives customer-centric innovation and fosters a “software mindset.”

Not only should organizations articulate software’s role in their culture but also ensure the culture drives customer-centric innovation and fosters a “software mindset.”

Embedding software into organizational culture requires, first and foremost, that companies outline a clear vision for their software business. That means explaining how the value proposition and strategy will impact customer experience, growth, and talent—and communicating this perspective consistently across internal and external forums. According to McKinsey’s 2022 Voice of Consumer Organizations Survey, managers at high-performing consumer companies are 1.6 times more likely to say their digital agenda is integrated into business units rather than siloed in an IT organization.

The right culture is also essential for driving customer-centric innovation. To build such a culture, companies can work to emulate the operating model of leading technology players that use small, cross-functional teams, or pods, to address specific customer needs or journeys. In this model, pods can include employees from software development, agile coaching, data science, product management, technical program management, and user design/research. The teams are typically empowered to own a customer problem space end to end, set their own objectives and key results, and determine their own product road maps and backlogs. They are actively encouraged to base their decisions on customer data, leveraging technologies such as AI and machine learning to predict customers’ needs and deliver value.

That approach dramatically alters the innovation process. The user design/research and product management functions are so deeply embedded in the pods that they help shape the mission statement and the problem’s definition from the get-go, rather than being used only for fine-tuning after overarching development decisions have been made. From our work with retail and CPG companies, we have seen this type of software-centric culture boost customer satisfaction rates by as much as 40 percentage points. Technology-inspired operating models also help push organizations’ performance closer to that of software players on other metrics. For instance, in one major North American retail brand, we saw this shift lead to a 60 percent improvement in time to market, from idea inception to software delivery, for such offerings as new app features.

Companies looking to make more significant changes should consider investing in a developer-centric culture that empowers software engineers to be a greater focus of innovation. To do so, organizations can create a culture of experimentation, learning, and safe failure that encourages developers to make more innovative, risk-taking decisions. This approach is a significant differentiator, as only 20 percent of executives from large enterprises believe their organization has successfully established a culture of safety for developers.4

Drive product management consumer expertise

Investing in product management (PM) capabilities is more critical than ever. Product managers help drive tech development by setting the strategy, road map, and feature definitions while serving as a liaison among consumers, business, data/engineering, and design teams. But in a recent survey, 80 percent of more than 300 consumer product managers said that their organizations’ PM functions were subpar or nonexistent.5 For the sector to fulfill its ambition of becoming true software innovators, that reality has to change.

Finding and developing PM talent are both challenging tasks. Technical skills are mere table stakes for a role as complex and multifaceted as product management. CPG and retail industry product managers need to deeply understand ever-changing consumer habits and preferences, as much of their job is to optimize the digital touchpoint and make the transitions among multiple channels seamless for customers.

Such consumer insight is just part of the overarching talent that sets successful product leaders apart—the ability to deliver enhanced customer experiences creatively. Product managers have a vast canvas of modern tools and platforms to help serve customers and consumers in new, innovative ways. This capability is particularly important in the consumer retail environment, where offering product experiences that deliver value as well as establish and exceed high user expectations is a true differentiator.

Companies looking to invest in empowered product managers should consider three actions:

  • Invest in generative AI tools to automate time-consuming tasks, such as compiling notes or updating documentation, and enable more data-driven decision making.6 This action is crucial because day-to-day responsibilities often limit product managers’ time to understand the market, competition, and consumer needs sufficiently.
  • Develop a culture that relies on continuous testing to improve the product and customer experience. Active testing can be especially valuable for allowing product managers to refine ideas about products and features iteratively.
  • Continuously upskill product managers on product acumen, and deepen their understanding of frequently changing consumer preferences.

Upgrade platform architecture and data management

Next-generation technology innovation requires a robust underlying tech architecture; however, most companies struggle with legacy systems and usually have more than 20 percent of tech assets in the “tech debt” category. Moreover, around 60 percent of CIOs have seen tech debt rise in recent years.7 At the same time, not all tech debt is bad: modernizing applications adds value, but only up to a point. Consumer companies should prioritize what, how, and when to migrate to a new solution.

A strong, platform-based approach8 enables better access to underlying services and creates opportunities for innovation in tech capabilities. A tech stack where all platforms talk to one another is critical. For example, platforms to automate marketing efforts should be able to pull from customer and product databases to personalize messaging and showcase the most attractive items. Failing to integrate these components into a unified tech stack will hinder the potential for technology-driven business improvement.

While certain companies take the more challenging route of leapfrogging to an entirely new tech stack that merges all existing customer and product data, more consumer-facing organizations are likely to take incremental steps. For instance, new business logic can be built out iteratively as modular microservices—self-contained units of code that execute specific functions with limited dependencies—effectively replace the legacy stack. Prioritizing platform upgrades according to the value at stake is important: drawing on our experience in the consumer space, we estimate that 20 to 50 percent of platforms may drive up to 80 percent of the value.

Companies eager to upgrade their platform architecture can gradually switch to a cloud-based approach. Those organizations can then leverage reliable data services and flexibility to adjust capacity while modernizing financial records and other important legacy systems. For instance, one leading European fashion retailer built an automated, cloud-based sandbox environment that allows fast access to data, along with flexible usage of analytics and isolated AI testing environments. Anheuser-Busch InBev used cloud infrastructure to create digital twins of its breweries—digital models of physical assets that identify operational inefficiencies in real time.9

Companies eager to upgrade their platform architecture can gradually switch to a cloud-based approach.

When upgrading platform architecture, companies can also optimize their systems to power generative AI technologies. In this context, it is critical to choose a suitable model, set up cloud and data architecture, use MLOps to reduce risk and continuously improve the model in production, and run “Live Ops” to monitor model performance and manage risk.

Another way to advance the engineering landscape is to manage data as a product. This approach helps to deliver high-quality, ready-to-use data sets that people across an organization can easily access and apply to various tasks, such as keeping up with changing customer buying patterns and trends. Traditional approaches, such as grassroots (managing data across the organization on a team-by-team basis) and big bang (managing data en masse in a centralized team), are highly complex and inefficient. In contrast, managing data as a product creates sustainable value by increasing reusability, interoperability, and speed of new application implementation. For example, customer and product data managed as two data products can be connected to drive hyperpersonalization; product and store data can be linked for assortment optimization.

Once an organization manages its data this way, it can more effectively unlock value from it. For instance, building innovative, data-centric applications is one promising vehicle for leveraging existing data in more meaningful ways than simply modernizing existing application features. Generative AI and foundation models now open many new opportunities for data-centric applications by automating content generation based on data. These models essentially supersede much more of the value chain than traditional, discriminative AI models that are used to predict labels or classifications. For example, companies can leverage marketing data and generative AI to automatically create and deliver hyperpersonalized messages with virtually no incremental costs.

 

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