Prioritizing flexibility to generate value from new technology —now and in the future
A wide range of technologies in fields such as sensors and data gathering, automation, and artificial intelligence (AI) have rapidly evolved in recent years, with the potential to transform the retail industry. One obvious trend has emerged: merchants who are able to adapt their businesses to extract value from new technologies across are more likely to be successful. This capacity to see and act on possibilities sooner than the competition has turned into a significant strategic advantage.
As a result, grocery stores have been on the lookout for technology that will enable them to outperform their competition. Given the rapid rate of change in grocery retail, yesterday's technological techniques are no longer sufficient. Online players, and inventive incumbent, are always on the move, so by the time corporations catch up, they'll be out of business.
With e-commerce now accounting for a significant portion of their revenue, retailers are attempting to create a true omnichannel offering and corresponding software platform. Simultaneously, merchants are increasingly using mostly automated, advanced systems to improve price, promotion, assortment, and sourcing on the commercial side. Retailers should follow five principles to lead their changes and attain the flexibility needed to succeed in order to realize the value potential.
Five guidelines for utilizing new technologies
1. Create a product-driven organization: Executives must focus on “products” (such as customer service or warehouse management) rather than applications when forming organizations. Leading grocery retailers identify business domains and product teams for each domain, which could have multiple products. They should be defined to the point that a small team of engineers can take ownership of the development process from start to finish. This cross-functional team, which includes both business and technical talent, is in charge of improving the product's overall vision, functionality, and performance. To capitalize on new market dynamics, goods and their teams might be increased, downsized, or mutated into new opportunities.
While most leading companies are developing product teams with in-house digital skills, retailers who use an outsourced or partner model are doing the same. Smaller companies, for example, may not be able to put together a full-fledged in-house team with expertise in all technology fields. Even so, product organization logic would apply; technology partners should mimic a retailer's internal product logic. Regardless of the model adopted, success will always necessitate some level of internal technical quality control.
2. Develop a true tech workforce: Cutting-edge grocers make sure they have the proper staff and provide continuing training opportunities to fill skills shortages to deal with underlying uncertainties and learn new technology. When it comes to strategic digital systems that are built and deployed in close collaboration with business and functional partners utilizing agile methodologies, creating internal capabilities is likely to be less efficient but more successful over time. The top companies build a strong internal engineering team that is supplemented by flexible access to external talent in areas where increased productivity or the provision of rare talents is vital.
Executive capabilities, in general, will require an upgrade. Skill development and lifelong learning on critical technology topics, such as developing an enterprise resource planning modernization center or a cloud-migration team, should not be limited to operational-tech. With technology increasingly serving as a key facilitator of the entire business model, executives at all levels of the organization should have a greater awareness of how technology works, what it can do for them, and how to avoid risks while pursuing value.
3. Build architecture modules based on the company's strategy: The dismantling of monolithic legacy systems into smaller, self-contained services that may be upgraded and deployed independently of the rest of the architecture is a good strategy. At the same time, without relying on fragile point-to-point integrations, these services should be able to provide data to other applications as needed. Breaking down architecture into modular components speeds up development and ensures a consistent customer experience across different consumer touch points (for example, in-store points of sale, web shops, and mobile apps).
Consider omnichannel basket calculation: this activity is often a pain point for grocers, but when it's modularized, it can be used across all channels, allowing for new features like coupons, targeted promos, and other types of loyalty programs.
4. Reduce time to market: Consumer behavior is changing at a faster rate than ever before. Shorter software release cycles enable merchants to better meet changing customer expectations by allowing them to experiment, quickly test new features, and scale up what works well. Instead of releasing large amounts of code a few times a year, which are difficult to alter or adapt, businesses must develop the capabilities to provide new releases faster. In customer-facing systems, this cycle might be as often as multiple times per day, with monthly releases in back-end systems to incorporate user feedback. Only if contemporary technological processes, such as automated code deployment and robust test automation, are in place, can this accelerated release speed be achieved.
5. All improvements should be based on data: Data is more plentiful and flowing faster than ever before, as well as across more boundaries. Monitoring structured transactional and supply-chain data is therefore no longer sufficient. Instead, supermarkets must record millions of everyday occurrences and interactions in order to analyze them and improve customer satisfaction and productivity. In this arena, new technologies are allowing for unprecedented flexibility in aggregating, processing, and analyzing data.
Companies can keep a close eye on changing consumer demand with this capability. Retailers must supplement experience with data-driven decision-making tools, which are frequently based on machine learning. Another genuinely strategic competence is tailoring these technologies to the business's most critical value drivers.
Data can also help you make better judgments about how to spend your IT money. Wherever possible, analyzing direct user feedback can help identify areas for improvement and drive future prioritization selections. Investing in crucial tools like A/B testing and performance monitoring allows businesses to better access and act on user feedback at scale.