A 3-Step Guide for Brands Navigating Today’s Retail Disruption
Small and mid-sized companies can leverage AI to be more nimble, proactively embrace change to avoid being eclipsed by the competition, and remain as relevant as large companies.
As the state of retail openings and potential shutdowns shift by the day, we’re currently witnessing an undercurrent of uncertainty for companies in the consumer goods market as we prepare to pivot towards the unknown landscape of a “new normal.”
With the industry undergoing an unprecedented wave of change in consumer behaviors, we’re seeing that companies can no longer merely respond to their customers’ needs. Rather, they must find ways to adapt quickly, proactively anticipate needs, adapt quickly to remain competitive and accelerate growth. Companies must embrace and adopt strategies, technologies and best practices in order to not only accept but champion a great reopening.
There’s an immense opportunity to come out in a better place with the great reopening, but leaders must understand the possibilities and act now.
Step 1: Embrace Change
Change has remained the sole constant this past year as consumers traded brand loyalty for availability, buying patterns shifted towards digital-first purchasing, product availability became misaligned with demand, and shipping patterns were disrupted across the globe. Therefore, the question is: How can consumer goods companies learn to embrace, rather than fear, change to overcome persistent challenges?
The answer lies in becoming data-driven to better understand demand forecasting, supply chain trends and customer behaviors. We know that data-driven companies are able to lean into a strong process and methodology around how data is captured, stored, archived and activated. Without a strong data foundation, companies are merely able to continue with their set routines and systems.
By investing in developing a data-first mentality, consumer goods companies are in a better position to recognize and adapt to trends in a meaningful way, using data instead of instinct. They can see trends evolve over time and develop a better sense of what is happening in their business.
But being data-driven is only part of the story. What we’re seeing now is companies that have become data-driven still need more.
Step 2: Become Nimble
Being data driven means that companies now have a wealth of data to rely on. But it still leaves them in a reactive position. Vast troves of historical data pose a challenge, where data analysts must sift through vast amounts of this information, running reports that don’t always connect the dots on trends. The process is time consuming and introduces delays in decision making. In some cases it’s not feasible to spot trends.
The reopening poses a challenge for companies to exercise their agility and go beyond even real-time to develop proactive strategies. More so than ever, companies must rely on their ability to act ahead of trends. Therefore, consumer goods companies should seek to remain nimble in their ability to consume and respond to trends.
This is where AI comes in. AI and machine learning are powerful technologies that help decision-makers see beyond the obvious. They quickly process vast amounts of data, faster than humanly possible, while surfacing trends and patterns to make recommendations. Through our experiences with developing and implementing AI systems, we’ve seen that, once built, AI models can help decision-makers uncover trends as they happen and even proactively predict outcomes.
Whether through using owned or third-party data, AI can help brands make decisions faster and more accurately than traditional data analysis methods. This ability to anticipate vastly improves a company’s agility, ultimately leading to improved productivity and growth. According to Boston Consulting Group, consumer goods companies that adopt AI can experience over 10% growth in annual revenue.
Global watch manufacturer Seculus was seeking a solution to avoid revenue loss from lack of supply, high overstock and liquidation pricing. Traditionally, they used historical data to place orders nine months in advance. With this method, they had difficulty predicting which designs would sell more than others, so they maintained a vast catalogue of products, some of which would sell better than others. Therefore, they often faced lost revenue due to lack of supply on more popular watches, had high overstock and liquidation pricing.
By implementing an AI platform, the company was able to predict which models would sell with greater accuracy, therefore, significantly reducing overstock and liquidation pricing while increasing operational efficiency.
Step 3: Build for Scale
Success stories around AI adoption typically come from well-established brands with highly developed teams of data scientists. While inspiring, these cases are difficult to relate to if you’re at a mid-market company without data science or data engineer resources to design, build and implement AI technology.
The fact is, doing so takes time, budget and is simply out of reach for many organizations. However, innovative technology solutions, namely enterprise AI software, are coming to market and putting implementing AI within reach for these companies.
Seculus exemplifies how mid-market companies can reap the full benefits of AI enterprise software. Within a few weeks, they were able to build their AI models with over 85% accuracy, using a data team that included only one junior data scientist and a CTO. Evidently, you don’t need headline-worthy investments, large teams, or even coding knowledge to leverage AI for noticeable business improvements.
Enterprise AI software seeks to democratize the use of AI for businesses that don’t possess data science expertise, previous AI knowledge, or vast budgets to build such teams. AI software has been designed with years of data science expertise, but it’s now available to companies without the need for coding or previous AI knowledge. All you need is a data set and directional insights that you want to capture. It’s an affordable entry point that gives quick results, sometimes in as little as a few months.
Small and mid-sized companies can leverage AI to be more nimble, proactively embrace change to avoid being eclipsed by the competition, and remain as relevant as large companies. As a consumer goods company, you have the ability to take this unprecedented industry change and leverage it into an opportunity to innovate and exceed previous standards during this great reopening.
Heather Black is senior marketing director at Stradigi AI.
We checked in with Guy Yehiav, general manager of Zebra Analytics and a member of the CGT/RIS Executive Council, to get his perspective on the promise of AI in retail and consumer goods, as well as some of the ways companies are leveraging prescriptive analytics right now.