The speed of production, marketing and activation, as well as the evolution of alternate channels and unpredictable consumer buying patterns, have presented a more challenging environment to interpret than ever before. CPG companies and retailers now have to balance unprecedented large data streams with the need to make decisions faster than ever, and increasingly fluid consumer buying trends make speed-to-insights imperative.
Traditional models don’t keep up, but artificial intelligence and machine learning can contribute greatly to the speed at which CPGs and retailers can identify and act upon trends. They can take advantage of high computation frameworks, advanced storage and the power of machine learning.
These technologies can analyze multidimensional historical data to identify trends and anomalies for products and attributes, and interpret their impact on growth and market share. Further, an entire company’s portfolio could be analyzed to identify risk factors to the entire business, or intelligence could be applied to a retailer’s total store across categories and pricing in order to identify misses and potential opportunities against other retailers. Speed and scalability allow for more impactful business strategies, and smart decisions are made and powered by deep analytics, benchmarking and solution automation.
Artificial intelligence possesses the power to finally put accurate demand forecasting within reach for CPGs and retailers. Demand forecasting plays an integral role in operations, finance and sales, however, the demand for high-quality forecasts often outstrips the pace at which they are produced. Fully automated forecasting at scale can select optimal models and provide forecasts from thousands of machine-generated models.
With the use of automation and learning algorithms, the need for human discretion is eliminated and thus so is human error. Processes — and actionable insights — are faster, and the complexity is minimized.
Pricing, Assortment Planning, Marketing
By examining pricing trends, deep learning algorithms can establish limitations and automatically set the right boundaries for pricing. This also allows for an easier understanding of growth opportunities and the ability to have good price intelligence to monitor weekly price changes and performance assessment at a granular level.
By using AI technology to study pricing and react to changes in the marketplace, pricing becomes more predictive than prescriptive, and much of the tedium of the process is eliminated. The realistic price combinations allow for CPGs and retailers to have a real and accurate assessment, saving time, energy, resources and driving growth and profit — minimizing complexity and maximizing results.
Similarly, artificial intelligence can help pinpoint the right assortment, helping retailers best utilize the highly valuable space within their walls. The next generation of assortment solutions is able to leverage machine learning algorithms to create prescriptive insights. This traverses all retailers, items and key product attributes to discover the opportunities that produce the most growth, by store.
Another way that machine learning contributes to this new wave of prescriptive insights is to identify the groups of attributes that consumers naturally consider as comparable or unique by discovering demand-based patterns. This shopper-centric approach means retailers will better connect with their specific markets.
In order to compete in an increasingly high-tech space, assortment planning must be more scalable and focused on the future. Artificial intelligence has changed — and will continue to change — the way retailers approach pricing, assortment and marketing. Speed to insights and action, scalability and managing complexity are daily challenges, all of which are alleviated with state-of-the-art technology that will take this industry and so many others into the future with great opportunities and growth.