Today’s retailers are struggling to cope with a variety of hardships including inflation, preparing for a recession, and learning to strategically manage their inventory.

Despite these challenges, the sector is growing at levels unseen for over two decades. In 2020, retail sales increased by 7%, and in 2021, they grew by 14%. According to the National Retail Federation (NRF), 2022 has been and continues to be one of the most impactful years in the retail industry.

Even in the face of adversity, companies like Walmart, Costco, and The Home Depot  have managed to maximize profitability and reach their target margins, leaving smaller retailers to wonder how they are succeeding in such a challenging market.

One reason these retailers are finding success can be attributed to their robust and optimized Advanced Analytics strategies. Specifically, their use of Advanced Analytics as a method to predict product and seasonal sales has enabled many retailers to spur their profitability. 

How does an organization begin designing a successful Advanced Analytics strategy? First, let's take a deeper look at the five main components of an Advanced Analytics strategy and the associated use cases below:


Prescriptive & Predictive Modeling

Potential Use Case: Optimizing Inventory Management

Prescriptive & Predictive Modeling involves identifying complex patterns over thousands of variables. For example, a buyer and planner can utilize analytics to validate and react to sales and inventory trends at the department, class, location, or stock-keeping unit (SKU) level. The ability to create models and evaluate item performance ensures that the product is in the right place at the right time, with the right price.


Optimization Algorithms

Potential Use Case: Optimizing Location-Driven Visual Planning

Optimization Algorithms enable accurate planning and merchandising decisions. etailers can employ this strategy to evaluate merchandise performance, for example, by tapping into AI-based algorithms to determine shelf placement based on inbound forecasted demand in the market. In addition, these algorithms can solve complex optimization problems with high accuracy. This drives tangible business value by reducing waste, increasing turnover, and maximizing gross margin.


Machine Learning & Artificial Intelligence (AI)

Potential Use Case: Reducing Churn Rate

For retail wholesalers like BJ's, Costco, and Sam's Club, retaining customer memberships is paramount to maximizing revenue and ensuring a steady stream of sales. In this scenario, AI can be applied using analytics and logic-based models like optimization algorithms. These algorithms can work together to create a robust and accurate churn prediction model, helping businesses take action to reduce and prevent membership cancellations.


Cognitive Intelligence

Potential Use Case: Rule-Based Automation

Cognitive Intelligence is the ability to generate knowledge by using existing information including intellectual functions such as attention, learning, memory, judgment, and reasoning. With Cognitive Intelligence, Retail Merchandise Planners can create predetermined rules to dictate a dynamic pricing strategy. At the same time, visual merchandising can optimize product placement in stores based on historical data.


Natural Language Processing & Text Mining

Potential Use Case: Sentiment Analysis

Natural-Language Processing (NLP) technologies involve the ability to turn text or audio into encoded, structured information that companies can use to identify customers and anticipate needs. Retailers can maximize opportunities by leveraging sentiment analysis. By analyzing customer sentiment toward your brand or products, you can make more informed decisions across business operations and focus your marketing initiatives on meeting demonstrated demands or trends in the market. Sentiment analysis can also be instrumental in analyzing competitors, spotting and/or reacting to market trends, and conducting market research.

Retail organizations can utilize these five pillars of an Advanced Analytics strategy concurrently or singularly to bring their business decision-making abilities to the next level. With retail sales expected to grow between 6-8% to reach more than $4.85 trillion in 2022, employing these methods is essential to remain agile.Leveraging an Advanced Analytics strategy may be the singular most important piece to success during one of the busiest years in retail. 

Contact us today to accelerate your seasonal sales and predictive capabilities and maximize your organization's overall Advanced Analytics capabilities. 


Check our content and resources below to learn how SDG is helping organizations remain agile!

Nestle Success Story

Retail Snowflake Webinar

NRF Retail Sales Forecast 2022


About SDG Group

Business agility is the ability of an organization to adapt quickly to market changes, both internally and externally. This can only exist by becoming a truly data-driven company. At SDG Group, we achieve this by co-creating optimal solutions with our customers, leveraging Data & Analytics services through a unique combination of business domain expertise and state-of-the-art technologies delivered by industry-leading talent.