Some of the major challenges retailers face today include coping with inflation, preparing for a recession, and learning to manage inventory.
Oct 28, 2022

Despite these challenges, the sector is growing at levels not seen in over 20 years. 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.

With these challenges in mind, companies like Walmart, Costco, and The Home Depot still manage to maximize profitability and reach target margins. So how can these companies achieve success in such a challenging market?

One of the reasons these retailers are realizing success can be attributed to a robust and optimized Advanced Analytics strategy. Specifically, the use of Advanced Analytics as a method to predict product and seasonal sales has enabled many retailers to spur their profitability. So how can you design a successful Advanced Analytics strategy for your company? First, let's take a deeper look at the five main components of an Advanced Analytics strategy and the use cases associated with them 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. For example, retailers can evaluate merchandise performance 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 turn, 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 maintaining revenue and maintaining a steady stream of patrons entering their stores to purchase goods. We can define AI as applying analytics and logic-based models like optimization algorithms. For example, having a robust and accurate churn prediction model helps businesses take action to prevent customers from leaving their membership.


Cognitive Intelligence

Potential Use Case: Rule-Based Automation

Cognitive Intelligence is the ability to generate knowledge by using existing information and includes intellectual functions such as attention, learning, memory, judgment, and reasoning. For example, 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 their 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 the predetermined 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 at to accelerate your seasonal sales and predictive capabilities and maximize your organization's 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.