CPG and Retail

How Is External Data Used in CPG Analytics Use Cases and Retail?

CPG (consumer packaged goods) and retail companies use data to analyze their organizations in order to encourage growth. AI machine learning programs gather product and customer data to plan short- and long-term business strategies.

Product data includes sales trends data, competitor data, historical price data, and distribution statistics. Customer data includes demographic data, transaction data, store and brand loyalty data, and abandoned products/carts data.

With all this information, CPG companies can analyze product trends and prepare marketing campaigns to grow their business.

How are Machine Learning Models Used in CPG analytics use cases and Retail?

The Consumer Packaged Goods (CPG) industry generates enormous data volumes with the very large number of transaction records, data coming from multiple sources etc. These unique characteristics of the CPG industry make it an ideal candidate for advanced predictive algorithms in combination with ML.

AI machine learning programs improve sales team and marketing campaign effectiveness by optimizing the supply chain and manufacturing sectors.

GPG retailers generate enormous volumes of transaction data, which are difficult to analyze manually. However, ML programs can easily handle these datasets and extract insights from them.

Further, the short shelf life and large volumes of consumer packaged goods run the risk of oversupply, which has huge impact on return-on-investment. However, businesses that use machine learning programs can solve these issues very easily.

Additionally, accuracy in sales forecasting leads to more efficient sales and marketing campaigns. Cutting-edge learning-based technology merges many data sources (sales, marketing, digital, demographics, weather, etc.) to improve sales forecasts. The programs do this by understand underlying demand drivers better than traditional predictions made with isolated and aggregated sales figures often restricted to demand history. In contrast, machine learning-based forecasting takes advantage of limitless data to determine what’s significant. As a result, companies can prioritize available consumer insights (demand sensing) to influence future demand (demand shaping). This improved sales forecast helps determine which product marketers should promote in a particular month, what type of campaign they should use for a particular product, what consumer segment they should target, and so on.

Moreover, almost all large CPG organizations use enterprise resource planning (ERP) systems that hold a wealth of hidden value in their data. Using machine learning, this data answers critical questions like whether businesses can guarantee on-time delivery or shorten product manufacture time.

Which Companies Lead the Way in Building Advanced Analytics and Machine Learning Products in this Field?

Nielsen data management company specializes in consumer goods and consumer media and behavior data. They provide services in countries around the globe.
SPINS is a syndicated data and retail measurement platform. It specializes in cross-channel point-of-sale reporting alongside data-based services and solutions. It covers both the Natural and Conventional Goods categories.
IRI – IRI market research company and digital data analysis company offers datasets covering purchasing, media, social, causal, and loyalty channels.