Case Studies
Maximizing Revenue With Machine Learning for Retail Pricing
Mosaic helped a nationwide retailer model the price elasticity of demand across their product catalog, empowering them to optimize prices and maximize SKU revenue.
Using data to inform marketing, sales, and customer experience decisions is increasingly essential for business success no matter your industry. If businesses fail to provide personalized customer experiences, they face the potential loss of brand equity, market share and — most importantly — loyal customers. Today’s consumers have more access to information than ever before. Information like where to shop, what to buy, and how much to pay are all at consumers’ fingertips. Companies that embrace new data streams and model them with internal data sources like historical sales and demand data will provide enhanced, more focused customer experiences.
Mosaic understands that predicting customer behavior is increasingly critical. When businesses gain deeper understanding of their consumers’ buying cycles and habits, they are better able to generate strategies to influence future behavior with relevant targeted advertisements, content, promotions or messaging. Armed with data, your company is more likely to succeed in keeping and growing your valued customer-base.
Mosaic helped a nationwide retailer model the price elasticity of demand across their product catalog, empowering them to optimize prices and maximize SKU revenue.
Graph analytics is a classic network science technique that is making waves due to the advancements in Graph Database Technology (GDB) and the integration of machine learning techniques (i.e., neural networks), to solve a wide range of use cases. No need for a map to figure this one out – Principal Data Scientist, Daniel Salazar is giving us the full scoop as our guide.
Given a set of nodes & connections, which can abstract anything from transportation networks, connections between customers, knowledge graphs, or molecular structures to computer data, graph analytics provide a helpful tool to quantify & simplify the many moving parts of dynamic systems.
Summary Our white paper explores the processes and opportunities presented by NLP for social media in extracting valuable data to drive improved operational and strategic decisions for R&D efforts. Natural language processing (NLP) is one of the most promising social media data processing avenues. It is a scientific challenge to Read more…
Both descriptive analytics and machine learning models can benefit greatly from using geographic data analysis to solve segmentation use cases.
We sat down with Senior Data Scientist Alex Tennant for his perspective on the opportunities and complexities of NLP and how Mosaic is paving the way for the consistent evolution of this powerful technology.
Mosaic used NLP algorithms to automatically extract various insights about people calling into a customer call center. We created a process to identify at-risk customers that called the previous day based on the transcript of the call.
In our whitepaper, Mosaic explores deep learning , including when to use deep learning over machine learning using practical examples.
Filtering search results is an essential part of any eCommerce website. Can you remember the last time you shopped online without filtering on product attributes such as color, size, brand, etc. Additionally, rich product attributes are critical to Google SEO which drives traffic and website sales.
Mosaic designed and deployed a custom machine learning model to help this retail energy company predict customer churn and inform a geographic growth strategy.