The world of retail is moving away from the manipulation of consumer decisions to a model that influences consumer decisions based on a careful analysis of vast amounts of available data. Whether it’s building recommendation engines, performing market basket analysis, optimizing pricing or inventory, or accurately predicting forecasts and seasonality, leveraging AI and machine learning in the world of retail is becoming critical to maintaining market leadership.
Optimizing Your Retail Strategy With AI
How AI Helps Retailers
Retailers are moving away from legacy approaches that attempted to manipulate consumers into a buying decision. The new strategy involves leveraging the vast amounts of data that are available, both in-house and through affiliate relationships, to help identify consumers that are most likely to respond and instead influence their buying decisions appropriately. Modern retailers leverage AI and machine learning to plan everything from seasonal staffing to the best product mix for any given market conditions, creating better shopping experiences in the process.
Understanding the conditions that impacted store management decisions used to be a process heavily influenced by opinion. Modern retailers leverage historical data along with publicly available data sets to create useful models to plan new store openings, manage multiple locations as well as to optimize staffing based on seasonal as well as regional demand. AI and machine learning provide effective means of giving retailers the flexibility to expand and contract operations to account for changes in market and competitive position with unbounded flexibility.
Retailers rely on pricing and product positioning to effectively influence buying decisions and remain competitive in markets that are often highly fragmented and filled with competitors. AI and machine learning are highly effective means for planning pricing changes, understanding consumer demand, and for modeling new product successes before high investments are made in planning and launching products in the market.
Retail AI And Machine Learning Use Cases
Leverage past consumer purchasing behaviors along with consumer actions, demographic data, and product characteristics to recommend products to consumers that are more likely to result in a sale.
AI and Machine learning are the foundation of recommendation engines that help drive the world of retail. dotData can help you leverage the vast troves of data necessary to create the best possible recommendation engine to drive your website-based business.
Market Basket Analysis
Leverage the vast amount of transactional data available from previous consumer interactions by using Machine learning and AI.
Identify product correlations that may not seem obvious and understand product combinations that are likely to result in losses of business or that are more likely to predict increased failures due to returns. dotData can help you leverage your historical information through an engine that can generate tens of thousands of features that are then applied to creating AI and ML models.
Whether it’s trying to prevent fraud or simply creating models to estimate coverage needed to maintain a high degree of customer satisfaction, retailers today use AI and machine learning to identify patterns in historical purchase data to create accurate predictions of possible fraudulent warranty claims and to continuously improve quality for consumers.
dotData can process millions of rows of data in complex table structures, even from transactional systems, making the process of leveraging data science for warranty planning fast, simple, and efficient.
Identifying the right pricing model is a careful balance between the cost of production of consumer goods with the propensity of consumers to spend a specific range on any given products.
AI and machine learning can provide retailers with increased planning tools that can take into account the impact of seasonality, location, competitive pricing, and other social and economic factors on pricing. dotData helps you automate the highly tedious and iterative data science process to ensure your pricing models are based on the criteria that are most likely to result in success.
Too little inventory means loss of potential sales. Too much inventory results in lost profits when products are sold at a discount and the wrong mix of stock can create the perfect opening for a competitor to steal business:
Retailers leverage AI and machine learning with platforms like dotData to expedite the building of predictive models. Retailers can take into account multiple elements, including changes in supply chain elements and other leading indicators to optimize inventory levels on even a store by store basis.
Knowing when and where to open new store locations used to be more art than science.
By analyzing customer demographics for any given zip code, and by taking other variables into account like locations of competitive locations as well as leading economic indicators to gauge spending power, retailers can effectively use AI and machine learning to plan store locations with exact precision. dotData helps accelerate the process with a 100% automated platform that reduces the need for armies of data scientists and accelerates the entire AI and machine learning process.
HR & Hiring
In the world of retail, hiring and retaining qualified staff can be an immense challenge.
Whether the problem is seasonality of demand or the high employee turnover rate that is common in retail, retailers can leverage data science automation platforms like dotData to plan adequate staffing levels, plan appropriate ramp-ups to account for seasonality and leverage AI to analyze employee satisfaction to help lower employee attrition rates.
Customer Sentiment Analysis
Machine learning and AI are the new kings of customer sentiment analysis for retailers.
Unlike traditional methods like polls or focus groups, leveraging platforms like dotData to automate the process of retrieving and analyzing text from social media and review platforms can help retailers identify customer loyalty trends and predict consumer response to new products, brand campaigns or special offers.
Fraud analysis and prevention in the world of retail can take many forms. Whether it’s guarding against in-house generated inventory shrinkage, product return fraud or credit default, retailers must continuously safeguard their bottom line and reputation without placing unduly onerous restrictions on the conduct of business.
By leveraging platforms like dotData, retailers can develop models that cluster potentially fraudulent activity and can help fraud prevention hone in on the most likely suspects, without impacting the consumers at large.
The Right Product for the Right User
Start by selecting the product you need, based on your environment, your use-case and your need to “get dirty” with the details of your data science workflow.
AutoML 2.0 & Data Science Automation
Leverage a full GUI experience to automate as much of your data science workflow as necessary. Empower citizen data scientists and data scientists alike.
How dotData Helps Retailers
Retailers have expanded their use of data to help boost consumer demand, identify key trends that impact their operations, and to manage complex logistics without missing a beat. dotData helps retailers accelerate these efficiencies by making data science a scalable, easy to perform process.
Chief Data Officers
Get projects out of the lab
Chief Data Officers at modern retailers must carefully balance the need for the business to leverage available information to make better decisions, with the demand from consumers for a high degree of privacy. Leveraging AI and machine learning automation platforms is critical, but they must provide real transparency of model building that allows the retailer to remain compliant with local, state as well as federal laws related to data privacy. dotData delivers one of the most transparent data science automation platforms available that can help your business maintain a competitive edge while staying within the bounds of the legal system.
Chief Data Scientists
The tools your team loves, without headaches
96% of AI and machine learning projects never leave the lab. The fundamental problems faced by any Chief Data Scientist are how to accelerate the myriad of projects “stuck” in the lab, while also providing results that are easy to deploy in production with minimal impact on the business. dotData helps address both needs with a data science automation platform that can accelerate the hardest parts of the data science process – like feature engineering – while also providing an API-based integration model that makes it easy to operationalize projects.
Chief Information Officers
Data science without the headaches
For retailers, data science is a mission-critical function. Finding enough data scientists, however, can be a daunting exercise, even for the largest of organizations. AutoML platforms are often a mixed bag of not enough power for your data science team, and not enough automation for your BI analysts to use unassisted. dotData changes all that. We provide data science automation products for the specific needs of your users – from a “full-cycle” data science automation platform for your so-called “citizen” data scientists, to a python-based add-on that is ideally suited for your most skilled data scientists.
AutoML 2.0: Data Science Automation Accelerates Your Business
Scaling a data science practice is challenging, time-consuming, and expensive. With Automated Data Science, you can empower data analysts, software engineers, and BI professionals to build and benefit from predictive models. Through Data Science Automation, you can embed models into applications seamlessly, while freeing up the time of your data science team to be more productive.
BI & Data Analysts
Unlike traditional AutoML systems that require users with in-depth knowledge of data wrangling and constructing feature tables, dotData automates 100% of the data science process. With dotData and minimal training, your BI team and data analysts can quickly learn to contribute to your ML and Enterprise AI initiatives, freeing precious resources and accelerating time to market for your AI & ML initiatives.
Data scientists spend 80% of their time in wrangling with data and constructing complex feature tables. By automating the entire workflow, you can liberate your data science team from the mundane tasks associated with data science and give them the power to provide tangible value to your business in a scalable, seamless manner that is not possible with hand-coded approaches or traditional AutoML platforms.
IT & Software
Integrating your AI and Machine Learning models into production environments is a crucial step in deriving value from your Enterprise AI projects. dotData gives your IT and engineering teams a seamless API-based integration model that enables Continuous Deployment and makes deploying and maintaining models fast and straightforward.
Executives and Line of Business
Giving executives and line of business leaders insights into the Machine Learning and Enterprise AI process provides the transparency and line of sight needed to keep projects moving and provide discernible ROI and value for the organization.