Smart retailers are using advanced revenue attribution and customer-level response modeling to optimize their marketing spends.
This session covers: Retail analytics with R -- how to optimize retail spend using Big Data Survival analysis for marketing using R Commercial grade scoring systems for R models Revolution R/Hadoop integration.
Overview of how Cluster Architectures compare and differ from traditional enterprise architectures. The first step in understanding Hadoop and other Big Data related topics is understanding the design architecture of a Cluster Architecture.
Combining analytics, cloud and mobile, organizations gain competitive advantage by delivering an excellent customer experience.
Mobility provides accuracy, precision and speed into customer-facing processes.
Using mobile-based Customer Relationship Management (CRM), warranty management, service and spare parts procurement strategies, small and mid-sized organizations can level the playing field with the big boys. What smaller competitors lack in breadth they can make up for in speed and responsiveness.
Gartner’s IT Market Clock for Enterprise Mobility, 2012 captures how mobility is changing the nature of competition.
Are We Carpenters, Cabinet Makers, or Furniture Makers?
The carpenter is a skilled craftsman who is adept at reading architectural plans and building what is prescribed. A good carpenter has a well-developed set of skills, high-quality tools, and the experience to build high-quality structures that will last.
The cabinet maker's work is designed to be seen and must be visually appealing. The joints must appear seamless, and the finish flawless. A good cabinet maker works with the customer to design a functionally effective configuration and select styles, color, and appearance. Unlike the carpenter's coarse tools, the cabinet maker's tools are precise and delicate.
The furniture maker's work must serve a specific purpose, but its actual design and appearance can vary widely. A chair might have arms or not, have a high or low back, be symmetrical or asymmetrical. An innovative furniture maker's vision is not tightly bound by the appearance of the chair, only by its functionality. The artistic furniture maker is not directed by the customer, but instead measures success by how many customers buy his work.
All three are skilled craftspersons who possess the right skills, tools, and experience. Data warehouse, BI, and analytics practitioners have historically been most like the carpenter, building what the architects prescribe. This talk will examine a blend of lean startup, Kanban, and agile techniques that offer the opportunity to work more like cabinet makers and furniture makers, infusing creativity, vision, and innovation into our results---and measuring how well our customers like what we build.
Predictive analytics turns data into valuable, actionable information. Predictive analytics uses data to determine the probable future outcome of an event or a likelihood of a situation occurring.
Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.
Three basic cornerstones of predictive analytics are:
Decision Analysis and Optimization
An example of using predictive analytics is optimizing customer relationship management systems. They can help enable an organization to analyze all customer data therefore exposing patterns that predict customer behavior.
Another example is for an organization that offers multiple products, predictive analytics can help analyze customers’ spending, usage and other behavior, leading to efficient cross sales, or selling additional products to current customers. This directly leads to higher profitability per customer and stronger customer relationships.
An organization must invest in a team of experts (data scientists) and create statistical algorithms for finding and accessing relevant data. The data analytics team works with business leaders to design a strategy for using predictive information.
Whether you're a wall street trader or Internet media buyer, knowledge is power. Engaged web users are now providing in-depth data about their actions and desires, powering a new generation of Predictive Analytics (PA).
Facebook, nearing 600 million active users, is the shining example of the meteoric rise of the engaged user. Through online engagement, users eagerly reveal more about themselves than slow old big-budget research studies could ever hope to uncover.
A new wave of PA start-ups are powered by social media, engaging brand awareness sites, on-line gamers, and always-on mobile web users. With APIs, platform providers create a virtuous circle - generating opportunities for start-ups and increasing the relevance of their platforms. Fueled by on-line ad spending approaching $50B, PA start-ups, their media buying customers, and venture capitalists are racing to discover the relevant insights which can predict users' needs and next actions.
Master data management (MDM) comprises a set of processes and tools that defines and manages data. MDM lies at the core of many organizations’ operations, and the quality of that data shapes decision making. MDM helps leverage trusted business information—helping to increase profitability and reduce risk.
Master data is reference data about an organization’s core business entitles. These entities include people
(customers, employees, suppliers), things (products, assets, ledgers), and places (countries, cities, locations). The
applications and technologies used to create and maintain master data are part of a master data management (MDM) system.
Data governance encompasses the people, processes, and technology required to create a consistent and proper management of an organization's data. It includes data quality, data management, data policies, business process management, and risk management.
Data governance is a quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information. It is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.