2. Customer Data Engine
Alleviates Big Data Challenges
Load Data
Access Data
Share Data
Analyze Data
Data
Store
Data
Science
Data
Manage
Choose
Customer
Data that
Matters
Managed Operational Expense plus Data Science Services
Take
Action on
Your
Data
3. Extracting Insights
Data Science
Advisory Services
“We want to work WITH you, because we
believe that creativity is the most powerful tool
in your problem solving arsenal.” Principal Data
Scientist, HUGEdata
Our Data Science team background in Econometrics and
Machine Learning allows our clients to:
• develop more robust customer segments
• mine for undiscovered product relationships
• build models that forecast future behaviors
• construct a sustainable analytical framework for making
decisions across the business
Enable marketing teams to answer the
“So, What? “
Editor's Notes
To get the most benefit from marketing analytics, you need an analytic assortment that is balanced – that is, one that combines techniques for:
Reporting on the past. By using marketing analytics to report on the past, you can answer such questions as: Which campaign elements generated the most revenue last quarter? How did email campaign A perform against direct mail campaign B? How many leads did we generate from blog post C versus social media campaign D?
Analyzing the present. Marketing analytics enables you to determine how your marketing initiatives are performing right now by answering questions like: How are our customers engaging with us? Which channels do our most profitable customers prefer? Who is talking about our brand on social media sites, and what are they saying?
Predicting and/or influencing the future. Marketing analytics can also deliver data-driven predictions that you can use to influence the future by answering such questions as: How can we turn short-term wins into loyalty and ongoing engagement? How will adding 10 more sales people in under-performing regions affect revenue? Which cities should we target next using our current portfolio?
High speed, High capacity Analytic database
Fully distributed, in database predictive and machine learning algorithms powered by MadLib
Processes, stores and analyzes Petabytes size of data (MPP)
Easy integration with existing IP and workflow
ANSI SQL 92, Views, DML statements (insert, update, delete)
Access by multiple clients: MS SQL, PostgreSQL, MySQL
Third party BI, stats and ETL
Standard database interfaces: ODBC, JDBC and native connections
Accelerators for machine data
Integration with wide range of statistical tools
Open source R, Python, SAS and more…
High Security, Onsite, Hosted or Cloud
Operates on commodity hardware and operating systems
Lower total cost of ownership and expansion
