How to Optimize Cloud Architecture for Marketing Analytics
September 17, 2020
“Zepl’s data science and analytics platform helps you unlock insights hidden deep within your data by analyzing multiple data sets using data science & machine learning algorithms.” — Snowflake
Data-driven organizations are looking to consume as many data sources as possible to fuel their marketing analytics initiatives.
Marketing analytic software is seeing exponential growth in data sources due to the advances in cloud technology. The marketing analytic landscape has developed to address the issues of data fragmentation to better build targeted engagement. This requires optimizing the customer journey through e-commerce platforms or special events and industry promotions with attribution management on the input variables used in data science for statistical review by ML.
Still, only 47% of enterprise businesses have a unified view of their customers, including major brands with global reach and hundreds of millions of customers.
Step #1: Get All Your Data in A Cloud Data Warehouse
Data silos — the practice of storing different types of information in separate, unconnected systems — are keeping you from getting a holistic understanding of your customers. Marketers are collecting more customer data than ever before, including purchase data, CRM data, website traffic data, paid-media data, and so much more. Without synchronizing this data, errors and duplicate records can become an ongoing problem.
Snowflake’s Customer 360 acts as the hub that links and synchronizes the information about your customers. It becomes the source of reference for finding the most up-to-date information. Many call this the “single source of truth” about the customer. The data can then be de-duplicated, aggregated, analyzed, and displayed on demand.
At Zepl we have built a native integration into Snowflake, which collects and unifies all of your customer data points — semi-structured or structured — into an easy-to-query SQL data warehouse. Once this data is in Snowflake, you can begin to run ML models against your data.
Step #2: Use A Cloud Data Science Platform to Analyze Your Data
Marketing analytics can be problematic for open source or legacy tools due to the size and scale of the workloads. Big data requirements typically begin at 100 million input variables, where fully trained ML models like GPT-3 have more than 175 billion language input variables.
The differences between GPT-2 and GPT-3 in machine learning are related to an exponential increase in the number of language variables the AI trained on for functionality. In GPT-2, the notebook data for machine learning ranged from 1.3 billion to 13 billion variables per unit.
Zepl’s cloud-based data science platform is built for workloads such as these. Zepl offers:
- Unlimited scalability of the cloud
- Collaborative across both technical and non-technical teams
- Python, R, Scala, and SQL in a single notebook
- Enterprise-grade security with SSO and data encryption
- Zero-management platform
- “Pay as you go” billing
Step #3: Start Exploring Your Data
There are three types of focus in the machine learning models used for marketing analytics:
- High-density sampling: “big data” in e-commerce and social networking
- Low-density sampling: where the pattern from data is not clear in ML
- “In Period” sub-sampling: examples of real-time AI-driven feedback
- This blog is an excerpt from Grover Righter’s webinar, “Using Data Science for Marketing Analytics. Watch the entire webinar here.
- Zepl’s data science platform lets data scientists and analysts rapidly prototype models in Python, R, Scala and SQL to create rich visuals for their marketing analytics use cases. Try it for free at www.zepl.com
- Snowflake helps marketing analytics teams drive more business value from their data by offering a scalable, elastic, and secure cloud data platform for all of their data workloads. Try it for free at www.snowflake.com
ABOUT THE AUTHOR
Grover Righter is the Chief Data Scientist at Zepl. He is focused on science-based marketing programs and runs a backplane of analytic engines measuring email, SEO and social media effectiveness. He has worked on every major know marketing automation platforms including Eloqua, Marketo, Pardot, HubSpot and Leadformix.
Grover has been working in the high technology sector since 1981. He began as a mathematician and design engineer and has been instrumental in the design and development of major technologies at RMS, Inc., AT&T, Unisys and Novell. But every time Grover built a product, he was immediately recruited to ‘sell it’, irrespective of the presence of another sales team within the organization. Eventually, Grover used up his lifetime limit of sales meetings as an engineer and was moved into marketing.