Why You Should Re-Platform Your Customer Marketing Database To Snowflake

We are often asked by CDOs and CMOs to document the business case for moving their marketing data platform to Snowflake. In this article I explore the key reasons why you should actively consider re-platforming your customer marketing database onto Snowflake.

 

Enhanced Performance and Speed

Marketing databases often struggle with processing and analysing large datasets, leading to bottlenecks and slower query response times. Snowflake processes queries using massively parallel processing (MPP) compute clusters. By providing elastic access to scaled compute at low cost it addresses some common speed and performance challenges in customer management.

 

There are 5 areas we find as the most transformative for clients:-

 

• Campaign execution & performance
• Cost
• Real time personalisation
• Model refresh
• Self- serve customer and campaign MI.

 

 

Improved Campaign management

 

By re-platforming your customer marketing database to Snowflake you get a quantum leap in performance and speed when running campaigns.

This is pretty critical as today’s customer marketing campaigns run off significantly larger data volumes, more complex data structure and wider range of data types than even 2 years ago. Campaign selections over large data sets that would have timed out or taken hours – now will run in seconds on Snowflake.

 

Snowflake’s support for diverse data types and semi-structured data empowers your marketing team with the capability and speed to harness the full spectrum of customer information. This includes unstructured data from devices, messaging, social media, clickstream data and more.  We have helped clients build high performing campaigns that query customer service chat, email & voice call transcript directly from nested JSON data in Snowflake. Its ability to function as a data lake query engine provides great scope to incorporate additional data for campaigns at speed.

 

Cost

What often surprises clients is the cost benefits for such increased performance. Usually there is line straight cost saving at time of migration –  especially of you have been on a managed service with an outsourcer.

Key FD friendly features

• Pay for what you use & avoid over- provisioning – Snowflake’s pricing model allows you to only pay for the resources they use, which can save money in the long run. You can automatically scale up or down their usage based on their needs, so there’s no risk of overprovisioning.
• Snowflake offers cost and workload optimization features help you to enforce cost control, and discover resources that need fine-tuning.
• Eliminate legacy software license fees

 

Many of the major Martech tool vendors run directly off the Snowflake data engine. 

For example we often set up Adobe Campaign to run directly off the Snowflake data engine. In addition to significant speed improvement we also eliminate the wasted effort, time and latency of data wrangling into campaign marts.

Most of the leading technologies in the modern martech and adtech space will talk directly to the Snowflake data engine.  These Martech tool vendors makers are driven by the need to get closer to the unified customer data store, equipped with native processing capabilities. This also means that you are not locked into any tool vendor. You can plug and play best-of-breed tools off your centralised data spine. This is why many CMOs favour going down a composable CDP route instead of a silo packaged CDP. By working with tools that are closer to the data, you’re optimizing the expensive time of your data engineers. You’re cutting the time and expense of getting your campaigns right. Marketers shouldn’t be hampered by data friction. Instead they should be working on what they love to do: delivering differentiated campaigns with optimal speed, accuracy, and agility.

 

Matching

Snowflake supports non-SQL code within Snowpark for complex transformations and simplifying data integration. We use our data matching & identity resolution platform AudiencePlus within Snowflake to link records and consolidate diverse datasets into a single source of truth. This breaks down data silos, enabling a holistic view of customer interactions and preferences. It uses AI to recognise partialand misspelt data from names, address , emails and contact numbers. It utilises a UK data universe to append data fields (forename, DoB, email, mobile) to assist matching algorithms and record linking . This provides a tunable matching environment for both deterministic and probabilistic matching.

• Link accounts for one person
• Create household views
• Link a customer’s digital activity across Web and app
• Bridge anonymous to known IDs
• Match and overlay external commercial data by name & address & other match keys
• Increase onboarding match rates with Google/Facebook & other ad platforms

 

Personalization data layer  

With a single customer view, you can drive great value through both digital and inbound calls. Snowflake supports Hybrid tables enabling high speed row level data look-ups as well as storage optimised for analytical queries. This means you don’t need multiple data technologies – you can drive on-site personalisation, customer identification and NBA within Snowflake without the overhead of synching multiple data marts with all the associated latency.

 

Machine learning and analytics

Snowflake’s support for machine learning and data science workflows further amplifies the value of your customer data, enabling predictive modelling, customer segmentation and propensity scoring. The ability to leverage advanced analytics within Snowflake’s environment eliminates the need for data movement, streamlining your analytics workflows and accelerating time-to-insight.

The game changer is to work across large volumes of data with rich intent signals and refresh models at great velocity to drive critical time sensitive customer campaigns and personalisation.

We have used these capabilities in telecommunication clients to build customer churn radars. Our models were able to detect customer disengagement from hundreds of millions of rows of PAYG transaction usage data.  We were able to trigger push messaging campaigns for data vouchers for those at risk of lapsing. These models driven by twinkling big data feeds can be game-changing campaigns in churn prevention and cross sell.

 

Gen AI

A key new feature of Snowflake is its role as a vector store with Gen AI capabilities

We classify and label inbound customer voice & chat transcripts using Cortex functionality. This allows us to detect multiple signals within conversations – call drivers, dissatisfaction, underlying cause of complaint, vulnerability and cross sell opportunities. Our models use this data with additional customer variables for highly effective triggered churn and cross sell customer campaigns.

• Speed- by using Snowflake and Arctic LLM we can achieve faster throughput at far less cost than many other foundation models.
• More accurate – trained industry specific classifier models
• Data security – no 3rd party data transfer – data stays in the Snowflake environment – no need for additional tech clutter and the complication of data transfer.

 

Streamlined Data Management and Collaboration

The migration to Snowflake redefines the landscape of collaboration within your marketing organisation. Snowflake’s cloud-based data platform offers a unified data spine and environment for storing, processing, and sharing all customer data. Secure data-sharing facilitates seamless collaboration and knowledge sharing across teams like advertising, operations and data sciences.  We have been able to help clients to securely share select datasets with external affinity marketing partners, agencies, or media vendors, fostering collaborative marketing initiatives and improved media ROI.  We have helped companies run some great affinity partnerships with directly measurable sales by match-back through secure data share. This frictionless data sharing empowers your business to leverage external expertise and enrich your customer insights, driving innovation and differentiation in your marketing strategies.

 

MI and Campaign analysis

Cloud analytics has emerged as a revolutionary approach to MI data self-serve through tools like Tableau, Power BI etc.  We commonly are asked to pair the Snowflake’s cloud data warehouse with BI tools. Customer operations dashboards and campaign analysis is one of the most challenging areas for BI tools. These projects have been traditionally plagued with slow speeds, incomplete data and conflicting results from source system reports. Snowflakes provides users with speed on large volumes of data through elastic compute and support for both structured and unstructured data. Net result – is more timely and consistent metrics across a wider set of data. One key feature we love is Snowflake’s Time Travel feature allows you to analyse data at different points in time, which is invaluable for historical trend analysis. This can provide insights into how your data has evolved and help you make better data-driven decisions

 

Conclusion

By investing in Snowflake, you are planting your flag in technology that everyone can invest in for the future and minimise the number disparate data technology repositories.    The enhanced performance and speed offered by Snowflake not only streamlines your marketing operations but also enhance the overall customer experience. By leveraging real-time insights, you can personalise marketing communications, deliver targeted promotions, and respond promptly to customer interactions, fostering stronger relationships and driving higher engagement with your brand.