PHASE 4:
OPTIMIZE PERSONALIZATION WITH DATA SCIENCE
Attribution modeling and personalization of content, offers, and experiences are foundational applications of advanced analytics, but they’re not the only ones.
Sophisticated data practitioners can harness predictive analytics to do things such as target ads and offers to segments that are similar to high-value customers, or identify existing customers at risk of churn and proactively improve their experience. They can also dramatically improve product recommendations on their websites, apps, and other touchpoints by using affinity scoring models to gauge people’s interests based on what they’ve looked at in the past.
Machine learning can also improve ad-bidding optimization, product upgrade propensity scores, lead scores, and next-best-offer recommendations. Cumulatively, these efforts can reduce costs and increase customer lifetime value.
Data science teams can tax legacy data platforms with resource-intensive queries that can cause delays and impact other parts of the business. Snowflake’s separation of compute and storage, as well as its ability to automatically scale up by increasing compute power and scale out by adding more compute clusters, enables all teams to query the same data with no resource contention.
Powering algorithms with a single source of data
Algorithms are only as good as the data that powers them. As a result, marketing organizations need to give their data science teams access to clean, merged data sets in an environment where all customer data is available.
With Snowflake, data science teams can operate on a single copy of customer data; there’s no need to copy or move data to a special environment. And since data doesn’t need to move between environments, there’s less of a delay between prototyping and deploying a model for marketing, sales, product experience, and other applications.
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