5 Best Practices for Integrating Data Science Into Your Marketing Analytics

January 8, 2021 Snowflake

Personalization enables marketers to send hypertargeted content and offers that are more likely to drive purchases and cultivate brand loyalty. Research by Accenture from 2018 shows that 91% of consumers are more likely to shop with companies that provide relevant offers and recommendations. 

Though personalization helps marketers optimize ad spend and drive improvements in customer lifetime value, basket size, and retention, it’s still untenable at scale in many organizations. A 2020 report from Econsultancy and Adobe indicates that only 30% of companies say their technology platforms enable them to combine known and anonymous data to activate real-time customer profiles across channels throughout the customer journey. 

To solve the challenges of integrating data science into their operations, forward-looking marketing teams are following six best practices, five of which are summarized below. For more details and recommendations, download our ebook, How Marketers Can Harness Data Science to Enable Personalization at Scale

Collapse Silos to Create a 360-Degree View of Customers

Marketing organizations today have numerous first-party data sets, which are often stored in separate, disconnected systems. Some of these data sets reside in third-party platforms, which compounds the problem.

By consolidating customer data sets in Snowflake’s Data Cloud and Snowflake’s platform, which can natively support structured and semi-structured data in the same system, marketers can harness more power from their marketing analytics tools. They can also access and query customer information in real time, which is critical for the holistic and up-to-date understanding of customers required for scalable personalization models.

Give Users Fast and Easy Access to Data

Once organizations have unified their data, they need the ability to support concurrent workloads. Marketing organizations should invest in a data platform that can instantly scale up capacity to deliver more computing power on demand, freeing up teams to produce outputs as quickly as they can. Instant elasticity removes the need to schedule and batch jobs, letting data scientists run complex models while at the same time allowing nontechnical users to access marketing analytics dashboards without bandwidth challenges. 

Build Efficient Data Pipelines

As data evolves from novelty into an essential part of operations, organizations build an increasing number of data pipelines to support critical use cases, such as personalization and regulatory reporting. While the price of getting started is low, as complexity increases, it can quickly compound to become a huge cost center, costing up to tens of millions of dollars a year.

This proliferation of pipelines also leads to challenges with data quality and maintenance, as well as efficiency and scale. And when underlying data or data formats change, pipelines often have to be rebuilt, which creates mounting technical debt. 

To help break this cycle, organizations need modern tools to support a flexible extract, load, transform (ELT) process that can handle data type changes in the source system without breaking. Legacy extract, transform, load (ETL) systems, on the other hand, tend to be slow, brittle and expensive, and they rarely meet the evolving needs of an entire organization. 

Embed Data Science into Business Teams

To create a successful culture of data, getting buy-in from the top is key. CMO and other C-suite executives should communicate the investment being made in data science and the value it will deliver to the organization. 

In many cases, it’s wise to embed data scientists inside business teams while creating alignment around  centralized data resources. By experiencing real business problems firsthand, data scientists will be in closer alignment with their internal “customers” (that is, brand and digital marketing teams), which can lead to quick and easy wins. 

Invest in Attracting and Retaining Top Data Science Talent

Notwithstanding how difficult it can be to hire data scientists, maintaining a high bar for talent is important. This is especially true for initial hires, who will be indispensable in ongoing talent acquisition efforts by tapping into their own professional networks to recruit colleagues and direct reports. Skilled professionals are more likely to hire others who are at or near their level of expertise and proficiency. It’s also important to look for good communicators with a track record of working cross-functionally with non-technical teams.

To learn more about how marketers can incorporate data science into their marketing analytics workflows to send hypertargeted content to customers and prospects, download our ebook, How Marketers Can Harness Data Science to Enable Personalization at Scale

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