Skip to content
  • AT SNOWFLAKE
  • Industry solutions
  • Partner & Customer Value
  • Product & Technology
  • Strategy & Insights
Languages
  • Deutsch
  • Français
  • Português
  • Español
  • English
  • Italiano
  • 日本語
  • 한국어
  • Deutsch
  • Français
  • Português
  • Español
  • English
  • Italiano
  • 日本語
  • 한국어
  • AT SNOWFLAKE
  • Industry solutions
  • Partner & Customer Value
  • Product & Technology
  • Strategy & Insights
  • Deutsch
  • Français
  • Português
  • Español
  • English
  • Italiano
  • 日本語
  • 한국어
  • 개요
    • Why Snowflake
    • 고객 사례
    • 파트너 네트워크
    • 서비스
  • 데이터 클라우드
    • 데이터 클라우드
    • 플랫폼 개요
    • SNOWFLAKE 데이터 마켓플레이스
    • Powered by Snowflake
    • 라이브 데모
  • WORKLOADS
    • 협업
    • 데이터 사이언스&머신러닝
    • 사이버 보안
    • 애플리케이션
    • 데이터 웨어하우스
    • 데이터 레이크
    • 데이터 엔지니어링
    • 유니스토어
  • PRICING
    • Pricing Options
  • 산업별 솔루션
    • 광고, 미디어 및 엔터테인먼트
    • 금융 서비스
    • 의료 및 생명 과학
    • 제조
    • 공공 부문
    • 소매 / CPG
    • 테크놀로지
  • 리소스
    • 리소스
    • Documentation
    • 핸즈온 랩
    • 트레이닝
  • CONNECT
    • Snowflake 블로그
    • 커뮤니티
    • 이벤트
    • 웨비나
    • 팟캐스트
  • 개요
    • 회사 소개
    • 투자정보
    • 리더십 및 이사회
    • 채용
Author
Snowflake
Share
Subscribe
2021년 01월 08일 2 min read

5 Best Practices for Integrating Data Science Into Your Marketing Analytics

  • 제품 및 기술
    • 데이터 사이언스
5 Best Practices for Integrating Data Science Into Your Marketing Analytics

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. 

Share

Marketing Analytics: The Foundation of a Data-Driven Business

Learn about marketing analytics, the combined processes CMOs and other marketing professionals use to measure marketing program success and value.

More to follow
Read More

4 Ways to Unlock Marketing ROI with Data Science and Machine Learning

Despite the emphasis placed on data-driven marketing over the last decade, 54% of senior marketing practitioners reported in...

Find Out More
Read More

SQL Versus NoSQL: What’s the Difference?

Comparison for SQL vs. NoSQL databases, including the benefits of each type. Gain insights into when to use NoSQL and SQL to make informed data decisions.

Discover
Read More

Operations Analytics for Retail

Operations analytics is the practice of using data to identify opportunities for improvement across the entire supply chain, from sourcing to fulfillment.

More to follow
Read More
Snowflake Inc.
  • 플랫폼 개요
    • 아키텍처
    • 데이터 애플리케이션
  • 데이터 마켓플레이스
  • Snowflake 파트너 네트워크
  • 지원 및 서비스
  • 회사
    • 문의하기

Sign up for Snowflake Communications

Thanks for signing up!

  • Privacy Notice
  • Site Terms
  • Cookie Settings

© 2023 Snowflake Inc. All Rights Reserved