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Best practices for integrating data science into marketing analytics

If you thought that the big data revolution is about to begin, think twice. We are in the middle of a big data revolution. And if your organisation is not data-driven still, it is time to act now.

People, organisations, and government agencies generate staggering amounts of data every day. Even devices such as smartphones, tablets, computers, and wearables produce a mammoth amount of data that can transform the way businesses operate. It makes strategic sense to leverage gigantic data, and data science is all about understanding and simplifying mammoth data to enable smarter data-driven decisions. And this takes us to marketing analytics best practices.

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Data science has a strong bearing on how your organisations' marketing and advertising functions eventually perform. Given that India has better growth prospects than other emerging economies, it is essential to understand its advertising landscape.

According to FICCI EY M&E Report 2021, advertising reduced to INR 199 billion in 2020, a decline of 25% compared to last year. Most brands preferred digital advertising in their marketing plan last year. Hence, digital advertising increased to 32% in 2020 from 24% in 2019. E-commerce advertising, a part of digital advertising, spends grew to INR 35 billion in 2020.

Small and medium enterprises intensified their focus on digital advertising and adopted diverse ways to sell their products on prominent e-commerce platforms. Media owners, in particular, news brands, saw their reach surpass the 450 million marks in 2020. As a result, their revenues improved drastically.

Personalisation enables marketers to send hyper-targeted content and offers that are more likely to drive purchases and foster brand loyalty. Although personalisation helps marketers optimise ad spends and drive improvements in customer lifetime value, basket size, and retention, it’s still untenable at scale in many organisations.

To solve the challenges of integrating data science into their operations, forward-looking marketing teams follow some best practices, five of which are summarised below.

Collapse silos to create a 360-degree view of customers

By consolidating customer data sets in the Data Cloud and the 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 achieving a holistic and up-to-date understanding of customers to scale personalisation models.

Give users fast and easy access to data

Once organisations have unified their data, they need the ability to support concurrent workloads. Marketing organisations 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 non-technical users to access marketing analytics dashboards without bandwidth challenges.

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Build efficient data pipelines

As data evolves from a novelty into an essential part of operations, organisations build an increasing number of data pipelines to support critical use cases, such as personalisation and regulatory reporting. While the price of getting started is low, as complexity increases, it can quickly compound to become a huge cost centre, costing up to tens of millions of dollars a year. This proliferation of pipelines also leads to challenges with data quality and maintenance and efficiency and scale. When underlying data or data formats change, channels often have to be rebuilt, which creates mounting technical debt. To help break this cycle, organisations 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 organisation.

Embed data science into business teams

To create a thriving culture of data, getting buy-in from the top is key. CMO and other C-suite executives should communicate the investments made in data science and the value it will deliver to the organisation. It is wise to embed data scientists inside business teams while creating alignment around centralised data resources. By experiencing real business problems first-hand, data scientists will be closer to their internal “customers” (brand and digital marketing teams), leading 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 professional networks to recruit colleagues and direct reports. Skilled professionals are more likely to hire others 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.

Conclusion

There are myriad ways companies can adopt to transform their marketing analytics. Without a structured approach and deep expertise in data science and marketing analytics, the entire exercise can become paralysed. The right choice is to seek specialist help – and at Snowflake, we partner our clients in every step of their journey to help them realise the business results they desire.

DISCLAIMER: The views expressed are solely of the author and Adgully.com does not necessarily subscribe to it. 

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