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The marketing world has been through a lot of major changes over the last two decades. Numerous new channels have emerged, but very few have been as impactful or transformational as digital retail media advertising. 

According to recent data by Insider Intelligence, retail media is growing faster than almost any other form of ad spend, with a growth of 21.8% Y-o-Y and a total spend of $140bn forecast globally for 2024.  

When you look at the benefits and value it can deliver for both retailers and manufacturers, it’s easy to understand why. 

Charting the rise of retail media advertising

Retail media advertising offers marketers something that no other channel can: direct and highly relevant visibility to high-intent customers at their point of purchase. When someone is ready to buy, retail media ads and placements ensure your brand is right there and at the front of their mind. 

It’s also a channel that’s perfectly aligned with many of the wider goals that modern marketing teams want to achieve. Retail media ads help marketers: 

  • Personalise customer experiences and journeys at the point of sale 
  • Learn more about customer buying trends using data provided by retail media networks 
  • Rapidly evolve their strategies and retarget ads to continuously improve advertising performance and ROI 

But in terms of tangible value, that’s just the tip of the iceberg. In addition to helping brands drive sales and satisfaction through relevance and personalisation, retail media can also offer margins in the region of 80%. 

As retail media appears at the point of sale, it is much easier to attribute ROI to it than other forms of advertising. At a time when many marketing teams have seen their budgets shrink, this direct, tangible link to ROI and sales can set retail media apart from other forms of advertising. 

It’s unique in many ways. But in terms of how teams manage it internally – and make decisions about where to invest their spend – that uniqueness is leading many to make a big mistake. 

The big mistake organisations are making with retail media 

Because digital retail media is still a new advertising channel for many, and the services offered by retail media networks have evolved so quickly, many CPG manufacturers still approach and manage the channel in isolation. 

As retail media becomes one of the biggest and most important areas of organisations’ advertising portfolios, teams must change how they use retail media data. They need to start using all the data at their disposal to make decisions aligned with their wider spend and strategy. 

The vast quantities of data generated by retail media networks are a mixed blessing for manufacturers. They offer deep insight into performance and campaign effectiveness, but also create additional data challenges. 

Three steps to long-term retail media success for CPG manufacturers 

Step one: Standardise and integrate data from retail media networks 

Retail media networks share valuable first party data with the companies that advertise through them. So, if you work with more networks, you’ll gain access to more data. However, the more networks you advertise through, the harder it is to maintain a consistent view of where you’re seeing the most return on your investment.  

The first step towards optimising your retail media spend is therefore gaining a consistent view of performance across the retail media networks you work with. Each network may provide data in different formats, so you’ll need to standardise the insights you receive to easily compare data points. 

Step two: Make standardised performance insights visible in real-time 

Another major difference between retail media and other advertising channels is that retail media networks can often provide you with a real-time view of performance across your ads. To make the most of that data, once you’ve built your standardisation layer, you’ll need to make it immediately visible to relevant stakeholders through accessible dashboards. 

That ensures that no matter which media network an insight comes from, the lessons you learn from it can be applied across your portfolio to optimise all your retail media advertising efforts. 

Step 3: Continuously compare performance to build an optimised retail media partner portfolio 

With a standardised view of performance, and clear visibility of how that performance differs between retail media networks, you can start making informed spend decisions to maximise ROI across all retail media networks and channels. 

Your standardised data foundation will even help you pull in other contextually relevant customer and marketing data. Using this data, you can ensure your retail media decisions are aligned with your wider marketing strategy and finely tuned to deliver maximum ROI. 

From there, you can integrate retail media into your wider marketing mix decision-making and review its effectiveness and ROI versus other channels, but be wary of the following challenges:  

  1. Retail Media Organisations (RMOs) and Retail Media Networks, such as Amazon Advertising, Walmart Connect and Target Roundel, often use different data formats, structures, and APIs, making integration with manufacturers’ systems complex.

  2. Data sharing agreements between manufacturers and RMOs must comply with privacy regulations and industry standards. Brands need to ensure that they have appropriate consent mechanisms and data handling practices in place to protect consumer privacy.  

  3. Metrics, terminology, and key performance indicators (KPIs) between different retail media networks and even other marketing channels are often misaligned making it difficult to compare effectiveness and ROI accurately.  

  4. Building effective optimisation models requires sophisticated analytics techniques and domain expertise. Manufacturers may lack the internal expertise and capabilities needed to effectively measure and analyse the performance of retail media campaigns and their contribution to drive revenue. 

  5. Digital marketing teams may struggle to make timely decisions due to delays in data processing and analysis, leading to missed opportunities and lower ROI. 

By being aware of these common pitfalls and having a strategy in place to mitigate against them, forming a clear view of digital media performance alongside other channels is within reach. As you model future marketing mix decisions and allocate budgets, this clarity will help ensure that you get the most from your advertising budget – wherever it’s spent. 

Integrating data to help a market leading CPG company model digital marketing ROI 

The Smart Cube recently worked through these steps with a major global CPG company, standardising and uniting its retail media advertising data to help its teams optimise spend and drive higher digital marketing ROI. 

This meant creating a scalable data layer on Azure as a first step – a Media Data Domain built on a data mesh architecture. This included migrating and integrating over 1,000 data streams from the likes of Meta, Amazon, Google Ads, Snapchat, TikTok, and other retail media networks, bringing diverse channel, network, and other marketing data sources together. 

Within that layer, data is automatically ingested, checked for quality, harmonised, and standardised. With that standardised data foundation in place, our teams applied advanced machine learning (ML) techniques to build a highly intuitive ROI calculator tool, and ‘what if’ scenario modelling capabilities. 

Using those new capabilities, the company’s internal teams can make rapid, informed retail media decisions that are aligned with their wider marketing mix. They’re empowered with a contextualised, always-on view of performance, and the ability to model any proposed changes to their strategy to see its potential impact. Together, those shifts have already improved ROI by an average of 9% across the company’s brands.  

Make the most of the retail media opportunity 

Digital retail media advertising represents an unprecedented opportunity for marketers. It’s already a cornerstone of most organisations’ advertising portfolios, and its meteoric growth will continue over the coming years. 

As CPG manufacturers embrace it, it’s important they do so in a considered, data-driven way. By building the right foundation for standardising retail media data today, those organisations can set themselves up for years – or even decades – of successful and optimised decision-making. 

While others are still approaching the channel in isolation, leaders with a mature approach to managing retail media data can make precise optimisation decisions in real time that help them maximise ROI and make the most of the retail media opportunity. 

At The Smart Cube, we’re constantly finding new ways to help our clients gain maximum value from their data and analytics. To discover how we could help you transform the way you make marketing mix, advertising, and merchandising decisions, get in touch. Or, if you’d like to learn more about our approach to marketing mix modelling and learn how you can measure ROI more accurately across your marketing efforts, download our marketing mix modelling eBook. 

Co-authored by: Nisha Purswani and Nitin Aggarwal
  • Nisha Purswani

    Nisha is an advanced analytics and consulting professional with over 12 years of experience in retail, CPG and pharmaceuticals. In her current role, Nisha is responsible for managing large analytics accounts, designing and developing data science and analytics solutions for retail and consumer goods. She is an expert in marketing strategy, CRM, measuring promotion and campaign effectiveness, test and learn, forecasting, time series analysis, and driver analysis. 

    When Nisha isn’t helping clients solve business problems, she can be found reading books, or in the kitchen trying out new recipes. She also enjoys travelling, meeting people of different cultures, and exploring new places.

  • Nitin Aggarwal

    Nitin is VP & Business Head of Analytics and Data Science, and a seasoned business leader with nearly 20 years of experience across industries and functions. Based out of our Chicago office, Nitin leads the Retail, CPG & Consumer Markets practice in the US. Prior to this role, Nitin developed and scaled the data analytics practice, and managed operations across the globe. He also drove the practice strategy in terms of new capabilities, solutions, and technologies from India.

    Nitin studied electrical engineering at Punjab Engineering College, Chandigarh, and has an MBA from the University of Notre Dame. An avid sports person, he loves playing tennis and badminton, and is a committed follower of American football.

Co-authored by: Nisha Purswani and Nitin Aggarwal
  • Nisha Purswani

    Nisha is an advanced analytics and consulting professional with over 12 years of experience in retail, CPG and pharmaceuticals. In her current role, Nisha is responsible for managing large analytics accounts, designing and developing data science and analytics solutions for retail and consumer goods. She is an expert in marketing strategy, CRM, measuring promotion and campaign effectiveness, test and learn, forecasting, time series analysis, and driver analysis. 

    When Nisha isn’t helping clients solve business problems, she can be found reading books, or in the kitchen trying out new recipes. She also enjoys travelling, meeting people of different cultures, and exploring new places.

  • Nitin Aggarwal

    Nitin is VP & Business Head of Analytics and Data Science, and a seasoned business leader with nearly 20 years of experience across industries and functions. Based out of our Chicago office, Nitin leads the Retail, CPG & Consumer Markets practice in the US. Prior to this role, Nitin developed and scaled the data analytics practice, and managed operations across the globe. He also drove the practice strategy in terms of new capabilities, solutions, and technologies from India.

    Nitin studied electrical engineering at Punjab Engineering College, Chandigarh, and has an MBA from the University of Notre Dame. An avid sports person, he loves playing tennis and badminton, and is a committed follower of American football.