Forecasting for Marketing in ecommerce was rocked by the pandemic and has remained fairly unsettled ever since. Year-on-year trends were skewed by a bumper 2020 for most online retailers, however further disruption has ensued with supply chain issues and the impact of Russia’s invasion of Ukraine.

Whilst many firms focused on revenue growth and stock sell-through during the pandemic, attention has been firmly placed on profitability since 2022. This is because as online has grown as a proportion of retail sales, an inverse correlation has seen pre-tax profit margins fall. With more than 33% of UK retail sales forecast to occur online by 2025, this trend is likely to continue to expand, meaning margin pressure could be here to stay.

In the midst of this, questions have emerged – how much weight should we put behind paid media for customer acquisition? Should investment in paid media be reduced to improve efficiency in the face of rising costs elsewhere? When you factor in increased difficulty to measure true channel impact with changes such as Apple’s App Tracking Transparency (ATT) framework, the challenge is complex.

But whilst short-term cost gains can be tempting, what impact would an efficiency drive have on your brand’s position long term? In this article, we look at the Scale vs. Efficiency trade-off when planning your media activity.

Finding the right balance

Let’s first compare the output of both, side by side:

A retailer has many things to consider in analysing ad investment, beyond return on ad spend (ROAS) there is margin on each product sold to account for. Not to mention cost per acquired customer and the expected lifetime value from them. With debate over what ‘lifetime’ even means, this gets complicated.

It’s therefore no surprise that setting the right target for media spend can be a challenge. When there’s doubt about the true performance of paid channels, brands tend to set a high ROAS target on activity which, whilst understandable because spend then leads to higher profitability, lower spend, and lower risk, also results in fewer auctions entered as algorithms find those most likely to convert. Essentially, implementing such a strategy may require cutting out your least ‘profitable’ sections and ultimately giving your competitors more space.

Whether it’s cutting a whole channel or cutting a section of your product catalogue from paid advertising, clear issues start to creep in – what if the products you’ve cut from paid advertising actually had a higher average margin? Sooner or later that reduced presence across Paid Social will translate to fewer new prospects, as the efficiency drive requires you to saturate your existing, brand-aware cohort. This user base in itself will shrink, as searches for your brand directly correlate to the amount invested in awareness-driving tactics.

Fewer overall sales, missing out on new customers and ultimately neglecting long-term growth are all likely outcomes of a drastic efficiency drive. With the stakes this high, it’s vital to be confident in the ROI target you are setting for Paid acquisition.

A flexible approach to efficiency

To drive true business value, a flexible approach to expected efficiency is required. Calculating metrics such as lost revenue due to budget is a great first step to understand whether your current setup is along the right lines.

A lower threshold for ROAS allows us to spend at a higher scale and therefore enter more auctions. Often it is only by spending more that we see how much headroom there is in potential sales. Emerging campaign types like Performance Max, that aim to simplify the process of creating and managing campaigns by leveraging automation and machine learning, are particularly strong at over-delivering on target ROAS.

A greater degree of ‘halo effect’ on other channels (or more accurately named ‘cross-channel interaction’) will also occur from an increased presence on paid media. Combined with the knowledge that a higher percentage of total sales will be coming from new customers, it makes sense to be as open as commercially possible in your ROI target setting.

Quantifying the ‘halo effect’

In truth, there is no silver bullet to measurement across channels. Advertisers must understand that a user’s path to conversion is often long-thought out and considered – with multiple touch points across different channels. Advertisers who find themselves stuck reporting on channel performance in silo will be left behind.

Connected measurement is essential for more robust reporting across media in a cookieless future. It starts with ensuring that in-platform media measurement is set up, enabling platforms to learn and optimise in the best way possible. It includes future-proofing  measurement with cookieless technology solutions like Google Ads Enhanced Conversions or Meta’s Conversion API. Taking this further, it requires a Customer Data Platform (CDP)  to centralise audience data and activate it across media to ensure consistency in communication.

By feeding in offline and backend sales data trends and combining in-platform reporting, a deeper understanding of whether scaling has been effective is available. Democratised data in the form of a dashboard alongside correlation analysis then visualises the strength of the relationships between key metrics, such as spend and sales. Specific solutions such as offline conversion imports then get the understanding of conversion value closer to Gross Profit.

At RocketMill, we have developed two products powered by a combination of our own proprietary tech, Octane, and open-source technologies. These products help advertisers better quantify the incrementality of specific channels of tactics, or compare the relative efficiency of different media mixes. 

• Incrementality testing is a method of measuring the incremental lift in performance, showing you the true impact of your marketing campaigns, without the use of cookies. It’s a form of A/B test designed to reveal the true impact of marketing campaigns. We can run incrementality tests on any media that can be targeted geographically, including offline media. Unlike digital attribution models, incrementality testing will not claim credit for conversions that would have been picked up by other channels anyway. This ensures that we optimise the media mix as a whole, rather than optimise one channel to the detriment of another.
• Dynamic Media Mix modelling uses historical data to create a statistical model that can be used to accurately report the contribution that each channel makes to overall performance. Unlike attribution modelling, both online and offline media can easily be included, as well as external factors such as pricing strategy and seasonality. Furthermore, the model can also be used for scenario planning, allowing us to identify the optimal media mix for any level of spend.

Driving lasting business value

Whilst strategies for growing sales and improving efficiency can be run in parallel, a natural plateau will eventually arise. The right media strategy can ensure you’re meeting the ever-growing share of online within retail, without neglecting profit.

It’s easy to slip into short-term thinking, but brand building and having an eye on long-term growth is key to driving lasting business value.

Need some support in planning your media strategy? Our team can help.