Why It’s Time for CPG Brands to Fall in Love With Machine Learning

By: NCS Marketing 

Is the idea of doing more with less becoming a reality with artificial intelligence? 

We’re on the cusp of the AI and machine learning age, and it’s time for marketers to embrace these technologies. There are a myriad of ways they can put them to optimal use. 

At NCS, we’ve been evaluating and testing machine learning methods for some time now, and we’ve learned they can be used to create greater efficiencies while also improving advertising effectiveness. We may not understand yet everything that is possible, but we’re convinced that all businesses must embrace this new age – or risk falling behind.

Consider, for example, that brand marketers are under constant pressure to be more agile and adaptable, make quick decisions and act fast. Every year, goals are pushed higher. This is true even in uncertain times. Marketer stress points continue to be the same year to year. Budgets are always tight, demands for a greater return on advertising spend (ROAS) are constant, and – of course – we all need to do more with less.

Meeting those expectations is challenging, especially without the right advertising insights. Two out of three marketers aren’t very confident in their current media marketing and advertising strategy to effectively produce desired outcomes, according to a survey conducted by the CMO Council in collaboration with NCSolutions. 

A key obstacle to success is knowing what makes a campaign effective. Only 23% of not-so-confident marketers say they understand why their campaigns are successful, compared to 39% of confident marketers. Meanwhile, 28% of all marketers say failing to understand which campaign aspects drive the best outcomes holds back their marketing success. 

When applied to advertising, artificial intelligence (AI) and machine learning can help marketers close these knowledge gaps and help them make faster - and smarter - campaign decisions.

The Basics – and Beyond

A subset of artificial intelligence, machine learning uses statistical models to analyze and draw inferences from patterns in large amounts of data.

In advertising, brands can leverage machine learning to analyze millions of data points and infer the relationship between advertising and incremental sales – faster and with greater accuracy.

Marketers gain insights that are:

Precise and reliable. The ensemble method of machine learning takes a data-adaptive, flexible approach to fit the best of multiple models to the data. When combined with deep industry knowledge and rigorous testing, this approach is capable of delivering more precise and reliable insights with greater consistency.

Actionable. With an early read of incremental sales results, marketers understand in near real-time which elements of their advertising – creative, audience, frequency, and platform are just a few examples – are working. They can adjust their strategy early or determine what they need to change in the future to improve outcomes.

Useful for scenario planning. Also known as counterfactual analysis, this method helps marketers understand what might happen under hypothetical situations. For example, it allows marketers to determine – with accuracy – what the sales outcomes would have been if people exposed to Creative A were instead exposed to Creative B.

Machine Learning is Complex and Challenging 

With all its benefits, you might wonder why more brands aren’t using machine learning to drive strategy. A key reason is that understanding the impact of advertising on CPG sales is a complex process. 

It’s important to remember that many variables impact whether or not a person decides to buy a product. The typical machine learning methodology will require millions of data points to ensure accuracy, and one function of automation is to help identify which data and variables have the greatest impact on CPG purchases.

Data on its own isn’t enough. Applying machine learning to advertising requires a unique blend of data science talent, investment in technology, and patience. Effective machine learning models aren’t marinated overnight but rather over the years.

A look at how NCS has implemented machine learning illustrates what it takes. We began our machine learning journey several years ago, so our methodology bears the imprint of our own intellectual property along with rich statistics, extensive testing, and a robust database of results. Over the years, we’ve run thousands of machine learning models, allowing us to deliver faster, more granular, more accurate, and more representative insights – at scale. 

What this decade-long effort means for NCS clients is that they’ve been able to turbocharge their advertising agility. By making adjustments based on NCS's early reads to their advertising strategies in near real-time, they can drive higher incremental sales and run more effective campaigns while also increasing ROAS.