The digital marketing landscape is constantly evolving – from IOS 14 privacy changes, to third party cookies to artificial intelligence (AI) innovations. Marketing and ecommerce leaders must keep up with these changes and leverage technology to optimize their campaigns, return on ad spend and continue to drive business growth.
Adaptability and staying informed are crucial to success.
In this ebook, Cart.com will discuss:
- The state of the digital advertising market
- Privacy changes, third party cookies and AI
- AI, machine learning and digital advertising
- How to incorporate AI and machine learning into your marketing strategy
- A plan to supercharge your brand campaigns (in three simple steps)
State of the digital advertising market:
Privacy changes, third-party cookies, and AI
Meta's ad share is expected to drop even further this year due to economic uncertainty, cookie removal and iOS privacy changes that prevent Facebook from tying conversions to specific visitors. With more people rejecting app tracking, Facebook's targeting is weaker, leading to higher customer acquisition costs and limiting marketing efficiency for brands.
Why iOS privacy changes and third party cookies sunsetting are bad news for brands?
These changes directly impact brands - but not in a good way. In technical terms, without Apple Identifier for Advertisers (IDFA) and cookies, ad platforms like Facebook can no longer tie a conversion to specific ads.
As a result, return on ad spend (ROAS) plummets making it more challenging for brands to measure the conversion rates and the overall effectiveness of their marketing campaigns as a whole.
Is there a Solution?
Management guru Peter Drucker famously said, “If you can't measure it, you can't manage it.” Like any other business initiative, the results of marketing activities should be measurable. Accurate measurement is crucial for marketing campaigns, as it provides a compass for effective ad spending. If brands can’t measure what they are doing, how can they improve and more importantly how can they invest ad dollars effectively?
The good news is that there is a solution. AI-powered marketing attribution software and machine learning is enabling a growing number of brands to gain the insights they need to win the advertising game, make decisions faster and drive business growth. Not surprisingly, a recent study from Adobe demonstrated how top-performing companies are more than twice as likely as their peers to use AI for marketing.
Let’s dive deeper into use cases of how marketers are leveraging AI and machine learning to optimize their campaigns.
AI, machine learning and digital marketing
Since November 2022, a lot of the conversation around AI has centered on Chat GPT: the popular OpenAI chatbot that reached 100 million monthly active users in January. (According to a UBS study, ChatGPT is the fastest-growing consumer application in history!)
And yet, the impact of AI on digital marketing goes way beyond ChatGPT: AI, machine learning and prescriptive analytics are already revolutionizing how marketing teams measure their campaign strategy and drive growth.
Before we dive into use cases, let’s cover some of the basics definitions:
How is AI and Machine learning transforming marketing analytics?
AI is transforming marketing analytics by enabling marketers to analyze vast amounts of data quickly and accurately, identifying patterns and insights that would be difficult or impossible for humans to recognize.
AI-powered analytics tools can save marketers a ton of time, by automating data collection, analysis and reporting, providing marketers with real-time insights into their campaigns' performance and allowing them to make data-driven decisions quickly.
AI can also help marketers identify new opportunities and target customers more effectively, leading to more efficient ad spend and better ROAS.
Use Case: How are the best marketing teams leveraging AI?
In working with several brands in the last few years, what we observed repeatedly is how the most successful marketing teams are the ones that are constantly monitoring performance, learning from each campaign and adjusting their plans quickly to incrementally drive growth.
Let’s look at an example of this in action. The below graphic shows the marketing campaign performance of a brand that spends sizably across various advertising channels. The underperforming campaigns in the lower quadrants take up over 30% of ad spending, areas where they are burning cash on ineffective campaigns.
So, how are smart marketers preventing burning cash on ineffective campaigns? Nine out of 10 times, these winning teams are able to limit the damage of underperforming campaigns by taking advantage of softwares that aggregate all of the data from diverse marketing channels (think: Facebook, Google,TikTok and Klaviyo) and then leverage AI and machine learning to gain critical insights and make decisions.
And while, not every marketing team has yet embraced the use of AI powered softwares, a recent survey showed that 84% of global business organizations believe AI will offer them a competitive advantage. Cart.com confirms that by using AI-powered marketing attribution software, brands can gain an advantage. They can quickly identify what campaigns are working and which ones are not and use that information to shift marketing budgets away from underperforming campaigns to high-performing ones.
How to incorporate AI and Machine learning into your Marketing Strategy: Introducing Cart Unified Analytics
Cart Unified Analytics helps brands improve business performance by converting complex data from across their ecommerce operation into practical, concise tactics. Our solution uses descriptive, predictive and prescriptive analytics to help marketing teams receive real-time reports and custom campaign recommendations that tell them where to invest ad dollars for the highest return - saving brands time and money.
A plan to supercharge your campaigns in three easy steps
If you are a marketer working at an ecommerce company, this will sound familiar: Every week, your team and/or your marketing agency has a meeting to review campaign performance and key metrics, such as ad spend, purchase conversion, CAC, ROAS, and LTV. And every week, it's the same thing: evaluating results across platforms that are time-consuming, complex and often inaccurate. Here are three better options:
1. Get a holistic view of your marketing campaigns across all channels
A recent McKinsey study found that brands typically spend 80% of their time organizing data and only 20% analyzing the results for decision-making. This is especially true for brands that run promotions across more than one channel, such as Amazon, Facebook, Google, Klaviyo, Attentive and Pinterest. Looking at each platform's data in isolation prevents brands from having a holistic view of their campaign performance. The difficulty in accessing, compiling and analyzing marketing data leads to continuous wasted investment in underperforming campaigns
Cart Unified Analytics is the only platform that automatically stitches all data sources across marketing, sales, and even inventory data into one dashboard for fast insights, so brands can get a holistic view of their marketing.
2. Evaluate each marketing channel performance – fair and square
Even brands that historically had exclusively focused on direct-to-consumer, and avoided channels like Amazon or Walmart, have now embraced the multi-channel approach and are expanding into various marketplaces and retailers.
But not all channels are created equal, and the best channel for each product in a catalog likely varies, which is why experienced brands regularly evaluate channel performance and find room to shift budgets and product offerings accordingly.
Then again, as explained in No.1, it’s easy to fall into the trap of evaluating channel performance in a silo, resulting in an inaccurate view. Reports from ad platforms can be misleading because they use attribution approaches beneficial to themselves – positively skewing performance so that brands continue to invest more dollars in their platform.
Powered by clickstream and Attribution AI technologies, Cart Unified Analytics reduces reporting bias and gives an honest “opinion” about each channel's performance. In fact, it’s not uncommon for Cart.com and its proprietary Attribution AI to uncover over 20% swing in ROAS across channels and campaigns, compared to the ROAS reported in Google Analytics or Facebook (which uses mainly last-touch attribution).
3. Fine-tune your ad campaigns for the highest return
After identifying your high-performing channels, the next step is a deep dive into campaigns. Even among the high-performing channels, some marketing campaigns are burning cash while only a few are generating revenue. The split between these two types of campaigns generally follows the 80/20 rule. Brands must distinguish the 20% top-performing campaigns among the crowd and continue to scale them.
We often notice that brands don't spend enough money on top-performing campaigns, leaving money on the table. With campaign-level attribution, Unified Analytics provides campaign recommendations within the platform enabling brands to quickly and easily optimize ad spend for the highest return across all marketing channels.
Want to learn more about how AI & machine learning can improve your marketing analytics and ROAS?
Connect with an expert to see our platform in action.