Where once advertisers had to carefully match sales data from Google Ads campaigns with the sales data on Amazon, Kate Cox, CMO at BrightBid, explains how advanced AI and attribution tools can provide accurate insights into how well ads are doing in real time.
Black Friday became the biggest shopping phenomenon of the year in 2001, dethroning the Saturday before Christmas in the process. Four years later, Cyber Monday was thrown into the mix – meaning shoppers have close to an entire month to delight in discounts and promotions on their favourite products, both in-store and online.
2023 looks set to be a retailer’s paradise, with Shopify’s research identifying that 53 per cent of UK shoppers are planning to increase their spending during this year's events compared to previous years. But that doesn’t mean it’s time to throw the confetti just yet – with many brands still struggling to find the most effective platforms to advertise on.
Two giants in the advertising arena, Amazon and Google, often vie for the attention of these retailers. While advertising on Amazon comes with margin challenges, its vast customer base and data-rich environment offers unique opportunities like driving conversions at a lower cost-per-acquisition (CPA). Google, on the other hand, offers powerful advertising tools like Google Shopping Ads, performance max and text ads that reach a broad audience.
But Google Ads and Amazon Advertising don’t have to be mutually exclusive. Leveraging analytics and AI can empower brands to unlock synergy between the two platforms, activating their unique strengths and opening the door to optimised spend.
It’s a numbers game
Access to data insights are invaluable to brands and retailers alike, helping them to make informed decisions, optimise advertising strategies and ultimately drive higher returns on investment (ROI). When it comes to sharing data to sellers, Amazon outshines other retail platforms.
But there’s a tricky part for advertisers. During Black Friday, when traffic is redirected from search engine platforms like Google to Amazon, there’s usually a manual job involved. Advertisers have to carefully match sales data from Google Ads campaigns with the sales data on Amazon. This takes a lot of time, and as more data piles up, it gets even more complex.
Using advanced AI and attribution tools that do this job automatically has now become extremely important. They help to connect how much is spent on ads with the actual sales data, giving accurate insights into how well the ads are doing in real time.
The Google-to-Amazon transition
The modern customer journey involves multiple touchpoints, both online and offline. Shoppers may find a product through Google, learn more about it on Amazon and then decide to buy it.
This level of complexity makes it difficult to determine which advertising efforts led to a sale – and subsequently harder to evaluate the actual ROI between the different platforms. The major challenge for this evaluation comes from difficulties in attributing conversions.
Attribution models are used to assign value to different touchpoints along the customer journey, ranging from first-click attribution (where all credit goes to the first touchpoint) to last-click attribution (where the conversion solely goes to the last touchpoint), or multi-touch attribution (where credit is distributed based on real customer journeys). Accurate attribution is crucial for informed, data-driven decisions and effective campaign optimisation.
The best-in-class brands are already leveraging advanced AI tools and methods for attribution, allowing them to analyse data from various touchpoints throughout the entire customer journey. Not only does this give them a more in-depth understanding of conversion dynamics – it ultimately helps them bridge the gap between Amazon and Google.
As simple as A… B…
Testing is the foundation of effective advertising. Controlled experiments and incrementality tests that assess various strategies, ad formats, targeting options and messaging are also vital to gauging the impact of ads on both platforms.
The more comprehensive the test plans are, the more likely it is to cover all the bases. This includes trying different ad creatives, experimenting with audience segments and evaluating bidding strategies. When a test is in place to test keywords, assess ad formats, refine landing page design and inform decisions on budget allocation, the most effective direction becomes clearer.
This, coupled with advanced attribution methods, provides a deeper understanding of campaign performance. Helping to consider the entire customer journey for businesses reach the ultimate goal of – real – return on adspend.
Changing the game
Just as Black Friday reshaped the shopping calendar in 2001, AI solutions have now reshaped the advertising landscape and bridged the competition gap for a more effective return on adspend.
Enabling a smooth transition of potential customers from Google Shopping ads to the Amazon store was a process that was not previously achievable, making it hard to fully take advantage of the power of both platforms. However, there are now AI solutions like BrightBid that help to bridge the data and bidding gaps between Google and Amazon.
Adopting AI solutions that provide comprehensive tests designed to streamline the attribution challenge, simplify the process and connect the two platforms through AI-driven bid optimisation is crucial to getting the maximum return at this ‘make or break’ time of the year.
By Kate Cox