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Aqurate Map 2021 - Electronics
The Aqurate Map considers Digital Presence and Digital Maturity for the top 8 players in the Electronics category of the Romanian e-commerce...
Ads have become an important tool in reaching out to users and driving conversion. What most businesses fail to accomplish is finding the right message and matching it with the right audience while also timing everything accordingly. The question to ask is “Can AI solve what human intuition is struggling to do?”. We’ve answered this question by using the 3 P’s formula: potential of data, pattern information, and perfect timing.
Did you know?
Implementing AI models in ads campaigns deliver up to 7% increase in CTR and a boost of up to 30% in the ROI of online marketing budgets, according to JOT Media.
Potential of data
Technology is evolving at a rapid rate and whether you are an ecommerce entrepreneur or a digital marketer in the retail zone, you should get updated about your business’s marketing strategy by making use of all the data it holds. Oftentimes, campaigns are driven by intuition-based calls that do not consider the full potential of data, but now is the time to get a competitive advantage through the technologies of big data.
In simple terms, such models require internal metrics such as keywords, ad text, bid, length and sentiment of the text to offer a forecasted result of dominant KPIs like clickthrough rate. For an even more accurate prediction, external factors such as data about the weather, media news and events along with bank holidays can be successfully implemented into the AI models.
Pattern information
It is not uncommon for individuals to debate on the controversy of AI, however, the process behind deep learning is quite compelling. For several years now, machine learning algorithms have helped minimize marketing budgets because of the way artificial intelligence learns repetitive behavior and looks for patterns in the customer’s journey. Simply put: the more data, the better the AI pattern recognition, and the more accurate the future ads for that user.
The information is then processed through machine learning methods like time series analysis using regression. The algorithm receives large amounts of precise and reliable data from your ad networks (e.g. Google Ads, Facebook Ads) that gets processed through standardized mathematical approaches to find conclusive relationships.
Perfect timing
Finally, after the model extracts pattern information and conveys which keywords are likely to increase impressions and CTR, the last step in shopping ads optimization is finding the right moment when the customer sees the personalized ad. It can also indicate the perfect timing for launching a campaign, depending on responsiveness patterns and other events that might negatively affect buyer behavior.
An ads optimization model can significantly increase the performance of marketing campaigns by leveraging valuable information that would otherwise not be fully tapped into or missed out on.
Find out what to look for when searching for an AI-driven product recommendation engine: How to choose the right product recommendation engine →
Useful resources:
Other AI solutions in ecommerce (Whitepaper)