In the words of Sundar Pichai: “Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web.”
This statement captures the essence of Google AdWords and social media advertising platforms as well, which harness the power of algorithms and machine learning to revolutionise digital marketing.
Understanding ad algorithms
At the heart of Google AdWords is an auction-based algorithm that factors in the relevance and quality of ads, alongside the bid amount, to determine ad placement.
When a user initiates a search, the algorithm sorts through advertisers’ bids on keywords, scrutinises the quality score of each ad – which includes click-through rate, ad relevance, and landing page experience—and decides which ads to display.
Social media platforms operate on similar principles, with algorithms designed to analyse user behaviour, preferences and engagement.
By scrutinising a plethora of data points, these platforms deliver personalised ad experiences to users, thus optimising the advertisers’ reach and impact.
Harnessing the algorithmic advantage
To take full advantage of Google AdWords and social media advertising, advertisers need to consider several key factors:
Quality content: Algorithms prioritise content that satisfies and engages users. High-quality, relevant content is not only beneficial for the audience but also leads to better ad placements and expanded reach.
Data-informed decisions: Machine learning tools help advertisers make decisions rooted in a deep understanding of consumer behaviours and preferences, thus shaping more effective marketing strategies.
Continuous testing and learning: The dynamic nature of the digital world necessitates that marketing strategies evolve through constant testing and adaptation, leveraging the insights gained from machine learning analytics.
Balance automation with human oversight: While automation enhances efficiency, it’s crucial to maintain human oversight to ensure that campaigns are consistent with the brand’s values and messaging.
Here are some practical use cases that illustrate the implementation of machine learning tools in digital marketing
1. Google analytics and predictive analytics: Google Analytics, coupled with machine learning algorithms, can predict user behaviour based on historical data.
By analysing user interactions, such as clicks, time spent on the website, and conversion rates, predictive analytics models can forecast future user actions.
For instance, an e-commerce store can predict which products a visitor is likely to buy based on their browsing history, allowing marketers to personalise product recommendations and promotional offers in real-time.
2. Natural Language Processing (NLP) for social media monitoring: NLP algorithms analyse social media mentions, comments, and reviews to gauge public sentiment about a brand or product.
Social media monitoring tools powered by NLP can categorise social media posts as positive, negative or neutral, providing valuable insights into customer opinions.
Marketers can then adjust their strategies based on this sentiment analysis, addressing negative feedback promptly and leveraging positive sentiments for marketing campaigns.
3. Email marketing optimisation with A/B testing: Machine learning algorithms can optimise email marketing campaigns by conducting A/B testing on a large scale.
These algorithms analyse user responses to different email variations, such as subject lines, content and send times. By identifying patterns in user engagement, ML algorithms can recommend the most effective email strategies, leading to higher open rates, click-through rates, and conversions.
Marketers can automate the process of A/B testing, ensuring that their email campaigns are always optimised for maximum impact.
4. Programmatic advertising: Machine learning powers programmatic advertising platforms by analysing vast amounts of data to optimise ad placements and bidding strategies in real-time.
With DV360’s custom bidding, an advertiser can optimise campaigns toward specific key performance indicators (KPIs), such as increasing the viewability of ads or driving higher conversion rates.
For instance, if the goal is to enhance online sales for a new product, the algorithm can learn which ad impressions are more likely to lead to a sale based on historical data and other custom signals, like the time of day or device used.
5. Customer Lifetime Value (CLV) prediction: Machine learning models can predict the CLV of individual customers by analysing their past purchase history, frequency of transactions and spending patterns.
Marketers can use these predictions to identify high-value customers and tailor their marketing efforts accordingly. For instance, offering exclusive loyalty rewards or personalised discounts to high-value customers can strengthen their loyalty, increasing revenue and customer retention rates.
Humanising the digital experience
The challenge for marketers is to humanise the digital experience, ensuring that amidst the data and algorithms, the personal touch isn’t lost.
To overcome this, we must balance the efficiencies of machine learning with genuine customer engagement strategies. This involves creating content that resonates on a human level, powered by insights derived from machine learning.
The future of digital marketing lies in the relationship between human creativity and machine intelligence.
By mastering this alliance, marketers can unlock unprecedented levels of personalisation, efficiency, and, ultimately, ROI in their digital campaigns.
By Prasad V. Sawant, Head of Performance marketing at Boopin
