Digital marketing services have come a long way in the last decade. From simple banner ads to complex AI-driven campaigns, the industry continues to evolve at breakneck speed. One technology that is emerging as a true game-changer is machine learning (ML).
Imagine you landed on a website and saw a banner displaying hiking boots. Though you love hiking, those boots don’t appeal to you. But the ad seems oddly specific. You realize the site’s AI has been tracking your browsing behavior and identified you as an outdoor enthusiast. The tech got it right, yet so wrong!
This simplistic use of ML is just the tip of the iceberg.
Through this blog, we will unpack the true capabilities of ML in digital marketing and elaborate how it helps marketing service providers to take personalization, predictive analytics, dynamic creative optimization, and more to the next level.
To get us started on the right foot, let’s first understand…
What is Machine Learning and How Does it Work?
Machine learning or ML refers to algorithms that can learn from data and improve themselves over time without explicit programming. ML algorithms help marketing service providers detect patterns in vast volumes of data to make predictions or decisions. The more data an algorithm trains on, the more accurate it becomes.
For instance, an e-commerce site can train an ML model on customer data including shopping behavior, preferences, demographics, etc. The algorithm will uncover patterns and build a customer profile to predict which products a user will likely buy.
Unlike hard-coded software with predefined rules, machine learning models continue to learn and upgrade independently. ML with a qualified marketing service provider brings unprecedented automation, personalization, prediction, optimization, and innovation to digital marketing services. But how does it exactly work its magic?
Let’s find out…
The Basics: How ML Models Learn
ML algorithms can be supervised, unsupervised, or reinforcement learning models:
– Supervised Learning: The algorithm is trained on labeled sample data to learn the correlation between inputs and desired outputs. It predicts outcomes for new data based on historical patterns. E.g. Predicting customer lifetime value based on purchase history.
– Unsupervised Learning: The model trains on unlabeled data to identify intrinsic patterns. E.g. Segmenting customers into groups based on common attributes.
– Reinforcement Learning: The algorithm is rewarded for right answers and corrected for wrong ones. It learns through trial-and-error interactions to achieve goals. E.g. Optimizing ad bidding strategy to maximize clicks at lowest cost.
ML brings three distinct advantages to marketing for a digital marketing company: automation, insights, and optimization.
Let’s analyze how ML delivers these benefits.
|Type of Learning||How It Works||Digital Marketing Examples|
|Supervised||Trained on labeled sample data||Predict lifetime value, sentiment analysis|
|Unsupervised||Finds patterns in unlabeled data||Customer segmentation, anomaly detection|
|Reinforcement||Rewarded for right answers, corrected for wrong||Ad bidding optimization, dynamic pricing|
Unlike rules-based software, machine learning models continue to learn and upgrade independently based on new data.
This empowers a digital marketing company in three key ways:
- The ML Advantage #1: Intelligent Automation
ML algorithms automate complex or time-consuming tasks that are impossible with traditional rules-based software. For instance:
– Sending personalized push notifications and emails to re-engage users
– Transcribing video/audio content for subtitles
– Analyzing sentiment on social media to gauge brand perception
– Calling out key moments in videos like product mentions
– Recommending related content onsite to increase time-on-page
– Fetching dynamic product/price data from catalogs to auto-populate ads
– Generating myriad ad creatives based on high-performing templates
These are just some examples of ML’s potential to automate digital marketing services and lift efficiencies. Mundane yet critical tasks can be automated to help focus strategic thinking on high-impact activities.
The ML Advantage #2: Actionable Insights
ML powers big data analytics to uncover rich consumer and competitive insights that would be humanly impossible to deduce. For example:
– Micro-segmenting audiences based on attributes like demographics, interests, values, and behaviors
– Predicting individual customer lifetime value to prioritize high-value accounts
– Uncovering channel preferences of user cohorts
– Forecasting demand for products before launch
– Detecting fraud in advertising
– Parsing competitor strategies by analyzing their digital footprint
The ability to gather granular consumer and industry intelligence allows brands to devise hyper-targeted strategies that resonate with niches. AI reveals insights that set leading marketers apart.
- The ML Advantage #3: Dynamic Optimization
The self-learning capabilities of ML algorithms allow them to continuously optimize tactics and campaigns.
– Programmatic Advertising: ML bids on each ad impression based on likelihood of it driving conversions. The model gets smarter at bidding optimally to lower cost per acquisition.
– Dynamic Pricing: ML tweaks product pricing based on demand signals like page views and add-to-carts to maximize revenue.
– Content Recommendations: ML suggests onsite content that will engage users based on individual user affinity.
– Creative Optimization: ML generates high-performing ad variants tailored to different segments for improved results.
ML essentially creates a feedback loop for continuous optimization by learning from ongoing data inputs. Any Digital Marketing Company tactics can be dynamically refined to boost KPIs.
The possibilities with ML are truly infinite. The technology will continue to revolutionize marketing for years to come. Marketing service providers who embrace ML early will have a first-mover advantage in driving impact.
So what’s your ML game plan?
In summary, Consagous Technologies, a premier digital marketing company, leverages ML capabilities like sentiment analysis and predictive analytics to understand customer pain points and evolving needs. This allows delivering personalized app experiences that customers love.
To explore how Consagous can build future-ready mobile apps enhanced by ML, get in touch with us today.