Machine Learning Unlock the Power of Predictive Analytics for Your Business

Making accurate predictions about your customers is necessary for your business, and with machine learning (ML), marketing teams can?transform customer data into actionable insights to drive growth.?

ML allows you to unlock predictive analytics at an unprecedented scale, revolutionising areas like consumer behaviour modelling, personalised recommendations, and predictive lead scoring.

AI expert Andrew Ng says, "Businesses live and die on their ability to make predictions." In this guide, we'll explore how machine learning can transform prediction for enterprises of any size and industry.

Demystifying Machine Learning

Many?view?ML as a?mysterious?black box. But in reality, it uses statistics and algorithms to find meaningful data patterns without explicit programming.?

For instance, an ML model can learn which customer profiles tend to purchase certain?products by analysing millions of purchase records.?It develops a complex mathematical function mapping input data (e.g. age, location, past purchases) to outputs (e.g. probability of converting).

Machine?learning automatically builds predictive models that are too complex for humans to code manually. This makes it possible to discover subtle insights in your data you may have never uncovered on your own.

Types of Machine Learning Models

There are three main types of ML algorithms for predictive analytics:

Supervised Learning

This involves "training" algorithms on labelled datasets where the desired output is already known. For example, a model could be trained on emails that have been marked as spam or?not.?It learns patterns to predict whether a new email is spam based on its text, sender, etc.?

Supervised learning is excellent for classification (assigning categories) and regression (predicting numeric values). Popular examples include logistic regression, random?forests,?and support vector machines.

Unsupervised Learning?

In this approach, algorithms must find patterns in unlabelled, uncategorised data. There are no predefined outputs. The model must group data points with similar behaviours and characteristics.

Unsupervised learning is often used for clustering, dimension reduction, and association rule learning. Key techniques like Apriori,?K-means,?and principal component analysis fall under this umbrella.

Reinforcement Learning

Here, algorithms learn by trial-and-error interactions with an environment. The model becomes more accurate over time by continually adjusting predictions based on feedback such as rewards and punishments.?

Reinforcement learning has powerful applications in areas such as financial trading, healthcare treatment plans, and gaming AI, such as AlphaGo.

Key Machine Learning Applications for Marketers

Now that you understand the inner workings of ML, let’s explore some of its most?potent?applications for growth-driven marketers:?

Predictive Lead Scoring

Lead scoring helps gauge the sales readiness of prospects in your funnel and prioritise the highest-value opportunities. This prevents wasted time?pursuing?deals unlikely to convert.

While rules-based scoring relies on heuristics, machine learning can analyse leads' complete profile and activity history to make more intelligent predictions. The model looks for signals correlated with?conversion, such as?email engagement, assets?downloaded,?and sales inquiries.

Over time, the accuracy of score predictions keep improving.?By switching to ML-powered lead scoring, companies have achieved up to a 75%?increase?in qualified?leads.

Churn Prevention

Acquiring new customers costs 5 to 25 times more than retaining existing ones, so minimising churn is?essential.

Machine learning algorithms can comb through customer data to detect the subtle patterns that indicate churn?risk,?often before?customers?decide to leave. Models can analyse in-product behaviours, service usage?trends,?and survey responses to identify at-risk users most likely to cancel or lapse.?

Brands can then proactively engage customers showing churn signals through promotions,?education,?and loyalty programs.?Machine?learning has reduced yearly customer defections by as much as 8% in some cases.

Recommendation Engines

Product recommendations are an?effective method?of boosting conversion rates. When done right, they influence between 10% and 30% of purchases on retail sites. However, the rules-based filters often?annoy?users with repetitive, irrelevant suggestions.?

Here,?machine learning truly shines. ML algorithms dynamically map user preferences to products based on historical data. They factor in each visitor's complete profile and behaviours to display personalised, timely suggestions likely to excite them.

For example, leading entertainment platform Spotify leverages deep learning to recommend music tailored to your taste. This produces a 2X higher click-through rate than generic suggestions.

Predictive Analytics in Marketing

Marketers?rely heavily on?their ability to make accurate forecasts to guide strategy:

• Predictive demand modelling shows expected lead volume and sales pipeline by market segment over time.?
• Response modelling uses past campaign metrics to estimate expected click-through,?conversion,?and engagement rates for future efforts.
• Customer lifetime value (LTV) modelling gauges the long-term revenue potential of current customers.

Without machine learning, creating such projections relies on risky guesstimates and lagging indicators instead of actual signals in data.?ML-powered?predictive analytics offers marketers statistical, dynamic and forward-looking forecasts to pinpoint new opportunities and optimise resource allocation.

Getting Started with Machine Learning

You’re?now convinced of machine learning’s incredible marketing applications. But ML also has notorious barriers to entry?such as?scarce data science talent and long development timelines.?

So, how can marketers practically leverage predictive models? Here are five key steps to efficiently launch ML without needing a PhD:

Start by?identifying your key business initiatives

Before?exploring machine learning?applications, have a clear roadmap aligned with revenue goals.?Know the 3-5 high-level?priorities,?whether that’s reducing churn, boosting lead conversion?rates,?or improving sales forecasts.

Understand Your Data Infrastructure

What customer data sources do you have access to? Can you easily combine them for analysis? Assess any pipelines,?warehouses,?and infrastructure. Clean, integrated data is?essential?for functional machine learning.

Find the Right External Capabilities?

Identify gaps in data science skills and technology on your team. Consider?using?machine learning consultants or managed platforms that provide models?tailored?to your?use case,?ready to use?- no coding required.?

Start With an Impact-Driven Pilot Project

Target a well-defined business problem to prove overall viability and gain confidence. Rather than boil the ocean, pick an area your C-suite cares?about, such as?increasing average order value or reducing payment failures.?

Measure Lift to Demonstrate Value

Compare model predictions/performance to key metrics over your existing business processes.?Demonstrating?small percentage gains in metrics like lead conversion can earn executive support for further AI expansion.

Examples and Results?

Still unsure about the potential of machine learning??Here are a couple of powerful examples from leading enterprises:

Company A Enhances?Online Ad Targeting?

The retailer leveraged external machine learning capabilities to optimise their Google Ad campaigns.?They built a propensity model predicting the likelihood of converting by?combining internal purchase data with demographics and search?data.?This enabled granular targeting of high-potential customers.

Results:

• 15% increase in clicks at?the?same cost?
• 14% lift in online sales
• Improvement?in conversion?rate of 25% or more

This had an impressive?annual?incremental revenue impact of over $8.5?million.

Company B Reduces Subscriber Churn?

The Fortune 500 media conglomerate used AI-based churn predictions to reduce customer defections. The model detected users likely to cancel subscriptions based on consumption habits, digital?behaviours,?and satisfaction survey data. High-risk subscribers received tailored promotions and loyalty incentives.

Results:?

• 48% more accurate churn predictions
• 7% year-over-year reduction in subscriber churn
• 10X?return on investment from additional customer lifetime value

Their machine learning initiative was critical for supporting a core business objective of maximising retention.

Time To Execute?

We stand at an inflexion point where AI is transforming marketing.?Now is the time to build your data science capabilities, with?87% of marketers planning to adopt machine learning next?year.?Prioritise?use cases?aligned to business value and move beyond?outdated?methods.

Machine learning offers us an analytics?tool?where historical data transforms into accurate?predictions?of the future. Will you?embrace?this disruptive force to lead markets or cede ground to early predictive analytics adopters? The choice is clear.

It’s time to unlock your data’s hidden potential using ML’s cutting-edge techniques. Become a visionary leader instead of?worrying about missed opportunities.

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