From Data to Decisions: How Machine Learning Platforms Can Transform Your Business Decisions

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When the savvy marketing team at Hydrant realized there was room for improvement in their email marketing campaigns, the hunt began for the perfect solution.

Despite sending promotional messages far and wide, open and click-through rates remained steady — which wasn’t what they wanted. They knew they could do better for their customers, and there had to be a smarter way to engage their audience.

Rather than continuing to blast out generic offers, the Hydrant team wanted to get strategic. They wanted to understand their customers’ interests and preferences and craft relevant messages to pique engagement.

In other words, Hydrant needed a way to identify their most valuable customers and those most likely to respond. They needed actionable information about their customers, not guesses.

But with limited resources, how could they determine which customers to target to optimize spending?

From hindsight to foresight with machine learning platforms

Situations like these demonstrate the pressing need for data-backed decisions in business. Relying on intuition frequently leads teams astray. Hydrant needed to find a better approach to understanding their customers and allocating budgets wisely.

Enter machine learning platforms.

With the power to uncover useful intel from data like never before, machine learning (ML) platforms held the promise of transforming Hydrant’s marketing strategy.

We’ll return to Hydrant’s story later to see how ML platforms delivered results. But first, what exactly is machine learning, and how does it enable businesses to advance from hindsight to foresight?

What is machine learning?

Machine learning transforms businesses across industries by allowing them to uncover useful information faster.

But what exactly is machine learning, and how can it impact your business?

At a basic level, machine learning is a type of artificial intelligence that allows computer systems to learn automatically through data without being explicitly programmed. Machine learning algorithms detect data patterns to make predictions or decisions without human intervention.

For example, machine learning powers many of the recommendations we see on sites like Netflix and Amazon — by analyzing data on customers’ viewing and purchase history, machine learning models can predict which new TV shows we enjoy or surface products we may want to buy.

While consumers experience machine learning through recommendation engines, this technology can also optimize essential business functions, from marketing to sales, using other kinds of powerful machine learning algorithms. 

And machine learning platforms make it easy for companies to leverage machine learning, even if they don’t have in-house data science experts.

What is AI predictive modeling?

A key capability enabled by machine learning is predictive modeling. But what exactly does this mean?

Predictive modeling uses machine learning algorithms to analyze historical data and predict future events. 

The models identify patterns and relationships in data that can be used to forecast outcomes like customer lifetime value or product demand.

For example, a predictive model could analyze a customer’s past spending, demographic data, and engagement metrics to predict how much that customer is likely to spend in the next 12 months. 

Or it could forecast future sales of a product based on past sales data, seasonality, pricing changes, and other variables.

The key difference between predictive modeling and more simplistic forecasting methods is that machine learning models can continually learn from new data and improve their accuracy over time. 

The models update their logic as they process more data examples to make better predictions.

With predictive modeling, businesses can shift from reactive to proactive planning across departments. 

Instead of relying on intuition or static historical reports, predictive models enable data-driven decisions on everything from budgeting to inventory planning.

AI predictive modeling takes this further by leveraging more advanced machine learning algorithms. AI models can process more complex data types and scenarios with greater accuracy.

These platforms make AI predictive modeling highly accessible. With drag-and-drop simplicity, any business can start uncovering predictions to optimize marketing, sales, finance, and more. The transformational power of prediction is now available to all.

3 use cases for machine learning platforms in your business

Optimize resource allocation across your organization

For companies with distributed operations, optimizing resource allocation is crucial yet challenging. How can you ensure the right resources in the right places and times?

This is one area where machine learning platforms can make a transformational impact.

Machine learning models can generate accurate forecasts for your future needs by analyzing your historical data on resource usage, demand patterns, sales, and other variables, enabling you to allocate resources in anticipation of demand. 

For example, models could forecast store traffic patterns to optimize staff schedules. Or predict future parts demand at warehouses to streamline inventory.

Case in point: CAA Club Group.

The CAA Club Group (CCG) optimized resource allocation across all facilities using AI predictive modeling capabilities. 

Previously, the CCG relied on manual forecasting methods that were time-consuming and limited in scope. One team member spent an entire week creating forecasts for just a portion of the club’s region.

With predictive modeling, the CCG rapidly built highly granular forecasting models that predicted every hour’s call volume and service needs across nearly 600 micro-regions.

As a result, the CCG reduced service response times and increased member satisfaction. 

And owing to the platform’s remarkable simplicity and ease of use, they could gain all these benefits without added data science resources.

Make more informed marketing decisions

Another area where machine learning platforms can drive operational transformation is in marketing decision-making. 

Machine learning models built to predict customer lifetime value can guide smarter marketing decisions. By analyzing early user data, the models forecast campaigns’ future return on ad spend (ROAS).

In effect, rather than best guesses, marketers gain data-driven confidence to pick better strategies. Marketing spend efficiency improves as teams decide based on predicted long-term value versus short-term vanity metrics.

Armor VPN is a study in ML-powered marketing decision-making, having gained the ability to make better-informed marketing decisions using predictive lifetime value (LTV) models. 

Previously, Armor VPN relied on intuition and short-term data to guide their user acquisition efforts. This made it difficult to optimize their marketing budget and campaigns.

With AI, Armor VPN built LTV models that predicted the revenue campaigns would produce using early user data. Models showed a 25% gap between expected and actual LTV. 

Armed with these predictions, Armor VPN can now:

  • Identify the most effective campaigns for long-term ROI and optimize budget allocation 
  • Spot “late bloomer” campaigns with high predicted LTVs despite slow early results
  • End or adjust campaigns that won’t deliver valuable users long-term
  • Share revenue predictions with stakeholders for improved planning

AI predictive analytics transformed Armor VPN’s ability to make informed marketing decisions. Machine learning can unlock similar benefits for your marketing team.

Refine retargeting strategies and save on marketing spend

SciPlay faced a challenge when they wanted to optimize their mobile game retargeting strategy.

In the past, they used rigid rules to determine retargeting audiences. But a more precise strategy could have retargeted only those customers likely to respond to those efforts.

It was a tricky situation, but machine learning platforms are well-suited to address challenges like these.

By analyzing historical user data, ML models can identify the specific audience most prone to re-engage if targeted. This enables precise, tailored outreach only to receptive audiences.

So that’s precisely what SciPlay did.

They built predictive models with AI that identified the players most likely to re-activate if retargeted. The models gave SciPlay unprecedented future visibility into user behavior, allowing them to tailor offers only to those receptive players.

In data-driven decision-making, machine learning opens doors to a future-oriented perspective.

With machine learning, you can predict customer actions. This future view enables a level of personalization that’s otherwise impossible to achieve.

And with that, you can personalize offers based on an individual’s predicted tendency to reactivate. You can also accurately measure retargeting campaigns’ direct impact and return on investment.

For SciPlay, the models enabled them to tailor messaging precisely based on a player’s likelihood to return.

In the end, SciPlay saw significant efficiency gains and cost savings, achievable for just about any company.

How AI helped Hydrant boost winback rates by 260% in just 8 weeks

Going back to Hydrant’s story, the marketing team wanted to improve their email campaigns’ results and needed a better way to optimize their marketing spend.

So they turned to AI’s predictive analytics.

In just two weeks, AI built a churn prediction model for Hydrant, analyzing thousands of customers’ purchase history data. The model generated individual predictions on customers’ likelihood to churn or purchase again.

Armed with visibility into which customers were most likely to churn, Hydrant could focus retention efforts on high-value customers worth retaining. They also built a winback model identifying customers likely to purchase again if targeted.

In winback campaign testing, customers AI predicted as least likely to return saw a 260% higher conversion rate when targeted than a control group. Their average revenue per reactivated customer also increased by 310%.

In only 8 weeks, AI’s predictions had transformed Hydrant’s customer retention and winback strategies. Marketing spend was optimized, and results skyrocketed.

Machine learning platforms make this type of transformation accessible to any business. 

By turning data into accurate predictions, these technologies enable data-driven decision-making that drives growth, efficiency, and competitive advantage.