The ultimate guide to predictive analytics in marketing

Are you using predictive analytics in marketing to reach the full potential of your campaign performance?

Using marketing analytics software that offers predictive analytics will improve the way you generate leads, make data-driven decisions, refine your campaign targeting, and much more.

In this article, we’ll cover what predictive analytics is, why it’s important, and how to implement it.

What is predictive analytics in marketing?

Predictive analytics in marketing is a specific type of analytics that aims to forecast and predict the likely outcomes of your marketing activities and processes.

This powerful approach uses different types of marketing data as well as machine learning – including artificial intelligence (AI) – to accurately predict future outcomes of consumer behaviours, trends, interactions, and more.

At its core, predictive analytics in marketing aims to answer the question “what comes next?” Will a certain customer phone call result in a valuable lead? Is a particular marketing channel likely to see increased engagement? Will a certain campaign yield the best results at this specific point in the customer journey?

Achieving 100% accuracy for future outcomes is near-enough impossible in marketing. However, with the right platform to analyse historical and real-time data, you can make accurate predictions to guide your marketing strategies.

Key concepts of predictive analytics

To fully grasp the power of predictive analytics in marketing, it’s essential to understand some key concepts:

• Marketing analytics: Marketing analytics is the practice of monitoring and analysing your marketing performance with crucial data and insights.
• Artificial Intelligence (AI): In predictive analytics, marketing software platforms use AI algorithms to process vast amounts of data and identify complex patterns that offer insights to improve marketing performance.
• Machine learning: Machine learning is a branch of AI and involves algorithms that can automatically learn and improve from experience without being explicitly programmed. Marketing technology platforms use machine learning to continuously refine their predictions as they process more marketing data over time.
• Data modelling: Predictive analytics software can create a statistical model of the data to identify relationships and patterns between different variables. This enables marketers to forecast outcomes based on various factors – such as predicted customer behaviour from a particular buying journey.

Understanding these concepts is crucial for marketers looking to leverage predictive analytics in the right way for their strategies.

Why predictive analytics is essential for marketing

Predictive analytics has become indispensable to marketers, helping businesses transform how campaigns are conceived, executed, and evaluated. Here are some reasons why predictive analytics in marketing is essential:

Accurate trend prediction

With factors like consumer behaviour fluctuating so frequently, it’s important to use predictive analytics in marketing to identify and take advantage of trends.

This foresight enables you to stay ahead of the curve and adapt your strategies promptly to meet emerging customer needs and preferences before they become mainstream.

For example, you could see an increase in leads from social media channels for a particular product or service. In response, you can adapt your campaigns to focus on driving more leads from this channel and making the most of this trend.

Refined customer segmentation

Traditional customer segmentation often relies on very broad demographic categories. With predictive analytics in marketing, however, you can take this to the next level by incorporating behavioural data and predictive modelling to create more accurate customer segments.

For instance, instead of simply targeting “men aged 25 to 35” in your automotive campaigns, you can use actual consumer behaviour data (such as speech analytics in phone calls) to create an audience of “eco-conscious men looking for electric vehicles”.

This allows you to refine your segments for more targeted marketing campaigns.

Data-driven personalisation

Personalisation has become a key differentiator in marketing, and predictive analytics makes it possible to personalise campaigns at scale. By analysing individual customer data, predictive analytics platforms can forecast customer’s preferences, likely behaviours, and most engaged times in their journey.

This enables you to create highly personalised experiences, from customised product recommendations to individualised social media ads. The result is a more relevant and engaging customer experience that significantly boosts conversions and customer loyalty.

5 key benefits of predictive analytics

There are several benefits of using predictive analytics in marketing. Here are five key advantages:

1. Create proactive marketing campaigns

Predictive analytics empower you to shift from reactive to proactive strategies. Instead of waiting for customers to act and then responding – and potentially missing out on enquiries and sales – you can anticipate customer needs and behaviours, and then effectively engage customers at the right time, in the right way.

With your pay-per-click (PPC) activity, for instance, you can feed information from phone conversations into Google Analytics 4 event parameters to build custom audiences. These can then be used as signals to refine how your Google Ads are shown, and more accurately meet current customer needs and behaviours.

2. Reduce customer churn

Since customer retention is often more cost-effective than acquisition, predictive analytics plays a crucial role in reducing churn. You can analyse patterns in customer behaviour and disengagement, to identify early warning signs of dissatisfaction.

This insight allows you to:

• Implement targeted retention strategies for any at-risk customers
• Address potential issues promptly, before they lead to churn
• Optimise the coverall customer experience based on factors that contribute to long-term loyalty

For instance, predictive analytics might show a software-as-a-service (SaaS) company that a customer is at risk of not renewing their subscription. The company can then provide targeted training to improve product adoption for users who haven’t engaged with certain key features.

3. Improve customer satisfaction

Predictive analytics in marketing is also important for improving customer satisfaction. It enables you to anticipate and meet customer needs more effectively in areas such as personalised product recommendations, more helpful customer service, and customised communication strategies.

For example, a marketing agency might use predictive analytics to forecast when clients are likely to be planning their yearly budgets – based on historical data – and use this as an opportunity to propose new strategies and services.

4. Increase conversion rates

By providing insights into customer preferences and behaviours, predictive analytics in marketing enables you to create more effective conversion strategies. This will lead to:

• More targeted and relevant marketing messages
• Optimised pricing and promotion strategies based on trends
• Improved lead scoring and prioritisation

For instance, you can use AI-driven predictive analytics from Mediahawk to measure the value of leads based on phone call conversations. This allows you to focus your efforts on the most promising prospects, significantly increasing conversion rates.

5. Generate higher revenue

Ultimately, the improved targeting, personalisation, and efficiency driven by predictive analytics in marketing will lead to significant revenue growth.

By using predictive analytics to increase customer lifetime value, identify upsell opportunities, drive conversions with targeted campaigns, and reduce customer churn, you can substantially improve your return on investment (ROI).

For example, your care home might see an increase in engagement from campaigns focused on ‘high-quality care and friendly staff’. Investing in these campaigns is likely to generate more leads and move-ins, making better use of your budget.

Best practices for implementing predictive analytics

To fully leverage the power of predictive analytics in marketing, it’s important to follow best practice. Some key considerations are:

1. Identify key metrics: Clearly define what you want to predict and why. This could be customer lifetime value, churn risk, or likelihood to purchase. These metrics should be aligned with your overall business goals.
2. Ensure data quality and compliance: The accuracy of your predictions is only as good as the data you use. Implement a market-leading platform to rigorously gather data and ensure all data collection and usage complies with relevant regulations.
3. Foster cross-functional collaboration: Predictive analytics shouldn’t be siloed within the marketing department. Encourage collaboration with IT, sales, and customer service teams for a holistic view of the customer journey.
4. Focus on actionable insights: The goal of predictive analytics is not just to generate predictions, but to inform actionable strategies. Ensure your analytics process includes steps for translating insights into concrete marketing decisions.
5. Operate on a small and large scale: You can use predictive analytics on a micro and macro level when analysing data like your phone call conversations. This will help you uncover the true value of campaigns for increasing overall leads across all channels.
6. Invest in skills and training: Build a team with the right blend of analytical and marketing skills. Your team will become more data-savvy, and know how to implement the findings into strategies for more successful campaigns.

By following these best practices, you can effectively implement predictive analytics in your marketing strategies to optimise activities, drive growth, and increase revenue across your whole business.

How Mediahawk can support marketers in making data-driven decisions

Having access to comprehensive, accurate data is crucial for predictive analytics in marketing. This is where Mediahawk comes in, offering a powerful platform with all the insights you need to significantly improve your predictive analytics capabilities.

This includes gaining the key benefits we’ve covered, such as running proactive campaigns, reducing customer churn, improving customer satisfaction, maximising conversion rates, and increasing revenue.

Mediahawk’s call tracking platform gives you an in-depth view of customer behaviour and campaign performance, offering real-time insights and predictive analytics:

• Multi-channel marketing attribution: Track customer interactions across multiple channels, including phone calls, website visits, and pay-per-click activity. This holistic view of the customer journey is essential for accurate predictive modelling.
• Real-time reporting: With Mediahawk’s real-time and customisable dashboards, you can continuously update predictive models with the latest data, ensuring your predictions remain precise and relevant.
• Integration capabilities: Mediahawk integrates with a wide range of Customer Relationship Management (CRM) and marketing automation platforms. This allows you to combine call data with other customer data sources for more comprehensive predictive analytics.
• Customisable reports: Mediahawk’s flexible reporting allows you to focus on the metrics that matter most to your predictive models – whether that’s call duration, conversion rates, or customer acquisition costs, for example.
• Keyword-level tracking: By providing insights into which paid keywords are driving the most phone calls and conversions, Mediahawk helps you refine your search marketing performance based on current and future trends.

With Mediahawk’s powerful analytics software, you can boost your predictive capabilities, leading to more accurate forecasts, better-targeted campaigns, and ultimately, improved marketing ROI.

Book a demo of Mediahawk to discover the power of predictive analytics in marketing.