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Is Predictive Analytics Marketing Reliable and How to Benefit From It

Is Predictive Analytics Marketing Reliable and How to Benefit From It

Artificial intelligence has become an inseparable part of contemporary digital marketing. We rely on various tools to automate repetitive tasks, track campaign KPIs, communicate with our clients, and bring in new leads. These solutions allow us to adopt a data-driven approach to decisions and improve how we do business.

However, if we rely only on data from past periods, we will always be a step behind. That’s where predictive analytics marketing comes in. It provides knowledge that enables us to prevent customers from leaving, decreases acquisition costs, improves CLV, and, ultimately, increases revenue.

In this article, we focus on the practical applications and reliability of predictive analytics in digital marketing. So read on for actionable tips on how to adopt the technology in your strategy.

What Is Predictive Analytics?

Predictive analytics (PA) is a technology that uses artificial intelligence (AI), machine learning (ML), and statistical modeling techniques to analyze past and present data, and identify potential trends.

The technology requires a constant flow of massive amounts of data in order to deliver accurate predictions. However, as modern software solutions accumulate a practically endless supply of information, predictive analytics has been on the rise and is developing rapidly. Furthermore, the advancement of cloud computing has accelerated its growth as it reduces the cost of maintenance, hardware, and IT staff.

In fact, research shows that the global predictive analytics market is expected to reach USD 25.2 Billion by 2026, showing a CAGR of around 20.8% over the period 2019 to 2026.

The technology is widely used in many industries, including, but not limited to, banking and financial services, insurance, telecommunications and IT, retail, eCommerce, healthcare, manufacturing, government, defense, energy, transportation and logistics, and so on.

PA allows businesses to capitalize on their data assets and, amongst other things, minimize investment risks, accelerate innovation, enhance security, reduce costs, and improve profits.

What Is Predictive Marketing?

Similarly to other industries, marketing can benefit greatly from predictive analytics.

Modern marketing tools deliver a constant flow of information, allowing the digital journey of the customer to easily be tracked and recorded.

Predictive analytics algorithms observe the customer’s behavior in real-time, and correlate it with existing data to identify patterns and provide us with a deeper understanding of our clients. Furthermore, the results may be cross-referenced with data from other departments such as sales and customer service, to deliver insights on what drives the customer to make a purchase.

Leveraging this knowledge, businesses may optimize their budgets, improve performance, and increase the ROI of communication channels.

As one of the top 15 predictive analytics providers in the world, Salesforce, has estimated that, if used consistently in your marketing strategy, it can boost conversions 22.66%, influence 26.34% of all total orders, and improve revenue by 40.38%.

However, while the results are impressive, it should be taken into account that machine learning algorithms need time to train and learn. This means that the reliability of the technology depends largely on the time you have been using it. The accuracy of the predictions increases dramatically after the first year of adoption and continues to progressively improve.

Applications of Predictive Analytics in Marketing

Understanding the customer and adopting a data-driven approach is a viable strategy that can result in improved personalization, performance optimization, and better targeting.

Applications of Predictive Analytics in Marketing

Some of the most successful applications of predictive analytics in contemporary marketing are:

Social Media Targeting

Facebook uses predictive analytics to create lookalike audiences. Information about your fanbase is extracted from your page, mobile app, and pixel tag. Based on your best customer profiles, the algorithm identifies potential viable clients to show your content to.

Ad Targeting

Google Ads uses the technology in paid searches to display PPC ads to the people who are most likely to click on them. The search engine company is one of the leaders in AI and ML development and its algorithms are constantly evolving, which makes its targeting ever better over time.

Email Marketing

The applications of PA in email marketing are numerous and can drastically improve the performance of campaigns. Intelligent automation benefits abandoned shopping cart recovery, drip marketing, advanced personalization, post-purchase emails, new arrivals notifications, back-in-stock emails, etc.

Product Recommendations

Analyzing customer behavior allows companies to make precise product recommendations and successfully cross-sell and upsell to their clients.

Advanced algorithms take into account factors such as repetitive purchases, buying intervals, seasonal changes, behavior trends and fluctuations, and so on. This allows them to predict with accuracy when a customer may buy a certain product and what else they may be interested in.

Lead Scoring

Lead scoring is one of the top applications of PA in digital marketing. There’s a variety of tools in the marketplace that allow companies to track and analyze user actions and identify when a lead is ready to convert. This enables marketers to approach the customer at the right moment with the right offer, and seal the deal.

Customer Segmentation

Predictive analytics allows you to segment your audience based on various traits. The algorithm processes your customer data and identifies patterns that you may never have noticed otherwise. Cross-referenced with behavior on communication channels, this information can help you sniff out your best customers, improve your buyer personas, target them better, and, ultimately, make more sales.

Conversion Attribution Modeling

Leveraging modern PA, marketers are able to use the data from their omnichannel strategy to build accurate attribution models. This allows them to distribute the credit for conversions amongst their channels and monitor performance. Using this information, they can improve efficiency and optimize the ROI of marketing campaigns.

Customer Attrition Prevention

Attrition, or customer churn rate, is an important metric for subscription-based businesses because, if left unattended, it may prevent growth. Analyzing customer behavior allows you to identify and monitor signs that show you a customer is about to leave. Leveraging this information, you can better address your clients’ needs and take action to keep them around.

Affordable Solutions

The advancement of machine learning and AI democratizes predictive marketing and makes it accessible to small and medium businesses, not only to corporations with big pockets.

The applications we’ve listed hardly exhaust the benefits of predictive analytics in marketing, but they highlight some of the most commonly used types of tools nowadays.

PA solutions provide convenience and reliability and, as the technology develops it may open doors to even more opportunities.

Predictive Analytics Marketing Tips

For those of you who are new to predictive analytics marketing, we’ve listed a few practical tips that may help you accelerate your progress with the technology.

Predictive Analytics Marketing Tips

  • Have Patience with Results. As mentioned, predictive analytics need massive amounts of data to deliver accurate results. The more information they process, the better machine learning algorithms learn and the more intuitive and precise their predictions become. This means that it may take longer than you’d like in order to obtain fully reliable results. However, the wait should be worth it.
  • Hire Trained Data Experts. Data management is not something you can learn overnight, and if you want to take full advantage of your tools, you should work with trained experts. They will know how to clean the data, process it, and analyze it.
  • Invest In a Flexible Solution. While out-of-the-box solutions may appear tempting, they may not be able to provide the expected results. As the investment will be substantial either way, consider opting for a flexible solution that can be adapted to match your needs.
  • Consider Omnichannel Integration. When building predictive models, make sure to integrate data from different channels. Furthermore, as mentioned, you should also consider adding up data from other departments, such as customer service, sales, finance, etc. This will make the results more relevant and may present unexpected valuable insights.
  • Regularly Update Your Models. Probably the greatest benefit of PA is that it allows you to monitor the market and behavioral changes in real-time. This means that if you regularly update your models, you can test out strategies and quickly respond to changes in the best possible way.

Furthermore, this will give you an advantage over competitors who build their strategies only on historical data from past periods.

  • Balance Personalization and Privacy. PA relies solely on customer data, and to gather it you should request informed consent. While people may enjoy the advantages of personalization just as much as companies do, they value their personal space and want you to respect it as well.

However, what’s known as the Personalization and Privacy Paradox is not a stalemate without a solution. If you adopt a transparent approach, gather only the information you need, and focus on cybersecurity, both you and your customer will be content.

Bottom Line

With the development of artificial intelligence, machine learning, and cloud computing, predictive analytics has seen tremendous growth and is expected to advance even further in the next few years. The tech has benefited marketing greatly by allowing us to rely on data, instead of on assumptions, and build resilience in a competitive marketplace.

Tech-savvy marketers, who take advantage of intelligent solutions and look for the latest innovations, have better chances of outperforming the competition and improving revenue. Furthermore, in the battle for the customer’s heart, companies who know their audience well, understand their needs, and respond to their behavior are bound to be one step ahead.

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