3 Best Benefits of AI-Powered Predictive Analytics for Marketing
In an era of rapid technological advancement, marketers are increasingly harnessing the power of artificial intelligence (AI) and predictive analytics to stay ahead of the curve. SaaS and other technology companies rely not only on analyzing the available data but also on predictive analytics to accurately forecast the future.
Marketing tools like IBM Watson Studio, Adobe Analytics, etc. leverage AI’s machine learning capabilities. It can provide a more nuanced understanding of market trends and help make data-backed decisions.
AI-enabled predictive analytics has transformed how teams function. The technology is empowering marketing teams to prove their contribution to business revenue. Teams are leveraging the power of analytics to drive their revenue marketing goals through data-driven decisions.
Here, we explore the top three benefits of AI-powered predictive analytics that works wonder for marketing
#1. Enhanced personalization
Predictive analytics, as a part of marketing strategy, can help gauge customer preferences with a high degree of accuracy. This enables marketers to deliver personalized content, products, and services, enhancing the customer experience and improving engagement and retention.
AI has affected the role of data professionals since predictive analytics leverages a range of data types, like historical purchasing behavior, personal characteristics, browsing patterns, etc., to predict what a particular customer might prefer. For instance, analyzing previous purchases and browsing history can let the AI predict what product category can interest a particular set of customers and thereby suggest relevant items.
Think of how Amazon recommends products based on ‘customers also bought.’
Moreover, AI can take personalization beyond product recommendations. It can help personalize marketing messages, emails, offers, and even website or app interfaces that enable marketers to tailor the overall experience for individual customers.
As a result, customers are more likely to engage with such personalized content, remain satisfied with their experience, and, ultimately, make a purchase. This personalization enhances the customer experience and fosters customer loyalty, which is invaluable in the competitive business environment.
#2. Near-accurate demand forecasting to choose the best option
Predictive analytics can also lead to demand forecasting, an asset for business planning and strategy. AI predictive analytics can scrutinize historical sales data and consider various external factors like economic trends, seasonal fluctuations, and competitor activities to forecast demand for a product or service. Such AI-drive capability allows businesses to manage inventory, optimize the supply chain, and make other necessary preparations, reducing waste and improving efficiency.
Demand forecasting can thus help marketers choose the next-best action approach, using a range of customer data and past behavior to forecast how a customer may respond to various potential marketing moves. In essence, it requires predicting and choosing the most beneficial action forward. This approach empowers marketers to identify subsequent marketing strategies that can yield better ROI, reducing wasted resources on less successful campaigns.
This approach also promotes better distribution of marketing funds. Traditionally, such funds were allocated to separate, unconnected categories like retail displays and promotional campaigns with little group collaboration. However, the next-best action approach based on demand forecasting can make it easier to consolidate these funds, breaking down the barriers that once existed between marketing silos.
#3. Customer segmentation
AI-powered predictive analytics can process and analyze vast amounts of data much faster than humanly possible, providing insights into customer behavior, needs, and preferences through CRM software. Focusing on these insights enables marketers to organize their customer base into distinct segments based on numerous factors like purchasing patterns, demographic details, psychographic characteristics, and more. This data-driven customer segmentation offers precision that facilitates more effective marketing tactics.
Marketers can leverage this approach to significantly enhance the revenue potential of their campaigns. This advanced technology rapidly processes and interprets vast amounts of data, uncovering valuable insights into customer behavior, needs, and tastes.
For example, customers who frequently purchase eco-friendly products can be grouped for targeted marketing of similar items. Such precise segmentation allows marketers to tailor their marketing messages and campaigns to appeal to the specific interests of each segment, resulting in higher engagement and conversion rates.
With AI, lead scoring can be dynamic and continually updated based on new data, ensuring that the sales team focuses on the most promising, “sales-ready” leads. This focused approach can enhance the efficiency of the sales process, reduce the sales cycle time, and improve conversion rates, leading to increased sales revenue.
Wrapping up
With predictive analytics, marketing teams can identify which channels and tactics yield the best results. This ability can help optimize the marketing budget, ensuring the highest possible return on investment. The transformative power of AI-powered predictive analytics in marketing is hard to overlook.
With these capabilities (and plenty more) of AI-powered predictive analytics, marketing departments can make informed decisions, increase their ROI, and secure a competitive advantage. As AI and predictive analytics continue to mature and become more accessible, their application in marketing is set to be the game changer for businesses striving for growth, relevance, and customer loyalty in a digital-first world.
Article by Hazel Raoult
Originally posted on OpenDataScience.com
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