If you want to stay in business for the long-term, build a name and come to the top of your consumers’ preferences. You must know what they want and what is happening in the market. Big data allows you to do so. It is a fuel for modern-day market research, or say market intelligence, which helps businesses make better decisions. The projected growth of this market from $18.89 billion in 2024 to $82.35 billion by 2030 clearly justifies it. From product development to sales strategy and post-processing, big data is inevitable at every step. Here, I will walk you through the exact role of big data in predictive analysis, and I will help you understand what predictive analytics is. Let’s start.
What is Predictive Market Analysis?
Predictive analysis in marketing is where the historical and present data are used to forecast and understand future market behavior. It helps anticipate customer demand, market fluctuations, sales trends, new products, or price changes.
All in all, businesses can make smart decisions with intelligent guesses as to what will happen next in the market.
How Does Big Data Support Predictive Marketing?
Pat Gelsinger, CEO of Intel, said, “Data is the new science. Bid Data holds answers.”
So here I will take you through components of predictive marketing analysis, but how big data amalgamates with it and makes marketing analysis more calculative.
1. Not just data collection but behavior tracking
Big data is not limited to just surveys, CRM entries, or historical data, but it includes real-time data from all sources in the business ecosystem. It gives you information on every click, scroll, search, view, and purchase. So here, marketing predictions are just assumption based but are based on real-time behavior, which is more accurate.
2. Includes free-form data
Legacy systems in predictive marketing only work with structured data like tables and numbers, which are clearly formatted. But big data includes all sorts of unstructured data like social media interactions, reviews, chats, videos, logs, and more. It makes a big impact as it not only shows transactions but also the intent of the customers.
3. Scalable storage and access for predictions
The new systems that work with big data offer diversified storage systems, including cloud data storage, data lakes, etc. They are not fixed like old storage systems, which have limited backward data. This helps in understanding long-term patterns and in-depth predictions, as you can review years of data.
4. Data study in real-time for spot-on decisions
Earlier market prediction followed batch processing of collected market data, which usually happened weekly, daily, or monthly. This resulted in slow decision-making or limited spontaneous adjustments in strategy. Whereas the vast data pools have made it much more flexible and convenient. Since you deal with real-time data from multiple sources, businesses do not just react to what has happened but also predict strongly about what can happen and act on it.
5. Advanced data cleaning
Instead of manual cleaning in traditional marketing analysis, which offered limited error handling. Large velocity data systems work with automated pipelines and processes, which clean large amounts of data at once, structure, spot errors, and organize. This improves the reliability and scalability of predictive models.
6. Intelligent systems and models
The legacy systems used in prior marketing analysis were rule-based and only offered basic stats to make decisions. But to deal with big data involvement in predictive marketing, modern systems are used, which are learning-based ML systems. They work with different branches of marketing intelligence to make more bang on predictions. Where predictions not only inform you ‘who bought what’ but also who will buy next and what they will buy?’
7. Speed and integration
The modern-day marketing demands speed and accuracy, which is impossible with slow-moving data in silos and static campaigns. Massive information assets have high-velocity data flow, which helps in adaptive and fast-moving marketing data campaigns. This helps your brand & business to reach the audience at the right time on the right platform. Also, modern systems are well integrated to cross-channel and departmental prediction. This gives a holistic prediction picture.
8. Understanding of data for predictive analytics
When you consider big data for marketing, it is more than just static reports & archives. You take predictive dashboards, visually rich reports, and intentional insights to take steps backed by thoroughly studied information.
9. Automation and execution
Previously, decisions were human-driven and manually executed, which was time-consuming and not resource-efficient. With new machine learning engines, the decisions can be automated and can be executed in real-time. This is important for predictive marketing to work at scale with fewer errors & fast pace.
So if you look closely and have read these nine points, you must have realised that I have not only discussed the role of big data in predictive marketing analysis but also how big data benefits predictive analytics.
Big Data Tools for Predictive Market Analysis
Here are some of the best tools with the relevant categories of tasks they perform in the big data analytics process, and how predictive marketing approaches them.
| Category | Tool Name | Task performed |
|---|---|---|
| Big Data Storage & Processing | Apache Hadoop | Open-source framework for storage and processing across multiple systems |
| Amazon Redshift | A scalable data warehouse that supports complex queries and large data sets | |
| Data Collection & Integration | Google Analytics | Widely used for collecting website traffic data and customer behavior data |
| Apache Kafka | All-inclusive tools that can collect, process, and integrate massive volumes of data in real-time, the architecture is very strong, with millisecond latency | |
| Predictive Analytics & Machine Learning | Tensor Flow | You can build machine learning models for various purposes, including customer behavior prediction models |
| SAS Analytics | An open-source distributed platforms which helps to build, train, and deploy predictive models on large data sets | |
| Marketing & Customer Intelligence Platforms | Salesforce Einstein | A tool from the Salesforce Customer 360 platform that leverages ML & NLP to predict customers’ actions across CRM systems |
| HubSpot | Predict leads, customer interactions, and track metrics to comprehend campaign success | |
| Visualization & Insight tools | Tableau | Best known for interactive data visualizations, tailored dashboards to your specific business needs, and its powerful statistical & trend analysis tools |
| Power BI | Integrating with Microsoft’s suite of products can automatically detect trends & anomalies with its built-in AI. Also, you can create customized reports and visualizations |
Big Data vs Predictive Analytics vs Marketing Intelligence
Till now, whatever we covered, it kept these three topics in a loop, so it can be confusing at times. Let me tell you the difference between them and how they work together with each other.
- Big data means extremely large, fast, and multifaceted data sourced from various components such as websites, apps, social media, transactions, and sensors. This data is about volume, velocity, veracity, value, and variety. While dealing with how it collects and manages to benefit businesses. The nature of big data is raw and loose.
- Predictive analytics is the process of using data to assess future outcomes using statistics and machine learning.
- Marketing Intelligence is about transforming the data and analytics into actionable business insights for marketing decisions. It answers to “ what business should do now” by applying insights from data and predictions.
So big data is the fuel, predictive analytics runs the engine, and marketing intelligence acts as a map that steers the business to follow the right direction and path.
Final Word
When big data is intertwined with predictive analytics, it excels market research and prediction to the next level, and businesses are powered to predict much more accurate outcomes. Businesses can read consumer behavior, preferences, market trends, sales patterns, and many other aspects. It can gain them a competitive advantage, save losses, and leverage resources to their maximum potential.
Also Read: Big Data Analytics in Procurement: Benefits, Use Cases & Examples
