Predictive Analytics Marketing helps businesses to showcase their products to new customers and reach new markets. This is a good way of making more sales for the growth of a business. Marketers have been using data to understand their customers, products, and the market and improve the effectiveness of their marketing campaigns. Data-driven marketing activities have continued to advance with time. By Analyzing data, Marketers are able to establish the best ways to market their brand for a high number of conversions. Thus, every business should consider using Data-Driven Marketing Strategies to promote their products.
Marketers can use Predictive Analytics to predict future behaviors. It involves using AI and Machine Learning techniques to extract insights from datasets. These insights can help marketers to know what will happen in the future and inform their Marketing Strategies. In this article, we will be discussing Predictive Analytics Marketing in detail.
Table of Contents
What is Predictive Analytics Marketing?
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Predictive Analytics refers to the process of using current and historical data together with statistical techniques, data mining, machine learning, and Predictive Modeling to determine the probability of a certain event occurring in the future.
In the Marketing Context, Predictive Analytics involves the use of Current and Historical Campaign data together with Statistical Techniques, Data Mining, Machine Learning, and Predictive Modeling to determine the probability of a certain event occurring in the future. By the use of Predictive Analytics marketing, marketers are able to establish accurate future predictions of consumer behavior. Such insights can help them know the marketing actions and strategies that are most likely to succeed. Thus, it drives Marketing Decisions.
Businesses that use Predictive Analytics marketing are already seeing the benefits. According to the Predictive Intelligence Benchmark Report, Predictive Intelligence recommendations influenced 26.34% of the total orders.
How do Predictive Marketing Analytics Works?
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Let’s use Amazon to give an example of Predictive Marketing Analytics. Amazon’s landing page displays personalized items for every customer. They collect data for a customer based on their past purchases and use that data to forecast what the customer is most likely to purchase in the future. This results in personalized recommendations. But it’s more complex than that.
Amazon will still consider the items the customer has clicked in the past, what the customer has shown interest in, and the most preferred suppliers for the customer. Such rich information helps Amazon to make accurate recommendations. The result of this will be more sales and increased customer satisfaction. There are many other companies using Predictive Marketing Analytics other than Amazon.
Key Features of Predictive Analytics in Marketing
The following are features of Predictive Analytics Marketing:
1) Customer and Audience Segmentation
Predictive Analytics can help you to segment your audience based on their demographics, behavior, interests, firmographics, or any other variable.
You can experiment with different cluster models and establish patterns that you didn’t expect. This can give audience segments that make sense for your business.
2) New Customer Acquisition
By taking your segmentation a step further, you can create identification models from the customer data. This will help you to identify and target prospects who resemble your current customers.
3) Content and Ad Recommendation
Collaborative filtering, a Predictive Analytics feature, can help you to create content and ad recommendations. Companies like Amazon and YouTube have used collaborative filtering to create recommendations for customers. This technique uses past behavior to create recommendations for cross-selling, upselling, or content consumption.
4) Personalization
You can use Predictive Analytics Marketing to take personalization further and improve your Customer Experiences. By creating meaningful audience segments and combining them with lead scoring and triggered content recommendations, you can increase the relevance of your marketing activities and their return on investment.
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The Predictive Analytics Marketing Process
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The following are the steps of the Predictive Analytics Marketing process:
Predictive Analytics Marketing Process Step 1: Define the Question to be Answered
Before jumping into the data, you should have a clear idea of what you will be doing. Define a question of the “What may happen in the future based on what happened previously?” variety.
For example:
“What audience segment should we target in my next Instagram campaign?” (Based on what happened previously).
Predictive Analytics Marketing Process Step 2: Collect the Data
In this step, you should collect data that can help you to answer the above question. The data should be historical in nature, describing the events of the past.
Predictive Analytics Marketing Process Step 3: Analyze the Collected Data
Now that you’ve collected all the data you need, it’s time to start analyzing it. The purpose of the analysis should be to help you find answers to your questions.
Predictive Analytics Marketing Process Step 4: Create and Deploy a Predictive Model
Create a Predictive model from your data. The model should be able to predict outcomes. You can use technology tools for this, for example, Python and R.
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- Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
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Predictive Analytics Marketing Process Step 5: Deploy the Model
Once the Predictive model is ready, start to put its results into practice. However, remember that the model will not make decisions for you. It’s your duty to look at the results and turn them into actionable insights.
That is how Predictive Analytics Marketing works.
Benefits of Predictive Analytics Marketing
The Benefits of Predictive Analytics Marketing are listed below:
- Enhances Marketing Performance.
- Improves Marketing Marketing budget planning which is made possible by Predictive Analytics
- Campaigns have been optimized and CPC models will have performed well.
- Increases lead generation as a result of Channel Optimizations
- Improves Customer Intelligence through Marketing Predictive Analytics
- Generates more precise Customer Lifetime Value as a result of Marketing Predictive Analytics
- Predictive Analytics will provide more Precise, Personalized Content.
Conclusion
This is what you’ve learned in this article, Marketers employ Data-driven Marketing Strategies to better understand their customers and other stakeholders. This provides them with insights into the best way to create and run Marketing Campaigns in order to increase conversions. Data-driven Marketing can be approached in a variety of ways.
To Predict future consumer behaviors, the Marketer uses Current and Historical Data, Statistical Techniques, Data Mining, Machine Learning, and Predictive Modeling. Such Insights assist Marketers in developing more effective Marketing Strategies. As a result, their Marketing decisions are influenced by the Insights.
To achieve this efficiently, you need to assign a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally, Load it to a Cloud Data Warehouse or a destination of your choice for further Business Analytics. All of these challenges can be comfortably solved by a Cloud-based ETL tool such as Hevo Data.
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