Using data to drive business decisions is certainly not a new concept.
Although we think of algorithms as being a modern feature, the first study of patterns and use of inference dates back to the 4th century when Euclid published his theorems in geometry!
The first predictive algorithm was created by Carl Gauss, who charted trends to predict the likelihood of a specific outcome and gave us the ubiquitous Gaussian curve in statistics.
Thankfully, we now have technology on our laps that makes complex but accurate analytics and forecasting part of everyday business operations.
Today, one of the most popular uses for AI technology in marketing specifically is predictive analysis, along with personalization.
Image Source: Marketing Charts
But can we really rely on AI or algorithms for all of our marketing decisions? Here’s how predictive analysis technology actually applies best for data-driven strategic planning.
AI complements human intelligence (and supplements human action)
If you asked marketers just a few years ago how they felt about AI adoption in the workplace, most would admit they had qualms.
The popular opinion about AI was that it would “take over” and make many marketing jobs obsolete. Others feared that machine learning and complex algorithms would be the only criteria used for marketing tasks like content creation, ad placement, and lead nurturing. This would eliminate the creative aspect of marketing and automate many of the fundamental tasks.
However, the fears of an AI vs human intelligence fallout is completely unfounded. The fact is that AI works best when it is regulated, directed, and fine-tuned by human intellect for better decision making.
Although predictive algorithms can provide data-driven insights, they are not designed to take into account data and factors other than those that have been fed into the model. Therefore, the decisions they “recommend” can only be logical at best.
Remember, computer systems cannot factor in human dynamics and reasoning, such as in-the-moment feelings and emotions, or values and ethics. On the other hand, AI can catch tiny mistakes that humans are prone to make and can supplement ideas and theories with long chains of predictions as well as cold, hard facts.
By working together, marketers can implement strategies based on data along with their own creative touch for improved results.
Predictive algorithms guide strategic planning
One of the greatest benefits of AI-based predictive models is that they can self-correct their estimations based on new data.
But predictive algorithm forecasting is an ever-changing process that requires continuous data-mining and refinement, especially in the enterprise. Additionally, variables often need to be included in the mix to predict “if this, then that” outcomes.
Image Source: McKinsey
Although the technology is certainly not perfect, predictive algorithms are steadily getting more sophisticated. Essentially, their usage falls into three broad categories:
- Top-line cases: These deal directly with the customer experience, such as targeted email campaigns, promotion optimization, pricing, stock replacement, and personalized lead nurturing.
- Bottom-line cases: This is used to improve internal processes, such as supply chain optimization, demand planning, fraud prevention, workforce planning, and data protection.
- New business models: With a fully equipped martech stack at their disposal, marketers can use predictive analytics to create new streams of revenue by mining customer insights and channeling feedback into improvements in products or services or entering new markets.
Ultimately, all of these approaches can make measurable differences in a business’s profitability. According to a study by McKinsey, companies reported 1.5% to 2% increases in revenue by applying predictive analysis to their sales strategies – while reducing marketing costs by up to a tenth!
Predictive algorithms can be specialized for specific applications
One of the key benefits of predictive technology is that it can be customized for all types of industries and marketing applications. There are essentially five different types of predictive algorithm setups that can be used for forecasting and decision making.
- Clustering: Creating subgroups of analyzed data for specific predictions of designated situations or categories, such as audience groups.
- Descriptive Classification: Identifying new opportunities based on past events and behaviors.
- Outlier Analysis: Perfecting predictions by factoring in anomalies and isolated instances with projected variables.
- Factoring: Using regression analysis and other methods to determine the relationship and interdependence between different data variables based on various factors.
- Time-Based Analysis: Collecting values over a period of time and creating data patterns that take into account things like trends and seasonality.
These different models can be used to create specific predictive analytical workflows and make data-driven decisions.
First, data is collected, organized, and importing into a singular algorithm program. Then any irrelevant factors are removed and the information is organized and formatted for analysis. Next, forecasting predictions are made by applying any of the appropriate predictive models.
Image Source: Mathworks
AI is becoming more practical than ever
At first, AI and predictive algorithms were only accessible for companies with massive budgets. Additionally, these forecasting tools were often used for major projects and had little to do with the day-to-day operations. That has now changed, as more and more companies are starting to adopt predictive analytics into routine business strategies across industries.
For instance, real estate firms use predictive models to target homeowners who may be interested in buying or selling in particular areas, based on behavioral data points.
Predictive algorithms can also assist supply chain management planning by stocking and distributing inventory based on projected sales.
It can even be used to improve customer service by providing insights on past customer behavior, thereby giving agents insight into factors like brand perception and preferences.
Predictive algorithms are essential for creating an optimized experience for both customers and employees. This technology benefits everyone, from key decision-makers and leaders to sales representatives who interact with customers every day.
Predicting the future
The fact of the matter is that predictive algorithms can help transform a business of any size – in various aspects. However, the starting point is typically in marketing-related decision making.
There are well-documented successful examples of key applications of predictive analytics across industries in customer segmentation and targeting, churn prevention, product quality assurance, and sentiment analysis.
That said, the power that this technology holds should not be pigeon-holed into just marketing or sales. Instead, AI and ML-based insights can and should be applied to all business functions in order to optimize outcomes. If used wisely, this approach can potentially drive all-round, wholesome growth in an entire economic sector.
Guest author: Rohan is an experienced digital marketer who has worked both agency and in-house, developing data-driven strategies for SEO, PPC, social media and content marketing. He is also an avid business and tech blogger; his insights are frequently published in publications like Fast Company, Fortune, and Adweek.
The post How Predictive Algorithms Are Transforming Data into Decisions appeared first on Jeffbullas’s Blog.
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Author: Rohan Ayyar
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