An integral part of many industries, including Artificial Intelligence and Machine Learning is none other than Marketing.
As we often talk about using or adding A.I. In marketing, what does this mean? What does this look like in practice?
Here are 7 examples of A.I. & Machine learning in the Marketing Industry.
1. Product / Content Recommendations
The process of clustering customer behavior to predict future actions began in 1998, with a digital report on digital bookshelves, In the same year, Amazon began using a “collaborative filter” to enable referrals for millions of users.
Some successful digital companies have built their product offerings around the ability to offer a highly relevant and personalized product or content recommendations, including Amazon, Netflix, and Spotify.
A brief history of artificial intelligence in advertising for consulting, “All this comes from AI-based clustering and profiling of information and demographic user data. These AI-based systems constantly adapt to your likes and dislikes and respond with new recommendations that are appropriate in real-time. “
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Publishers are implementing AI-powered content recommendation widgets that allow readers to identify relevant content on the surface and personalize those recommendations based on readers’ browsing habits.
2. Data Filtering & Analysis
Marketing is becoming a data-driven discipline and the key to using data more effectively is to improve customer experience, personalization, targeting, and more.
However, after collecting that data, integrating it, and analyzing it to determine patterns for humans. This is where A.I. Comes: One of the greatest strengths of A.I. The workplace is capable of carrying out complex organizational and analytical tasks that are difficult or impossible for humans. Freeing humans to do more vivid, creative work that suits them better.
For example, A.I. Account-based marketing can be used to improve account selection when carried out on an ABM scale. Targeting and personalization company Demand Base has found that it can use A.I. These AI filter companies from a list of prospects that will eventually lose company money in the long run.
3. Search engines
Based on Artificial Intelligence portfolios, We have a frequent take on 2019, which has had a profound impact on the way we search, and on the quality of the search experience.
Google first introduced the A.I. Rank Brain, which was in search of 2015 with the introduction of its machine learning-based algorithm. Since then, many e-commerce websites (including Amazon) have followed in the footsteps of Google and A.I. into their search engines to make product searches smarter.
With innovations such as natural language processing and semantic search, search engines can determine relationships between products and suggest similar things. Finding relevant search results and automatically correcting mistakes can help customers find products even when they’re not sure what they’re looking for.
4. Social listening & sentiment analysis
Advances in natural language processing have proven very useful for marketers who want to evaluate their brand presence and conversations around their brand on social media and use them to target campaigns.
A.I. It enables brands to analyze sentiment on social interactions and understand their attitude towards their brand and products. This allows them to identify potential problems and deal with them before they become too widespread.
For example, Samsung — it works with A.I. Consumer Insights Crimson Hexagon — The newly released S8 smartphone model has been able to detect and prevent customer dissatisfaction with red on the screen.
5. Predictive Analytics
Predictive analytics can be of significant impact in improving customer service and customer experience, as is the method of gathering information from data sets to predict future trends.
Predictive Analytics is the revolutionary capability of A.I. This is because predicting trends from data sets is only possible in the past. Thanks to artificial intelligence, things that could only be predetermined once can now be reliably modeled and decisions made based on those models.
6. Audience target & segmentation
With the level of personalization that most expect, marketers need to target the granular sectors in which they are growing.
A.I. It can be used to achieve this. Marketers can train machine learning algorithms against “gold standard” training to draw data they already have about their customers, identify relevant variables and popular features, and select contacts that have been identified incorrectly.
It comes down to data on how marketers can differentiate their customers — whether the categories are as simple as gender and age, or as buying past behaviors and personalities.
7. Sales forecasting
Sales forecasting is another assessment-oriented application of A.I. — This time, for sales.
Using past sales data, industry-wide comparisons, and financial trends, Artificial Intelligence can predict sales results and help companies make business decisions and predict short- and long-term performance.
Sales forecasts can also help predict product demand, although sales teams must be careful to consider other factors.
For example, a company experiencing manufacturing problems may sell only a certain number of units due to a lack of stock, not due to a lack of demand for the product. Therefore, using only sales statistics to estimate demand produces an inaccurate forecast.