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If you’re looking for a way to apply machine learning to your marketing strategy, this article is for you. We will discuss how Amazon uses machine learning in its marketing campaigns and tips on incorporating it into your strategy.
Each product goes through several stages of production before it reaches the customer. Amazon has taken advantage of its extensive supply chain experience by applying predictive analytics techniques such as forecasting demand, calculating risk probabilities for suppliers based on their management abilities, ranking suppliers based on performance history, etc., thereby creating a more efficient supply chain.
How brands use machine learning
Since the supply side is handled, let’s look at how Amazon uses machine learning on the demand side. The simplest way to do so is by looking at some examples of products featured in their advertisements. Advertisements are particularly interesting because several factors influence which product gets advertised, such as internal data from past ad campaigns and external data from past customer purchases, both of which can be analyzed using machine learning techniques.
In one example, a LEGO set was featured in an ad for three consecutive days from November 28th to November 30th in 2014. They were selling a special six-month subscription pack where customers could get one free LEGO every month after paying a monthly fee for six months
Predicting customer behavior
Amazon predicted that many customers would purchase this offer when they saw it advertised based on past customer purchases. It is interesting to note that Amazon knew which products would be featured in advance because it was based on internal data from previous ad campaigns! After all, it wouldn’t make sense to advertise something that no one will buy.
We can imagine that millions of ads are being served every day with only a handful featuring certain products, but once in a while, an ad gets lucky and ends up being clicked by thousands of people. But wait, how does Amazon know how many people will click on the advertisements to begin with? That’s where predictive analytics come in and RemoteDBA.com can help.
For example, Amazon knows which advertisements (amongst millions of ads) will be clicked and for how many times because it uses machine learning models that calculate the probability of an advertisement being clicked based on different factors such as but not limited to:
– Price sensitivity of product (e.g., a lower priced item may have a higher clickthrough rate than something more expensive)
– Click-through rate history (over time, you can predict the likelihood of someone clicking on an ad over time with good accuracy)
Amazon also predicts how many customers are likely to purchase this offer after seeing the ad by using, you guessed it, machine learning! They might use regression techniques or clustering algorithms depending on what they’re trying to predict.
Predicting what products to sell
Amazon does a great job of using machine learning algorithms to predict which products will sell and where to place them, but what about personalization? Personalization is a hot topic these days, and it’s something that Amazon has been doing for years, even before it became en vogue! For instance, the “Sponsored” section under each category on their website suggests different products based on past customer purchase history. In other words, they know what you’re going to buy before you’ve even clicked on it 🙂 Their predictive analytics strategy uses data from previous purchases such as items purchased together in one order and item attributes such as brand and color. This information helps determine the likelihood of an individual purchasing certain items.
Things customers are most likely to shop
Something more interesting is Amazon’s ability to predict some of the products you are most likely to shop for on their website! I’d say some of this is pure statistical analysis since e-commerce websites have a lot of historical data that can be mined for customer purchase behavior, but some of it has more to do with personalization. Let me give an example. Suppose you’ve been searching for “running shoes” on Amazon recently.
How would they know what you want before you even search for it? Then one day, without any new searches on running related products, Amazon serves up an ad with discounted Nike running shoes. Not only did they serve up this ad because of your past purchase history alone, but they also took into account your browsing history on the website by observing your search behavior on related products. Your past purchase history on Amazon alone is enough to give them an idea of what you might be interested in. Still, by adding more information such as how frequently you’ve browsed certain items and even the time of day you browse different product categories, they can get a pretty good understanding of what new items would interest you most.
Indeed other factors play into Amazon’s ability to accurately predict which new products or advertisements will appeal to their customers. They probably use software that calculates these probabilities based on complicated machine learning algorithms suggesting which products would sell best for each customer depending on their demographics, geography, psychographic (e.g., lifestyle & personality), search history, purchase history, etc. But the beauty of machine learning is that it makes good predictions even with inadequate data by using sophisticated statistical techniques that can analyze large amounts of data to extract structure and predict future behavior in a way that minimizes error.
There are many other great examples out there for companies who have successfully implemented machine learning algorithms to enhance their marketing strategy. It’s best to implement these algorithms if you have decent-sized data sets so you can get good insight from them. Otherwise, all you’ll have are just educated guesses!
However, they are very simple to deploy via various APIs available online that don’t require a lot of data science knowledge to use and the insight needed to extract valuable information from your customers. Now that you understand how machine learning can be used in marketing to enhance the customer experience start thinking about ways you could apply this at your own company!
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