How data science can improve retail
The strides in ecommerce represent an entire paradigm shift in retail. Despite only making up around 10% of all retail purchases, ecommerce accounts for more than $2 trillion dollars in sales. Mapping the interactions between the offline and online world seems like an arduous task, but when we focus on each customer and their purchasing paths, it becomes something that can be broken up into a few different paths. We’re going to take a look at a few surprising ways that data science can increase your sales, both offline and online.
Do you know who you are selling to? You have quite a few different systems for gathering information about your client. They scattered about and do not take them into consideration. You have loyalty information from in-store purchases because your front line is methodical asking but your online purchase history does not take this information into account. More precisely, groceries and big-box stores optimize separately for online and offline. We lose value of our marketing endeavors if we don’t take a wider look at our data and search for insights.
Amazon is a great example of this, with its anticipatory shipping. Products are shipped before customers even order them. Past behavior and predicted purchases are used to plan on decreasing next day shipping to next hour shipping. Is it crazy? Of course it is! Products are being shipped without having anyone to receive them. But that doesn’t matter. Once these products are out of the warehouse and in a given area, they can be marketed to others at a discounted rate or kept at the final hub. This is more of a logistic miracle than an e commerce one, but shows how forward thinking you have to be if you want to lead the future. I would wager that you are not Amazon, but with such a behemoth so far ahead, it makes sense to try your hand at staying competitive in your niche. It is certainly working for Amazon, with over $2 billion in profits last year.
An image from amazon’s anticipatory shipping patent illustrating the logistic paths
But how does this actually work? There is some machine learning that goes into predicting client behavior. Machine learning takes in data to train a model. Training is the process of feeding data into a model for it to apply statistical weights allowing the model to automatically recognise future purchases. For example, John purchases a new book every two or three weeks. Based on this behavior, we know what to expect from him. We do not use all of our data, but divide it into train and test data. If we use too much of our data as test set, then we could end up overfitting; a situation when our model identifies artifacts in the data that do not exist.
This a simplified example that isn’t representative of the insights pulled out of millions of clients’ purchase history. These behaviors are then juxtaposed with each other to segment clients into various cohorts that overlap and vary. Machine learning methods can be used for a variety different use cases such as product recommendations, churn predictions, logistics planning and automatic personalized marketing. There are quite a few more use cases that I’ll let your imagination come up with.
Let’s get started and build some interesting applications of data science. First off, we need to agglomerate all of the relevant data we have on our clients. Certain regions have legal restrictions on what sensitive information you can store. There is a way around this; I am not advocating for breaking the law but rather, to anonymize your data. Many times this can be done with a common user ID across every system you use. I also want to be clear and point out that there are certain types of data that we simply can not collect. Always ask a lawyer about your options when dealing with sensitive information.
Map of global data protection.
