Data Analytics and Data Science projects for eCommerce businesses
The growing accessibility of complicated data science tools in everyday life has been a major boost for small eCommerce businesses, who can now use data analysis to improve the way they work more than ever before. Research that would have taken a team of specialists many expensive hours to complete in the past can now be done in seconds, but how can a business most efficiently use these amazing new tools to make the most of their limited time? Let’s take a look!
One of the biggest unqualified successes of data science in eCommerce has to be the product recommendation engine, that little section of the page dedicated to the ‘customers also bought’ section. The figures are startling: Amazon generate 35% of their massive sales from their recommendation engine, while Salesforce say that recommendations increase the likelihood of sales by 4.5 times! This is perhaps one of the easier projects to undertake, combining a history matrix with a co-occurrence matrix to establish patterns. However, you need to remember to use statistics to remove anomalous results: It’s no good recommending something that everybody buys, after all!
Using past purchases to recommend products to customers is one thing, but you want to give your buyers something new, not something they already want! That’s why the way to supercharge your data science strategy is to anticipate the customers’ next move based on what their likely spending habits are going to be: Based on the cost and type of their itemset, what is likely to be their budget for the next purchase? By running the numbers and doing the market research to make that leap from one type of product to another, you can sharpen up your recommendations whilst also taking customers logically from one type of product to another without them having ever bought it before.
Not all of your data insights are going to help sales, for many retailers returns are a major expense that isn’t going to go away, so how do you reduce their impact on your business? Using a machine learning algorithm that compares your returns with a predicted number of returns, based on known fault rates with your products or the terms of their warranties, will help you establish how many returns are happening above the norm and why. This will allow you to make changes based on this, of supplier for example, and cut those costs.
A lot of data science solutions are focused on immediate sales, getting the customer to go to the right place next. However, the real money is in the whole life of a customer’s interactions with your company. AI can help you create a data visualisation of a customer’s predicted interactions based on their past spending, allowing you to decide how much it’s worth spending on that customer in the form of marketing or discounts to boost their spending. Otherwise, you end up with a blanket approach that wastes most of your spend on customers who aren’t coming back or won’t spend much.
At DataWrk, we are home to the best data scientists and analysts. Contact us today and let us help you put together the best data science team that suits your company’s needs, on-demand.