TechsPlace | The competition amongst eCommerce businesses has led to business owners, and the marketers resort to innovative technologies like Data Science. The collection and analysis of data are quite effective for businesses that would like to increase conversion rate statistics.
If you are a business owner or marketer interested in Data Science projects being used by eCommerce businesses, here are eight examples of them.
Customer recommendations are one of the most popular data science projects that affect e-Commerce businesses. When businesses use recommendation systems, they heighten the chances of getting more sales.
A customer makes return purchases from a business that recommends relevant items. Data science has improved this initiative by offering more sophisticated recommendations that are not redundant.
Before this improvement, businesses were making the mistake of advertising the same items again, and that did not reach the goals of recommendation systems.
Nowadays, a customer receives diverse recommendations of different items that have been purchased by individuals of the same buyer persona. These sophisticated recommendation systems have reached the goal of getting repeat purchases, and all e-Commerce businesses should have them.
Customer lifetime value
Customer lifetime value is an important KPI metric that can help businesses predict future revenue income. The customer lifetime value metric does not calculate the future predicted revenue at large but calculates each client.
Therefore, you can know how much each customer will benefit the business for the rest of their predicted lifespan. That helps businesses plan their future expansions and budget properly based on the value brought by each company.
An IT expert at one of the top resume writing services, says that you can also optimise marketing strategies using this data by adjusting its budget to fit the value each customer averagely presents to the business. The latter is called defining customer acquisition costs using this useful KPI metric that is data-driven.
The churn model
E-Commerce businesses derive a lot of revenue from repeat purchases made by retained customers. That highlights the importance of understanding when do customers grow cold and why do they stop returning to your online shop.
The Churn Model can help you get answers to those questions and has become an important part of e-commerce.
Data science can help you predict future churn rates and provide insight into how many customers have left the business. Once you get predictive churn rate statistics, it can help business owners determine a turnaround strategy.
Data science insight used in analysing the churn rate of your business can help you identify signals of customers growing cold early. That gives you ample time to respond accordingly and retain as many customers as possible.
Fraud detection and security improvements
Security is another great concern for e-Commerce businesses because threats loom everywhere on the internet. Very advanced attacks can happen right under the noses of business owners that thought they were safe.
Cyber attackers have also overrun some systems that had a certain measure of security. Therefore, it is important to implement security systems that are data-driven and offer protection for both known and unknown attacks.
You can implement online fraud detection systems that prevent identity theft and credit card scams. Customers should have peace of mind when using your eCommerce site and implementing comprehensive data-driven security solutions can make your appeal to the masses. That will also save you from trying to fix the damage done by a successful attack, which can cost thousands of dollars to repair.
Easier customer sentiment analysis
Customer sentiment analysis can help businesses determine the level and quality of their products and customer service. Knowing this will help them improve where they need to attract and retain more customers.
You do not have to ask customers what they think about the business directly, but rather, data science-driven solutions will gather various information and provide business owners with a report.
The data collected and analysed by customer sentiment analysis tools are analysed using natural language processions and text analysis to determine whether customers are happy or not. The data is collected from various online sources like social media platforms, online reviews, and surveys as well as feedback forms.
Efficient inventory management practices
“Sold out!” are the worst two words you can ever tell customers because the business might lose valuable sales. Not only will this make customers look for the same product amongst competitors, but they also might never return to your online shop ever again. If the competition offers the same product range as you do, those customers that left will start shopping frequently there.
Therefore, it is essential to have a solid inventory management system that will alert you whenever a restock should be conducted. Data science projects have come up with the best inventory management systems that study customer behaviour and use that information to alert business owners when they need to restock.
Optimising the pricing structure
Pricing products can play an integral role in increasing the sales of eCommerce businesses. You would like to sell at a competitive price when compared with competitors, but it can be hard finding just the sweet spot.
The reason being is that you do not want to go bankrupt just because of setting lower prices. Same way, overpricing the products will not get a lot of sales.
Big data initiatives have enabled businesses to easily set their pricing structure based on a lot of industry and competitor information. Above that, businesses can even predict the response of customers when prices change.
That insightful data can help you understand your customer’s perception of the current pricing structure and if it is possible to increase the product’s price when needed.
Improving the overall customer service
Businesses can improve their customer service using data science-driven technologies like chatbots. Customers expect to receive prompt responses from businesses they are buying from, and any delay could lead to a customer converting to the business’ competition.
Thus, it is essential to have functional chatbots that will improve the customer’s experience when engaging with the business on direct messaging platforms.
Although regular chatbots have worked incredibly well, data science is taking this technology to the next level by implementing natural language processing. Chatbots that process voices and stores the data for future reference are the Hail Mary of eCommerce enterprises and manifest the impact of data science in this industry.
The bottom line
These 8 data science projects promote efficient predictive models for eCommerce businesses and can help retain more customers. You can also accurately plan for future expansions and acquisitions using these data insights tailor-made for eCommerce businesses.
Kurt Walker works as an academic writer and editor for Academized. He is from London and has 3 years of work experience. His strong interest in academics and his great writing skills means he can craft everything with equal ease, be it a thesis or dissertation.