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Hire NLP ExpertsWhen you shop online from a particular merchant for the first time, you handpick a few items you really like. The successive searches you do to buy again in future reveals a nice selection of products which you both find appealing and valuable. Do you think these items that you found interesting and relevant just happen to land in front of you by chance?
In reality, this is big data e-commerce and data science e-commerce at work. ML (machine learning) algorithms have actually handpicked those items for you, because they have learned from the choices you made when buying products and what you searched for. Based on your inputs, they knew almost exactly which type of products you’d be interested in the next time you browse or buy.
E-commerce data science isn’t a new concept. In fact, it has been around for almost as long as online marketplaces like Amazon and eBay have. Data science in e-commerce lets companies collect, analyse and apply valuable information in their internal systems to improve sales and marketing strategies, and boost revenue. Businesses also use data science in e-commerce to learn about customer preferences, and then present almost the exact item in front of them – all nice and shiny, ready to be bought!
Before we share a few e-commerce data science use cases and big data e-commerce use cases with you or discuss some of the exciting big data application in e-commerce, we should bring you up to speed on what data science in e-commerce actually is and how it all generally works.
As an online business grows and scales, data science and big data application in e-commerce allows them to gain specific insights to make more informed decisions. If you’re wondering how big data is used in e-commerce, then it involves going through massive volumes of data that are generated from multiple sources – e.g. user behaviours, buying patterns, reviews, transaction histories, and so on.
The beauty of e-commerce data science projects really comes into its own when specific patterns are discovered in the collected data that can reveal insights which can directly help to improve customer satisfaction, increase revenue, and become more competitive. Data science and big data in e-commerce is also used to come up with predictive models, optimise business operations as well as processes using ML, statistical analysis, and other techniques applied within the data science e-commerce sphere for many different reasons, such as:
Algorithms for product recommendations provide personalised product recommendations to people, thus, improving customer service satisfaction and retention levels, as well as boosting sales both in the present and future using deep, insightful data analysis.
Customer segmentation algorithms help to identify customer segments according to specific characteristics such as demographic, behavioural and personal preference, so that marketing campaigns can be tailored to further improve customer satisfaction.
Pricing optimisation algorithms to help increase profitability and market share, gain insights on competitor prices, analyse market trends around pricing, and how customer behaviour is impacted by various pricing strategies.
Inventory management algorithm which utilises predictive analytics to identify slow-moving products, as well as optimise inventory levels to improve productivity and reduce costs.
Fraud detection algorithms based on machine learning to track and prevent financial loses.
From warranty analytics and new store locations to lifetime value prediction and market basket analysis, how big data is used in e commerce can have a dramatic effect on your success as an online merchant and overall competitiveness, as we will soon discover through these unique data science and big data applications in e commerce.
Here are at least 9 examples/use cases of how data science, data analytics, and big data is used in e-commerce (in no particular order of importance):
A common application of data science in e-commerce is inventory management.
Inventory refers to the goods an organisation stores in order to meet demand. Efficient management is absolutely vital to ensure that the organisation purchases the right levels of stock and always has the optimal quantity available to meet demand.
Powerful machine learning (ML) algorithms can be trained to evaluate item-to-offer data which can be used to discover specific patterns and correlations between various purchases. The data analyst then examines this data at length to develop strategies to boost sales, while also ensuring that inventory is always delivered on time and managed so as to prevent surpluses and shortages.
Proper inventory management plays a major role in ensuring optimal supply chains and maximising profits for online merchants. Artificial intelligence (AI) and ML algorithms trained specifically for inventory management can help retail stores create projections which, in turn, can help them guarantee timely deliveries, increase gross sales, and maintain excellent quality control all year round.
The analysis of warranty-related data is an important aspect of how big data and e-commerce converge; it helps manufacturers and retailers check their products, particularly their respective lifespans; any possible issues customers may face following a purchase; optimise returns processes, and; keep a check on fraudulent activity.
Analysis of warranty data is done via estimating the distribution of failures – this is done based on specific data such as the age of the product, the number of total returns, the total number of ‘surviving units’ currently being used, etc.
After data scientists have analysed this data, manufacturers and retailers can quickly determine how many units were successfully sold and how many came back due to problems. This data can also be used to uncover anomalies or unusual activities within warranty claims. It’s a great way for online merchants to transform warranty claims into actionable information which can then be used to improve the warranty process, returns process, warranty terms, and other aspects of the warranty itself.
At the end of the day, this ‘goldmine’ of data which can only be acquired through e-commerce companies' big data efforts, helps retailers to significantly improve product quality and customer satisfaction levels – better warranty terms and processes means people will always be happy to buy goods from you, both now and in the future.
The importance of recommendation engines should never be underestimated or overlooked as they are one of the most important tool’s in an online retailer’s arsenal.
Retailers utilise recommendation engines to coax more customers to buy a specific product based on their past purchase history. By offering such recommendations, you can dramatically increase sales and even dictate future buying trends.
Sounds all too familiar, doesn’t it? Remember how you visit Amazon or Netflix to subscribe to a specific TV show and get recommendations to download/stream other shows like it? Or when you buy an Xbox/PlayStation game online and start getting recommendations for similar games? Those are recommendation algorithms at work.
Recommendation engines are powerful data science models for e-commerce delivery, consisting of complex ML and deep learning (DL) algorithms which can record individual buyer behaviour; analyse their buying or consumption patterns, and; make future suggestions based on that data.
This is why every time you see a movie/TV series recommended by Netflix, you are highly likely to watch and enjoy it because they already know it appeals to your tastes in movies or TV shows!
The same applies to Amazon, for instance; you get recommendations and discounts/special offers based on your past purchases, inquiries, searches, and reviews.
Providing recommendations is an essential part of e-commerce data science projects as it allows you to not only increase sales but also influence buying trends. You may be aware of how Amazon is using its recommendation engine to personalise their main page for users and email marketing campaigns, utilising data like demographics, user behaviour, product attributes, and buying history. This directly helps them increase user engagement, sales, and customer satisfaction levels.
The process involves extensive big data e-commerce analysis and filtering through a ML algorithm which reads through and filters massive volumes of data to come up with the right recommendation.
Selling something at a price that your customers find competitive and reasonable or, for example, that your dealer/manufacturer considers “good”, is a very important task for any business involved in e-commerce data science. This price must account for the product’s manufacturing cost and your customer’s ability to pay for it. At the same time, you must also keep your competitor’s prices in view to ensure that in both instances, you arrive at a win-win.
As with the previous big data e commerce example, ML algorithms are also used in this case to analyse multiple parameters from the data – e.g. customer location, price flexibility, competitor’s price, each customer’s buying behaviour or attitude, etc. Based on these parameters, the algorithm comes up with an ideal price where both parties are happy, i.e. you and your customer or retailer/manufacturer.
However, it’s worth noting that machine learning algorithms are not to be relied on solely for the purpose of determining your product’s optimal price. There are several other factors that work along with it, in fact, such as consumer demographics, design, costs, and market trends.
With that said, price optimisation algorithms are a powerful way for retailers to form their marketing strategy, especially when that price fully aligns with their business goals.
Market basket analysis is among the most traditional data analysis tools being used today, with retailers historically profiting from it for as long as we can remember. It is based on the concept of determining how likely your customer is to buy a set of related products after buying one specific set or category of products.
For example, if you sit down at a restaurant and order appetizers first (no drinks), then you are more likely to follow it up with a main course (with drinks) or dessert. So, the standard or regular set of items that your customers buy from you is called the item set (appetizers), while the chances or likelihood of them buying another set of related products is called the confidence (main course/dessert).
In the world of retail, people often tend to buy things on impulse. Market basket analysis capitalises on this principle and predicts what your customer is most likely to buy next after having purchased a specific kind of item from you.
In e-commerce data science terms, the best way to identify potential impulse-driven purchases is by looking at consumer data. And, much like one of the previous big data applications in e-commerce we covered (recommendation engines), market basket analysis also utilises a DL/ML algorithm.
As an online merchant, you must master the art of knowing when and how to take advantage of these waves of impulse buying. As you calculate your customers' unplanned purchases, you gain knowledge and insights on what their possible shopping list will look like in future. So, in a way, the market basket analysis will provide you a forecast of what they will spend money on. For this reason, search recommendation algorithms and market basket analysis algorithms are often used in conjunction to arrive upon some very interesting and insightful conclusions!
Customer sentiment analysis has long been practiced in the business world – way before we even had any algorithms to enhance the process. But now, things have changed. Now we have powerful ML algorithms which can simplify and automate customer sentiment analysis, as well as get it done in almost a fraction of the time.
Social media is probably the most accessible and reliable channel for analysing customer sentiment. So, the customer sentiment analysis algorithm looks at specific words which might bear a favourable or unfavourable attitude from the buyer towards a specific brand. This is done via machine learning and natural language processing (NLP), a sub-field of AI. You can then use this feedback to serve your customer better as you are now well-aware of their pain points, and would know what kind of goods and services appeal to them the most.
Customer sentiment analysis is an important big data e-commerce use case to understand as it grants you a superb opportunity to understand how your audience sees you and your brand. With the help of some very advanced ML and NLP algorithms, you can significantly reduce your efforts while gathering such insights, while also increasing the accuracy of the data you acquire.
This is why social media channels play such a vital role in the above e-commerce data science project because the NLP algorithm specifically helps to analyse content containing both negative and positive perceptions about your brand. This context is something you can take full advantage of and respond to by tailoring your services as per your customer’s needs, desires, and pain points.
Every business must spend a certain amount of money to acquire customers. However, your business model can only succeed and yield profitability once the customers you bring on board contribute desirably more than what was spent to acquire them in the first place. The money that a customer spends on your business, from the very first transaction (initial contact) to the last (successful purchase), is referred to as CLTV or customer lifetime value.
CLTV is an important metric for businesses to calculate after acquiring customers. However, to reap the full benefit, businesses must take more of a proactive approach rather than a reactive one – where you might actually spend more to acquire a low-value customer without actually having it affect your profitability. Taking this proactive approach ensures that your business model continues to make progress and generates an appreciable amount of profit.
This is another great data science model for e-commerce delivery, as it helps to proactively calculate CLTV (rather than reactively) using predictive analysis. It helps you collect, decipher, and generate the critical insights needed from customer data to better understand the LTV of each customer. The data may contain anything from their purchasing quantities and behaviour to their purchasing frequency and recency or general buying preferences – after which it is compiled using ML algorithms to provide a fairly accurate presentation on your customers' LTV.
It’s important to be able to leverage this information to your benefit as it better prepares you to direct your marketing spend only on customers who promise a high return, and ultimately, help you build a more sustainable, successful, and profitable business model.
Let’s assume your predictive analysis algorithm informs you that the CLTV for Customer A is approximately £300 while for Customer B, it is around £1300. So, you now know that you should be spending under £300 to acquire Customer type ‘A’, and a little more than that to acquire Customer type ‘B’.
When you predict CLTV this way, your e-commerce data science can help you build a more robust marketing strategy which can potentially yield a more positive return on investment.
Merchandising forms a very crucial part of any retail business, be it online or offline, where the aim is to boost product sales, as well as promotion and advertising.
Merchandising also plays a pivotal role in influencing customer decisions when they are exposed to different visual channels. For example, visual details like rotating items, labels, and attractive wrappers can help drive customer decisions.
Again, ML algorithms can be leveraged to analyse data, uncover insights, and generate buyer rankings on the basis of fashion, relevance, and seasonal trends, which can help you make better merchandising decisions.
Location analysis is now a fundamental aspect of data science and big data application in e-commerce, helping companies narrow down the perfect location for new stores through ML algorithms.
The exercise is typically focused on demographic assessment, where postcode data and potential business ventures are assessed to deduce the perfect new store location. While the algorithm is fairly straightforward compared to other e-commerce projects in data science, it is still highly efficient.
The data analyst attempts to understand the market potential through demographic analysis, examining specific demographic data and zip code data. In addition, a study of the retailer’s network is also conducted, and once all these factors are considered, the algorithm churns out optimal results as far as new store locations go, which may be relevant to both online (which network to be on, for example) and offline store locations (specific areas or neighbourhoods where the product will sell best).
Through four data types namely: Diagnostic Analysis, Descriptive Analysis, Prescriptive Analysis, and Predictive Analysis.
Many!
For instance, brand data, shopper data, consumer review data, in-store data, retail data, product data, and many other related categories, are used for the purpose of marketing and analytics in e-commerce.
Let’s answer the first question:
Data is used in e-commerce to help businesses make better decisions around sales, customer support, marketing, promotions, campaigns, etc. Furthermore, customer data analysis can help predict trends to help online businesses fine-tune their product and service offerings.
As for the second question:
Yes!
Big data in e-commerce and data science in e-commerce helps businesses gain a deeper understanding of their customers' behaviour, interaction, and preferences, and what led to their purchase which helps them retain customers better and address their needs in a more direct way.
Every online retail business can benefit from the use of data science and big data in e-commerce. It is an indispensable asset that can help you gain a serious competitive edge, cut back losses, and boost revenues across the board. Learn more: +44 20 3488 5740.
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Meta description: How is data science in e-commerce used?
How big data is used in e-commerce depends on the goal of each retail business but there are many use cases of data science in e-commerce that you should know of.
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