How data science can increase ecommerce business profitability

How data science can increase ecommerce business profitability

Ecommerce retailers have one massive advantage over brick-and-mortar business owners: easy access to critical data. 97% of data executives believe data is crucial for maintaining a business’s profitability. That is because data can enhance your decision-making process and consequently help improve your bottom line. However, capitalizing on the humongous volumes of available data requires effective data management and continues to be among the top challenges facing ecommerce merchants today.

Data science methodologies can help ecommerce merchants tackle this problem, enabling them to streamline their business processes and improve revenue. This article will demonstrate how data science can boost an ecommerce business’s growth and improve its profitability.

Data science: A force that promises increased revenue

Data is no longer an option for ecommerce businesses. Modern online shopping is dependent on the ability to access, interpret, and use data effectively. The onset of the digital age and its proliferation has led to excessive data production. According to some resources, 2. 5 quintillion bytes of data is produced every day. This number reflects the volume of profit-driving insights and value you might be able to get your hands on if you manage to tap into this data.

Data science can help you do that. Helping people interpret data, data science enables marketers and business owners to gain critical insights into their business performance, customer behavior and demographic, inventory and competitors. Data science transforms meaningless, raw data into meaningful, valuable insights that guide all business activities, including decision-making and strategizing.

Businesses are quickly adopting data science, with steady investments made in AI and ML initiatives. As a result, data science is expected to grow by 300% in the upcoming years. Here are a few of the many areas data science works on to enhance your business’s profitability:

Increased sales

As a business owner, either ecommerce or brick-and-mortar store, you wouldn’t mind having more sales, would you? You wouldn’t mind more sales, of course. You should aim to make as much sales as possible because that will translate into more revenue.

Psychology plays an important role in the buying process and data science can explicitly help with increasing your business sales by helping you learn consumer behavior. As humans, we tend to buy things in pairs or groups. If we go out to buy bread, we may buy milk and eggs as well. We tend to purchase accessories for our mobile phones such as screen protectors and headphones, earbuds and chargers.

Data science helps you capitalize on this aspect of human nature and maximize your sales. A market basket analysis (also known as affinity analysis) is an analytics and data mining technique that helps to identify common relationships among certain items. It works by analyzing large datasets and uncovering a combination of items that are often bought together in transactions. This helps progressive retailers understand purchase patterns and use this understanding to increase sales.

How?

When you know bread and eggs are bought together, you can put up offers for eggs on the bread’s page to remind people they may like to buy eggs with their bread. Market basket analysis is said to be one of the best machine learning applications in retail. This allows you to gain insight into the product affinity of your products and makes it easy for you to recommend the best product. And it is this approach that has led to the success of recommendation engines in the ecommerce space.

Recommendation engines also build on market basket analysis and generate relevant recommendations for people. For example, on Amazon, when you are looking at something, you also see “buy it with” and “customers also viewed these products” sections that display other relevant products. 35% of Amazon’s revenue comes from these personalized product recommendation engines. Moreover, Best Buy, a U.S.-based tech retailer, recorded a 23. 7% increase in sales using product recommendations.

A global data analytics and advisory firm helped a food retailer increase their quarterly sales by 50% and reduced marketing costs by 15% using market basket analysis. So, we can safely conclude that understanding product categories that are often bought together can help increase sales.

Apart from increasing sales by building on human insights, market basket analysis-driven recommendation engines also build a positive customer experience, which in turn promises revenue as customers may be willing to spend as much as 17% more for a good experience. [NOTE: Citation for this stat?]

Price optimization

Price is the first feature 60% of online shoppers worldwide consider as they make a purchase decision. Customers will lose trust if your price is too high. And if it is too high, you push the customer toward your lower-priced competitor. Therefore, getting your price just right is critical for business profitability.

The price you choose for your products or services depends on many variables like customer behavior, psychographic and demographic data, market geography, operating costs, LTV and churn rate, etc. The presence of data and the need for effective data analysis calls for data science.

Technology-driven price optimization effectively considers all the factors that go into setting the right price and reads the available data to generate an optimal price. Machine learning-enabled price optimization leverages both qualitative and quantitative data, plugging it into predeveloped algorithms that give retailers a well-informed and granular approach to setting optimal prices.

Customers are more likely to pick your products if they are optimally priced, which inevitably increases sales that reflect in your revenue. This is why a 1% improvement in pricing can bring up to an 11. 1% increase in profit.

Inventory management and optimization

Inventory management is the process of managing a business’s inventory to avoid shortages, as it can result in deferred profit. Being out of stock means potentially losing your customers, as 31% of online shoppers tend to switch to a competitor if a product is unavailable on their preferred site. On the other hand, overstocking can lead to increased warehousing and logistics costs, as warehouse space comes at a price, and in the U.S., that is around $5. 08 per sq. ft.

Knowing how much to keep in stock, what and when to order, and forecasting demand is a challenge that plagues many business areas, and ecommerce is no exception. 75% of all supply chain management professionals wish to improve their inventory management practices. Implementing data science is the best way to achieve this.

The supply chain, just like most areas of ecommerce, overflows with data. You can either ignore it or capitalize on it and use it to your advantage with the correct data analytics methods. Modern inventory management software and apps are grounded in data science. They use current and historical data to ensure accurate inventory.

These programs leverage past sales data and seasonality, among other factors, to anticipate future demand. You can use this information to determine the inventory needed and keep stock levels at an acceptable level.

Customer segmentation and personalization

Customer segmentation is the process that divides a business’s customers that have common characteristics into discrete groups. This helps marketers develop targeted marketing campaigns that resonate more with the audience and promise better results. This could be why 77% of the returns generated from marketing campaigns come from the ones built with customer segmentation. Therefore, this approach helps you optimize your marketing spend, enhance your ROI and eventually enjoy better profits.

Your customer data is scattered all over the internet.

Data science helps you collect all of this data, clean it, and use it to divide your customers into segments. Data science, which is based on efficient data analysis, is the key to customer segmentation’s effectiveness. Once your customers are divided into discrete segments, you can target them with personalized messages on their preferred channels.

For example, for a health and fitness brand, you can reach your Gen Z audience on TikTok and Instagram with messages to look fit and fab. You can also communicate with your Baby Boomers through Facebook and emails, with messages that emphasize the benefits and importance of being fit as an older age.

When people come across personalized messages from brands, they feel connected to them and are more inclined to buy from them. In fact, 49% of buyers have made impulse purchases because of a more personalized experience, while 59% claim personalization influences purchase decisions. Data science can help you target customers more effectively, increase sales, and improve your profits margins.

CLTV prediction

You spend money on customer acquisition, and your business model can be profitable only if the customers you acquire contribute more than what was spent on acquiring them. The money your customer spends on your business, from the first transaction to the last, is called customer lifetime value or CLTV.

Normally businesses calculate CLTV after they have acquired customers. This is not an efficient way to calculate CLTV. It’s more reactive and could result in higher costs for acquiring low-value customers, which can impact your profitability. You have to be proactive to make sure your business model sustains good progress and generates appreciable profit.

Data science can help you be proactive with using predictive analytics to calculate your CLTV. This technology helps to collect and clean customer data. It can also generate important insights such as their behavior, frequency of purchase, recency, and number of purchases. Based on this data, machine learning algorithms churn out a presentation on the possible lifetime value of each customer.

With this information on hand, you are better equipped to focus your marketing spend on customers that promise more returns and build a more sustainable and profitable business model. For example, predictive analytics have informed you that the CLTV of customer type A is around $200, while that of customer type B is around $1000. Now you know that you have to spend less than $200 on trying to acquire customers from group A and can spend a bit more on type B customers.

By predicting CLTV, data science can help build a marketing strategy with a positive ROI.

Final word

Data science is the tool businesses must use to carve their success in the modern ecommerce environment. Data science can directly influence sales, helping to optimize marketing strategies and empower stakeholders to make better decisions. The key to all its benefits is how data science principles are implemented. Therefore, you’ll have to invest in some excellent data analysis resources before you can enjoy the perks that come with it.

Atul Jindal is a web design and marketing specialist.

The post How data science can increase ecommerce business profitability appeared first on Venture Beat.

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