How E-Commerce Can Use Data Analytics
The outbreak of the COVID-19 has risen to a high crescendo. With so many countries acting quickly to take preventative measures, nations like China have reported having deployed big data and AI in fighting against the widespread of this phenomenon. The outbreak has affected retailers and the supply chain in many ways. This is especially true with e-commerce brands. As an influx of consumers will begin to shop online, now is the time for e-tailers to use data analytics to maximize their profits.
How data analytics helps e-commerce
Application of data science goes a long way in helping e-commerce brands maximize sales and improve ROI, especially during this period of the coronavirus pandemic. Having cleaned, data can help these brands prevent losses through the following ways:
Personalized product recommendations
According to Barilliance reports, about 31% of the revenue in the eCommerce industry worldwide is due to personalized product recommendations. In fact, 35% of Amazon’s sales are made through the personalized product recommendation engine. This is further evidence for why e-commerce brands should look into compiling data on online shoppers’ purchasing histories and make recommendations based around this.
Predicting product trends
Knowing what is a top-selling product beforehand is landing on a gold mine for any online retailer. Up till now, long hours of market research and cross-referencing trending keywords is how e-commerce brands have toiled through the labor. Yet, with the help of big data, brands can remove the guesswork and clearly identify what best-selling product is next. From ad-buying to looking through sentiment analysis, which determines the context of a product being discussed online, e-retailers can use this to predict top-selling items in specific categories. Brands can also use trend forecasting algorithms to sift through social media post data and compare with web browsing habits to see what is trending.
Therefore, in times like this, you know that people are searching for hand sanitizers, safety-related products, etc. how can you make sure the online store is stocked for these times.
In addition, cashflow strained during this economic crisis, it’s important to purchase the right assortment in the right quantity as mistakes during this critical time could hurt the bottom line.
Analyze buying and customer behavior
Any e-commerce brand that wants to avoid losses during the current pandemic must turn to data science in determining customer shopping patterns. Consumer behavior has changed dramatically since the advent of the pandemic, so being able to predict this can help brands stay on top. As governments at various levels impose restrictions on movement and social gatherings, the desire for physical shopping in stores will definitely reduce. This naturally means more consumers will migrate online.
Tools like Hot Jar allow websites to track user behavior by giving a heat map on what areas of the site visitors are looking through most. Using this data, e-commerce brands can better identify how to organize their assortment better. They can A/B test on what products in their online store are luring in more views and optimize their inventory accordingly. This is far more convenient than offline retailers since physical stores have to adjust what they can sell and are stuck with slow-moving goods. Thus, knowing how to edit their assortment based on behavior analytics can definitely help to maximize sales.
Product price optimization
The big problem in identifying optimal price points at the product level for most companies is that the process of sifting through all that data manually is not only time-consuming but nearly impossible. Roughly 75% of a company’s revenue comes from its standard products, often in the thousands.
With data analytics, companies can go granular and make insights from the treasure trove of data they are sitting on. Moreover, with the use of AI, they can identify many more factors that affect price, from the economic situation, preferences, and sales. This can help to reveal what drives prices for each customer segment, ultimately helping businesses shape a more refined pricing strategy instead of using a standard discount across product categories and locations. Moreover, with intelligent automated systems, companies can save time by automating the data-crunching for price optimization analysis.
How the coronavirus has impacted e-commerce
There is no doubt that e-commerce has been on the rise for the past few years. This number will only continue to increase, especially after the events of this recent pandemic. In fact, it is expected that there will be around 2.14 billion digital buyers by 2021, and depending on how the spread of this virus continues, it may be more.
According to Statista, last year alone (2019), retail e-commerce sales worldwide had amounted to USD 3.53 trillion, with revenues projected to grow to USD 6.54 trillion in 2022.
One of the key reasons why people enjoy shopping online is the ability to shop 24/7, compare prices, find lower prices, save time, and not have to go out.
With that pattern of behavior only increasing, it is only natural for people to convert their shopping behaviors by going online. According to Technomic, roughly 52% of consumers are beginning to avoid crowds, with 32% leaving their house less often.
This is evident, not only in the US but in Asia as well. A report by YouGov illustrates (down below), 85% of internet users in China have avoided public places to protect themselves. Countries like Hong Kong, Taiwan, Malaysia, and many others follow this trend as well.
Based on U.S. government data, Q4 of 2019 showed that online sales made up 11.4% of total retail spending, however, from how this pandemic has been spreading out, e-commerce is projected to reach 12% or higher.
Sales of China’s largest online retailer, JD.com is one example of how e-commerce activity has seen a boost. Their sales of common household staples quadrupled in comparison to the same time last year.
It may not all be gold
While it is true that e-commerce sales have increased, many retailers are saying that these sales are primarily for categories in health-related items and essentials like toilet paper. This has been evident through research done via site search provider Bloomreach, where online sales increased in the week of Feb. 22-29 had gone up for health-related items in comparison to the week before. Items that saw a major boost in sales from the week prior included:
- Masks (590% increase)
- Hand sanitizer (420% increase)
- Clorox/Lysol wipes (184% increase)
- Disinfectants (178% increase)
- Gloves (151% increase)
- Bottled/packaged water (78% increase)
- Vitamins sales (78% increase)
- Tissues (43% increase)
- Hand soap (33% increase)
- Toilet paper and paper towels (26% increase)
Moreover, along with this influx in shopping comes a delay in delivery. This drastic increase in demand has led to many retailers having shorter supply. Although there were quite a few retailers who expect e-commerce to increase their sales (roughly 38% of 304 retailers surveyed by Digital Commerce 360), interestingly a majority of them believed the sales would actually decline or go flat.
Whether its due to supply chain issues, a shortage in product inventory or a decline in consumer demand, the pandemic has raised a lot of real concern for an economic recession. Although more people may be staying home, this doesn’t necessarily equate to a boost in all e-commerce stores, as the only items people may be buying are the essentials. Discretionary spending could, and perhaps may most likely, decrease.
How predictive analytics can help save costs
Better predictions can help e-commerce brands curtail their operating costs in diverse ways. Any brand that wants to survive in the long-run must be quick to predict what the consumers will want in the future. Data science is not only for the enterprise. It can help e-commerce brands save costs in different ways through better predictions.
- It helps you determine what your customers will most probably purchase in advance.
- Improves supply chain management thereby minimizing the cost of stockouts.
- Reduces the cost of marketing through target recommendations and promotions.
- It helps you decide how to tighten your assortment for the economic downturn and slowing sales. Since there is so much data to be gained from online shopping – from behavior analytics to purchase history – you can use predictive analytics to know how to strategize on what will sell, knowing the best and worst sellers.
- Monitors what other competitor brands are doing by scraping their data and taking that information into consideration.
According to Salesforce, Room & Board increased its ROI by a whopping 2900% with the help of a predictive forecasting engine. With AI technology, you can leverage many data sources, from customers’ search histories, previous purchases, economics characteristics, and other demographics, to make the best recommendations on what to buy, when to purchase, and how much to get.
Focusing on data is key
Working with data is crucial to transform your business in this digital age. Yet, while companies are scrambling to maximize their sales and keep up with changing consumer demands, executives from large corporations are illustrating the lack of data-driven focus in the modern corporate world.
In a 2019 Big Data and AI Executive Survey by NewVantage Partners, they found that 72% of survey participants (which consisted of c-level technology and business executives) stated they had yet to establish a data culture, with 69% reporting that data-driven organization had yet been created.
Furthermore, what was even more alarming was that 53% state that data has not been treated as a business asset, with only 52% of them expressing non-action on data and analytics. What comes as a huge surprise is that the most surprising aspect of this was that these executives were from corporations like American Express, Ford Motor, General Electric, Johnson & Johnson, etc.
Simply having data is not enough. What is important is making use of that data, and pulling the right insights from it. Big data analytics helps to manage inventory and have less deadstock. What this does is help online retailers free up cash flow, which can only help to improve your business. This is incredibly important for smaller brands, as unlike the Nikes or Louis Vuittons of the world, most businesses do not have a pit of money to grab from. With Chain of Demand’s inventory health check, you can learn what and when to buy to increase your sell-through.