Countless entrepreneurs aspire to start successful online retail businesses. They are enamored with the dream of working from home and having the opportunity to make virtually unlimited wealth. Unfortunately, the probability of success in this field is often considerably lower than they would like to believe. Nevertheless, some e-commerce entrepreneurs can thrive by taking advantage of big data technology.
Predictive analytics helps savvy entrepreneurs make realistic projections and develop sound strategies to meet them. They should understand the benefits of big data analytics and use it to their fullest potential.
The dismal state of the e-commerce industry indicates that predictive analytics is more important than ever
Online entrepreneurs often get overly optimistic when they hear data about the growing e-commerce industry. They have read statistics showing that the industry is expected to grow to $1, trillion by 2024. At first glance, this figure suggests that there is a tremendous opportunity for them to enrich themselves by capturing a small share of the market.
Unfortunately, entrepreneurs that take a more introspective view will see that their outlook is much bleaker. They will notice that the industry is heavily consolidated. A small percentage of e-commerce companies are dominating the market, while the vast majority struggle to get by.
According to research from PipeCandy, there are approximately 240,000 web-based retailers in the United States. An article from Forbes recently showed that around 40% of those online retailers generate less than $12,000 in revenue each year.
This doesn’t mean that new retailers with tightly focused product lines can’t succeed. However, they need to execute a carefully planned strategy to yield a decent net revenue.
Predictive analytics helps e-commerce entrepreneurs develop lucrative business models. Here are some of the core benefits this technology provides.
Determine the lifetime value of leads acquired through various channels
Online entrepreneurs are often skittish about investing in digital marketing channels with a high CPA. One major apparel company was nervous about using social media traffic to attract potential customers because the advertising costs were higher than with search PPC.
They were tempted to withdraw their advertisements from those social media channels. However, after they used predictive analytics, they were able to determine the expected lifetime value of those customers within a mere two weeks. They discovered that the expected lifetime value of customers acquired through social media ads was extremely high, which actually resulted in a higher ROI than their search engine marketing campaigns, despite the fact that the traffic costs were also higher.
Identify future crises before they occur
A number of changes are constantly taking place in the e-commerce sector. Brands need to adapt to new legislation on a nearly daily basis. More mundane events can also have a major impact. For example, they could face a serious problem if a software license expires. This is one of the reasons that they will use a software license management tool. These tools use predictive analytics to identify the expiration dates of various applications and provide timely warnings to have them renewed.
Get a deeper understanding of customer behavior
Every business model must be carefully structured around the expected behavior of the company’s target customers. The biggest challenge is not building a funnel that aligns with customers, but rather developing an understanding of those customers in the first place. Too many brands make poor assumptions about their customers, which inevitably causes their conversion rates to plateau.
Machine learning algorithms are able to monitor customer behavior in real time. The data that these algorithms provide helps e-commerce brands optimize their marketing strategies by:
- Optimizing the placement of website navigation links and visual elements on e-commerce websites
- Providing suggested search options to customers using internal search engines
- Offering promotions and incentives to encourage customers to follow through on purchases
- Providing timely reminders to encourage customers to follow through on purchases to minimize shopping cart abandonment rates
Customer behavior is exceedingly complex and difficult to predict. Machine learning algorithms can give insights that are much more accurate than the assumptions you would make.
Predicting demand for new products
Most drop shipping entrepreneurs I have spoken with have stated that A small percentage of products account for the vast majority of their sales. Identifying the most profitable products to promote is One of the most important decisions they can make.
Predictive analytics models can track long-term market trends. Entrepreneurs that leverage them can determine the products that are most likely to be profitable.
