We have watched as our world has experienced various revolutions. Among the most significant are the E-commerce and data-driven digital marketing aspects. The inception of data-driven marketing began with Customer Relationship Management (CRM).
The evolution continued, and now the leading trend in data-driven marketing is social media. Data-driven marketing refers to the strategy of using massive data on customer trends and behavior to personalize experiences and optimize business efficiency.
The first requirement is data collation, as multiple data are waiting to be harvested. But, what do you do if you have the wrong information? Or do you lose hold of relevant information? Or information is disorganized? Or you don’t know how to implement and employ your information?
This is where data analytics comes in. Data analytics include:
- Optimal business performance due to informed decisions
- Website traffic, more clicks, views, and sales based on revealed customer behavior
- Lesser expenditure as trends are predicted and demands forecasted
- Creation of better products and services as a result of personalization
Types Of E-commerce Data Analytics
Data analytics is the science of analyzing raw data to reach conclusions about various information. The technologies and processes associated have been automated into algorithms and mechanical processes that work over raw data.
Data analytics encompasses all forms and steps of data analysis and goes a step further to reveal trends and metrics that you may have missed. You can use the insight to optimize processes to increase business efficiency and make accurate decisions, thus reducing cost. The types of data analytics are:
1. Descriptive Analytics
Descriptive analytics simply refers to the process of interpreting historical data. This process facilitates a better understanding of every change that occurs in the business.
Furthermore, this describes an event after a given period using a range of historical data. It asks the question, “what happened. ”
For instance, are sales better this year than last year? It employs techniques like segmentation, clustering, and summary statistics.
2. Diagnostic Analytics
Diagnostic analytics assists with deeper business processes understanding. It assists companies with the creation of clear connections between data and behavior patterns.
This type of data analytics focuses on the question “why,” the reason for an occurrence. It involves hypothesizing and techniques such as data mining, drilling down, and correlation to analyze situations. It is often used to identify customer trends.
3. Predictive Analytics
This attempts to predict or forecast near-future events or the outcome of scenarios in relation to potential responses. It studies possible patterns based on statistics, modeling, and data mining.
With a scoring system, predictive analytics indicate the probability of a forecast occurring.
4. Prescriptive Analytics
While predictive is in terms of future events, predictive relates to the past, prescriptive is more of the present.
This gives recommendations for unfolding situations based on available data. It suggests a favorable course of action.
5. Cyber Analytic
This is new to data-driven marketing and e-commerce. It’s a combination of cyber security skills and analytics knowledge.
Sophistication tools and software detect threats and vulnerabilities and close off attack vectors with a data-driven approach.
How to Use Data Analytics to Boost E-commerce Sales
Data analytics is a great tool that you can use to work wonders in sales and revenue. In addition, e-commerce businesses that use statistical information from online data-driven marketing and sales activities have a 40.38% influence in revenue after a year and a half.
This shows that usage of data analytics gives an edge. Data analytics gathers data from all areas that impact your e-business, and you understand trends and changes in your customers’ behavior. Some of the ways you can use data analytics to boost commerce sales are:
1. Price Optimization
In data-driven marketing, analytics tools can help increase sales by setting optimal prices for your goods and products based on various factors.
The best price options will be offered to you after considering your customers’ purchase patterns, location, prices charged by competitors for similar products, etc.
2. Better Segmentation
We all know that a one-size-fits-all approach to data-driven marketing and sales has ceased to be effective. After considering statistics and demographics, data analytics helps boost sales by giving ideas on how to implement segmentation and targeting.
You could segment your customers based on age, gender, location, time zone, etc., for better advertisement targeting.
3. Recommendation Engines
You could use Demographics from data analytics to deduce behavioral patterns of customers on a website, e-store, or online catalog. Machine learning and deep learning algorithms track every user’s online behavior. After analyzing customers’ purchase patterns, you could prompt them to make more purchases by suggesting similar products and services.
This aids cross-selling through recommendations of accessories to go with an item. You could also use it to enhance upselling of a new and improved product with more features.
4. Increased Return on Ad Spending
With the various types of data analytics, you can find out where you are facing deficits. Prescriptive analytics can then suggest to you ways to maximize ad spend.
Ecommerce analytics helps you to determine the kinds of ads that produce results. It also enables you to choose the right target audience to direct your ads to.
5. Retargeting Ads
With analytical data, you can track the activities of your customers. Research has shown that a business runs at a loss every time a customer abandons their shopping cart.
To minimize this, you can use personalized emails to send a pop-up reminder about a product they forgot stylishly. Chip in the benefits of that particular product as a reminder of why they chose it in the first place.
You may offer a small discount to trigger the purchase of the product or offer similar products. Retargeting previous customers is effective and can be done through emails and adverts. Retaining customers is easier than acquiring new leads.
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