Data analysis is presented as an essential task in companies to find valuable information found among a large number of records currently available. In this way, they can detect opportunities for improvement that allow them to deliver a higher quality service to their customers. Nowadays, providing a satisfactory service to your customers is one of the master keys to opening the door to success for your business.
Recent studies have revealed that 61% of consumers are more willing to spend at companies that gave them a positive experience. In particular, achieving an efficient flow of customers in your e-commerce can make a significant difference between success and failure, therefore, it is of great importance that you optimize this process as much as possible.
But how can data analytics help streamline operations?
Customer flow is related to the process of interaction between a consumer and a company, either physically or virtually through an online sales site. In other words, it encompasses the full journey of the consumer’s shopping experience.
In this sense, elements such as the difficulty in locating the products available in the store, little information about their characteristics and benefits, excessive requests for personal data, and limited payment options can seriously affect the purchase process, so it is especially important to control these and other variables to optimize customer flow.
For this reason, the correct classification of data and information based on a detailed analysis becomes essential to deliver a more satisfactory experience to your customers. In this aspect, it is necessary to evaluate variables such as:
- Most used payment methods.
- Percentage of complaints due to problems in home delivery.
- Best selling and quoted products.
- conversion rate.
- The number of daily visits.
- Average product shipping time.
Thus, through the control and statistical and real-time analytics of the data related to these variables, it will be easier to implement measures that aim at a higher quality customer flow in your e-commerce that allows, for example, to reduce the cart abandonment rate (studies indicate that this percentage is close to 70 % on average).
Data analysis techniques
To optimize the evaluation and analysis tasks of your e-commerce, you need to know different data analysis techniques that you can use to find valuable information, among which are:
- Description techniques: We seek to interpret the data through tools such as tables or graphs that facilitate the analysis to obtain valuable information.
- Correlation or association techniques: These consist of establishing how related two or more characteristics under study are.
- Comparative techniques: Two or more data sets are compared. For example, you can compare the level of sales of products in your store.
- Regression or cause-effect techniques: The dependency relationship between a dependent variable and one or more independent variables is investigated to seek to explain the behavior of a data set.
- Predictive models: The aim is to project behaviors based on historical data. Thus, for example, you can estimate the sales of your e-commerce in a given month.
As you can see, the data and information analysis and classification tasks are key to understanding the current situation of your e-commerce and improving the shopping experience of your customers to gain their trust and loyalty which represents a higher level of sales in the future market.
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