How Business Intelligence Analysts Help Online Shops Drive Sales

Why Every Online Shop Needs a Business Intelligence Analyst?

Imagine you are a business intelligence analyst for an online shop that sells clothes. Your job is to use data to help the shop improve its sales. You do this by identifying trends and patterns in customer behavior, analyzing the performance of marketing campaigns, and optimizing the pricing of products.

This is how we'll do it.

1- Collect data

• Review the shop's website analytics: This could involve looking at the following metrics:

  • Daily sales
  • Traffic (number of visitors to the website)
  • Conversion rate (percentage of visitors who make a purchase)
  • Top selling products
  • Bounce rate (percentage of visitors who leave the website after viewing only one page)
  • Average time spent on the website

• Analyze customer reviews: This could involve looking at the following:

  • Common complaints
  • Areas where the shop could improve
  • Positive feedback
  • Suggestions for new products or services

• Monitor social media mentions of the shop: This could involve looking at the following:

• Hashtags used to tag the shop
• Positive and negative comments about the shop
• Sentiment analysis (determining the overall tone of the comments)
• Trends in customer sentiment over time

2- Clean and organize data

• Remove any duplicate or erroneous data from the shop's database:
•  This could involve using a data cleaning tool to identify and remove duplicate records, or manually reviewing the data to identify any errors.
• Standardize the data format:
•  This could involve converting all dates to the same format, or ensuring that all product names are spelled the same way.
• Create dashboards and visualizations:
•  This could involve using a data visualization tool to create charts and graphs that show the distribution of the data, or to identify trends or patterns.

3- Analyze data

• Running statistical tests to identify trends and patterns in the data:

  • A business analyst conducts a t-test to compare the average sales of two different products.
  • A marketing analyst performs a chi-squared test to see if there is a relationship between the gender of a customer and the type of product they purchase.
  • A data scientist applies a regression analysis to predict the probability of a customer making a purchase based on their age, income, and other factors.

• Developing hypotheses about why these trends are occurring:

  • The business analyst proposes that the higher sales of one product are due to its lower price.
  • The marketing analyst speculates that the relationship between gender and product purchase is due to different marketing campaigns being targeted to different genders.
  • The data scientist concludes that the probability of a customer making a purchase is higher for customers who are younger and have higher incomes.

• Testing these hypotheses using data analysis techniques:

• The business analyst gathers more data to see if the lower price of the product is indeed the reason for its higher sales.
• The marketing analyst conducts a survey of customers to see if they are aware of the different marketing campaigns and how they influence their purchasing decisions.
• The data scientist builds a machine learning model to predict the probability of a customer making a purchase based on their age, income, and other factors.

4- Create reports

• Creating reports that summarize the findings of the data analysis:

  • A business analyst might create a report that summarizes the findings of a study on the effectiveness of different marketing campaigns. The report would include charts and graphs that illustrate the results of the study, as well as a summary of the key findings and recommendations.
  • A marketing analyst might create a report that summarizes the findings of a study on the customer journey. The report would include information on how customers interact with the company's website and products, as well as recommendations on how to improve the customer experience.
  • A data scientist might create a report that summarizes the findings of a study on the company's customer churn rate. The report would include information on why customers are churning, as well as recommendations on how to reduce the churn rate.

• Tailoring the reports to the specific needs of the shop's management:

  • A business analyst might tailor a report on the effectiveness of different marketing campaigns to the specific needs of the marketing team. The report would include information that is relevant to the marketing team's goals, such as the cost-effectiveness of different campaigns and the impact of campaigns on brand awareness.
  • A marketing analyst might tailor a report on the customer journey to the specific needs of the customer service team. The report would include information that is relevant to the customer service team's goals, such as the most common reasons for customer complaints and the steps that can be taken to improve the customer experience.
  • A data scientist might tailor a report on the company's customer churn rate to the specific needs of the sales team. The report would include information that is relevant to the sales team's goals, such as the characteristics of customers who are most likely to churn and the steps that can be taken to prevent customers from churning.

• Highlighting the key findings and recommendations in the reports:

• A business analyst might highlight the key findings and recommendations of a report on the effectiveness of different marketing campaigns in a summary at the beginning of the report. The summary would be written in a clear and concise way so that the management team can quickly understand the key findings and recommendations.
• A marketing analyst might highlight the key findings and recommendations of a report on the customer journey in a section at the end of the report. The section would provide a detailed explanation of the key findings and recommendations, as well as suggestions for how the company can improve the customer experience.
• A data scientist might highlight the key findings and recommendations of a report on the company's customer churn rate in a table or chart at the beginning of the report. The table or chart would make it easy for the management team to see the key findings and recommendations at a glance.

5- Communicate findings

• Presenting the findings of the data analysis in a clear and concise way:

  • A business analyst might present the findings of a data analysis in a meeting with the management team. The presentation would be clear and concise, and it would use visuals such as charts and graphs to help the management team understand the data.
  • A marketing analyst might present the findings of a data analysis in a written report. The report would be well-organized and easy to read, and it would use clear and concise language.
  • A data scientist might present the findings of a data analysis in a presentation to a technical audience. The presentation would use technical terms and concepts, but it would be clear and concise so that the audience can understand the findings.

• Answering any questions that the management may have about the data:

  • A business analyst might answer questions from the management team about the data during the presentation or in a follow-up meeting. The business analyst would be prepared to answer questions about the methodology used, the results of the analysis, and the implications of the findings.
  • A marketing analyst might answer questions from the management team about the data in a written report. The report would include a section that addresses frequently asked questions.
  • A data scientist might answer questions from the management team about the data in a technical presentation. The data scientist would be prepared to answer questions about the technical aspects of the analysis.

• Working with the management to develop and implement strategies based on the data analysis findings:

• A business analyst might work with the management team to develop strategies to improve the effectiveness of marketing campaigns. The business analyst would use the findings of the data analysis to identify opportunities to improve the campaigns.
• A marketing analyst might work with the management team to develop strategies to improve the customer experience. The marketing analyst would use the findings of the data analysis to identify areas where the customer experience can be improved.
• A data scientist might work with the management team to develop strategies to reduce the company's customer churn rate. The data scientist would use the findings of the data analysis to identify customers who are at risk of churning and develop strategies to prevent them from churning.

6- Stay up-to-date on trends

• The data analyst attends industry conferences and workshops to learn about the latest trends in data analysis. This helps the data analyst to stay up-to-date on the latest techniques and tools that can be used to analyze data.
• The data analyst reads data analysis blogs and articles to stay up-to-date on the latest research. This helps the data analyst to learn about new ways to use data to improve business performance.
• The data analyst experiments with new data analysis techniques and tools. This helps the data analyst to stay ahead of the curve and to find new ways to use data to improve the shop's business.

Summary

Think of the Business Intelligence Analyst as the secret ingredient that transforms your online clothing shop. They roll up their sleeves, diving into the world of website clicks, customer thoughts, and social media buzz. It's like they're detectives, solving the mystery of what makes your customers tick. Armed with data spells and analysis tricks, they reveal patterns that fuel smart decisions. Their reports? They're like treasure maps, guiding your marketing, customer service, and sales crews. And they don't just stop – they're on a quest to stay ahead of the data game, making sure your shop is always one step ahead in this ever-evolving adventure.