In ecommerce, expecially in DTC business understanding the customers is the turnkey for overcome the wrench and analytics is not only about making good business decisions, it's provide information in a way that best suits decision-makers.

They usually read product/company reviews from Trustpilot before place an order, to quickly compare prices between online stores or understand how is the value of customer care.

To be the first choice in their mind, you should provoide a exceptional shopping experience and an outstanding service covering the gap on what your competitors doesn't provide yet.

There’s only one thing that will help you do it right: data.

And that’s where ecommerce analytics come in.

What is e commerce analytics?

Ecommerce analytics is the process of gathering data from all areas that have an impact on your online store and using this information to understand the trends and the shift in consumers’ behavior to make data-driven decisions that will drive more online sales.

Ecommerce analytics include metrics related to the full customer journey from discovery, to acquisition, to conversion, finally to retention and advocacy.

Why ecommerce analytics are so important nowadays?

Both direct and indirect impacts of COVID-19 have forced people to purchase goods online. Considering the evolution in consumer demand, combined with technological innovations, will drive growth in global ecommerce sales. According to Statista, the number of people buying goods and services online is expected to reach 2.14 billion in 2021 and the digital buyer penetration worldwide is still increasing up to 65.2%.

The key drivers of success over the next decade will be centered on building a deep understanding of consumer through measurement and analyzing.

These topics are strictly related to RFM customer segmentation and signals gathered and sent to marketing advertising platforms.

Data from Deloitte shows that 49% of respondents say that analytics helps them make better decisions, 16% say that it better enables key strategic initiatives, and 10% say it helps them improve relationships with both customers and business partners.

Source Deloitte: The Analytics Advantage (pdf)

Data analytics in e-commerce

In today’s complex business environment, the field of data analytics is growing in acceptance and importance. It is playing a critical role as a decision-making resource for executives, especially those managing large companies.

Design a Data-Driven Customer Journey Map
Data analysis will help you uncover what your customer cares most. Prescriptive, Descriptive and Predictive make the difference to grow sales.

Structured Vs Unstructured Data

Structured data are for examples: phone numbers, ZIP codes, currency or dates. It tends to reflect the past, which is great for machine learning forecasting algorithms to predict the probability of certain event.

Unstructured data reflect the mainly the present and they are for example: email, social media posts, articles, satellite imagery or sensor data. It may be stored within a non-relational database like NoSQL.

Once the data is collected, normalized and fixed issues related to data quality that may affect the quality of the analysis, you're ready to run data profiling processes to ensure the dataset is consistent. It consists in run processes for data cleansing to ensure duplicate information and errors must be eliminated.

The data now is ready for forecasting, presciption and analysis using data visualization tools to discover hidden correlations, patterns, and trends that can be used to drive business decisions.

Consumer behavior are determined by product feature or needs
Habitual buying behavior, variety-seeking behavior, dissonance-reducing buying behavior, complex buying behavior.

Blending all sources to analyze User data

Gathering and blending user behavioral data gives you information on browsing preferences along customer journey and purchasing process as well as throughout the checkout.

Shopping Behavior Analysis — gives you insight on the number of sessions at each stage of the funnel. Starting from the visitors that only view your products, moving to those who add products to their cart, how many initiated checkout, and lastly, how many sessions finish with a transaction. This report gives you a clear picture of how visitors move down the funnel and helps you identify what you can fix in the process to increase the number of transactions.

Checkout Behavior Analysis — this report is similar to the Shopping Behavior report, but it’s focused on the steps included in the checkout process. Using this report, you can easily track how customers behave during checkout and at what point most of them decide to give up.

For a guide to blend data, below a link to the reference:

Blending Data using Google Analytics and other sources in Data Studio
I will show you how to merge data from different sources, using information provided by Google Analytics and merging them in Google Data Studio.

Benefits of e commerce data analytics

With enough data, businesses can predict customers' needs and respond in real-time by changing or adding products to meet the indicated demand. This can result in a competitive advantage, improved customer experiences, and improved acquisition and retention of new customers. Data analysis boosts revenue by allowing people to make faster and more informed decisions.

Descriptive vs Predictive vs Prescriptive

  • Descriptive Analytics, which use data aggregation and data mining to provide insight into the past and answer: “What has happened?”
  • Predictive Analytics, which use statistical models and forecasting techniques to understand the future and answer: “What could happen?”
  • Prescriptive Analytics, which use optimization and simulation algorithms to advise on possible outcomes and answer: “What should we do?”

Descriptive Analytics

Descriptive analysis or statistics does exactly what the name implies: they "describe", or summarize, raw data and make it something that is interpretable by humans.

They describe the past

The past refers to any point of time that an event has occurred, whether it is one minute ago, or one year ago. Descriptive statistics are useful to show things like total stock in inventory, average dollars spent per customer and year-over-year change in sales.

  • Summarising past events such as sales and operations data or marketing campaigns
  • Social media usage and engagement data such as Instagram or Facebook likes
  • Reporting general trends
  • Collating survey results

Predictive Analytics

Predictive analytics has its roots in the ability to "predict" what might happen. No statistical algorithm can predict the future with 100% certainty but it provides an estimation about the likelihood of a future outcome. They can be used to forecast customer behavior, purchasing patterns and identify trends in sales activities.

They combine historical data from different data sources to identify patterns in the data and apply statistical models and algorithms to capture relationships between various data sets.

Predictive analytics can be used throughout the organization, from forecasting customer behavior and purchasing patterns to identifying trends in sales activities.

  • Efficiency, which could include inventory forecasting
  • Customer service, which can help a company gain a better understanding of who their customers are and what they want in order to tailor recommendations
  • Fraud detection and prevention, which can help companies identify patterns and changes
  • Risk reduction, which, in the finance industry, might mean improved candidate screening
  • Recommendations – predicting customer preferences and recommending products to customers based on past purchases and search history

Prescriptive Analytics

The field of prescriptive analytics allows users to "prescribe" a number of different possible actions and guide them towards a solution. These analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes.

They predict not only what will happen, but also why it will happen.

They use a combination of techniques and tools such as business rules, algorithms, machine learning (ML) and computational modelling procedures. These techniques are applied against data from many different sources including historical and transactional data.

  • identifying the best cluster of customers next to payment or the best for upselling

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