The application of various data analysis types in ecommerce is now essential for success. It is the process of accumulating data from all of the areas that have an impact on your store. You should then use this data so that you can comprehend shifts in customer behaviour and online shopping trends.

Ultimately, you can make more intelligent decisions by basing them on data, which should result in more online sales being made.

Ecommerce analytics can include a wide range of metrics relating to the full customer journey, such as discovery, acquisition, conversion, retention and advocacy.

Analysis of data is a vital part of running a successful business. When data is used effectively, it leads to a better understanding of your business’s previous performance and better decision-making for its future activities. There are many ways that data can be utilised, at all levels of a company’s operations.

Four types of data analysis are in use across all industries. While we separate these into categories, they are all linked together and build upon each other. As you begin moving from the simplest type of analytics to more complex, the degree of difficulty and resources required increases. At the same time, the level of added insight and value also increases.

Four Types of Data Analysis

The four types of data analysis are:

• Descriptive Analysis
• Diagnostic Analysis
• Predictive Analysis
• Prescriptive Analysis

Below, we will introduce each type and give examples of how they are utilised in ecommerce.

Descriptive Analysis

The first type of data analysis is descriptive analysis. It is at the foundation of all data insight. It is the simplest and most common use of data in business today. Descriptive analysis answers the “what happened” by summarizing past data, usually in the form of dashboards.

The biggest use of descriptive analysis in business is to track Key Performance Indicators (KPIs). KPIs describe how a business is performing based on chosen benchmarks.

Business applications of descriptive analysis include:

• KPI dashboards
• Monthly revenue reports
• Sales lead overview

Diagnostic Analysis

After asking the main question of “what happened”, the next step is to dive deeper and ask why did it happen. This is where diagnostic analysis comes in.

Diagnostic analysis takes the insights found from descriptive analytics and drills down to find the causes of those outcomes. Organisations make use of this type of analytics as it creates more connections between data and identifies patterns of behaviour.

A critical aspect of diagnostic analysis is creating detailed information. When new problems arise, you may have already collected certain data about the issue. By already having the data at your disposal, it ends up having to repeat work and makes all problems interconnected.

Business applications of diagnostic analysis include:

• A freight company investigating the cause of slow shipments in a certain region.
• A SaaS company drilling down to determine which marketing activities increased trials.
  

Predictive Analytics

Predictive analysis attempts to answer the question “what is likely to happen”. This type of analytics utilises previous data to make predictions about future outcomes.

This type of analysis is another step up from the descriptive and diagnostic analyses. Predictive analysis uses the data we have summarised to make logical predictions of the outcomes of events. This analysis relies on statistical modelling, which requires added technology and manpower to forecast. It is also important to understand that forecasting is only an estimate; the accuracy of predictions relies on quality and detailed data.

While descriptive and diagnostic analysis are common business practices, predictive analysis is where many organisations begin to show signs of difficulty. Some companies do not have the manpower to implement predictive analysis in every place they desire. Others are not yet willing to invest in analysis teams across every department or are not prepared to educate current teams.

A highly recommended explanation of how predictive analytics works is available here.

Business applications of predictive analysis include:

• Risk Assessment
• Sales Forecasting
• Segmentation
• Predictive analytics in customer success teams

Prescriptive Analysis

The final type of data analysis is the most sought-after, but few organizations are truly equipped to perform it. Prescriptive analysis is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision.

Prescriptive analysis utilises state-of-the-art technology and data practices. It is a huge organisational commitment and companies must be sure that they are ready and willing to put forth the effort and resources.

Artificial Intelligence (AI) is a perfect example of prescriptive analytics. AI systems consume a large amount of data to continuously learn and use this information to make informed decisions. Well-designed AI systems are capable of communicating these decisions and even putting those decisions into action. Business processes can be performed and optimised daily without a human doing anything with artificial intelligence.

Currently, most of the big data-driven companies (Apple, Facebook, Netflix, etc.) are utilising prescriptive analytics and AI to improve decision-making. For other organisations, the jump to predictive and prescriptive analytics can be insurmountable. As technology continues to improve and more professionals are educated in data, we will see more companies entering the data-driven realm.

The application of predictive analytics in personalisation

Marketers using predictive hyper-personalisation software track both website and application activity perpetually. It is an autonomous personalisation system, so requires zero human input whatsoever, and it makes a 20x greater ROI. It removes not only the cost of human involvement but their errors and omissions too. 

Dynamic content becomes an integral element in all predictive personalised emails, which smashes ROI. Using dynamic content with personalised emails packs a powerful punch. For those researching this software, it is essential you choose autonomous software with zero human interaction involvement to achieve the greatest returns or you could spend time and money, wasted on an inferior solution. 

Context should be always and perpetually relevant to the email recipient. Context is the future of email hyper-personalisation. Dynamic content can change or update with every action made by the consumer interacting with both your site and your emails. That should include the item (SKU code as we are talking personalisation to that degree of sophistication), colour, style, fabric, size, cut, price but also time of day viewed, then purchased, those actions compared and about previous purchases and the pattern made to deliver this purchase to you.

Multiple surveys say that using dynamic email content achieves the highest ROI – including Forrester, McKinsey, Bain and Statista. Advanced email marketers using PPS know it will be presenting the exact image of each product with the highest buying propensity for that individual and unique customer, at precisely the right moment.

Conclusion

As we have shown, each of these types of data analysis is connected and relies on each other to a certain degree. They each serve a different purpose and provide varying insights. Moving from descriptive analysis towards predictive and prescriptive analysis requires much more technical ability, but also unlocks more insight for your organisation.

Solutions that run autonomously, and offer predictive analytics for hyper-personalisation are the future of ecommerce. there are however many distinctions of hyper-personalisation software, and essential to know what they are.