What is data analysis?

Data analytics is the process of analysing raw data to form conclusions and make decisions.

Any type of information can be subjected to data analysis and the techniques will often differ depending on the end goal. For example, manufacturing companies may be looking at improving the efficiency of their machines so they may focus on the runtime, downtime, and work queue of the machines and then make iterations based on their findings. Retailers would likely focus on the online behaviour of their customers, looking at their session duration, average order value, and interaction with products. As e-commerce grows, retail has become an increasingly data-rich environment, creating many more opportunities for capturing actionable customer insights.

Why do data analysis?

With more retail traffic online, retailers need to remain up to date with their consumer purchasing behaviour. As discussed in our previous blog posts (this one, for example), customers are also changing the way they view the online shopping experience in relation to their in-store experience. For example, Google’s Zero Moment of Truth (ZMOT) research found that 70% of consumers research online before purchasing in-store. This means that data analysis of online behaviour can provide invaluable information for forming omnichannel strategies that capture the customer from the very beginning of their purchasing journey.

Here are 3 reasons to pay attention to data analytics in your e-commerce business

  1. You can adapt your business strategies to shopping patterns, as they emerge. As seen throughout the past 2 years, customer behaviour online can change rapidly and it’s important to have the most up to date insights. Data analytics can reveal spikes in demand, product preferences and brand preferences, which all help to inform inventory management. Before the technology existed, it was difficult to predict required stock levels throughout the year. Measured short-term data shows a clear formula for determining price based on supply and demand.
  2. Data analytics can reduce your costs. Analytical data can eliminate a lot of guesswork when planning and designing marketing campaigns, creating products, and identifying bottlenecks in processes or supply-chain. Analytical techniques can reduce the lead time associated with blind iterative R&D, thus significantly reducing the cost of various aspects of retail including product launches.
  3. You can find opportunities for innovation. Consumer preferences and needs are often hidden if you are only looking at data types in isolation (just item data, for example). Complex data analysis allows for a more algorithmic approach to finding causal interactions between variables, such as how the time of year impacts different brand preferences whilst controlling for the budget of a customer.

How to conduct data analysis?

There are many different techniques and processes paired with highly capable technologies. Some of the technologies that make modern data analytics so powerful are:

  • Machine learning: Artificial intelligence (AI) uses computers and machines to offer the problem-solving and decision-making capabilities of the human mind. Machine learning (ML) is a subset of AI that involves algorithms and unsupervised learning. This enables applications to conduct data analysis and predict outcomes without requiring a human programmer to manually input instructions. Therefore, it provides a scalable solution that often improves performance over time.
  • Data management: To analyse data, you need to have the infrastructure in place to manage the flow of data in and out of your systems and to keep it organised. Often this will involve the use of a central data management platform, where the data remains until it is needed for analysis.
  • Data mining: The term data mining refers to the process of finding anomalies, patterns and correlations between data points. This organises the data into useful categories and allows you to sift through large datasets to see only the most relevant figures.
  • Predictive analytics: Technology like this can predict future events. It uses artificial intelligence (AI), data mining, machine learning, modelling, and statistics to make these determinations. Predictive models are used for all kinds of applications including language translation, weather forecasting and investment portfolio development. All of these applications use descriptive statistical models of historical data to predict the likelihood of future outcomes.

Insights from BOON

Whilst all of these technologies and statistical models are undoubtedly useful, it can be difficult for retailers to conduct seamless analysis if they are using multiple service providers, especially if this data is to be accessed by multiple departments.

Some solutions, such as BOON, offer an all-in-one approach where each step of the data analysis process is contained on one platform. At BOON, we analyse session-based, anonymous zero-party data, collected through shopping-assistant quizzes. We organise and aggregate all the data for our clients, and provide significant and actionable insights to inform future campaigns, find gaps in product ranges and understand the demographics, intention and personality of customers.