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  • Writer's pictureGraas

Why is it important for eCommerce business to make data-driven decisions?

Updated: Oct 24, 2023

Data & Insights eCommerce | Graas

Ever since doing business went digital, the amount of data it has been generating on the side continues to get bigger and bigger every day. Even the simple act of buying and selling something online involves multiple data points, such as

  • The journey a prospective buyer took to reach your product listing online

  • The process the buyer follows on your online store to reach checkout

  • Potential drop off points along the buying journey

  • How coupons, discounts, and external factors like festive sales periods drive buying decisions

And that’s just skimming the surface. This data can translate into actionable insights that businesses can indeed utilize. However, this data is often bulky, unstructured, and sometimes difficult to process. The challenge of too much data but very few insights is all too real. eCommerce companies stand to benefit a lot if they are able to unify their data sets and extract real value out of the data. Data, in fact, can form the crux of decision-making in business. Often, data tends to get stacked in information silos of its units, departments, or divisions. This is where technology comes in as an ally to pull real value out of these data banks and silos. Data is a goldmine. When harnessed right, technology-driven data analysis in eCommerce can help drive the following business insights or strategies:

  • Quantifying demand for the service or product

  • Getting realistic estimates on future demand

  • Planning product and inventory

  • Understanding the buyer journey and buyer behavior

  • Gathering competitive business intelligence

  • Visualizing responses to marketing campaigns

It all boils down to how accessible the information is and what tools you need to mine it. The answer lies in technology in the form of the right data analytics tools. These tools can help sift through vast amounts of noise and pick up on the precise signals that can help facilitate data-driven decision-making.

Why use data to make decisions ?

Decision-making, when backed by data, is vastly more reliable than any decisions you make based on your gut feeling, experience, or personal opinions. With data analytics and insights, you take more calculated risks and reduce the vulnerability factor in your business growth practices.

Let us take the example of a relatively modest brand with 100 SKUs that sell across 3 countries, through 5 channels and across 3 advertising mediums. If you take into account all the variable and complexities, the brand would need to make and execute approximately 45,000 decisions per month. The more variables we add to this equation, the greater the complexity. If you are curious about how many decisions you would need to take for your business, check out our Decisions Calculator.

If businesses skimp out on using data to make decisions, they miss out on valuable opportunities for growth. Certain types of data have a limited shelf-life. They are actionable only up to a point, beyond which they lose their significance. For example in eCommerce, the type of purchases that happen online during a holiday flash sale, the segment of customers who respond quickly to SMS prompts, how timed notifications and messaging contribute to the online traffic, are all things worth knowing in real time while planning marketing campaigns.

Not only does data analytics afford a look at the big picture, but it also looks at relationships and patterns among several of its components, before delivering insights.

How can you harness the power of data for your eCommerce business?

The two challenges when it comes to data driven decision making are:

  • Data from multiple sources are typically siloed

  • Any meaningful analysis is extremely labour intensive

In order to use data to make effective decisions, brands need to do the following:

Connect the data silos across disparate systems and platforms into a unified data pool, thereby reducing operational complexity. Having a unified pool of data is critical for the next step of analysis.

Secondly, run the data through an AI engine that works to analyze the data. Attempting to do this manually is almost impossible, when you consider how vast the data is. Machine algorithms can identify the outliers and anomalies, and derive trends and patterns from the data. Using this they can arrive at insights and actionable recommendations.

Lastly, turn the insights into action across your entire eCommerce value chain. Take data backed decisions in real time, and track and measure performance to see the real impact on your business.

Previously, AI-powered business processes were out of reach for most companies. Building the AI engines in-house, and also the supporting infrastructure and teams with the right skill sets can be very expensive.

Today, by outsourcing these functions under a “Growth-as-a-Service” solution, this massively reduces the resources brands need to commit in order to get the same level of functionality. By leveraging the economies of scale and having dedicated R&D teams, Graas can provide the functionality of having a larger data science and operational team at a fraction of the price that it would cost to build similar capabilities in-house. If you'd like to see how Graas' predictive AI engine can help your business take effective and data backed decisions that have real bottom line impact, register here for a demo.


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