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Does your eCommerce site need big data analytics?

big data analyticsThere’s been no shortage of coverage of the impact big data has had on the internet economy in the last decade or so. A key sector of this coverage that shouldn’t be left out of the news is eCommerce.

The eCommerce industry is a highly-competitive sector. Whether you’re running a major online platform or not, the number of people offering the same services as you is going to be large. Not to mention the challenge of building the right strategy to attract customers, drive traffic to your website and the added competition of offline retailers and the constantly-changing customer interests. This is how big data analytics can help your site.

How can big data analytics help with eCommerce?

In general, big data’s claim to fame has been its ability to allow companies to implement a number of useful features. This includes data mining, predictive analysis, and business security enhancements. Each of these can then be used to facilitate better decision making within a business, including eCommerce websites.

Customer experience personalization: eCommerce sites are particularly at an advantage with the rising importance of big data. Every customer that visits an eCommerce website presents that platform with a new opportunity to study and improve intricate details about the site that would otherwise be easy to miss. ‘

For example, a common problem that all eCommerce websites continue to face is how to fine-tune their recommendation engines.

Customers don’t want to be shown random products when they visit your website, they want something relevant to their interests. Utilizing big data analytics for eCommerce helps companies to develop products and features that the consumers actually want, and thus sets out to improve customer retention and build brand loyalty.

Dynamic pricing: Dynamic pricing is a practice that has come under a lot of criticism because of the way it has been applied in various consumer markets. It’s worth visiting the effectiveness of tailoring prices according to factors such as demographics and location because a 1% price increase leads to as much as 8.7% boost in profits.

Adjusting prices according to such metrics has always been a challenge because of how quickly customers adjust to the changes themselves, which warrants a response from the business. Noticing such a change in the mass market is a challenge because humans take time to collect and study the data.

Big data enables better dynamic pricing because most platforms are currently able to react to such changes almost immediately. This is where the difference between Hadoop and Spark is most visible. Spark is a big data platform that can process massive amounts of data in real-time.

It can achieve this thanks to its in-memory processing, as opposed to Hadoop’s disk-based processing. Streaming data allows it to respond to both small and major changes in supply, demand and other factors attached to the pricing model accordingly.

Faster response to market changes: The availability of these vast amounts of data also puts companies that utilize it at an advantage because they can respond to market changes more easily. Rapidly-shifting consumer interests can be studied better and responded to preemptively.

An interesting way to illustrate the opportunities offered by big data is Google’s DeepMind AI. It managed to defeat world champions in games that we would otherwise have considered impossible, such as Go and Poker, by ‘predicting’ the opponent’s next move. It’s an extreme example, granted, but modern AI works in somewhat the same way.

Enhanced checkout security: The most important part of an eCommerce website is arguably the cart/checkout functionality. Most businesses choose to outsource this to a third party such as PayPal or use add-on functionality from plugins and widgets. This, in turn, takes away many of the headaches the business would face in implementing its own functionality.

The soaring popularity of big data has made both integrating third parties and having home-grown solutions much simpler. Big data can be used for threat visualization, which allows you to cybersecurity threats by evaluating patterns. Data collected from such threats should then be stored to get better insights from future threats and how to mitigate them.

Better inventory management: Inventory management is a task that is traditionally carried out using a myriad of excel sheets and a lot of manpower. Excel sheets come with the caveat that one wrong function will mess up all of your data, and tracking down such errors isn’t a simple task.

Big data in inventory management provides companies with an opportunity to analyze historical data on sales and attempt to predict when they are likely to be stocked out; create better supply chain visibility, where every product can be monitored and tracked in real-time; and forecast when demand is going to be high to enable better stocking.

Does your business really need big data analytics?

With all the apparent benefits big data has, you’d think answering this question is as simple as saying ‘yes, your company is falling behind’ and moving on. Not many people will take you up to the task of challenging the viability of big data, after all.

The main thing every person has to keep in mind is that adopting a big data platform isn’t the same thing as a database migration (which in itself is a commendable feat) or changing CMS platforms.

Big data comes with many caveats, and not all businesses are ready to take on the burden of setting up and maintaining a Hadoop cluster, regardless of the benefits. A few factors you need to consider before settling on a big data platform are: the cost implication, the amount of data you handle and the intended use cases.

Most platforms don’t need a big data solution, in part because they don’t handle the obscene amounts of data that Apache Spark was meant for, for instance, and also because it’s an expensive affair.

In addition to which, the intended use cases for the data just doesn’t really justify pouring so many resources into it. For instance, rather than pay hundreds of thousands of dollars to improve their checkout experience, most businesses are better off just using Stripe or PayPal in the long run.