How Big Data Can Help Personalize Your Ecommerce Store

Data is everywhere. Every single detail you have ever provided online, from your address to the advertisements you’ve clicked on, is stored by browsers and applications.

There will hardly be anyone who hasn’t been followed by ads for the same products they’ve searched for earlier.

Is it a bad thing?

Well, not necessarily. While some might have a few ethical issues with collecting so much information, there’s plenty of wiggle room that allows businesses to gather and analyze big data for eCommerce purposes.

The logic behind collecting data is quite simple. Marketing and selling require businesses to understand customer’s needs and pain points. And the information collected (and sometimes provided by customers) helps BigCommerce stores understand shoppers and design actionable steps to improve a customer’s shopping experience.

Let’s now take a closer look at how big data can make BigCommerce stores provide a more personalized experience to shoppers.

How does big data help BigCommerce stores provide personalized shopping?

The information that stores used to take years to collect is now possible within a few days or even hours. Companies that have used big data analytics have seen at least a 5–6% increase in their productivity compared to firms that haven’t utilized big data.

1. Demand forecasting using Big Data

Big data is most useful in demand forecasting and trend prediction. Every business performs some sort of market research to forecast demand and predict trends. However, market forecasting is easier said than done – as accurate demand forecasting needs large quantities of data to be analyzed. Although big data is a collection of immense information, it needs to be sifted through and studied to draw actionable insights.

BigCommerce allows stores to use big data analytics to gather historical data about customers and analyze usage behavior, customer preferences, and transaction records. Predictive forecasting is vital for eCommerce stores to stock inventory, limit overstocking, and enhance order management. BigCommerce developers can help you set up big data analytics. 

2. Insights into Shoppers Psyche

As an eCommerce retailer, you are aware that not all traffic is, in fact, generated equally. The intent of visitors to your site will vary from one traffic segment to another. And each segment deserves attention and appropriate action.

In this regard, BigCommerce helps e-retailers with tools for traffic segmentation so that you can provide dynamic content to visitors based on their shopping behavior. Big data helps you define your ideal market segment so that you focus your marketing efforts on that specific target segment to improve sales. 

The chance to get a deeper glance into the shopper psyche is something no eCommerce store owner will let go of. BigCommerce allows merchants to access, gather and track customer shopping data, which the stores can use to develop actionable plans. With so much information, stores can understand customer preferences, the products they usually like to purchase, their preferred shopping timings, and their payment choices.

By gaining a sneak peek into buyer behavior through BigCommerce’s Insights Analytics, it is possible to develop promotions, product recommendations and even understand unexpected shopping behaviors.

3. Product Recommendations

Product recommendations are one of the most commonly used big data applications. Your store might have thousands of products that are drastically different from each other or a select few that are similar but not exactly the same. It becomes crucial to have critical information about your customer’s preferences to place required products in front of your customers.

BigCommerce development services offer e-retailers a plethora of analysis tools that allow them to provide product recommendations targeted at the right customer group. The main idea behind product recommendations is to increase the average order value of each customer per session. Smart e-retailers are using big data to offer cross-selling and up-selling as well.

If e-retailers can employ insights about customer’s previous purchasing history, their wish list, browsing history, it is possible to offer highly targeted product recommendations to customers. With big data analytics, BigCommerce stores can build a perfect preferred product catalog for customers. 

Another aspect of product recommendation is product customization. With big data, instead of offering a wide range of products, you can allow your customers to build the products they need. BigCommerce’s analysis tools allow e-retailers to know exactly what product specifications are being preferred by customers.

4. Personalize Communication with Customers

Offering customers personalized communication goes a long way in establishing mutual trust. Presenting targeted advertisements to customers depending on their requirements can be done using big data.

72% of shoppers say they engage only with stores that offer personalized messaging.

Most eCommerce stores constantly communicate with their shoppers, but the emails don’t usually have any personal touch. They seem very generic, probably because most of these emails are sent to customers en masse. However, with big data, BigCommerce stores can personalize the actual content of their emails.

Imagine how effective an email would be if it started with 'Hi Jason' rather than a bland 'Hi there'. 66% of customers expect brands to understand their unique requirements and preferences, yet 66% say they are generally treated like ‘numbers’ by retailers.

The best-targeted communication is possible by collecting points from datasets based on previous purchases, mail interactions, on-site activity, pricing requirements, and abandoned carts. It is clear that customers do not always find personalization to be a ‘creepy’ subject, but they, in fact, prefer personalization to a great extent. 

5. Targeted Promotions

52% of customers expect retailers’ offers to be personalized, increasing from 49% in 2019.

With BigCommerce, you can personalize the shopping experience of customers using 'group targeting'. Start with defining your customer groups – based on their orders, revenue generated, and purchase history. You can then create a separate customer group – a group you wish to target. You can then offer advertisements or even discounts to this specific group.

Large datasets – based on buying habits, age, demographics, customer value, and on-site activity – are needed to develop targeted promotions.

If you have a first-time visitor to your BigCommerce store, you can make use of this information to provide targeted promotions – like discounts or coupons – to encourage them to make a purchase. 

6. Personalized Pricing

BigCommerce stores can make use of big data to offer personalized pricing to individual customers. That means two shoppers buying the same product can end up paying different prices. 

Demand-based pricing strategies require vast amounts of buyer data. Big data can go a long way in allowing retailers to gain complex information which can be used to segment traffic and offer multiple price points based on the groups.

Using big data analytics, eCommerce stores can track the rise and fall of customers’ demands and adjust product prices accordingly. BigCommerce simplifies this complicated process by allowing stores to set up groups, specific pricing, offers, and discounts for each group from its store’s dashboard.

Wrapping Up

Big data indeed provides businesses with a ton of information – critical points regarding eCommerce store’s traffic, personal data, on-site interactions, and purchases, keywords used to search, pricing, and preferences.

Having crucial shopper information at your disposal doesn’t solve many challenges faced by stores unless they know exactly what points to track, whom and when and the tools used to track. Big data is certainly no magic wand, but it is a vital arsenal among your BigCommerce marketing tools.