7 Ways Big Data Will Impact Ecommerce in 2020

As eCommerce continues to grow, so does the data that is stored. eCommerce businesses are only able to utilize a fraction of that data. But that is likely changing this year, as data scientists are getting better and better at merging, standardizing, and analyzing.

All of this will impact eCommerce businesses. What follows are the data predictions for 2020.

Impact of Big Data on eCommerce in 2020

Personalized stores

Merging the search history, purchase history of customers and lookalike visitors will help eCommerce businesses to create a much more personalized shopping experience. This will result in higher conversion rates and more cross-sell opportunities.

Personalized marketing

Marketing is becoming increasingly sophisticated day by day. eCommerce businesses will send numerous email variations based on customer segments. For example, if a customer only buys the sneakers, sending him an offer for formal shoes will likely be ineffective. Similarly, customers who buy only discounted goods will presumably not respond to a full-priced offer. Marketing to both customer types requires collecting and segmenting the data.

Increased automation.

Automating recurring tasks not only saves human resources. It also improves the overall customer experience. A good example is using chatbots for customer service, which helps improves accuracy and response time.  This year, eCommerce businesses need to find ways to automate repetitive tasks. Keep in mind, however, that not all such tasks can be automated. Many of them require human intervention.

More cross-border sales

Automated language and currency translation, streamlined shipping, and local payment options will help eCommerce businesses penetrate global markets like never before with minimum investment. Shipping platforms and plugins can calculate at checkout the exact worldwide transit cost, which comes in handy.

Better forecasting

Business intelligence tools can effectively forecast sales, optimize product prices, and predict demand — in detail. This results in lower inventory costs and better-targeted promotions based on a product’s demand. eCommerce businesses can now move faster without spending a lot of money. To start, eCommerce stores can acquire an intelligence platform or hire a machine learning expert who can forecast in R or Python.

Research with social media

Marketers will now focus on understanding the customer and their behavior leveraging the massive, public data on social media sites. Retailers can shift from using net promoter scores and surveys to analyzing qualitative and quantitative data. eCommerce stores can start by manually categorizing the opinions of customers and prospects around products, product types, and the business overall.

More privacy laws

Governments worldwide are imposing strict privacy laws on the collection and use of consumer data. Examples include Europe, Korea, and California. eCommerce stores will be a lot of spending money on legal fees, employees, and consultants. Marketing capabilities will presumably decrease, as will customer experiences.

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