As e-commerce grows, so does data stored and used. However, only a fraction of this data is used. But this is likely to change, as computer scientists are better at merging, standardizing and analyzing.
All of this will affect e-commerce. The following are my data predictions for 2020.
The impact of data 2020
Personal shops. Merging search and purchase history for customers and lookalike visitors will create a much more personalized shopping experience. This will translate into higher conversion rates and more opportunities for cross selling.
Personal marketing. Marketing will become increasingly sophisticated. Merchants send multiple email variants based on customer segments. For example, if a customer only buys t-shirts, it will probably be ineffective to send him an offer of pants. Similarly, customers who purchase only discounted goods will probably not respond to a full offer. Marketing to both customer types requires data to be collected and segmented.
Increased automation. Automating repetitive tasks not only saves human resources. It also improves the customer experience. An example is using chatbots for customer service, which can improve accuracy and response time. In 2020, you can find ways to automate by asking each employee to describe repetitive tasks. However, keep in mind that not all such tasks are candidates. Many have variations that require human intervention.
More cross-border sales. Automatic translation of languages and currencies, streamlined shipping (including customs duty) and local payment options help merchants penetrate global markets with little investment. Also human translators (like at Fiver) are getting cheaper. And delivery platforms and plugins can calculate the exact global shipping cost at checkout.
Better forecasts. Business intelligence tools can now predict sales, optimize prices and predict demand – in detail. The result is lower inventory quantities and targeted campaigns based on a product's demand. Businesses can go faster without spending a lot of money. For starters, traders can acquire an intelligence platform or hire a machine learning expert who can predict R or Python.
Research with social media. Marketers will focus on understanding the customer and her behavior utilizing the big, public information on social media sites. Dealers will go from using net promoter points and surveys to analyzing qualitative and quantitative information. Traders can begin by manually categorizing customer opinions and prospects for products, product types and operations overall. Over time, this data can be collected for ongoing insights.
More privacy laws. Governments all over the world introduce strict confidentiality laws on the collection and use of consumer data. Examples include Europe, Korea and California. No doubt more will come. Merchants will spend money on legal fees, employees (for example, data requirements officials) and consultants. The market capability will probably decrease, as will the customer experience.