The rise of ecommerce has generated an avalanche of data, most of it unused. In 2020 and beyond, merchants will increasingly use the data to improve their businesses while also protecting consumer privacy.
As ecommerce grows, so does the data that is stored and used. Only a fraction of that data is utilized, however. But that will likely change, as data scientists are getting better at merging, standardizing, and analyzing.
All of this will impact ecommerce merchants. What follows are my data predictions for 2020.
Impact of Data in 2020
Personalized stores. Merging search and purchase history of customers and lookalike visitors will create a much more personalized shopping experience. This will translate to higher conversion rates and more cross-sell opportunities.
Personalized marketing. Marketing will become increasingly sophisticated. Merchants will send multiple email variations based on customer segments. For example, if a customer buys only t-shirts, sending him an offer for pants 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 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, find ways to automate by asking each employee to describe repeated tasks. Keep in mind, however, that not all such tasks are candidates. Many have variations that require human intervention.
More cross-border sales. Automated language and currency translation, streamlined shipping (including customs), and local payment options will help merchants penetrate global markets with little investment. Even human translators (such as on Fiver) are becoming less expensive. And shipping platforms and plugins can calculate at checkout the exact worldwide transit cost.
Better forecasting. Business intelligence tools can now forecast sales, optimize prices, and predict demand — in detail. The result is lower inventory quantities and targeted promotions based on a product’s demand. Businesses can move faster without spending a lot of money. To start, merchants can acquire an intelligence platform or hire a machine learning expert who can forecast in R or Python.
Research with social media. Marketers will focus on understanding the customer and her behavior leveraging the massive, public data on social media sites. Retailers will shift from using net promoter scores and surveys to analyzing qualitative and quantitative info. Merchants can start by manually categorizing the opinions of customers and prospects around products, product types, and the business overall. Over time this data can be aggregated for ongoing insights.
More privacy laws. Governments worldwide are imposing strict privacy laws on the collection and use of consumer data. Examples include Europe, Korea, and California. More will undoubtedly come. Merchants will spend money on legal fees, employees (such as data compliance officers), and consultants. Marketing capabilities will presumably decrease, as will customer experiences.
