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International dropshipping is a very promising business model for savvy entrepreneurs. The industry accounts for 33% of all e-commerce sales and the returns in many untapped markets can be huge. A case study from Oberlo discovered that the value of an e-commerce website visitor from Norway was 54% higher than one from the United States. After accounting for the lower advertising costs to reach Norwegian customers, the ROI could be up to 300% higher. Dropshipping entrepreneurs will find that there are lots of other untapped markets in other regions too.
However, developing a profitable international dropshipping business is going to be much more complex than one that solely serves domestic customers. You will want to take advantage of predictive analytics to develop a lucrative funnel for your international customers. Here are some ways that predictive analytics can be invaluable.
Creating highly targeted Promotions
The importance of online promotions for any e-commerce business cannot be overstated. A poll from Oracle found that 98% of e-commerce marketers felt promotions were vital to their bottom lines. Unfortunately, less than 50% of e-commerce marketers are confident in the tools they use to launch and monitor their promotions.
Making ad-hoc promotions for domestic customers is difficult enough. Managing promotions for customers in other parts of the world adds numerous more layers of complexity to the equation. You need to account for:
- Seasonal differences between countries
- Cultural differences that make some promotions more appealing
- The proportion of elderly people in the population and other demographic factors that come into play
- The timing of holidays in various parts of the world and the relative significance of those holidays
Administering a promotional campaign for so many different countries would be hopeless without the availability of big data. Fortunately, there are a number of predictive analytics models that can help you plan your promotional strategy.
Developing a pricing strategy
Predictive analytics has been very helpful with pricing strategies. Uber and AirBnB both used it to boost their ROI. International dropshippers can benefit from it as well.
Setting prices is another very important element of any dropshipping business. You can’t necessarily set the same prices for customers in every location. You’re going to need to know what the equilibrium price is for similar products in the regions you are marketing too. If you simply used prices for products in the United States as a benchmark and adjusted for cost-of-living differences, then you might be drastically overpricing or underpricing your products. You need to account for differences in demand and supply availability in those regions.
Predictive analytics can help you tweak your pricing strategy at the regional level. Tools such as Import.io can collect pricing data on thousands of competitors. You can use this tool to collect data from different online stores in every country that you intend to compete in. Collecting this data over time and creating separate spreadsheets for each date the data was collected with relevant notes will allow you to track changes and conduct a regression analysis to identify events that trigger pricing changes.
Adapting this strategy at the regional level allows you to set future prices ahead of time and adjust after the importance of certain events, such as regional natural disasters.
Assessing the future demand for various products
Choosing the right inventory for your dropshipping business is one of the most important things you will do. One international dropshipping entrepreneur that I spoke with said that his global split-testing data showed that fewer than 4% of products will be profitable in any market.
Unfortunately, products that sell well today may not sell at all two months down the road. Demand for some products is cyclical, so you need to time the marketing campaigns for them carefully. Other products permanently disappear after the market become saturated, people lose faith in the brands behind them or they become obsolete as more effective products take their place.
Predicting the future demand for products is not easy at all. Fortunately, predictive analytics has made it much easier.
