In October 2018, a research showed that 52% of shoppers would have agreed to full prices for products they saw in promotional offers.
This highlights the need for promotion optimization.
Well-aimed promotions not only make a product recognizable and popular among the target market, but also help to avoid losses selling out seasonable items or simply pushing sales for more revenue.
One of the best promotion tools in ecommerce is a discount.
What Is a Discount?
A discount always means a price reduction, so the customer pays less for the item he wants. Discounts are often used as a “reward” for good customer behavior as it may be the first purchase, the sign up for a newsletter, staying loyal, or being an Influencer.
A discount should not be mistaken for a bonus which gives the client a higher amount of the same product or a free item for the initial price. There are different forms of discounts and the most important ones are:
- Bundled Discounts – discount if a group of products is bought together
- Early Payment Discounts – lower price if paid in advance
- Volume Discounts – paying less per piece if buying a larger amount
- Seasonal Discounts – lower price if season ends
Discount Optimization Using AI
Artificial Intelligence (AI), in combination with Machine Learning, helps marketers to design an optimized discount campaign. Using combined customer data, AI can offer every client, via different channels, an individual price the client most likely will be willing to pay.
This way, companies can prevent avoidable losses and yet present an attractive reward for desired customer behavior.
How to Predict the Right Customers for Discounts Using AI
A common mistake when granting discounts is that companies often tend to have an inconsistent discount system. A good discount system should have clear thresholds with a fixed amount of revenue and discount volume. Besides discounts that are granted based on a certain sales volume, companies also provide discounts as an incentive for new customers or to customers with a high churn-risk. Hereby, AI helps to identify customers who are most likely to churn and also potential new customers. Predicting new prospects with a high potential to buy is usually made by using a predictive purchase behavior model. I.e., a machine learning algorithm that analyzes the company’s pre-purchase behavior of thousands – or even millions of visitors – who made a purchase in the past. Important criteria, or features, for this model are transactional customer data, for example:
- a sign-up for a newsletter
- opened emails
- website visits
- viewed products
- if available, past sales or previous purchases are also very valuable data points.
You can find more about AI in marketing and sales in our posts Smart Selling with Predictive Lead Scoring and AI in Sales Forecasting – knowing the future is not just a dream anymore. Additionally, we have also shown how to speed up the sales process with predictive churn scores in this case study.
How to Predict the Right Percentage of Discount Using AI
With AI-based predictive analytics, companies can tailor individual offers based on the discount level that attracted a special customer in the past. This way, clients that are very likely to buy a product anyway, will get a much lower discount or maybe even no discount at all. On the other hand, customers known to be persuaded by good discounts before, might get a better discount to encourage them more.
With an AI-driven, standardized, and well-aimed discount model, a company can manage demand, influence customer‘s behavior, and by doing so increase the revenue significantly.
Our team is happy to help you choose and establish your AI-based ecommerce discount system. For further information on discount systems and related topics, please send an email to email@example.com and check out our solutions for ecommerce.