How Machine Learning Is Reshaping Retail Pricing and Promotions
For retailers, being local and offering customers exactly what they need when they need it is the key to success. With technological advancements, customers now expect more personalized offers and promotions based on their needs. Retail businesses sustain in the market when it has a regular flow of new and recurring customers. Therefore, retailers and businesses, in general, go to great lengths to satisfy their customers. Furthermore, making a customer happy is one of the best business strategies, retailers are always devising creative promotions and special offers to engage with their customers.
This drive to keep customers happy is supported by emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML), which are opening new realities to eCommerce and retailers as a whole. Retailers are aggressively integrating these modern technologies with the hope of improving their customer experience, easing operational friction, and improving their bottom line.
We’ve seen AI and ML used to improve advert targeting and product recommendations. Now we see these technologies being used in the crucial aspect of product pricing and delivery of promotions and special offers.
How Machine Learning Improves Pricing and Promotions
The role of ML in the delivery of promotions and special offers appears on both sides. ML predicts the best promotions for each customer segment while ensuring their satisfaction, and these promotions have an overall positive impact on the bottom line of the business as well.
For the benefit of newbies, Machine Learning is “teaching a computer system, how to make accurate predictions when fed with data.” So an ML model when “trained” can recognize patterns from data without being explicitly programmed. Therein lies its primary advantage, when a retailer deploys an ML model, it can continuously integrate with new product and market data, determine trends, and make accurate predictions about the demand and prices. Usually, the model which is developed by an expert data scientist allows the retailer to play around with different strategies and considerations to obtain the optimal pricing and promotional offers.
Here are a few specific ways ML is helping retailers in the area of promotions and product pricing:
1. ML Is Used To Justify Promotions
Promotions are justified only when they make business sense — that is when the discounts are recouped by the business in terms of economic value such as increased turnover, increased customer acquisition, brand loyalty, etc. With ML algorithms, managers can more accurately predict:
- The optimal discount value for a product
- The best time to start or end a promotion
- The best parameters for the promotion
- The overall impact of the promotion on the business balance sheet
The keyword here is accuracy. Where managers without ML attempt to answer these business questions by looking at past data, ML models learn both past and present market trends as well as other underlying factors to return more reliable recommendations.
2. ML Helps Retailers Avoid Promotional Waste
Unless it’s a loyalty scheme, giving a promotion for a product to a buyer who doesn’t need them seems a bad idea, right? However, 52% of buyers in a Forrester Study confirmed that they were getting weekly to monthly promotional offers for products that they would have gladly paid the full price for. AI and ML-based models can help retailers plug these loss opportunities by analyzing buyer data and the expected impact of promotion across various channels.
3. ML Determines Optimal Product Prices
Instead of the common markdowns, ML helps the retailer determine the best price for a product considering myriads of factors; not the least, total spending, number of transactions, profits, expected demand, promotions data, demography, and competitor prices. Taken further, ML can power dynamic pricing, an automated system where prices change real-time in response to some of these factors.
4. ML Gives the Managers the Tools To Meet Several KPIs
With ML models, whether the goal is to increase overall revenue through increased turnover, to maximize profit per unit, or a combination of goals, an ML-enabled pricing system is the best bet for that. ML is becoming more sophisticated, increasing marketing capabilities with clean data, unlocking the growth opportunities with key KPIs. Depending on the considerations, the ML model can predict the best price for revenue increase, for sales, or for promotion.
5. ML Enables Personalization of Promotions
An ML model that studies the behavior of individual buyers can recommend promotions for buyers with similar patterns. For instance, the model could suggest a cart-based offer to incentivize buyers to exceed some cart-value thresholds.
6. ML Saves Time and Effort
The machine learning model analyzes the volume of data and large sets of variables that would be impossible for traditional systems to manage. Because ML models are “trained,” they integrate new information and predict with being explicitly programmed over and over again.
7. ML Reduces Promo Risks and Improve Business Bottom Line
Accurate ML models drastically reduce the risk faced by retailers making promotional offers. The algorithm predicts likely promotion outcomes making it easier for the product manager to determine which promotion and product price will improve the business balance sheet. With the model in place, the retailer can carry out what-if analysis for revenue and profit for different scenarios before embarking on a sales promotion.
Start Leveraging Your Data To Boost Business Impact
With the help of Data Science and Machine Learning, you can leverage both past and present market trends as well as other underlying factors to return more reliable recommendations. Data can help you determine the best price for a product, increase overall revenue through increased turnover, and help you maximize profit per unit. While ensuring customer satisfaction, you can get rid of promotional waste and create an overall positive impact on the business. More data means more insights, you might also want to start with data modernization in your organization to get the most out of your customer data.
Note: This article has first been published on Nisum
