According to a Deloitte study, price is what matters to consumers the most. Another report by PricewaterhouseCoopers suggests 60% of online shoppers opt for retailers based on pricing. If pricing holds such importance then certainly it can be decisive of the fate of a retailer.
The Current Challenges for Price Optimization and their AI-driven Solution
Certain variable factors pose a challenge for retailers while pricing their products viz. time of the year, demand, product features, and the fair price considering the market. The variability of these factors is even bigger in the E-commerce market making it difficult to stick to one price. Hence, large retailers such as Amazon change their pricing often during a day. But for retailers not as large as Amazon, doing so is a tough task.
Here, Artificial Intelligence (AI) can help optimize prices that not only attract customers but also keep profit margins. This allows pricing managers to switch to strategic tasks, enabling retailers to reap incremental profits. According to a report by IBM, 79% of retail and consumer products companies plan on using intelligent automation for customer intelligence by 2021.
Following are a few ways how AI and ML can positively transform the retail price and promotional aspects across all channels:
Suggesting optimal prices
Optimal prices neither ward customers away nor affect marginality and do not compete with other items in the product portfolio. A brand manager must be equipped with unimaginable analytical and computational powers to manage such pricing for thousands of products regularly. Here, AI comes to the rescue.
Retailers that choose AI-driven price optimization learn to set optimal prices by stock-keeping unit (SKU), by product portfolio, channel, point of sale, and by customer. Optimal pricing at every level subsequently improves profitability.
Picking products for pricing made easy
Mostly, algorithm optimization is required for niche products that differentiate you from competitors such as private label items that can be sold at a higher price. AI can also be utilized for items that are common between you and your competitor but don't need the lowest price to attract buyers. For example, baby food. Customers would prefer a shop specializing in baby food rather than buying it from a supermarket at discounted rates, as shoppers might prefer advice apart from an enticing offer. In this case, AI can offer help by recommending prices apt to lure customers while maximizing your revenue simultaneously.
Trying different price points
Due to their prediction capabilities, AI and Machine Learning also reduce the risk concerning changing prices. A retailer can leverage ML data to test out promotional and pricing strategies to understand its effect. Overall, price optimization using Machine Learning lends a retailer more than one potential price for a product, considering multiple scenarios providing the best price for revenue maximization and promotion, etc.
Choosing the right strategy
ML-led pricing gives retailers a lot of room to experiment with their knowledge. Thanks to its predictive prowess, one can foresee customer reaction to their strategy and implement the best suited one, be it competitive, dynamic or keystone pricing. Irrespective of what they choose, retailers know the likely outcome and the best price for it.
Getting insights for personalized pricing
AI & ML can consider multiple current variables ranging from sales patterns and customer footfalls to other factors viz. recent events, weather, weekends, and holidays, all in real-time. Based on such events, these technologies help retailers analyze popularity on social media and estimate a more detailed probability of demand levels. It provides deep actionable insights on customer preferences, ultimately, helping them to generate more personalized price offers.
Self-learning algorithms scan huge amounts of data and sift through infinite price scenarios to suggest the most relevant offering. Algorithm-backed mechanisms take into account multifarious hidden yet dependable factors between the products in the portfolio to determine individual prices that maximize revenue and sales of the entire product portfolio.
Leveraging AI-based pricing, retailers can maneuver with parameters to satisfy their pricing strategies such as implementing data algorithms against varied strategies on products that draw more traffic vs. those that successfully drive margins.
AI can be used to automate time and labor-intensive tasks that allow teams to focus on customer-centric decision making. The longer they are implemented, the better and precise they become up to a level wherein they can factor in shopper sensitivity and competitive flexibility, down to the store-item level. AI-led pricing keeps brands ahead of the curve giving them an edge over competitors driving higher sales and profits.