Jonathan Treiber, co-founder and CEO of RevTax, says it's time artificial intelligence made its impact in retail, just as it has with the travel, auto and financial services industries.
November 4, 2019
By Jonathan Treiber, co-founder and CEO, RevTrax
Many businesses struggle with customer engagement in today's retail landscape — especially in their pricing and promotions strategies. Finding a single price for a product or service is tough work. Pricing a product for a specific customer is even harder work. How retailers engage their customers on this front will make or break the chances of future loyalty — enter AI.
Advanced artificial intelligence has made strides in the travel, automotive and financial services industries. The technology is leveraged in customer service through chatbots, in inventory management for product tracking and to create a more seamless experience through product recommendations. Recently, however, AI is becoming the norm in retail, with e-commerce businesses and traditional retailers adopting new technologies, leading the way for more effective customer engagement methods.
An example of how retailers are leveraging AI is through dynamic pricing. But there's an issue with the practice: it's challenging, especially from a systems standpoint. Pricing systems cannot support the concept of multiple prices for the same item, a lost opportunity for more effective engagement. The logistics and workflows for dynamic pricing simply aren't supported by mainstream processes at this point. Because of this, businesses often decide on one price for an item, even if that price is too high or low for most customers, which is easier and less complicated.
Much how retailers have adopted machine learning into their customer service channels at scale, AI-driven dynamic discounting can cure the woes of customer price engagement. With a dynamic discounting strategy, price points don't change. Varying the customer discount value can alleviate back-end systems issues while increasing customer perception of value and conversion rates.
For example, say a product or service sells for $100. With a 10% discount, the price to the customer is $90. This is often referred to as the net price or net-down price. The customer wins in this example, where they see a $100 price but pay $90. In the customer's mind, the product is worth $100 but costs $90. This perception of value can play a powerful role in motivating purchase by triggering the customer's "hot state," using behavioral economics parlance. Hot states increase the tendency for consumers to purchase and subsequently increases conversion rates.
But how do retailers come up with specific goals for their pricing and promotion strategies of the future? Dynamic pricing or discounting strategies are fundamentally geared to capture the maximum amount the customer is willing to pay. However, we need to understand what that amount is in order to accurately implement each strategy. This is where AI and machine learning play a role in effective engagement, by identifying the recommended discounts that will generate the maximum profits.
This can be done by implementing AI-based methodology to recommend and change offer values based on engagement levels. Linking consumer responses to offers shows that businesses are willing to adapt to customers needs, which ends up benefiting both parties. Businesses will be able to eliminate wasteful spending and consumers receive offers more geared to their needs.
Customer engagement and proper expectation setting are critical considerations for any business. Customers typically respond more favorably to dynamic discounts than to dynamic pricing. That's because everybody likes getting a deal, no matter how much. In a world where brands are touting personalized offers to customers via loyalty programs based on engagement history and AI technologies, there is a precedent set by marketers treating customers unequally. This is the window of opportunity for businesses to capitalize more effectively on customer engagement, and more specifically, dynamic discounting.
So how can businesses get started with a successful dynamic discounting strategy? From a workflow and implementation standpoint, the above strategy won't be easy to execute without an AI-driven Offer Management Platform. An OMP provides the capabilities and integrations to create and manage the discount-offer-pool, decide which discount-value is appropriate for a given customer, securely present that offer to the customer, and measure the performance of the offer. With this strategy, every dollar of each promotional budget works harder, driving value for retailers, but more importantly, for their customers.