Artificial Intelligence (AI) is the talk of the town. The hype cycle is in full effect and marketers are being told that the technology is going to solve most – if not all – of their problems. However, despite its promise, new research shows that many marketers aren’t buying into the hype and are hesitant to incorporate AI into their martech stack. For now, it’s very much an “in the future” concept. Brands are afraid to get burned as AI used incorrectly is very visible to your customers and can sour the customer experience (CX).
Instead of avoiding AI – and its potential benefits – altogether, marketers need to take a strategic approach to how they use AI. The key to AI’s success in a brands’ approach is identifying and understanding the roles it will play within their marketing strategy not as their marketing strategy.
Laying the Groundwork
Brands that effectively implement AI have an in-depth knowledge of their architecture. Any gaps in the martech stack must be addressed before anything else can move forward. Without a finely tuned CX strategy in place to guide AI, marketers run the risk of delivering customer experiences that could potentially damage their brand.
Collect and act on NPS-powered customer feedback in real time to deliver amazing customer experiences at every brand touchpoint. By closing the customer feedback loop with NPS, you will grow revenue, retain more customers, and evolve your business in the process. Try it free.
Like any marketing technology, AI must be accounted for and optimized to fit into the overall business strategy and must be viewed as a method to fulfill marketing strategy; a tool in brands’ arsenal. Oftentimes, AI isn’t strategically placed within the martech stack which results in implementations that
are either too niche – and therefore don’t add a lot of value – or too broad and don’t deliver meaningful insights.
Marketers should start small with their AI initiatives. Optimizing the bottom of the funnel experience can pay immediate dividends and is a nice entry point for most marketers. As an example, look at how AI can be effectively used in setting dynamic pricing structures.
Almost since the dawn of time, dynamic pricing approaches have been an equal mix of strategy, black magic, and luck. How do you price things low enough that you get your customer to feel like they are getting value while still making as much money as possible (you are in business to make money, right)? Brands can use AI to sift through data from a business intelligence perspective, then use those insights to get to know customers better. This method enables marketers to avoid being “creepy” and deliver experiences customers will appreciate.
Every step of the pricing journey can be impacted by AI. Beginning with data capture, modern AI-enabled analytics engines act as the reconnaissance team, able to go out and discover an ocean of environmental information. Business Intelligence systems then rely upon machine learning algorithms to mine that data for previously unknown patterns. Dynamic (AI-enabled) Pricing algorithms then use these insights and combine them with specific business factors to ultimately arrive at the optimal price.
Want to know how to optimize the sale of yellow bicycle shorts in Poughkeepsie next Wednesday? AI can help.
AI is neither a silver bullet nor a way to eliminate staff. Instead, AI should be thought of as a means to deliver powerful experiences that can have direct bottom-line impact. Brands that take a strategic approach to data aggregation, analytics and mining will be able to reap the full benefits of an optimized customer journey.