I’ve talked with some great companies this year who are in the pursuit of price optimization. Across the board, models are getting more sophisticated, software roadmaps are getting more ambitious, and AI is pushing all of us to be more efficient. There’s a lot of development moving people in the right direction of understanding our customers better and incorporating more attributes to come to a price point.
There are also some places where we’re getting caught up in the hype cycle of tagging everything as AI. We’re far from utopia, but it benefits all of us to talk frankly about how far we are from true price optimization. Here are some of the things I’m seeing.
Understanding customer behavior
We first need to understand how customers make decisions and how that process is changing. Think about the evolution from “spray and pray” marketing to more gunshot tactics – where we now have a better sense of what products and solutions people are searching for, and we can send emails and texts to promote specific offers tailored to their needs. With AI, we can now get even more specific based on the person or business’ needs at that moment in time. Many companies are making strides here towards more personalized efforts.
Today, optimization looks like “John bought 14 things last quarter. Other people like John bought those 14 things, but they also bought these 4 things. John isn’t buying those 4 things. Why don’t we give him low pricing to get him to buy them?” But what about all the other factors that go into making a decision? Where we are heading is beyond the standard math of order and customer level attributes. The next layer of optimizing price requires us to understand each person as a buyer and as an individual – not just by buckets of persona, account, or activity.
Think about a buyer who has limitations on IT expenditure or how they can pay based on budget cycles. We need to understand the things that could prevent or accelerate their purchase behavior. As an example, how should we change price metrics or purchasing modes to incorporate those restrictions? This will allow companies to reduce friction in the purchasing process – through options to customize a solution and how it is presented for the individual.
The role of the sales team
If a buyer has restrictions or preferences in how they buy, the sales team needs ways to collect and incorporate that information into the offer they’re presenting. Does this person like to be walked through a solution, or would they prefer to demo on their own? Do they have a good relationship with the sales rep? Our ability to understand that data must become much easier.
Properly leveraging the commercial team is an important part of this process. In a B2B environment, customers buy to benefit the company, but they also buy to benefit themselves personally. We have to understand this well in order to price and present our offers in a way that makes the purchasing process easy. If a seller gets to know and understand a potential buyer, how do they work with the right tool to introduce the right offers in the right manner, depending on the metrics and modes of purchase they prefer? How do you know their emotional components behind the decision, and what makes them feel comfortable to make a purchase?
This is also where AI plays a big role in offer presentation. You need all of this data to be in harmony to understand the buyer at a deeper level. The most important thing is that we’re serving buyers with the right product, with the right offer, at the right time to instigate a purchase and provide value to that person. This requires knowledge of them as a person, as a buyer, and as a consumer. What motivates them, and what choices can you give them?
Driving for incremental value
This is how you build trust with your customer base from the early stages of the relationship. You need to truly understand people and what they’re looking for on a very individual and personalized level. Looking only at how people buy and who they get influenced by will keep your teams in a frame of old school math and visualizations. But we’re reaching a new cap to that, and we need to start connecting new levels of data to provide more options for customizing a solution and how it is created and presented to each buyer.
We need to go beyond selling products and solutions, and step into the next level of creating personalized value to our buyers. Delivering more tailored value will then feed the next level of innovation to drive overall CLTV and long-term partnerships. This is how we influence the next iteration of products, innovation, and raising the bar for true value alignment between companies and markets moving forward.
