Harnessing AI's Potential for a Shift in B2B Pricing Approaches

 

In the ever-changing world of B2B commerce, businesses are turning more and more to AI to make their pricing strategies better. A survey by Gartner Quadrant showed that using pricing algorithms could boost revenue by 1% to 5% and increase the time customers stick around by 20%. The market for AI-based solutions in retail is expected to hit $24.1 billion by 2028. Many companies have already added custom Gen AI and ML solutions to their work, but these can be costly, not always reliable, and pretty complicated. There's still a lot of room for making them better.

Being the Chief Technology Officer (CTO) of a top ITO company, I've seen that pricing managers and sales managers can fill a big hole in their pricing plans by changing how they think about it. Instead of just asking, "How much info can we get from the AI?" we should also think about what kind of info we can put back into the AI to get even better results. Let's look at a few examples of how we can do both.

**Outliers and Margin Leakage**

One way we can make the most of AI's brainpower is by using it to figure out what's happening in our current sales and pricing setups. For instance, AI can help with customer comparisons and suggest prices for tricky deals. By digging into past sales data, AI can spot those times when things are a bit strange and find gaps or leaks in our profit margins.

Getting pricing strategies right depends on how well we understand our customers and products. AI can step in here by making these groups (called microsegments) better through a detailed understanding of how people buy things and using the info we have on how customers act and how well products do.

When we talk about outliers, we mean those odd times when things don't go as expected. AI can help us catch those moments and figure out what went wrong. On the other hand, margin leakage is when we're losing money because of pricing errors or other issues. AI can spot these leaks and help us plug them up.

**Feedback Loop for Better Results**

Now, let's talk about the back-and-forth between us and the AI. It's not just about taking info from the AI; it's also about giving info back. This creates a loop where the AI gets smarter over time.

When we're dealing with outliers, we should feed the AI info about why those odd things happened. Maybe it was a one-time event, or maybe there's a pattern we didn't notice. By giving this feedback, we help the AI understand the context and make better suggestions in the future.

For margin leakage, it's crucial to tell the AI why we think we're losing money. This could be due to pricing errors, customer complaints, or other issues. The more info we share, the more the AI can learn and suggest ways to stop the leaks.

**Nuanced Understanding through Microsegmentation**

Microsegmentation is like zooming in on specific groups of customers or products. AI can help us get really detailed here. Instead of thinking about all customers or all products as one big group, we can break them down into smaller, more similar groups.

For example, let's say we sell widgets. AI can look at the buying habits of different customers and group them based on similarities. This way, we can set prices that match what each group is willing to pay. It's like giving each group its own special treatment.

**Customer Behavior and Product Performance**

Understanding how customers act and how well products are doing is gold. AI can help us analyze this info and make better decisions. If we know which products are flying off the shelves and which ones are gathering dust, we can adjust our prices accordingly.

Similarly, knowing how customers behave – do they buy more during certain times or prefer certain features – helps us tailor our pricing strategies. If the AI knows these patterns, it can suggest prices that match what customers are looking for.

**Conclusion**

In the big world of B2B commerce, using AI for pricing is like having a super-smart assistant. It can find the weird stuff, plug up the leaks, and even suggest prices that make customers and businesses happy. But it's not just a one-way street. We need to keep the conversation going by telling the AI why things happened the way they did. This creates a loop where the AI learns from us, and we learn from the AI. It's a win-win for everyone involved. So, let's not just ask what the AI can do for us – let's also think about what we can do for the AI to make things even better.