SalesTechStar Interview with Kate McCullough, Co-founder and Chief GTM Officer at Nue.io

Why should modern B2B sales leaders focus on having better unified data models to power sales journeys? Kate McCullough, Co-founder and Chief GTM Officer at Nue.io weighs in:

 

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Welcome to this SalesTech Series chat, Kate, tell us about yourself and more about Nue.io?

I’m a serial entrepreneur, and co-founded Nue with our CTO Tina Kung. Before that, I was working within the VC space after being the VP of Integrated Marketing at Anaplan, where I was part of the early GTM team that scaled the company from Series A to D. I’ve also held key marketing and operations positions at NextWorld Capital, RichRelevance, and Kiva.org.

As for the product itself, Nue is a new way to manage your customer’s revenue lifecycle. We enable sophisticated pricing for both your direct sales team and your product-led growth motions (like website or in-app commerce). Nue is Salesforce native, so despite its extensive capabilities, it’s straightforward to implement and learn.

Think of Nue as a way to increase sales velocity. You can sell however you want, whether that’s usage, subscription, physical goods, and/or one-time milestone services. And you can price what you offer in a way that makes sense for you. That could mean creating completely hybrid bundles in which one of your products is priced as a subscription, and another runs on consumption of credits. You can choose between giving customers self-service purchasing options on your website, or having direct sales teams sell companies a managed application in Salesforce, among many other possibilities.

Either way, if midterm alterations happen, the Nue platform fully automates, calculates, and bills all complex upsell and expansion revenue changes. Typically, companies would have to cobble together different tools to get something that can handle “quote-to-cash” and reconcile any modifications across the board. We finally make it simple.

What are some of the common challenges B2B teams face when moving prospect conversations from quote to revenue? What optimized processes (and tech tips) can help here?

B2B companies commonly face what I call “the big chasm” — the rift between what sales puts out and what finance needs.

Sales has a tool called CPQ (configure, price, quote) that it uses to configure pricing for quotes and then the final signed sales order. It’s usually a manual or partially automated operation for sales teams to send the order over to finance.

Because many finance teams have no automation to transform that sales order into an invoice, they have to waste time toggling in manual orders and invoices into a billing or ERP system. And any time the customer changes what they’re using — you expand the account, upsell them more, cross-sell them extra products, and so on — that requires another manual adjustment by finance.

This is happening because most common sales tools (including CPQ) and billing tools trace their origins back 15 years or more. They were built in a different era, and don’t support a full view of the customer journey or today’s modern pricing models, which demand flexibility.

My biggest advice is this: bridging the “big chasm” requires a diverse pricing data model that covers the entire customer lifecycle. This means a clean handoff between sales, post-sales, and finance. It gives you the best chance at landing and expanding revenue efficiently, without the need to jump between several systems to figure out which customers would benefit from which products.

Perhaps the biggest benefit of a unified data model is gaining visibility into the analytics of the entire customer journey. There are so many data points that can inform future pricing decisions and go-to-market motions: Where did the customer buy more? What opportunities did they have to buy more? Which products perform best out of the entire portfolio? How do reps discount, and how effective is discounting at keeping customers? There are hundreds of questions that can only be answered with comprehensive analytics.

So to summarize, I believe that optimizing the quote-to-revenue journey requires connecting data end-to-end, while also expanding it out to new product-led growth sales motions like website self-service or in-app purchases. Up until very recently, this was very difficult, and many companies have thrown lots of staff and custom code at the problem. But customer lifecycle management has now entered the market so that this data streamlining can be achieved.

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Why in your view can the larger B2B tech SaaS marketplace benefit from automated pricing models?

The irony is that most “automated” pricing models are not especially forward-facing, since they’re quite rigid and not very agile. As I see it, automation matters most right now for the role it can play in supporting agile pricing models.

If you have highly configurable offerings in Salesforce, for example, a rep should be able to make a few tweaks and then the quote should automatically adjust to those changes. This would open the door to A/B testing discounts, enabling sales teams to make specific discounts for specific customers, automating multi-variable pricing, and more.

Self-service customers also need agile pricing fuelled by automation. If self-service customers and prospects make a few selections, can the process finish automatically based on their choices? The holy grail of self-service is custom pricing, but most systems tend to support one specific pricing model in a rigid way. This means actually implementing custom pricing is a massive custom lift and design, and most companies hesitate to do it.

But automating agile pricing is becoming easier thanks to the advent of new models that aren’t just traditional sales-led CPQ systems, but extend pricing configuration functionality to self-service motions. These models are built from the ground up to support flexible pricing and product-led growth. And they simultaneously automate the entire quote-to-invoice process in a way that facilitates whatever sales can think of.

What can B2B tech brands do to make pricing conversations via their website more conversion oriented? Can you highlight some of the top B2B tech SaaS brands that have got this right over the years?

A lack of customization holds many pricing conversations back. When you give someone the choice of “good, better, best” pricing options, as most companies do, that’s not tailored to the prospect’s specific goals and needs.

Conversations become much more engaging when prospects can configure products to their needs in a guided selling motion. Pricing models and engines have historically been too rigid to support this, but new technology changes that.

A great example of a company that does pricing customization well is Sendgrid. The company’s pricing page offers very intuitive slider buttons to let visitors forecast the cost of Sendgrid’s usage-based pricing model. It’s a simple and elegant way to engage customers, because they can now answer some of their most critical questions without needing to talk to a rep.

I imagine that the endgame of guided selling motions will completely transform sales. In the future we’ll see sales-assisted motions become the norm, where the salesperson is a strategic guide for an empowered customer. In this context, customers will configure what they want themselves by mixing and matching what they need. This will require bringing together purchase options that are typically not handled together — including (but not limited to) subscriptions, services, consumption-based models like credit burndown and usage overage, and physical goods like hardware.

What are some of the top trends that will drive the B2B tech and SaaS market in 2024?

We’re going to see a heavy focus on getting more visibility into the customer journey, as well as supporting agile pricing. Ongoing economic uncertainty is going to put a lot of pressure on sales teams to upsell and cross-sell while keeping churn to a minimum. Agile pricing allows sales teams to experiment with pricing to prevent churn, and comprehensive analytics will help identify white space in the customer base for upsell and expansion revenue. As part of agile pricing, many companies will likely start relying on AI to suggest optimal pricing adjustments after processing huge amounts of usage analytics data.

In the upcoming year, customer and prospect pressure to offer self-service purchasing options will change the way many companies approach sales. I expect we’ll start seeing what I call a “hybrid channel motion” — where salespeople and self-service exist side-by-side, and prospects can move back and forth between them freely. This will give prospects the satisfaction of being able to learn on their own, while having a salesperson intervene at select articulated moments during the customer journey.

If you had to share a few AI and other trends that will impact the RevOps sub segment; what would you throw light on?

AI pricing optimization will benefit sales, RevOps, post-sales and executive clarity into revenue opportunities; but throwing AI at a system doesn’t automatically bring benefits. You’ll first need tools that unify data across the entire customer journey to get properly informed AI suggestions. In the near future, sales reps and post-sales will receive AI-driven suggestions identifying customer white space, self-service customers ready to be upsold, or potential synergies between products a customer has purchased and ones they’ve yet to pay for. AI will especially shine when it comes to tracking and forecasting product usage.

Highly trained chatbots will also expedite RevOps work in the near future. Team members can work with the chatbots of revenue lifecycle platforms to iterate through product and pricing design, ensure that accurate billing configuration and closing details like RevRec for finance are working properly, and much more. This will mark a major milestone in democratizing previously technical work to business users instead of expert consultants. Think TurboTax wizard for revenue management and pricing configuration, so you can speed up changes to your go-to-market motions.

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