Generative AI is already transforming both the way B2B organizations operate, and how the individuals that comprise them execute their day-to-day responsibilities. In our October webinar, we explored the potential ways AI can help companies identify sources of customer value, quantify it in financial terms, and help communicate it to customers.
During the session, Coralie Bordeaux, Global Marketing Manager at ABB and Brian Hannon, VP of Sales at LeveragePoint, shared their perspective on the ways GenAI is impacting their daily responsibilities and their outlook on how this emerging technology can be applied to value management and value selling today and in the future. In this blog, we recap their live conversation.
How do high-quality value conversations positively impact marketing and sales execution?
Coralie Bordeaux:
I believe that value conversations are really transformative, both for marketing and sales execution. I think the conversations are fundamental in building trust, but also credibility with potential customers. They also shift the focus from pure pricing to the real pains and gains experienced by our customers, and I think that approach helps in highlighting the unique value your solution brings by making it more compelling. With these high-quality value conversations, they’re not about selling a product, they’re really about creating meaningful connections that can drive sustained business growth.
Brian Hannon:
So obviously we’re big fans of high-quality value conversations – it’s what we do every day. So I think if we had a hundred marketing people here and asked them, “is value important as part of a sales process?” You’d probably get a hundred people saying yes, it’s something that is pretty much accepted.
While value being a core part of a good sales and marketing process is accepted, getting it right is the challenging part. I think having a value conversation as part of every sales cycle is important. And there are a lot of things that need to happen to make the people involved in that successful, from the marketing people creating the content, to the salespeople sharing it. I would put an emphasis on creating an environment that makes that happen will allow you to see the benefits of the high-quality conversations.
Coralie Bordeaux:
I agree with Brian – getting it right and having that mindset and the environment that gives our sales team the chance to have these high-quality value conversations is so important. And because it also has a lot of steps, it’s not that easy to turn around.
What are some of the challenges you’ve seen or experienced in creating high-quality value content?
Coralie Bordeaux:
Thinking about challenges, there are four that really come to mind:
1. Focusing on Features Instead of Benefits: One common challenge is the tendency to emphasize product features rather than the benefits they provide. It’s crucial to articulate how your product or service addresses the customer’s pain points and delivers tangible value. This shift from a feature-centric to a benefit-centric can significantly enhance the effectiveness of your value proposition.
2. Time Constraints for Product Managers: Product Managers often juggle multiple responsibilities, making it challenging to dedicate sufficient time to crafting high-quality value content. The breadth of topics they need to cover is vast, so the more we can improve and optimize efficiencies with tech like GenAI, the better in the end for our stakeholders and customers.
3. Difficulty in Quantifying Financial Impact: Accurately quantifying the financial benefits of a product, solution or service can be complex. Identifying the right metrics and obtaining precise estimates requires thorough analysis and reliable data, which can be time-consuming and challenging to gather.
4. Global vs. Local Competitors: For Global Product Managers, balancing the global value proposition with local market nuances is a significant challenge. Local competitors may have a better understanding of regional customer needs and preferences, making it essential to tailor value content that resonates on both global and local levels.
Brian Hannon:
I think of two main things. First, there’s the getting started aspect, where you have product managers who may be doing this once per year, maybe at best, twice per year. So they’re not experts at the process, so sometimes they just avoid getting started – it’s a natural thing.
The other challenge is the interactivity. When you have customers in many cases who have invested time and energy in a ROI spreadsheet, for example, and hand that to their sales team and say, okay, this spreadsheet is accurate, it works. We spent months on it, don’t screw it up and use it. And the salespeople go, “Great, I don’t know what’s going on in the spreadsheet. There’s about a 0% chance I’m going to use it, but I’m going to smile and tell you I’m going to use it and then hopefully six months from now you don’t ask me if I’ve opened it up.”
They’re not going to show it to customers because it doesn’t have the interactivity, it doesn’t have the back and forth that they’re looking for. It’s really important for a customer to feel comfortable sharing information with a salesperson and for the salesperson to accept that, use it, and build it into the back-and-forth conversation. A spreadsheet just doesn’t fly. So even though it might be correct, it just doesn’t get used.
How are you currently seeing AI being used day-to-day in your role/organization?
Coralie Bordeaux:
ABB is a very large organization, so AI is used in different areas and different levels, but when it comes to ABB Electrification, Marketing and Sales (the area I belong to), AI is already instrumental in several key areas. It’s all sorts of speed, efficiency, and quality improvements. One of the key areas would be business intelligence right now. We use AI for enhanced decision-making and strategic planning for in-depth analysis of business market competitor data along with efficient data integration as well. We also use it for things like campaign concepts and briefs, campaign strategy development, and to enhance and combine existing content. We also leverage AI for generating marketing content.
An area where I think it’s really helpful these days already is in driving quality improvements. For instance, tools like LeveragePoint could be used to draft initial value drivers for specific value models which not only speeds up the process, but also enhances creativity by suggesting value drivers that our team sometimes might not even have initially considered. And I think that’s particularly powerful for existing products, because that allows us to compare and refine our value. It also provides real-time feedback and suggestions so that we really can improve the effectiveness of our value proposition, ensuring they resonate better with our target audience.
Brian Hannon:
As salespeople, we spend a lot of time researching new companies, inbound leads, and new people we’re talking to. We want to understand their company quickly. We want to understand their competition quickly. We also need to know the industry that they’re in, what changes are happening, what initiatives there are, what threats are happening, and get an understanding of new technologies. A lot of that gets to the value that these companies are providing.
Personally, I want to understand if the companies we’re talking to are established in their industry. Maybe they’re the premium price supplier, maybe they’re getting competition from lower price competitors and that’s their biggest threat. Learning who those companies are and learning what the technology drivers are is what AI is really great at. So that’s what we use it for a lot.
What aspects of harnessing AI to quantify and communicate value do you find most exciting?
Coralie Bordeaux:
I think it’s exciting for several reasons. In ABB Electrification, I think AI significantly accelerates the process of creating value proposition content. It provides a solid starting point with draft value drivers making it much easier for colleagues to get started and also onboard this methodology. I think we all agree that it’s always easier to start working on something that you have a bit of a foundation for rather than starting from scratch.
The benefit extends beyond newcomers. So even with our well-educated product managers, they find it really useful. It offers a quick and efficient way to validate ideas around value drivers, often uncovering insights in the process. It’s also great to see how enthusiastic they seem about it. People genuinely appreciate the new technology and its potential to streamline their work. This excitement and acceptance of AI tools foster a more innovative and collaborative environment.
Brian Hannon:
The idea that I’ve been thinking about more and more, which we’re not touching on a lot in this particular webinar, is really combining innovation with value. So if we go back to product development, when a product manager is trying to think about a new product to build, what should they be building? What is the value of these ideas that they’re thinking about building?
Generating those ideas for new products, thinking about what the value is of those new ideas, quickly ranking them and being able to make decisions about what to build based on the amount of value that it provides, I think will become more and more interesting in the future. I think AI can help understand that quickly, and you don’t hear a lot about that today. The idea of innovation and value becoming more closely aligned throughout the development process all the way through, of course, to what we’ve been talking about today, I think is the most exciting developments coming down the road.
Are there any concerns you hear about as the AI landscape evolves?
Coralie Bordeaux:
I think these technologies are obviously not neutral. They fall under certain data regulations already. For example, the EU AI Act regulations in Europe are quite good at protecting end users. But like how we saw with GDPR, not all the markets are moving at the same pace, and we are a global organization operating across different countries, so we must always ensure all countries are compliant with global policies.
I think there also are ethical and bias issues that the systems really could perpetuate and even amplify if they are trained on biased data. This could lead to unfair treatment of people in different areas such as hiring or lending. An important part of the process is to ensure all the topics are raised in the right way so that we see how the models are trained.
I also see technical challenges. I think we all know AI systems are not infallible. They can make errors, sometimes with significant consequences. Ensuring the reliability and accuracy of AI systems is a major technical challenge. We need to ensure that data and content is of good quality, as also a lot of time can be wasted trying to get the correct output. If you have low-quality prompts, it will be tricky to get the right output. Finally, developing AI systems that can scale effectively across different applications and environments remains a really complex task.
Brian Hannon:
I’m concerned if, in a year from now, there is a cartoon Brian here who will be talking to everyone and I’ll be sitting on the couch listening. But seriously, I think it can go lots different ways. Things evolve so quickly now that I think even what we’re thinking about today might change completely in a few months. Staying on top of and responding to the latest developments is the most important thing companies can do.
