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Sales Discussions that Work
Selling is challenging work, and often, “we” (sales and marketing teams) make it even more challenging than it has to be. How many times have you seen a selling script, elevator pitch, or initial presentation that is long, boring, and undifferentiated? People have a short attention span, and nobody wants to interact with someone who does not listen to them or is pushy.
Your initial discussion is crucial to your success. Instead of going over a list of features, reading a slide deck, and telling why you and your product are so great, let’s try something else.
1. Understand why people buy. Any change has the potential to be difficult, risky, and painful. So, the pain they are facing has to be even greater, or they won’t bother changing. Your main job early on is to listen and try to learn what their pains are. You may have a perfect solution, but if it doesn’t solve their pain, it isn’t worth much.
2. At the start of the meeting, ask, “What would make this time well spent for you? What would you like to walk away from this meeting with?” Get them thinking about their problems and the value you may be able to provide, even if they don’t fully articulate it.
3. Ask questions and follow-up questions. People don’t lead with their significant issues, and someone unwilling to divulge anything likely isn’t a buyer. The more the prospect talks, the more you learn. So many people do not understand this simple concept.
4. Once you think that you have identified a pain, qualify and quantify that. For example, “You mentioned that your product release cycles are too long and complex. What is the business impact of that, and what would the impact be if you could reduce that time and effort by 50%?” Write that down because it could be vital later.
5. If you are giving a presentation, pull up the most relevant slide (customer problem/benefit slides work well here) and ask if this sounds similar to the problem they are facing.
6. Don’t worry if you are not able to cover everything you intended, as long as the meeting is productive. I’ve also seen salespeople cut someone off and move on to a new slide rather than discussing something of substance.
7. Next steps. Keep in mind that your time is valuable, and qualifying out a prospect that is not a good fit is essential – it helps you avoid false hopes and lets you focus on people who might want your help. There are many ways the next meeting could go but ask the prospect. Would they like to expand the audience? Is there a specific problem they would like to focus on? Would they like a product demo or a technical discussion? Is something like an NDA (non-disclosure agreement) keeping them from opening up?
Here is a mini success story. In 2010, my team and I began selling the first commercial vector high-performance analytics database. There were several products already out that claimed to be 70x-100x faster than other products. Our pitch was supposed to be that we were 70 times faster than other products. That was self-limiting before we even started and likely kept people from contacting us.
After two months of minimal success (I closed a deal to a small hedge fund, which was the only sale in all regions), we started a weekly webinar called “Why Fast Matters.” The focus was on positive business outcomes rather than specific technology and features (“speeds and feeds”). We opened with some “What if?” statements, such as: What if you get answers from complex queries faster than your competitors? What if you could do that without the cost, complexity, delays, and limitations of a Star Schema or pre-aggregated data? What if you could do this on commodity x86 hardware? We would then briefly cover the breakthrough technology (which was a precursor to Snowflake) and offer a free half-day meeting with a consultant.
Within the first two weeks, we met with a company that was later acquired by PayPal a few months before eBay acquired PayPal. This company was about to spend $500K on a proprietary hardware expansion that would have only provided additional capacity for the following year. Their customers bought advertising based on queries against the last six months of their data. I asked the question, “What if they could query against five years of data and get answers faster than they do today? Do you think that would help them buy more advertising? Do your customers ever ask for this?” The response was that their customers frequently ask for 12 months of data and would be willing to pay more for these capabilities. Still, they did not have a way to do this cost-effectively.
I closed a $250K ARR subscription deal in less than two weeks, and they purchased $140K of commodity Dell hardware for our software to run on. They saved 20% over their planned purchase, and more importantly, they rolled out advanced querying capabilities (against six years of data) in less than a month. There was incredible value to them and their customers, which we would not have uncovered if we focused primarily on features and technology.
As an aside, I was initially chastised for going off message, but after the Australian team adopted our approach and began closing deals, it became the new corporate standard. If something isn’t working, focus on finding ways to improve it.
In the words of Tony Robbins, “If you do what you’ve always done, you’ll get what you’ve always gotten.”
The Imperative Of Customer Trust In 2024
I read a Forbes article that outlined findings for increasing and maintaining customer trust in business today. The summary points are below, and here is a link to the full article.
These points were foundational to the Customer for Life program that I implemented a decade ago. Treating people right is timeless. So, check out the Forbes article and the linked post, and then go out and build stronger relationships with your customers!
Lessons Learned from Selling Kubernetes
Cloud-native, containerization, microservices, and Kubernetes have become very popular over the past few years. They are as complex as they are powerful, and for a large, complex organization, these technologies can be a game changer. Kubernetes itself is a partial solution – the foundation for something extraordinary. It can take 20-25 additional products to handle all aspects of the computing environment (e.g., ingres, services mesh, storage, networking, security, observability, continuous delivery, policy management, and more).
Consider the case of a major Financial Services company, one of my clients. They operated with 200 Development teams, each comprising 5-10 members, who were frequently tasked with deploying new applications and application changes. Prior to embracing Kubernetes, their approach involved the deployment of massive monolithic applications with changes occurring only 2-3 times per year. However, with the introduction of Kubernetes, they were able to transition to a daily deployment model, maintaining control and swiftly rolling back changes if necessary. This shift in their operations not only allowed them to innovate at a faster pace but also enabled them to respond to opportunities and address needs more promptly.
Most platforms use Ansible and Terraform for playbooks, configuration management, and more. The configurations could become very long and complex over time and were error-prone. And for more complicated configurations, such as multi-cloud and hybrid configurations, the complexity multiplied. “Configuration Drift,” or runtime configurations that were different than expected for a variety of reasons led to problems like increased costs due to resource misconfiguration, potential security issues based on incorrectly applied or missing policies, and issues with identity management.
The surprising thing was that when prospects did identify those problems, they would look to new platforms that used the same tools to solve their problems. Sometimes, things would temporarily improve (after much time and expense for a migration) but then fall back into disarray since the underlying process issues still needed to be addressed.
Our platform used a new technology called Cluster API (or CAPI). It provided a central (declarative) configuration repository that made it quick and easy to create new clusters. More importantly, it would perform regular configuration checks and reset incorrect or restore missing policies as part of its automatic reconciliation. It was an immutable and self-healing Kubernetes infrastructure. It simplified the overall management of clusters and standardized the implementation of any infrastructure.
All great stuff – who would not want that? This technology was new but proven, but it was different, and that scared some people. These were a couple of recurring themes:
- Platform and DevOps teams had a backlog of work due to existing problems, so there was more fear about falling further behind than believing in a better alternative.
- Teams focused on their existing investment in a platform or on the sunk costs spent over a long period attempting to solve their problems. The ROI on a new platform was often only 3-4 months, but that was challenging to believe, given their experiences on an inferior platform.
- Teams would look at outsourcing the problem to a managed service provider. They could not explain how the problems would be specifically resolved but did not seem concerned about that.
- There was a lack of consistency on the versions of Kubernetes used, the add-ons and their versions, and one-off changes that were never intended to become permanent. Reconciling those issues or migrating to new clean clusters both involved time and effort, which became an excuse to maintain the status quo.
- Unplanned outages were common and usually expensive, but using the cost of those outages as justification for something new tended to be a last resort as people did not like calling out problems that pointed back to themselves.
It was interesting, but focusing on the outcomes and working with the Executives who were most affected by those outcomes tended to be the best path forward. Those companies and teams were rewarded with a platform that simplified fleet management, improved observability, and helped avoid the problems that had plagued them in the past.
Working with satisfied customers who appreciated your efforts and became loyal partners made selling this platform that much more rewarding.
Six Ways AI Can Become a Sales Management Enhancer
As a VP of Sales, I would spend the first 60-90 minutes of every day reviewing a dozen different news sources, looking for information about new technology, competitor announcements, proposed legislation, and M&A news. I looked for anything relevant in critical industries, news about our customers or their top customers, and staffing changes within their companies.
My goal was to identify anything that could negatively impact deals in play, threaten the customer base, disrupt the run rate business, as well as seize opportunities to break into a new company or displace a competitor. You feed your findings and speculations back to your team, along with suggestions, talking points, or specific directions to help them maintain or increase their success for the current quarter plus the next few quarters.
You look for trends and leading indicators that could help your team and your organization achieve greater success. Winning feels good, and the rewards make you want to achieve even more.
Artificial Intelligence (AI) will be able to do all this and more. It will be more consistent, analyze information without bias, and do so on a more timely basis.
1. Automated Intelligence Gathering
Focus your efforts where they add the most value. AI can automate the collection and analysis of data from multiple sources, including news feeds, legal updates, social media, and competitor websites. This automation can save considerable time and minimize the chances of missing relevant information. Natural Language Processing (NLP) can identify, categorize, and correlate relevant information, providing actionable insights without the need for manual review.
2. Enhanced Lead and Opportunity Identification
In addition to the correlations above, Machine Learning (ML) models can analyze trends and patterns in data to identify potential leads or opportunities for expansion. By understanding market movements, customer behaviors, historical behavior, and presumptive competitor strategies, AI can suggest new targets for sales efforts and highlight areas where teams could gain a competitive edge.
3. Improved Internal Communication and Collaboration
Sales is not just about selling but also about collaborating internally to create the best possible products and services to sell and identify the best approaches to generate awareness and interest in your offerings.
AI systems can serve as a central hub for information that benefits various departments within a company. By integrating with CRM, marketing, support, and other internal systems, AI can distribute tailored information to different teams, ensuring that everyone has the best insights to perform their roles effectively and promoting a more cohesive and coordinated approach to achieving long-term business objectives.
4. Forecasting and Predictive Analytics
With the ability to process vast amounts of data, AI should significantly improve forecasting accuracy. Predictive analytics can estimate future sales trends, customer demands, and market dynamics, providing businesses with more opportunities to make better-informed decisions—better resource allocation, optimized sales strategies, and, ultimately, higher revenue will be the result.
5. Increased Efficiency and ROI
By automating routine tasks and providing deep insights, AI can free up sales and management teams to focus on strategic activities. The efficiency gains from AI can result in significant cost savings and a higher return on investment (ROI) as teams do more with less, capitalize on opportunities faster and more effectively, and ultimately make more money for themselves and their company.
6. Continuous Learning and Improvement
Machine learning models will improve over time as they process more data, meaning the insights and recommendations provided by AI will become increasingly accurate and valuable, helping businesses continuously refine their strategies and operations for better outcomes.
Future Perspectives
While it’s true that AI may not yet be ready to take over complex roles like enterprise sales, its potential to enhance these roles is undeniable. As AI technology continues to evolve, its ability to provide highly accurate forecasts, improve win rates, shorten sales cycles, and enhance competitiveness will only grow. The future of AI in sales and business management is not just about automation but about augmenting human capabilities to create more effective, efficient, and thriving organizations that are better able to compete in an increasingly competitive global landscape.
So, what do you think? Will this work? Will it be good enough if everyone is doing it? Leave a comment and let us know.
Finding the Right Fit in Sales
I won’t sell a product or service if I don’t believe in it or in the company behind it. But that is only part of the picture. Not all products or services fit everybody, but most are a fit for somebody. Whether you are a seller or leading a sales team, understanding the best-fit use cases helps you create a repeatable sales motion that allows you to:
- Find and prospect the best candidate companies.
- Demonstrate benefits for a credible and relevant use case.
- Find a sponsor who benefits from your offering.
- Accelerate the deal velocity – even in a large enterprise business.
- Close large deals faster – and more of them!
When I started at my last company, I was told that the typical deal size was $75K-$80K, having a 9-12 month sales cycle with a midsize company. I was selling a Kubernetes Fleet Management platform, and I quickly found that most midsize companies lacked the containerization needs that Kubernetes provides. Most also needed to gain the skills required for fairly complex solutions, which can take months.
Large Enterprise companies had the need and the expertise to support Kubernetes, which started my profile development exercise. Large companies with a corporate standard containerization product were less likely candidates with a much longer sales cycle. Financial Services companies require strong end-to-end security and cannot afford breaches (reputationally and actual costs). Therefore, they had larger budgets and immediate needs, so they became a primary focus.
While looking at the environments for these companies, it became clear that an initial deal size could easily be in the $500K – $1 million range. And, if you successfully delivered what you promised, there could be several more significant follow-on deals. The icing on the cake is that by selling those companies what they need, solving significant problems or concerns, and treating them like the valued customers they are, they would reward you with loyalty and long-term business.
Finding the right fit for your product or service takes analysis, investigation, testing, and time. Getting this right provides the perfect opportunity to be successful and scale the results through the entire team. It also provides credibility when new customers are willing to speak with prospects and sing your praises. Success breeds success.
Where Ideas, Experiences, and Lessons Learned Intersect 