How disqualifying customers can actually boost sales
Far too often, salespeople lead their interactions with customers from a very seller/product-focused mindset, raving about the bells and whistles of their product features without thinking about their relevance to the customer. In delivering a product or solution meant to solve your customers’ problems, you have to understand their problems first.
To ensure this happens with your sales process, adopt a sales qualification framework like MEDDIC (explained below), which can help your team hone in on your ideal customer profile (ICP), focus on what matters in a deal cycle, and, most importantly, lead from a place of curiosity. That curiosity and structure can lead to progressively more thoughtful questions that show empathy and insight and help salespeople qualify and disqualify sales opportunities to move the right deals forward in a meaningful way.
This guide is based on SignalFire’s Sales Mastery Series session on leveraging MEDDIC as a sales qualification framework, facilitated by SignalFire’s Head of GTM Development Kelechi Nwadibia, featuring expertise from guest speakers Ian Gilbert, CEO at Revelate, and Google Sector Leader and Sales Coach Mark Marinacci.
Key takeaways include:
- Customer-centric approach: salespeople should focus on understanding the customer’s needs and problems, adopting a framework like MEDDIC to align with the ICP.
- Curiosity and structure: MEDDIC encourages curiosity, leading to thoughtful questions that qualify and disqualify opportunities effectively.
- Leveraging MEDDIC: Metrics, Economic buyer, Decision criteria, Decision process, Identify pain, and Champion play a vital role in successful sales processes.
- Learning from lost deals: analyze lost deals to gain valuable insights to refine sales strategies for future opportunities.
- Effective discovery questions: use insightful questions to reveal customer needs, build trust, and understand the buying process, positioning solutions effectively.
MEDDIC was born out of a detailed analysis done in the mid-90s at PTC (formerly Parametric Technology Corporation) to understand the key components of why business deals were won, lost, and slipped. The learnings were codified, and the acronym MEDDIC was born:
- Metrics: the KPIs that measure pain, success, and the value you provide your customer
- Economic buyer: who in your customer’s org has the ultimate purchase authority
- Decision criteria: what your customer is basing their decision on
- Decision process: your customer’s internal decision process
- Identify pain and implications: what pain points you’re solving for and how serious they are
- Champion: the people that support your solution internally and help move the deal forward
A successful sales process with the MEDDIC framework will have demonstrated your salesperson’s ability to leverage the aforementioned list at varying stages of the sales cycle.
Deals can slip or be lost for various reasons, but likely a gap in one or a few of the aforementioned areas of MEDDIC can be the cause. For example, was the downside impact of the identified pain painful enough? Was there a naysayer who was part of the decision process that overruled the champion? As a sales team, the insights from studying lost deals can be just as valuable as the revenue from a won deal.
Guide deals with the right questions
Within the sales process, knowing your product in and out and how it’s designed to solve the challenges you’ve identified provides a unique advantage for you—something you always have in your back pocket. Early on, prospects may want to rush into seeing a demo, thinking they already know more than they do. You may feel inclined to acquiesce, jumping right into the product or pridefully listing features they missed. However, it’s important to remember that whoever is asking the bulk of the questions is driving the conversation. You’re also there to learn and solve a problem. Without knowing about your prospect’s problem, talking about product features wastes time without getting you closer to a sale. Without mentioning your product, the questions you ask can subtly reveal insights to your buyer that indicate you can solve their needs and pain points.
Following are discovery questions within the MEDDIC framework to consider the next time you engage with a sales prospect.
Discovery questions to reveal needs for inbound/outbound leads
- What prompted you to reach out? (Inbound) (Identify pain)
- This can reveal their challenge or gap in how they address it. This answer may be superficial, which is an area to probe in another way.
- Tell me about your current process for [challenge]. (Inbound/outbound) (Identify pain)
- How is that working/not working for you?
- Where are the most significant gaps within that? / Have you noticed any areas where things could be improved?
- If you made those improvements, how would those improvements impact your day-to-day/business?
- How much time/money has this cost? (Inbound/outbound)
- (Metrics)/(Implication of pain)
- Gently push here to quantify the pain.
- What’s different now that has you considering changing your process? (Inbound) (Identify pain)/(Implication of pain)
- What would you need to see to make a change in your process? (Decision criteria)
- What approaches are you considering to solve this? (Decision criteria)
- If you could solve this, how would [significant KPI] for the business improve six months or a year from now? How would things improve for you, personally? (Inbound/outbound) (Metrics)/(Decision criteria)
As you can see, different questions highlight different areas of the MEDDIC framework. As these questions are asked and answered, follow-up questions will naturally arise, allowing for a more conversational discussion. It shouldn’t feel like an interrogation but more like a consultation with a professional. You’re working together to reach better business outcomes.
Questions for revealing urgency / building deeper trust
- Tell me about your goals (and any key metrics) personally? For the team? For the organization? (Metrics)/(Decision criteria)
- How did you arrive at these goals?
- When do these goals need to be achieved? (Decision process)
- Have you identified any roadblocks? What are they? (Decision process)
- What happens if the goals aren’t achieved? (Implication of pain)
- What happens if you do nothing and keep things operating as is?
- What other solutions have you looked at to address this?
- What did you like or dislike about those?
- What do your decision criteria look like?
- Where does this fall on your current list of priorities? (Decision process)
With the knowledge of your product, these questions should reveal opportunities for you to position the value of your solution as the sales cycle progresses. You can tailor your messaging to use cases, how pain can be relieved, and how goals can be achieved.
The questions can also reveal just how important this initiative is for them.
Questions for understanding the buying process
This may occur later in the process, but it’s a critical step to avoid unnecessary hiccups.
(Champion)/(Decision process)/(Economic buyer)
- Aside from yourself, who else would need to be involved in the process?
- Can you bring them into our next meeting so we all align?
- If you’re not speaking with the final decision-maker, their ability and willingness to bring in other critical stakeholders shows seriousness and their role as your champion.
- Can you bring them into our next meeting so we all align?
- What has the buying process been for other solutions? (Are they aware of the buying process?)
- Procurement? Legal? Etc.
- Can we start the procurement process alongside the trial/proof of value if there’s a specific procurement process?
- What can you foresee as a roadblock in moving forward with us?
- What concerns, if any, do you have that I can help clear up?
- What does success look like to you in this process?
- Once we’ve achieved that, would there be any hesitation in moving forward?
These questions are not meant to be a script but a guide and framework for your next sales interaction. It’s a way to keep the focus on the customer and their needs, not your own.
Ultimately, before every call, you should have a baseline understanding of who the individual you’re speaking with is, their business, and maybe any recent news/trends to make for a more impactful business discussion. With these questions in mind, combined with the MEDDIC methodology, if you can simultaneously disqualify low-probability prospects while building trust with those more likely to buy, every sales interaction becomes worth your time and an opportunity to be positioned as a trusted consultative partner and boost your sales.
How startups can sell AI to enterprises
- Banks and other regulated enterprises are already aggressively testing proofs of concept for generative AI.
- Large language models are more risky to integrate than traditional machine learning models given their black box nature.
- Enterprises want an FDA-style dedicated regulatory body to ensure fairness, explainability, and accountability of AI models.
- Easier AI applications for enterprises to adopt include customer support and market research, but there is more excitement about the potential of coding automation and predictive analytics tools.
Enterprises want to buy from startups that emphasize practical use cases with clear ROI and adequate compliance features.What’s keeping regulated enterprises from adopting generative AI? Fears around risk management and the auditability of AI models were the top concerns, according to leaders at top financial institutions and the startups that sell to them. SignalFire and Truist Ventures brought together a roundtable of founders and banking executives to explore what enterprises need to see in order to trust AI startups.
Overcoming LLM unpredictability
Each bank at the event had already spun up around a dozen proofs of concept in the past few months to aggressively test where their business could benefit from AI. Eight months into the explosion of large language models, leaders said the new technology is experiencing significantly faster adoption than the internet. But the consensus is that much of the technology, or at least the maturity of the products delivering it, isn’t quite ready for mass deployment.
The big difference between LLMs and traditional machine learning models is that LLMs like OpenAI’s GPTs aren’t transparent or reproducible. They can provide different answers each time they’re queried, making it challenging to ensure robustness and to test coverage.
That unpredictability introduces significant issues around risk management and governance. Banking executives expressed the need for industry consensus and regulatory adjustments to address the unique challenges posed by LLMs. While some LLMs are highly advanced and user-friendly—with features like content customization, cognitive search, and the ability to integrate into existing systems—banks are wary that the rapid progress of LLMs may outpace governance and regulatory frameworks, creating challenges for both regulators and internal risk management teams.
Startups selling to the enterprise should emphasize how they’ve reined in rogue outputs with proper safeguards, human-in-the-loop feedback, and testing. Instead of pitching the unlimited potential of AI, founders should frame how their products control and harness its power.
Receptive to regulation
Executives believe regulators and auditors will need to develop new approaches to ensure the responsible and safe deployment of LLMs. Overall, the inclination of the roundtable was pro-regulation: there is a strong need for external oversight to ensure fairness, explainability, and accountability in LLM usage. The federal government is thorough in their scrutiny (especially for banks) and will emphasize the importance of explainability in the context of financial institutions. They discussed the role of regulators in guiding and facilitating the adoption of LLMs while balancing the need for innovation and mitigating potential risks.
One approach would be an FDA-like approval process for commercializing LLMs, where experienced individuals would assess the safety and potential risks of these models. One banking exec drew a parallel to the automotive industry: “Before there was a [National Highway Traffic Safety Administration] every car company had their own safety standards. They just made it up. That’s where we are now [with AI], right? Pretty much every bank has their own audit process. And I think it would be hugely beneficial, honestly, to have one certification authority.”
Developing such a regulatory body would certainly be complex. Still, the consensus is that it is necessary to ensure responsible and safe deployment of LLMs so that enterprises and the public can benefit from their capabilities at scale. Appearing resistant to regulation could scare off enterprise customers. Getting involved in public policy to help shape and cooperate with regulations can enhance trust.
The low-hanging AI fruit for enterprises
There are some use cases for AI that are less fraught for regulated enterprises. Executives see the lowest-hanging fruit as chatbots, digital front-end applications, customer support, and research tools. Internal solutions are also less likely to trigger regulatory scrutiny than public-facing tools. Executives pointed out that AI can greatly accelerate the progress of these areas, though they have yet to see truly transformative business use cases for generative AI. AI products that they would be excited to see include coding automation tools to solve for technical debt and predictive analytics within specific sectors.
Banking executives agree that startups may be best positioned to solve some of the deeper issues of model governance than enterprises themselves. “We don’t expect to build this type of capability and are looking to rely on external innovation,” one attendee said.
In order to reach industry-wide adoption and build confidence in the technology, there is a strong need for startup partners to provide tools for spot-checking, middleware layers, and governance frameworks. Top needs include solutions for data leakage, model inversion, and model explainability. But as Truist’s head of AI Bjorn Austraat put it, “the last thing we want is a black box explaining another black box.”
Early-stage founders should internalize that enterprises care less about the underlying technology or dreams of what it could do in the future. Founders who frame their products around commercial impact, feasibility, and security will have better success converting enterprises into lucrative customers.
You know you need a customer success team when…
Your sales team is overworked, big accounts are in danger of churning, and leadership lacks insights about your key accounts. It’s time to build your customer success team.
This guide is based on SignalFire’s Sales Mastery Series session on Customer Success taught by Figma Senior Director of Customer Experience Shani Taylor and Growth Molecules Customer Success Strategist Sabina Pons.
Key takeaways include:
- You should split customer success from sales when your account managers are too busy to proactively check in with key accounts and relay churn probability to leadership.
- Hire CS talent that has a mix of empathy, time-management skills, and a repeatable method for measuring customer health.
- Build a CS scorecard that combines interviews, surveys, metrics into a renewal probability score.
- Set up an information workflow to relay notes about customers’ needs and risk of churning from sales to customer success to leadership.
The purpose of customer success is to ensure customers are getting the most out of your product. That means helping them better use your current product offering, as well as learning how your product could evolve to meet more of their needs. Customer success helps you grow net revenue as customers use your product more, increase retention as they remain satisfied, and expand your business as your happy customers become advocates.
Customer success managers are a two-way bridge between customers and leadership. They check in on customer health, relay their findings to product and leadership to influence your roadmap, and then communicate planned solutions to customer needs so they don’t churn before fixes are delivered. It’s about plugging the hole in your bucket so you keep more of the customers you acquire, allowing you to scale revenue without the costly work of acquiring short-lived customers.
When to create a discrete customer success team
At first, founders do everything customer related, from sales to support to success. This is critical for getting the right insights to keep building, selling, and supporting your customer base as you grow.
Eventually, as you build your early sales team, that team will likely serve several purposes—account managers, customer success, and support. But when should you split up sales and support? Here are a few signs:
- Overworked: Account managers can’t handle all the sales and scheduled success calls, resulting in weak customer engagement or slow revenue growth.
- Reactive: You have no time for proactive success calls with top customers.
- Time study: An hour-by-hour analysis shows teams aren’t prioritizing or focusing on success as part of their daily work.
- In the dark: Leadership can’t predict churn of key customers.
Once you’ve seen these signs, or even better have anticipated the need to stand up a CS function, it’s important to hire the right leadership and team. You should also be thoughtful about who the CS team reports into, the CS team structure as you grow, and the hiring criteria to build a high-performance CS team.
What makes a top-performing customer success manager
- Empathetic, adaptable, and level-headed
- Researches to understand your product and roadmap as well as customer needs
- Has a system for measuring customer health
- Proactive about outreach and making customers feel they have an internal champion
- Strong time management skills and attention to detail
If you’re open to having customer success as a remote function, check out SignalFire’s Work From Home Hiring FAQ. Otherwise, our guide to employer branding can help make your company appealing to the best customer success talent.
Setting up your customer success team to win
As you are creating your initial CS function, it’s important to set up key measurement systems and processes internally to ensure you get the greatest insights, customer retention, and account expansion growth.
Establishing a customer success scorecard
Develop a standardized process for measuring and reviewing your CS health measures on a regular cadence with these steps:
- Interview customers about key metrics, but also collect surveys in case they’re shy about sharing problems.
- Measure quantitatively if they use the product through engagement, seat utilization, session time, bug reports, and feature requests.
- Measure qualitatively if they like your solution through NPS, satisfaction, and executive relationship.
- Measure a customer’s business health to anticipate if they’ll shut down through business slow-down, layoffs, and late payments.
- Assign a renewal probability score based on the impact of health measures above on historical churn.
- Understand the potential impact of churn by assessing acquisition cost, lifetime value, and renewal probability.
Coordinating customer success, sales, product, and leadership
Once your customer success team is up and running, it’s crucial to drive collaboration with the rest of your company with a clear flow of information:
- Sales provides notes to success managers on what drove signups, feature requests, and customer concerns.
- Leadership ensures swim-lane clarity on whether sales, success, or support handle different types of requests and upsells.
- Success shares renewal probabilities and executive summaries on key customers with leadership.
- Success relays feature requests to product teams, and then feasibility and timelines to customers.
- Tools track customer health, whole customer base health, and success productivity so you can measure improvement across time periods and CS managers.
With this customer success process in place, you should have smooth hand-offs from sales to success, teams focused on where they’re experts, early warnings on customers who might churn, and clear translation of customer needs into roadmap changes. This leads to a more predictable business, less churn, useful product insights, and greater internal efficiencies.
SignalFire’s Sales Mastery Series is a set of interactive workshops connecting founders and sales leaders from our portfolio with functional experts in the space. For more insights from these sessions, sign up for email updates from SignalFire.