Ciente > Articles by: Ciente Editorial Team

Customer Lifecycle Management: Why you are underutilizing your CRM

Author: Ciente Editorial Team

Customer-Lifecycle-Management--Why-you-are-underutilizing-your-CRM
Blogs

Customer Lifecycle Management: Why you are underutilizing your CRM

Customer Lifecycle Management: Why you are underutilizing your CRM

A lot of businesses use CRM to store data such as names, emails, contact numbers, deal stages, call notes, etc. But that’s only half the story. A CRM can be helpful when it helps you manage the entire customer lifecycle, not only parts of it.

Customer Lifecycle Management is about keeping track of where your customers are in their journey with your business. It starts from when they first learn about you and goes on even after they make a purchase, much like a structured approach explained in customer journey mapping.

When done correctly, it changes how your team views your customers. You no longer think of customer interactions as events. Instead, now you will see them as a long-term relationship with your customers.

What is the Customer Lifecycle?

The customer lifecycle is the path a customer takes with a business, closely aligned with how businesses design their customer journey. These are the five stages of the customer:

  • Awareness
  • Acquisition
  • Conversion
  • Retention
  • Loyalty

These steps don’t always go in a straight line or in a neat order. A customer might hear about you three times before they actually buy something. A customer who has been with you for two years might leave if a competitor has better prices. The lifecycle is more complicated than any diagram shows, but the framework is still a good way to think about how you deal with customers, especially when supported by insights from customer journey analytics.

How CRM Supports Customer Lifecycle Management

The operational backbone of lifecycle management is a CRM platform. It’s where the data is stored, where teams work together, and where actions happen based on how customers act or what their status is.

This is how it works at different stages:

  1. Awareness & Acquisition

The Customer Relationship Management system, or CRM for short, is doing a job of keeping track of new leads. It knows who these people are, where they came from, and when they got in touch with us. People can get in touch with us in ways such as filling out forms on our website, clicking on our ads, signing up for our events, or calling us on the phone.

A good CRM will make a note of where each lead came from so we can see which methods are actually working, similar to how businesses evaluate their customer acquisition strategies.

This is really important to know. Let us say we are spending a lot of money on one way of getting customers, but the CRM shows that our best customers are actually coming from a different method. The CRM is telling us that some methods are better than others. The Customer Relationship Management system is giving us information about our leads and customers, which becomes even more powerful when combined with customer data platforms.

  • Conversion

The sales team spends most of the time in CRM, as the deal pipeline, email-threads, proposal history, follow-up reminder, etc., all are live here, often supported by lead nurturing strategies to keep prospects engaged. The goal is to make things easier and keep deals going. A CRM can help you find out which leads have gone cold, which one recently responded, and where your process tends to get stuck.

One thing you might notice about companies that have trouble converting is that their CRM data isn’t always the same. Some reps write down everything, while others write down almost nothing. For lifecycle management to work, the data has to be accurate. Platforms like Zoho CRM do a great job of this. If your business is thinking about or already using Zoho, a Zoho Implementation Partnercan help you set up these features in a way that makes sense for your sales process.

  • Retention

A lot of businesses don’t spend enough money after the sale, even though customer success plays a key role in long term retention. The sales are done, the customer is passed on, and not much happens until they call with a problem or a renewal is due.

The changes when you use a CRM for lifecycle management. It can keep an eye on things like customer health signals, the number of support tickets, products, the date of last login, NPS responses, and more. It will also let teams know when something seems off. If a customer has sent in three support tickets in the last two weeks and their renewal is in 45 days, that’s a pattern that should be dealt with before it happens.

  • Loyalty

Long-term customers who become advocates are very valuable, but it’s easy to forget about them because they don’t have clear “stages” to follow. This is where CRM segmentation comes into play, especially when businesses follow structured B2B SaaS customer segmentation practices. You can find customers who have been with you for a certain amount of time, spent more than a certain amount, referred others, and then treat them in a way that is appropriate for them, such as giving them early access to features, special pricing, or even just a personal check-in from an account manager.

Key Components of Effective CLM in a CRM

Customer lifecycle management is a combination of practices and CRM practices working together. For businesses wanting help pulling it all together, CRM Masters is a CRM consulting company that also helps in setting up customer lifecycle management workflows properly.

  1. Segmentation: This enables segmentation of customers by behaviour, stage, value, etc, and allows businesses to interact with their customers based on their relevance.
  2. Automation: It helps in handling welcome emails, re-arrangement campaigns, and renewal reminders, which are key components of effective marketing automation strategies. These tasks are all important, but are repetitive and take unnecessary time.
  3. Reporting & Analytics: It shows how your customers move through stages, where they are dropping off, and what’s keeping them coming back, similar to insights gained from customer analytics solutions. Most of the CRMs have built-in dashboards for this, despite how useful they are for data quality.
  4. Integration: It helps your CRM connect with other tools that customers interact with, such as your product, your support desk, and your marketing platforms.

Why It’s Worth Getting Right

When life cycle management is done right, you benefit from it. Instead of talking to customers at random times, your team talks to them at the right times. You catch accounts that are about to leave before they do. You spend your acquisition budget more wisely because you know which groups keep customers the best.

Over time, your CRM will become more than just a list of contacts; it will become a real source of business information, especially when paired with data analytics to improve customer experience. There is no need for a complicated setup for any of this. It starts with agreeing on what each stage of your life looks like, making sure your CRM is collecting the right data at each stage, and getting into the habit of using that data all the time.

FAQs

Q1. Which stage of the customer lifecycle is most important?

Ans. The retention stage is really important because getting customers is more expensive than keeping the old ones. We think that keeping the existing customers is a deal. So companies that are good at keeping their customers tend to do.

Q2. Do small businesses need CLM?

Ans. Yes, but the scale looks really different. Small businesses should not use enterprise-level software; they should use a lightweight Customer Relationship Management system. Having CRM software helps you in keeping track of every customer and responding to them properly.

Q3. How do I know if our lifecycle management is working?

Ans. Take a look at the numbers like churn rate, customer lifetime value, repeat purchase rate, and time taken to close a deal. It’s a good sign if these things get better after you start working on CLM.

Q4. Can lifecycle management be fully automated?

Ans. You can easily automate touchpoints like reminders, check-in emails, welcome sequences, etc. But mostly, a human involvement is needed in renewal conversion, relationship building, escalations, etc.

Beyond Complex Pricing Structures: Snowflake's Usage-based Model
Blogs

How Snowflake’s Usage-Based Model Moves Beyond the Market’s Complex Pricing Structures

How Snowflake’s Usage-Based Model Moves Beyond the Market’s Complex Pricing Structures

Snowflake’s unique market positioning stems from its culpability to adapt to market demand. And its pricing structure is a solid proof.

Traditional pricing models leave users frustrated with underutilized resources or even unpredictable costs.

Users continue to contend with a list of complex pricing charts, a stack of bills, and additional price points they weren’t even aware of. It’s a prevalent challenge at the helm of most subscription pricing structures and for flat fees incurred for a fixed storage space.

Snowflake, the next-gen leader in cloud-based data storage, has chosen to move away from these traditional pricing charts. Unlike its competitors, BigQuery and RedShift, it reflects a broader shift in how modern businesses approach cloud computing fundamentals.

It’s vamping cloud data warehousing not only through tech innovation, but also by introducing a new methodology for pricing data infrastructure in the modern cloud era.

A Detailed Glimpse at Snowflake’s Current Pricing Model

Snowflake’s usage-based model is transparent at the philosophical level. Pay for what you use. Simple enough, right?

But here’s where it gets nuanced: your actual cost per credit isn’t fixed. It shifts depending on the edition you’re on. And most teams don’t realize that until they’re already locked in.

Snowflake offers four editions: Standard, Enterprise, Business-Critical, and Virtual Private Snowflake (VPS). Each tier unlocks progressively more capabilities, but each one also comes at a higher per-credit rate. So, choosing the wrong edition boils down to a budget problem.

Here’s a rough breakdown of how per-credit pricing typically shakes out across editions on AWS US East (On-Demand):

  1. Standard: ~$2.00 per credit. Entry-level. Core warehousing, data sharing, standard security. Right for smaller teams or early-stage workloads.
  2. Enterprise: ~$3.00–$4.65 per credit. Adds multi-cluster warehouses, materialized views, extended Time Travel (up to 90 days), and column-level security. This is where most mid-market SaaS companies land.
  • Business-Critical: ~$4.00–$6.20 per credit. Built for regulated industries. HIPAA compliance, enhanced encryption, private connectivity, Tri-Secret Secure. If you’re in healthcare, fintech, or any environment with strict data governance requirements, this is typically non-negotiable and closely tied to cloud security considerations.
  • Virtual Private Snowflake (VPS): ~$6.00–$9.30 per credit. Completely isolated infrastructure. Pricing is negotiated directly with Snowflake. Reserved for workloads where shared cloud infrastructure isn’t an option.

The jump from Standard to Enterprise alone can mean paying 50–100% more per credit for the same compute work. Before you move tiers, audit which features you genuinely need versus which ones are just nice to have.

Paying enterprise rates for workloads that only need standard capabilities is a remarkably easy way to inflate your bill without adding any business value.

And if you’re evaluating Snowflake for the first time- there’s a 30-day free trial with $400 in usage credits. It expires when the credits run out or after 30 days, whichever comes first. There’s no permanent free tier, so the trial window matters.

Snowflake ‘s pricing follows a simple, transparent, and agile structure. One based on usage (consumption) that operates on a very innovative motto: Pay only for what you use.

The logic behind this is straightforward- be unique and value-driven, much like organizations aiming at successful cloud adoption today.

You merely pay for what you use. Whether it’s storage space, compute (virtual warehouses), or cloud services, the underlying architectural layers make up the nucleus of Snowflake’s umbrella model.

Here’s how.

For data storage and transfer

The cost depends on the average volume of compressed data (in bytes) stored on the platform on a daily basis. You can store, access, and process this data, irrespective of its format, at any volume. And you pay for the space that you utilize.

“More value, lower the cost of ownership”

– Snowflake’s guiding principle

Unlike its competitors, Snowflake doesn’t offer a basic storage volume at a flat fee or recurring fee. Instead, it entails additional features such as zero-copy cloning, which allows for more storage at a reduced cost.

What happens is that the platform has automatic storage compression, where table data gets automatically shrunk and optimized, meant for bulk onloading and offloading. On the other hand, zero-copy cloning allows users to copy the exact database without duplicating existing data or encroaching extra storage space.

How are customers charged? – per terabyte (used) per month for the compressed storage space. The pricing changes when data is transferred within the same cloud but across different regions, or different clouds.

For compute usage

Snowflake’s compute pricing is dependent on the number of compute resources leveraged. And they aren’t billed the traditional way.

The platform leverages its unique currency called ‘credits.’

They are units that determine how many billable compute resources (virtual warehouse) an user has consumed. It tracks the billable units only when the virtual warehouse is running, not when it’s suspended, i.e., while running a workload, loading data, or performing a query.

The credits differ according to the compute type- virtual warehouses, serverless capabilities, and cloud services.

Virtual warehouse compute consumes credits depending on its size and runtime (billed per second), with a minimum requirement of 60 seconds. And if less than a minute, it can incur additional charges.

One of its key benefits is that you can control the number of Snowflake credits it consumes. It’s user-configured, meaning you can choose size, the runtime, and additional usage caps.

Snowflake allows for resizing while the performance remains linear. For example, doubling the warehouse size will halve the operating time while maintaining the original cost. But resizing to one size larger will cost a full minute’s worth of usage.

Virtial warehouse credits per hour

Source: Snowflake

Cloud services are powered by compute resources, so they follow the Snowflake credits framework just like virtual warehouses. But there’s something more to note here.

Cloud services are charged only when they exceed 10% of daily compute resources usage. And the 10% adjustment is calculated based on that day’s warehouse usage.

For example, you’ve utilized 200 compute credits and 100 cloud credits on the same day. The 10% adjustment is then subtracted from the compute credits, i.e.,

  • 200 * 10% which equals 20 credits.

So, the overall billable credits would boil down to

  • 100 cloud credits – 20 adjusted credits = 80 billable credits.

And if in another scenario the overall usage is less than 10% of the daily compute resources, then Snowflake charges for 100 cloud credits in this scenario.

Snowflake’s approach to pricing its resources is unarguably forward-thinking.

The focus is on user needs, not vendor convenience. And the control is relinquished to the customers, helping them exercise flexibility, similar to benefits seen in cloud native environments. By doing so, Snowflake is facilitating ease of use that only such a unified and managed service model like theirs can deliver.

It’s a single product, with only different editions with higher levels of service and features.

Snowflake most popular thing

Source: Snowflake

But there’s a small underlying complexity- users must closely monitor and manage their credit usage to avoid any surprise costs later. With tactical management practices, even this stumbling block can be cracked.

To navigate this complexity, Snowflake adds another tier to its pricing structure, and this is where it all truly ties neatly together- the account type you are leveraging.

An on-demand or a committed capacity purchasing option?

a decision often influenced by your broader cloud migration strategy? With on-demand, you’ve the promised flexibility to store as much and as little data as you wish. There are no commitments involved.

To avail the on-demand account, you sign up for the service on Snowflake’s website and pay through a credit card every month. The final amount depends on the edition you’re entailing, and the geographical location of the cloud services.

Meanwhile, the capacity account type basically works as an agreement. The user agrees, or instead, commits to spending on a particular amount of storage space, of course, in exchange for bulk credit discounts. And that space has to be utilized entirely within a specific contract period.

This account type comprises a diverse set of services, from hands-on training to professional assistance and price guarantees for the long term.

Irrespective of the account type you opt for, the policy remains the same: you pay for what you use, which is critical when managing cloud data platforms efficiently.

Overall, this agile pricing philosophy is insightful. One that has facilitated large enterprises and start-ups in scaling analytics effortlessly and mapping innovative data initiatives without financial guesswork.

Making it a win-win opportunity for both customers and the brand alike.

The Hidden Costs Most Teams Discover Late

Snowflake is transparent about its pricing model. Where teams get caught off guard are the corners of that framework they didn’t know to look at.

Time Travel and Fail-Safe Storage

Every table you create in Snowflake comes with two features that quietly add to your storage bill: Time Travel and Fail-Safe.

Time Travel helps query historical versions of your data, which is incredibly useful for data recovery or debugging. But the default retention window can be set as high as 90 days on Enterprise. Every version of every changed row gets stored for that entire window.

On a large, frequently updated dataset, that isn’t a minor line item. Teams that set Time Travel to maximum retention across all their schemas without overthinking have reported their actual storage footprint ballooning to 60–70% more than their raw data volume.

Fail-Safe adds another seven days of protected recovery storage on top of Time Travel. You can’t configure it, and it’s factored into your storage bill automatically.

The fix is straightforward: audit your Time Travel settings.

Not every table needs 90-day retention. Historical or archive tables with low update frequency probably don’t need any Time Travel at all. Reducing retention on the right schemas can meaningfully shrink your monthly storage bill without any actual loss of functionality.

Serverless Features That Don’t Auto-Suspend

Virtual warehouses have auto-suspend. Once they go idle, they stop consuming credits.

Serverless features don’t work that way. Once you enable Snowpipe, Search Optimization, Materialized Views, or Snowflake Tasks, they run on a continuous credit consumption model until you explicitly turn them off. There’s no built-in idle detection.

That is where numerous teams get blindsided.

A data engineering team enables materialized views across several large tables during a migration project. The migration wraps up. The Materialized Views keep refreshing every 30 minutes against staging tables nobody is querying anymore.

Weeks later, that forgotten configuration becomes thousands of dollars of unexplained spending.

The practical safeguard is building lifecycle management into your workflow- a policy that deactivates serverless features tied to non-production environments when those environments are no longer active. That doesn’t have to be complex.

A scheduled task that checks for and terminates idle serverless features in development schemas is enough to prevent the most common version of this problem.

Data Transfer Fees

Snowflake doesn’t charge for data ingress. Moving data into the platform is free.

Moving data out is a different story.

Egress costs vary based on destination. Cross-region transfers on the same cloud run roughly $20–$140 per TB, while cross-cloud or internet-bound transfers can reach $90-$150 per TB depending on your cloud provider and region.

None of these numbers is large on a per-GB basis. But at scale, they compound fast.

A team replicating 300 GB daily from AWS US-East to EU-West for regulatory compliance will have a meaningful monthly transfer bill that has nothing to do with their compute or storage usage.

Teams building multi-region architectures without mapping their data flow to Snowflake’s regional pricing structure often encounter this unpleasantly.

The straightforward mitigation: align your Snowflake account region with the regions where your downstream data consumers actually live, especially when working across hybrid cloud strategies. Cross-region replication is sometimes unavoidable, but it should be a deliberate architectural choice with a clear business justification. It shouldn’t end up as an accidental cost.

Practical Cost Optimization: Where to Start

The good news is that Snowflake’s cost structure, once understood, is highly controllable. The most impactful optimization levers are also the most accessible.

1. Right-size your warehouses before anything else.

The most common driver of Snowflake overspend is when teams default to Medium or Large warehouse sizes for workloads that run perfectly well on Small or XS. Each size increment doubles your credit consumption rate.

Running a query that takes four seconds on a large warehouse would take eight seconds on a small one. But you’d pay a quarter of the price. For most interactive BI queries, that tradeoff is an obvious win.

2. Configure auto-suspend, but don’t set it too aggressively.

Warehouses that suspend immediately after every query lose their data cache, which means the next query has to reload data from scratch. That’s slower and often more expensive than keeping a warm warehouse available for a few minutes.

A 60-120 second suspend threshold typically strikes the right balance between minimizing idle spend and preserving cache performance for follow-on queries.

3. Monitor cloud services usage separately.

Cloud services are free up to 10% of your daily compute credits. Most workloads stay comfortably within that buffer. But environments with heavy automation, frequent schema changes, or large-scale cloning operations can drift past the threshold and start generating additional charges.

Checking your ACCOUNT_USAGE.METERING_DAILY_HISTORY view regularly takes two minutes- surfacing the issue before it compounds.

4. Pre-purchase credits if your usage is predictable.

On-demand credits carry a meaningful premium over pre-purchased capacity. For teams with stable, foreseeable workloads, committing to a capacity plan (sized to cover roughly 80–90% of expected usage) delivers consistent savings without the risk of overbuying credits you can’t roll over.

Snowflake’s pricing strategy could prove to be the guiding principle for modern businesses.

There’s a lack of transparency in a market that facilitates hidden costs without any real value or uniqueness in its offerings.

This is where Snowflake’s pricing strategy makes a 180-degree shift.

Its pricing framework is built on offering businesses true clarity and control over their spend. Snowflake believes that rigid billing practices shouldn’t throttle innovation. But keep pace with the rhythm of modern cloud businesses, especially across fluctuating workloads.

Each pricing for the different architectural layers of Snowflake’s platform is based on paying only for the value that users gauge from it.

As the pricing remains constant, the value increases. And as the value of the Snowflake credit also rises, the pricing remains the same.

Snowflake has built on what customers want the most: value. And a promise that rarely gets delivered on: value for money.

Snowflake’s Pricing Is Honest. Your Usage Needs to Be, too.

The framework Snowflake built is genuinely fair. You pay for what you consume, and the structure rewards teams that are deliberate about how they consume it.

But “pay for what you use” only works in your favor when you actually know what you’re using. Time Travel retention settings that haven’t been reviewed in six months, Materialized Views refreshing against forgotten staging tables, warehouses sized for the peak workload that happens twice a year- these aren’t Snowflake’s design flaws. They’re operational blind spots.

The teams deriving the most value out of Snowflake’s pricing model treat cost visibility as a first-class concern alongside performance and reliability. Not as an afterthought when the bill arrives.

Sales Techniques
Blogs

10 best sales techniques for B2B sellers in 2026

10 best sales techniques for B2B sellers in 2026

The techniques haven’t changed as much as everyone claims. What’s changed is the buyer. Here’s what actually works in 2026 and why most lists get it wrong.

Every year, a new list. New frameworks, new acronyms, new tools promising to close the gap between quota and reality.

Most of them are the same techniques with updated vocabulary.

This one is different not because the techniques are all new, some are genuinely old, but because it is honest about why they work. The mechanism matters more than the method. A rep who understands why something works can adapt it. One who memorized the steps cannot.

Here are ten that hold up.

Here is the Top 10 Best Sales Techniques in 2026

1. Sell to the problem before you sell to the solution

A structured approach like a defined sales journey can support this shift, as outlined in your broader framework. B2B buyers in 2026 are not short on vendor options. They are short on people who understand what they are actually dealing with.

The rep who opens a conversation by describing what their product does is already behind. The one who opens by accurately describing the problem the buyer is living with gets a different response entirely. Something shifts. The buyer leans in slightly. The conversation stops being a sales call.

Problem-first selling requires genuine research. Not a LinkedIn stalk and a quick glance at the company’s homepage. Real research. What is this company trying to do this year? What is in the way? What has probably already been tried and why might it not have worked?

Arrive with a hypothesis about the problem. Test it in the conversation. Adjust. The rep who does this is not pitching. They are consulting. Buyers pay attention to consultants.

2. Map the buying committee before the first call

Single-threaded selling is how deals die quietly.

The champion who responded to outreach is one person in a room full of people who will have an opinion about this purchase. Some of them the rep will never talk to. All of them will influence the outcome.

Mapping the buying committee before the first conversation changes what questions get asked in it, especially when you understand how multi-threading impacts deal outcomes across stakeholders. Who else is involved in this decision? What does the IT team need to see? Who has said no to something like this before?

These questions do not feel like intelligence gathering when asked correctly. They feel like the rep taking the deal seriously. Because they are. The information they produce shapes every touchpoint that follows, who gets contacted, what they receive, and when.

Multi-threading is not aggressive. It reflects how organizations actually make decisions and aligns closely with how modern sales pipelines are built and managed.

3. Use silence as a technique, not as a problem to fix

Most reps fill silence. Immediately. With a clarification, a rephrasing, a pivot to the next talking point.

The instinct is understandable. Silence in a conversation feels like failure. It feels like the other person is losing interest or the rep has said something wrong.

It is almost always neither.

A buyer who goes quiet after a question is thinking. The thought that comes after the silence is usually more honest than the one before it. It is the answer after the prepared answer. The thing they have not said yet because they needed a moment to find it.

The rep who holds the silence gets that thought. The one who interrupts it gets the prepared version and goes home thinking the conversation went well.

Sit with it. Thirty seconds of silence in a sales call is not a problem. It is usually the most productive thirty seconds in the whole conversation.

4. Run a pre-mortem on every deal

This one comes from research psychology and almost nobody applies it to sales.

A pre-mortem is simple. Before a deal progresses to the next stage, the rep imagines it is six months from now and the deal has fallen apart. Not if it falls apart. It fell apart. Working backwards from that assumption: why?

The champion lost internal support. The budget got reallocated in Q3. A new VP arrived and froze all vendor decisions. The technical evaluation surfaced an integration issue nobody anticipated.

Running through the failure modes before they happen produces two things. The rep catches the risks that were always there and gets ahead of them. And they ask different questions in the next conversation, the ones that probe the assumptions the deal is currently resting on.

Pre-mortems do not make deals pessimistic. They make them honest.

5. Teach buyers something they did not know they needed to learn

The consultative selling model has been discussed for decades. The version most reps practice is a softened version of traditional selling with more questions and less hard closing.

Challenger selling, documented by Matthew Dixon and Brent Adamson, takes it further. The highest-performing reps do not just understand the buyer’s situation. They reframe it. They introduce a perspective the buyer had not considered, challenge an assumption the buyer was carrying, and show them the problem from an angle that makes the solution feel inevitable rather than optional.

This requires the rep to know more about the buyer’s industry, competitive landscape, and category dynamics than the buyer expected. Not a standard pitch. An actual point of view.

Buyers remember the rep who taught them something. They forget the one who presented well.

6. Qualify out faster

The pipeline looks healthy until someone examines what is actually in it.

Most B2B pipelines contain two types of opportunities, which is why tracking the right pipeline metrics becomes critical for maintaining accuracy ones that will close, and ones that will not close but nobody wants to admit it yet. The second category consumes time, energy, and forecast credibility. It also prevents reps from focusing on the ones that could actually move.

Qualifying out is the discipline of ending pursuit of an opportunity that does not meet the criteria for real pipeline. Not leads received. Not meetings booked. Opportunities where there is a problem that needs solving, a budget to solve it, a timeline with some urgency, and a path to the people who make the decision.

When any of those is genuinely missing, the right move is to exit cleanly and shift attention. This is hard. It feels like giving up. It is actually the most productive decision a rep can make with an opportunity that was never going to close, because it frees up the capacity to work one that will.

7. Make the next step specific before leaving every conversation

supported by a well-defined sales cadence that keeps momentum consistent. Vague next steps are where deals go to stall.

Following up to touch base is not a next step. Sending over the proposal when you get a chance is not a next step. Connecting again after the holiday break is not a next step.

A specific next step has a date, a time, a clear action, and a shared understanding of what will be different after it happens. The follow-up call is scheduled before the current call ends. The proposal has a walkthrough meeting booked, not just a delivery date. The evaluation has a defined criteria and a defined endpoint.

Buyers who leave a conversation without a specific commitment tend not to come back to it at the same level of urgency. The rep who locks the next step in the current conversation keeps the momentum. The one who sends a follow-up asking for availability loses three days and a degree of interest they will not fully recover.

8. Use the customer’s language back to them

which is a critical part of scaling personalization across modern B2B sales interactions. This one sounds small. It is not.

Every buyer has specific language for their problem. Specific words they use, specific phrases that carry weight in their internal conversations, specific framings that reflect how they and their organization think about the challenge.

Reps who listen carefully enough to capture that language and reflect it back in the proposal, the follow-up, the next conversation, create a recognition response in the buyer. This organization understands us. The rep gets it.

Reps who translate the buyer’s problem into their own product language lose something in the translation that the buyer notices without being able to articulate why. The fit feels slightly off. The response is slightly cooler.

It is a small thing. It accumulates into a meaningful difference in how the buyer experiences the relationship.

9. Stop using urgency you did not create

instead align your messaging with genuine buyer timelines and decision triggers. End-of-quarter discounts. Pricing that expires Friday. Limited spots in the implementation queue.

Manufactured urgency works once, on buyers who did not realize it was manufactured. Which is fewer and fewer of them. Experienced B2B buyers have been through enough sales cycles to recognize artificial pressure, and when they feel it, the trust that the rep has been building takes a measurable hit.

Real urgency comes from the buyer’s situation, not the seller’s quota. A compliance deadline is real urgency. A budget cycle closing is real urgency. A competitive situation where delay means conceding market position is real urgency.

When real urgency exists, the rep’s job is to surface it and reflect it back: you mentioned the board review is in eight weeks, which means the implementation timeline needs to start no later than this. That is a legitimate conversation. It helps the buyer see their own situation clearly.

When real urgency does not exist, the rep’s job is to find the underlying reason the buyer should move, or to honestly determine that the timing is not right and nurture accordingly. Inventing a deadline is not a technique. It is a shortcut that borrows against future trust.

10. Review calls for what you missed, not what you said

as part of a broader effort to continuously improve sales performance through deeper analysis. Call review culture in most organizations is a performance assessment. Did the rep follow the methodology? Was the talk-to-listen ratio acceptable? Were the right questions asked in the right order?

That framing optimizes for process adherence. It does not optimize for learning.

The most useful question in a call review is not what did the rep do well or poorly. It is: what did the buyer say that the rep did not follow? The moment where the buyer offered something, a comment, a shift in tone, a throwaway line that contained a real signal, and the rep moved past it because the script had somewhere else to be.

Those missed moments are where deals are lost. The buyer told the rep something important. The rep did not hear it. The call ended and both parties went away thinking it was a good conversation. Six weeks later the deal stalled for a reason that was visible in that moment and got ignored.

Training reps to watch for what they missed, rather than to evaluate what they did, builds a different kind of awareness. It is slower to develop and harder to measure. It produces reps who close deals that their peers are still trying to understand how they won.

The techniques that hold up in 2026 are not the newest ones. but they often align with the ongoing evolution of sales teams and how they adapt to changing buyer behavior.

They are the ones built on an honest understanding of how buying actually happens: slowly, non-linearly, inside organizations full of people with competing priorities and limited time.

The rep who understands that sells differently than the one working a script. And the difference shows up in the number.

SPIN selling
Blogs

Architecting Certainty: How SPIN Selling Solves the B2B Problem-Realization Gap

Architecting Certainty: How SPIN Selling Solves the B2B Problem-Realization Gap

A research loop traps B2B buyers. They don’t need another pitch; they need a framework to quantify the cost of doing nothing. That’s the real SPIN.

In the late 1980s, Neil Rackham and his peers at Huthwaite conducted 35,000 sales calls. Not to torture themselves- to find out what actually separates elite B2B performers from everyone else scrambling for quota.

What came out of it was SPIN selling. Four question types: Situation, Problem, Implication, Need-Payoff. It still aligns closely with a structured approach like a proven sales process that guides reps through each stage intentionally.

Simple on paper. Brutally hard to execute well.

And here’s the thing: the psychology behind it stands.

Even with AI in the mix, even with buyers conducting 70% of their research before they’ll take a call, the framework still works especially as modern teams rethink their approach with AI in sales. Because human decision-making hasn’t changed. What has actually changed is how impatient buyers have become, and how quickly they’ll tune out an SDR who hasn’t done their homework.

The real problem in most B2B sales cycles? SDRs pitch too early, often without fully understanding effective sales prospecting practices that set the foundation for better conversations. They walk in, start talking about features, and the buyer hasn’t even admitted to themselves that they have a problem worth solving yet.

SPIN fixes that. It turns the rep from someone pushing a product into someone helping a buyer think- guiding them through their own discovery rather than dragging them through a deck.

The Philosophy of the Question-Led Journey

Here’s something that surprises people: top salespeople aren’t the best talkers. They’re the best listeners.

Conversation intelligence platforms have been tracking this for years now, and the data repeats the same thing which aligns with insights from sales performance metrics that truly matter. SDRs closing the biggest deals ask more questions and talk less. But it’s not just about asking more. The type of question is everything.

SPIN works because of one core insight: buyers don’t buy because they understand your product from the inside out. They buy because they feel like you understand their problem.

Get a prospect through all four stages well, and you’ve built enough internal certainty that even a skeptical buying committee starts moving.

1. Situation Questions: Avoiding the Data-Dump Trap

Situation questions are the basics. Where are you now? What are you working with? What does your current setup look like? They’re necessary. But they’re also where bad reps lose the room before the conversation even begins.

Today’s buyers will not sit through a discovery call that feels like a questionnaire. If you’re asking a VP of Operations what software they use when that information is sitting right there on their website, you’ve just told them you didn’t prepare.

That’s a trust problem that’s hard to retrace.

Trust builder vs trust killer

The best SDRs keep situation questions tight. They use them to confirm what they already suspect, or to receive the one piece of context that isn’t public. Nothing more.

Gong’s research found that top performers actually ask fewer situation questions than their peers, largely because they rely on strong data sources and B2B databases before the call. Because they’ve already done the work before the call. The goal isn’t to gather information.

It’s to set up the next stage without wasting anyone’s time.

2. Problem Questions: Identifying Implied Needs

Most buyers don’t walk into a sales conversation knowing exactly what’s wrong. They know something feels off. A process that’s slower than it should be. A tool the team complains about, but nobody’s formally flagged. A gap they’ve learned to work around.

These are implied needs. And problem questions are what pull them to the surface.

The mistake most reps make here is asking something that puts the buyer on defense. “Are you happy with your current vendor?” Almost never works. People reflexively say yes even when they’re not.

The better approach? Ask something that makes them think about the friction they experience daily. “How often does your team end up manually reconciling data because your systems fell out of sync?”

That’s not a threatening question. It just invites them to reflect- and when they start reflecting, the cracks in the status quo show up on their own.

Never forget that in B2B, inertia is your real competition which is why aligning your sales and marketing strategy becomes critical to drive momentum. Not the other vendor on the shortlist. Inaction. If a buyer doesn’t feel the pain of the problem, they will choose to do nothing. Every time.

Problem questions are what start chipping away at that comfort.

3. Implication Questions: Quantifying the Cost of Inaction

This is where most reps blow it. They find a problem, feel good about it, and immediately pivot to the demo. Big mistake.

Implication questions are the hardest part of SPIN. They’re also the most valuable- by a significant margin.

These questions take whatever problem you just uncovered and stretch it- what happens downstream because of this problem? What does it cost the business? Who else does it affect?

“If that manual reconciliation is eating four hours a week, what’s that doing to your team’s ability to hit launch deadlines?”

That question changes the nature of the conversation. You’re no longer talking about a software inconvenience. You’re talking about missed targets, stretched teams, and lost revenue.

Suddenly, the problem is a liability.

This is where loss aversion kicks in, reinforcing why a strong sales pipeline strategy is essential to keep deals moving forward.

The buyer stops thinking “It would be nice to fix this someday” and starts thinking “We’re actively bleeding because we haven’t fixed this.” That mental shift: that’s when your solution stops being a budget line and starts being a business case.

How an SDR handles implication questions is the clearest predictor of how they’ll perform in the back half of the funnel.

4. Need-Payoff Questions: Turning Problems into Explicit Needs

The last stage is the payoff, literally and figuratively. But there’s a catch most reps miss. You don’t get a say in what the value is. The buyer has to say it.

“If we took that reconciliation process completely off your team’s plate, what would they be doing with those four hours instead?”

When a buyer answers that question, something shifts. They’ve now articulated (out loud, in their own words) what life looks like with the problem solved. They’ve moved from vague dissatisfaction to a clear, stated desire for something better.

And because they said it themselves, they believe it in a way they never would if you’d said it for them.

There’s also a practical upside here.

That answer becomes their internal pitch when the CFO asks why they’re recommending this spend, similar to how qualified opportunities are justified in a sales accepted opportunity framework. They’re not repeating your talking points- they’re defending a conclusion they reached themselves. That’s a much harder thing to poke holes in.

You’ve essentially helped them become your advocate without them realizing that’s what happened.

Why Methodology Matters

Rackham found something counterintuitive in his research: the techniques that work great in small, fast sales often actively hurt you in large, complex ones.

Hard closes, urgency tactics, feature dumps- these can work when you’re selling something low-stakes on a single call. Try them in a six-month enterprise deal with eight stakeholders, and you’ll lose trust faster than you built it.

In a complex sale, closing isn’t a moment. It’s a sequence that depends heavily on managing a structured sales cadence across touchpoints.

Every interaction needs to end with a commitment, i.e., a next step, a follow-up, a decision, that keeps the deal moving forward.

SPIN was built for exactly this environment. It’s designed for slow burns. Its whole purpose is cultivating the buyer’s internal conviction over time, so that by the time you make a decision, it doesn’t feel like a leap- it feels obvious.

When the problem feels bigger than the price tag, the deal closes itself.

Data-Backed Best Practices for Modern Teams

Data says top performers do differently

A few things modern sales data consistently shows that pair well with SPIN, especially when supported by strong sales analytics and ROI-driven decision making.

1. Silence is underrated. Pausing after a buyer finishes talking consistently leads to richer outcomes. Buyers fill the silence with context they wouldn’t have offered otherwise. Most reps are too uncomfortable with silence to let it work.

2. Talk less. The best SDRs in complex deals entail around a 46/54 talk-to-listen ratio. They’re not quiet because they’re passive- they’re quiet because they’ve asked a question worth sitting with. SPIN questions are the tool that makes this ratio natural rather than forced.

3. Measure the right things. Call volume is a weak proxy for discovery quality. which is why tracking the right sales KPIs becomes far more important. Track how many specific business problems reps actually quantify per conversation. That number tells you far more about pipeline health than dials-per-day ever will.

4. Don’t rush the solution. Bringing up your product before the buyer has genuinely felt the implications of their problem often weakens your positioning within the broader sales funnel. is one of the fastest ways to invite price objections. You haven’t earned the right to pitch yet. Let the implication stage do its job first.

The Spin Method Needs to Make a Comeback.

Four spin questions

Better sales performance isn’t a hustle problem. It’s a precision problem.

SPIN has lasted this long because it doesn’t try to manipulate anyone, much like modern approaches focused on improving overall sales performance. It takes the buyer seriously. It recognizes that no SDR, no matter how good, can convince a company to change. Only the buyer can do that.

The SDR’s job is to create the conditions for unavoidable realizations.

Work through the situation carefully. Dig into the problem. Spend real time in implication- more than feels comfortable. Then ask the need-payoff question and let the buyer land there themselves.

Done right, you’re helping someone find the clarity they’ve been stuck without.

Stop training your SDRs to pitch. Train them to ask better questions.

Alphabets Quarterly Revenue Exceeds Wall Street
News

Alphabet’s Quarterly Revenue Exceeds Wall Street Expectations

Alphabet’s Quarterly Revenue Exceeds Wall Street Expectations

Google Cloud just silenced the AI skeptics. With a massive revenue surge, the search giant is proving that AI has become a hot profit machine.

If you’re still waiting for the AI bubble to burst, Google Cloud just threw a bucket of cold water on that theory.

Alphabet’s latest earnings aren’t just a win; they’re a loud, expensive proof of concept. While critics spent the last year wondering when all those billions in GPU spending would actually turn into profit, Google just looked at the camera and said: Now.

Google Cloud’s revenue didn’t merely beat estimates. It surged nearly 30%. But the real story isn’t the number- it’s the velocity. We’re seeing a clear pull-through effect for the first time.

Companies are no longer experimenting with Vertex AI or Gemini in a sandbox. They are diving face-first into full-scale production. The cloud has officially transitioned from a storage locker to a high-octane AI engine room.

Here’s the nuance that the headline misses: this wasn’t just about selling more compute power. It’s about ecosystem gravity. Google is finally leveraging the fact that they own the entire stack- from their custom TPU chips to the Gemini models, all the way down to the Workspace apps people use every day.

By integrating AI so deeply into their existing infrastructure, they’ve made switching costs higher than ever. If your data is already in BigQuery, moving to another cloud for your AI needs now feels like trying to change your car’s engine while driving 80 mph.

But let’s observe the hidden cost of winning. Alphabet’s capital expenditure is still eye-watering. They are spending billions to build the cathedrals of the AI age, and while the revenue is finally showing up, the pressure to keep this growth vertical is immense.

It’s a high-stakes arms race where steady growth is no longer an option- you’re either accelerating, or you’re invisible.

The takeaway?

The skeptics who called AI a hype cycle are having a very bad week. Google Cloud has proven that enterprise AI is an accurate, revenue-generating machine, not just a series of fancy demos.

With this, we’re watching the incumbents fortify their kingdoms in real-time.

PayPal to Make Venmo a Separate Segment Within the Company
News

PayPal to Make Venmo a Separate Segment Within the Company

PayPal to Make Venmo a Separate Segment Within the Company

PayPal is finally letting Venmo move out. Is this a strategic masterstroke or a surrender? Here’s why the fintech divorce of the decade matters.

It’s official: PayPal is looking for a clean break.

After years of trying to force Venmo into the boring parent brand of traditional payment processes, the rumor mill (and balance sheets) assert a massive spin-off is finally on the table. It’s the corporate equivalent of a parent admitting their kid is way cooler than they are and finally letting them move out.

But here’s the thing: it’s a desperate attempt to fix two fundamentally different business identities that have been stifling each other for a decade.

PayPal is the dependable workhorse of the early internet. It’s the checkout button we trust because it feels safe, corporate, and a bit clinical.

Venmo, on the other hand, is a cultural verb. It’s how we split mimosas, pay the dog walker, and, weirdly enough, spy on our exes’ social feeds. By keeping them under one roof, PayPal has essentially been trying to run a high-security bank and a social network at the same time.

And the result? A bloated Super App vision that nobody actually asked for.

The real nuance here is the monetization trap.

PayPal makes its money from transaction fees; Venmo is a goldmine of user data and peer-to-peer volume, which has struggled to turn a profit. Investors are bored with PayPal’s slow growth, and they’re frustrated that Venmo’s massive cultural footprint hasn’t translated into significant dividends.

A spin-off allows Venmo to finally lean into crypto, social commerce, or even neo-banking without being dragged down by PayPal’s legacy compliance baggage.

Of course, there’s a catch.

Without PayPal’s massive treasury backing it up, Venmo has to grow up fast. It will be flying solo in a shark tank filled with Cash App, Zelle, and Apple Pay. Is Venmo a strong enough brand to survive without its parent’s deep pockets?

This restructuring sounds more like a confession. PayPal is admitting that the everything app dream is dead, and specialization is the only way to survive. The great divorce is coming- let’s see who gets to keep the users in the settlement.