Not only do we typically work with very large data sets, but those data sets are extremely rich. That is, each row of data typically has many, many attributes. When you combine this with the temporal sequences of events for a given user, there are an enormous number of ways of looking at the data. 
Contrast this with a typical academic psychology experiment where it's trivial for the researcher to look at every single data point. The problems posed by our large, high-dimensional data sets are very different from those encountered throughout most of the history of scientific work.

New Frontiers In Data Analysis

Most practitioners use summary metrics (for example, mean, median, standard deviation, and so on) to communicate about distributions. 
However, you should usually examine much richer distribution representations by generating histograms, cumulative distribution functions (CDFs), Quantile-Quantile (Q-Q) plots, and so on. These richer representations allow you to detect important features of the data, such as multimodal behavior or a significant class of outliers.

Data Distributions

Examine outliers carefully because they can be canaries in the coal mine that indicate more fundamental problems with your analysis. 
It's fine to exclude outliers from your data or to lump them together into an "unusual" category, but you should make sure that you know why data ended up in that category.

Consider The Outliers

Randomness exists and will fool us. Some people think, “Google has so much data; the noise goes away.” This simply isn’t true. Every number or summary of data that you produce should have an accompanying notion of your confidence in this estimate (through measures such as confidence intervals and p-values).

Look At The Noise

Anytime you are producing new analysis code, you need to look at examples from the underlying data and how your code is interpreting those examples. Your analysis is abstracting away many details from the underlying data to produce useful summaries.
How you sample these examples is important:
<ul>
<li>If you are classifying the underlying data, look at examples belonging to each class.</li>
<li>If it's a bigger class, look at more samples.</li>
<li>If you are computing a number (for example, page load time), make sure that you look at extreme examples</li>
</ul>

Look At Examples

Slicing means separating your data into subgroups and looking at metric values for each subgroup separately. We commonly slice along dimensions like browser, locale, domain, device type, and so on. If the underlying phenomenon is likely to work differently across subgroups, you must slice the data to confirm whether that is indeed the case. 
Even if you do not expect slicing to produce different results, looking at a few slices for internal consistency gives you greater confidence that you are measuring the right thing.

Slice Your Data

With a large volume of data, it can be tempting to focus solely on statistical significance or to home in on the details of every bit of data. But you need to ask yourself, "Even if it is true that value X is 0.1% more than value Y, does it matter?" 
This can be especially important if you are unable to understand/categorize part of your data. If you are unable to make sense of some user-agent strings in your logs, whether it represents 0.1% or 10% of the data makes a big difference in how much you should investigate those cases.

Consider Practical Significance

You should almost always try slicing data by units of time because many disturbances to underlying data happen as our systems evolve over time. (We often use days, but other units of time may also be useful.)
During the initial launch of a feature or new data collection, practitioners often carefully check that everything is working as expected. However, many breakages or unexpected behavior can arise over time.

Check For Consistency Over Time

Looking at day-over-day data also gives you a sense of the variation in the data that would eventually lead to confidence intervals or claims of statistical significance. This should not generally replace rigorous confidence-interval calculation, but often with large changes you can see they will be statistically significant just from the day-over-day graphs.

Check For Variation

Almost every large data analysis starts by filtering data in various stages. Maybe you want to consider only US users, or web searches, or searches with ads. Whatever the case, you must:
<ul>
<li>Acknowledge and clearly specify what filtering you are doing.</li>
<li>Count the amount of data being filtered at each step.</li>
<li>Often the best way to do the latter is to compute all your metrics, even for the population you are excluding. You can then look at that data to answer questions like, "What fraction of queries did spam filtering remove?"</li>
</ul>

Acknowledge And Count Your Filtering

The most interesting metrics are ratios of underlying measures. Oftentimes, interesting filtering or other data choices are hidden in the precise definitions of the numerator and denominator. For example, which of the following does “Queries / User” actually mean?
<ul>
<li>Queries / Users with a Query</li>
<li>Queries / Users who visited Google today</li>
<li>Queries / Users with an active account (yes, I would have to define active)</li>
<li>Being really clear here can avoid confusion for yourself and others.</li>
</ul>

Ratios Should Have Clear Numerators And Denominators

Validation: Do I believe the data is self-consistent, that it was collected correctly, and that it represents what I think it does?Description: What's the objective interpretation of this data? For example, "Users make fewer queries classified as X," "In the experiment group, the time between X and Y is 1% larger," and "Fewer users go to the next page of results."Evaluation: Given the description, does the data tell us that something good is happening for the user, for Google, or for the world?

Three Stages Of Data Analysis: Validation, Description, and Evaluation

Before looking at any data, make sure you understand the context in which the data was collected. If the data comes from an experiment, look at the configuration of the experiment. If it's from new client instrumentation, make sure you have at least a rough understanding of how the data is collected. 
You may spot unusual/bad configurations or population restrictions (such as valid data only for Chrome). Anything notable here may help you build and verify theories later.

Confirm Experiment And Data Collection Setup

As part of the "Validation" stage, before actually answering the question you are interested in (for example, "Did adding a picture of a face increase or decrease clicks?"), rule out any other variability in the data that might affect the experiment. For example:
<ul>
<li>Did the number of users change?</li>
<li>Did the right number of affected queries show up in all my subgroups?</li>
<li>Did error rates change?</li>
</ul>
These questions are sensible both for experiment/control comparisons and when examining trends over time.

Check For What Shouldn't Change

When looking at new features and new data, it's particularly tempting to jump right into the metrics that are new or special for this new feature. However, you should always look at standard metrics first, even if you expect them to change. 
For example, when adding a new universal block to the page, make sure you understand the impact on standard metrics like “clicks on web results” before diving into the custom metrics about this new result.

Standard First, Custom Second

Especially if you are trying to capture a new phenomenon, try to measure the same underlying thing in multiple ways. Then, determine whether these multiple measurements are consistent. 
By using multiple measurements, you can identify bugs in measurement or logging code, unexpected features of the underlying data, or filtering steps that are important. It’s even better if you can use different data sources for the measurements.

Measure Twice, Or More

Both slicing and consistency over time are particular examples of checking for reproducibility. If a phenomenon is important and meaningful, you should see it across different user populations and time. But verifying reproducibility means more than performing these two checks. If you are building models of the data, you want those models to be stable across small perturbations in the underlying data.
If a model is not reproducible, you are probably not capturing something fundamental about the underlying process that produced the data.

Check For Reproducibility

Often you will be calculating a metric that is similar to things that have been counted in the past. You should compare your metrics to metrics reported in the past, even if these measurements are on different user populations.
You do not need to get an exact agreement, but you should be in the same ballpark. If you are not, assume that you are wrong until you can fully convince yourself. Most surprising data will turn out to be an error, not a fabulous new insight.

Check For Consistency With Past Measurements

If you create new metrics (possibly by gathering a novel data source) and try to learn something new, you won’t know if your new metric is right. With new metrics, you should first apply them to a known feature or data.
If you have a new metric for where users are directing their attention to the page, make sure it matches what we know from looking at eye-tracking or rater studies about how images affect page attention. Doing this provides validation when you then go to learn something new.

New Metrics Should Be Applied To Old Data/Features First

Typically, data analysis for a complex problem is iterative. You will discover anomalies, trends, or other features of the data. Naturally, you will develop theories to explain this data. Don’t just develop a theory and proclaim it to be true. Look for evidence (inside or outside the data) to confirm/deny this theory.

Looking For Evidence

Good data analysis will have a story to tell. To make sure it’s the right story, you need to tell the story to yourself, then look for evidence that it’s wrong. One way of doing this is to ask yourself, “What experiments would I run that would validate/invalidate the story I am telling?” Even if you don’t/can’t do these experiments, it may give you ideas on how to validate with the data that you do have.

A Story To Tell

When doing exploratory analysis, perform as many iterations of the whole analysis as possible. Typically you will have multiple steps of signal gathering, processing, modeling, etc. If you spend too long getting the very first stage of your initial signals perfect, you are missing out on opportunities to do more iterations in the same amount of time. 
Further, when you finally look at your data at the end, you may make discoveries that change your direction. Therefore, your initial focus should not be on perfection but on getting something reasonable all the way through.

Exploratory Analysis Benefits From End-To-End Iteration

We typically define various metrics around user success.
You can not use the metric that is fed back to your system as a basis for evaluating your change. If you show more ads that get more clicks, you can not use “more clicks” as a basis for deciding that users are happier, even though “more clicks” often means “happier.” Further, you should not even do slicing on the variables that you fed back and manipulated, as that will result in mixed shifts that will be difficult or impossible to understand.

Watch Out For Feedback

<ul><li>As you work with data, you must become both the champion of the insights you are gaining and a skeptic of them. You will hopefully find some interesting phenomena in the data you look at. When you detect an interesting phenomenon, ask yourself the following questions:</li><li>What other data could I gather to show how awesome this is?</li><li>What could I find that would invalidate this?</li></ul>

Skeptic And Champion At One Go

There’s always a motivation to analyze data. Formulating your needs as questions or hypotheses helps ensure that you are gathering the data you should be gathering and that you are thinking about the possible gaps in the data. Of course, the questions you ask should evolve as you look at the data. However, analysis without a question will end up aimless.
Avoid the trap of finding some favourite technique and then only finding the parts of problems that this technique works on. Again, creating clear questions will help you avoid this trap.

Data Analysis Starts With Questions, Not Data Or A Technique

When making theories about data, we often want to assert that "X causes Y"—for example, "the page getting slower caused users to click less." You can not simply establish causation because of correlation. By considering how you would validate a theory of causation, you can usually develop a good sense of how credible a causal theory is.Sometimes, people try to hold on to a correlation as meaningful by asserting that even if there is no causal relationship between A and B, there must be something underlying the coincidence so that one signal can be a good indicator or proxy for the other

Correlation and Causation

The previous points suggested some ways to get yourself to do the right kinds of soundness checking and validation. But sharing with a peer is one of the best ways to force yourself to do all these things. A skilled peer can provide qualitatively different feedback than the consumers of your data can. Peers are useful at multiple points through the analysis. 
Early on you can find out about gotchas your peer knows about, suggestions for things to measure, and past research in this area. Near the end, peers are very good at pointing out oddities, inconsistencies, or other confusions.

Share With Peers First, External Consumers Second

Deep-dive into google's machine learning models and how it relies more on logic than data

computerscience

Computer Science

artificialintelligence

Artificial Intelligence

softwareengineering

Software Engineering

technologyandthefuture

Technology & The Future

Machine Learning With Google

business

Business

Good Data Analysis  |  ML Universal Guides  |  Google Developers

A sales objection is an explicit expression by a buyer that a barrier exists between the current situation and what needs to be satisfied before buying from you. 
In other words, it's a clear signal that you have more work to do in the selling process.

Defining a Sales Objection

Sales objections often signal that you haven’t made the value proposition case to the buyer as powerfully as possible.
<ul>
<li>Need: Buyer doesn't yet perceive, or doesn't yet admit, the need to solve a problem.</li>
<li>Urgency: The buyer doesn't yet see why the issue is pressing.</li>
<li>Trust: Buyer feels uncertainty about you, your solution, your company, or your outcomes.</li>
<li>Money: Buyer communicates that money is going to be an issue.</li>
</ul>
While these common sales objections might seem like steep hills to overcome, don’t despair: an objection indicates that the buyer is engaged, which sure beats apathy.

The Most Common Sales Objections

Your first reaction when you hear an objection may be to jump right in and respond immediately. Resist this temptation.
 Instead:
<ul>
<li>Take the time to listen to the objection fully</li>
<li>Don't react defensively</li>
<li>Train yourself to ignore any negative emotions you may be feeling</li>
<li>Stay focused on what the buyer is saying and the business problem you’re helping to solve</li>
<li>Listen with the intent of fully understanding the buyer's concerns without bias or anticipation</li>
<li>Allow your body language and verbal confirmations to communicate to the buyer that you're listening intently.</li>
</ul>

Overcoming Sales Rejection: Listen To the Customer

Many objections hide underlying issues that the buyer can't or isn't ready to articulate. Often the true issue isn't what the buyer first tells you. It's your job to get to the heart of the objection—to fully understand it and its true source.To do this, ask permission from the buyer to understand and explore the issue. From there, restate the concern as you understand it. Sometimes when you restate the objection, the buyer sees the issue more fully, and you get closer to the true source of the objection as a result.

Understand the Objection Completely

After you're confident you've uncovered all objections, address the most important objection first. Once you work through the greatest barrier to moving forward, other concerns may no longer matter as much to the buyer.
You should do your best to resolve their issue right away if possible. The more effectively you can resolve issues in real time, the greater chance you have of moving the sale forward. If you need more information to resolve a specific concern, you may have to look something up or investigate further.

Respond Properly

Once you've responded to the buyer's objections, check if you've satisfied all of their concerns. Just because they nodded during your response doesn't mean they agreed with everything you said. Ask if the buyer is happy with your solution and explain your solution further if necessary. Sometimes you need to go through a process to overcome sales objections, rather than a quick answer or a-ha moment.If the buyer isn't ready, don't try to force a commitment. Be sure not to accept a lukewarm "yes" for an answer though, either.

Confirm You've Satisfied the Objection

As much as you might like to respond, “You get what you pay for,” or, “Those are our fees and we’re worth every penny,” don’t. There's no glib, pat answer to money objections.

Money Objections in Sales: Choose your words wisely

Price is often a “red herring” objection. Work to uncover the real objection. (Refer to the 6 types of money objections below.) Ask questions. 
Find out if money is really the issue with one simple question: “If money wasn’t an object, what then?” This will usually lead you to the root objection to the sale.

It’s Not All About The Money

Communicate a clear picture of the value of the solution you established in the selling process. The right buyer can usually “find” the money if the value is too strong to pass up—if the solution you’ve proposed answers their needs especially well.
Most times when buyers say, “Your price is too high,” what they’re really saying is, “I don't see the value of your solution.”

Get Back To Value

Review the component parts of the solution. This may lead to either a reduction in scope or having the buyer realize the whole package is the best solution.
You’ll end up going down a slippery slope if you start justifying your price by what your costs are.

Ask, “Which Part Don’t You Want?”

If you’re willing to simply drop your price, you’re telling buyers this is the way you operate. Instead, explore new possibilities, change scope, or make a trade that could change the price. 
An arbitrary price reduction can sow mistrust and set the precedent for lower prices for as long as you work with that buyer. Always trade for value.

Don’t Drop Your Price

Don't appear to look upward for guidance at your organization, even if you might have to.
You may be dealing with the wrong buyer who isn't high enough in the organization or isn't the economic decision-maker. Money is certainly an objection for them because they can't pull the trigger even if they wanted to.

Maintain A Peer Relationship With The Decision Maker

What the buyer says: “Wow, that’s a lot. Can we do it for less?”
What it means: This comes from the buyer who always asks for a price reduction because it’s worked in the past. Their philosophy is that it can’t hurt to ask.

Types of Money Objections: Whack Back

What the buyer says: “Oh, I don’t have the money. We’ll need to do it for less.”What it means: The buyer has the money, but doesn’t want you to know that. It’s a ploy to see how low you’ll go.

Types of Money Objections: Bluffing

What the buyer says: “It costs too much. Money is going to be a problem.”What it means: The buyer doesn’t see the value your solution delivers. Perhaps they can’t justify spending more than they did previously.

Types of Money Objections: Value Challenge

What the buyer says: “It’s not in the budget.”
What it means:
(a) It’s true, and they’d like to see what can be worked out.
(b) It’s true, and they’re using that as a bargaining chip.
(c) It’s not true, and they’re just saying it.

Types of Money Objections: Budget Pushback

What the buyer says: “We received other proposals and your price is the highest.”What it means:(a) It’s true, and they’re using that as a bargaining chip.(b) It’s not true, or at least not the whole truth.

Types of Money Objections: Competitor Pressure

What the buyers says: “Too much money. Call me back if you can go lower.”What it means:(a) It’s a bluff, they assume you’ll come back with a lower price.(b) It’s not a bluff, they hope you’ll lower price to do business.

Types of Money Objections: We’re Done

When faced with sales objections, don't lose sight of your end goal: overcome the sales objection and make advances towards gaining commitment from the buyer.In a transactional sale, sellers are taught to overcome objections at all costs. This doesn't work for more complex sales. If you just plough through the objection without addressing it fully, the underlying reason for the objection will usually come back to haunt you.Remember, you have to work with these people once you're done selling to them!

Keeping Your Eyes on the Prize

Objections have merit: they're often a sign that something else is going on. Your purpose is to understand the objection fully, isolate it, and respond to it appropriately—not necessarily rebut, counter, and argue. You may need to build a case for overcoming an objection instead of answering quickly on the fly.

Objections Are Valuable

Expert tips on how to tap into the human psyche and turn potential customers into actual ones

books

Books

leadershipandmanagement

Leadership & Management

personaldevelopment

Personal Development

Understand yourself and those around you.

psychology

Psychology

How to Sell Anything

Your prospect is not your adversary. Their objections are valid! Here are four proven steps to overcoming sales objections, no matter how complex.

Discover the world of business and economics.

economics

Economics

marketingandsales

Marketing & Sales

4 Steps to Overcoming Sales Objections

Partnerships (at least the successful ones) encourage a series of characteristics—trust, teamwork, a regard for someone else, and continuing checks and balances.You can’t do everything on your own.  Even Einstein wouldn’t have been successful if there weren’t other people he didn’t talk to all the time. Total isolation does not work. You need interaction, putting your own thoughts into expression; you learn things just from doing it.

Two Is Better Than One

The best combination comes from partnership, when two people balance each other, constantly reminding the other of the need to keep the conscious and unconscious in harmony, to make each other smarter, make each other better.

The Best Partnerships

They have complete trust, complete faith, and complete belief in each other. And that reverberates through every phone call they have, every deal they discuss, and every decision they make.You cannot keep score. It just doesn’t work with the best of human relationships. It shouldn’t be even suppressed—it should be something that doesn’t even exist.

Not Keeping Score

Does the partnership cause the success, or does the success cause the partnership?

It’s human nature to seek the quick and easy path to success. History is full of stories about people’s quest for the Holy Grail, the search for the fountain of youth, the philosopher’s stone, the proverbial ‘silver bullet’. And while there are common elements that can guide us more surely along the path to success, the reality is we must alter the recipe to suit our own unique strengths, limitations, desires and situations. The same is true for creating winning partnerships.

The Holy Grail

Partnerships all made these people happy, and happier than they would have been had they worked for their success alone. They had someone else with whom to experience the challenging lows and the ecstatic highs; another person in the trenches, another person to pop the champagne

Splitting The Challenge

Who here doesn’t want to be happier in their work and personal lives? Who would prefer to organize a party for one (be it of a celebratory nature or a pity party)? Who wants to work harder alone rather than alongside others who share your dreams and aspirations? Finding the right partner for our various dreams and aspirations is itself hard work. Through trial and error, I did find my life partner. I have not yet experienced the same success in a business context.

Finding The Right Person

In Working Together, a fascinating and invaluable look at why great partnerships succeed, former Disney CEO Michael Eisner discusses how professional partnerships have contributed to his success. In addition, Eisner tells the stories of nine other highly successful business collaborations, including Warren Buffett and Charlie Munger, Valentino and Giancarlo Giammetti, Bill and Melinda Gates, Joe Torre and Don Zimmer, and Brian Grazer and Ron Howard.

Michael D. Eisner

Aaron R. Cohen

Working Together

Hiring is difficult and costly. It’s important to find the right person for a job, but sometimes people are not willing to work somewhere else without certain conditions or benefits.
In those cases, it’s important to compromise so that you don’t lose out on top talent. You should also be sure to do everything possible in order to keep that candidate from leaving your company for another one.

The Importance Of Right Hiring

Studies have found that hiring the wrong person can cost a company 15 times their salary. This is because some of those mistakes are made by the employee, and other mistakes are made when they’re fired and replaced with someone new.
If you hire a manager who makes $100,000 per month and your decision turns out to be a bad one, it could cost the company up to $1.5 million. Hiring the wrong person is a huge mistake, but it happens all the time. Most managers make at least half their mistakes during hiring decisions.

Hiring The Wrong Person For a Job Can Be Costly

The hiring process can be compared to that of an amateur art critic. They are not familiar with the language used by real art critics and their knowledge is limited to whether they like or dislike a piece of art. 
This means that talented artists can easily trick them into thinking they’re good when they aren’t. Similarly, people who hire based on instinct may be fooled by charismatic candidates who have little true ability.

The Art Critic

In some cases, managers act like prosecutors who try to trick candidates into saying something wrong. 
This approach only makes the candidate feel defensive and prevents you from finding out whether he has the right skills for the job.

The Procecutor

Many leaders focus on the “Whats” in their organizations, e.g. the strategies, products and services, systems and processes.
However, without the right people in the right place, whatever problems you fix will only resurface again. When you address the “Whos”, the Whats” will naturally fall in place.

Focus on Who, not What

Most traditional recruitment and interview techniques are ineffective because they don’t test if someone can really do a job well. After interviewing many CEOs, leaders and management experts, and then analysing the data in detail,  the author found 4 key mistakes that lead to hiring failures at all levels:• Lack of clarity on what the job requires• Lack of good candidates• Inability to choose the right candidate• Losing the selected candidates

The Four Key Hiring Mistakes

The A Method for Hiring is a simple, proven 4-step process to identify and hire A players at all levels of the organization.The most common approaches (or “voodoo hiring methods”) lead to hiring failures.

The A Method

Would you hire someone to work on your house without a license? Probably not, since it’s hard to know whether they are qualified and have the skills necessary for the job. The same goes for hiring new employees. You need to define what qualifications they should have before you make them an offer.
Recruiters often don’t have a clear idea of what they’re looking for. This can cause problems in the hiring process.

Know What You Want

Scorecards describe the mission for the position, outcomes that must be accomplished, and competencies that fit with both the culture of the company and the role.The scorecard is composed of three parts: the job’s mission, outcomes, and competencies. Together, these three pieces describe A performance in the role—what a person must accomplish, and how. They provide a clear linkage between the people you hire and your strategy.

Scorecards

The mission is an executive summary of the job’s core purpose. It boils the job down to its essence so everybody understands why you need to hire someone into the slot.

Scorecards: Mission

Outcomes, the second part of a scorecard, describe what a person needs to accomplish in a role. Most of the jobs for which we hire have three to eight outcomes, ranked by order of importance… While typical job descriptions break down because they focus on activities, or a list of things a person will be doing (calling on customers, selling), scorecards succeed because they focus on outcomes, or what a person must get done (grow revenue from $25 million to $50 million by the end of year three).

Scorecards: Outcomes

Competencies define how you expect a new hire to operate in the fulfilment of the job and the achievement of the outcomes.” Critical ones are:<ul><li>Efficiency</li><li>Integrity</li><li>Organization</li><li>Aggressiveness</li><li>Follow through</li><li>Intelligence</li><li>Analytical skills</li><li>Attention to detail</li><li>Persistence</li><li>Proactivity</li></ul>

Scorecards: Competencies

You can only build a house with a blueprint. Likewise, you can only hire the right person with a scorecard. The scorecard is a job blueprint—it describes exactly what success in a role looks like, including the 1. mission (why the role exists), 2. outcomes (what the person must deliver)3. competencies (how the job should be done given the job requirements and company culture).

Scorecard: Developing Your Success Blueprint

Most managers don’t plan in advance. When a position opens up, they pull out an old job description and rush to fill the vacancy. 
HR shortlists several candidates with the limited time and info, and the hire one of them using voodoo methods. Sourcing is about systematically looking for and identifying potential talents before you actually need the people.

Building A Pipeline Of Players

Traditional interview techniques don’t predict job performance because they focus on how someone behaves during the interview rather than on their track record. You need a structured approach to gather info and evaluate candidates against the Scorecard. Specifically, you’re looking for A Players who have a history of delivering the desired outcomes, have competencies that fit the role and culture, and are passionate about the role.

Identify The A Players

Sift out the B and C Players quickly using a short phone-based interview. 
You can potentially weed out 80-90% of the candidates at this phase, thus saving yourselves lots of unnecessary time and energy later on.

The Screening Interview

This interview covers the candidate’s career in a chronological order, using 5 questions to draw out specific facts and stories about each career phase. By the end of 3-5 hours, you’ll have a much clearer assessment of the candidate.

The Topgrading Interview

This interview takes about 45-60 minutes, and the goal is to dig deeper for info on specific outcomes or competencies in the scorecard.

The Focused Interview

You’re now ready to test what you learned about the candidate through reference checks. Never skip this step.
Only move someone to the next phase of the interview if you’re truly excited about them and convinced that they could be the ideal candidate. After the 4 interviews, complete the scorecards and give each candidate an overall grade of A, B, or C. Only hire A-graders. If you have no A-graders, keep sourcing; if you have multiple A-graders then hire the best.

The Reference Interview

Your job is not done until the candidate is delivering results as your employee. To get them through the door, you must sell to them by sincerely appealing to the 5Fs that people care about: Fit, Family, Freedom, Fortune, and Fun. You’ll have to sell throughout the recruitment process, not just at the end. Specifically, you must sell across 5 phases of the hiring process: during sourcing, during interviews, while waiting for the candidate to accept your offer, after the offer has been accepted, and during the new hire’s first 100 days on the job.

Securing The Candidate

Hiring A Players takes deliberate effort, hard work and persistence. However, the results are worth it, since the right talents will influence/shape your company culture/direction and define your long-term success. Eventually, your goal should be to build an A-team, i.e. a team of A Players, each with their own strengths suited for a specific role.You don’t have to be a CEO to use the A method of hiring—it can be applied at any level in an organization, be it a department, business unit or a function.

Implementing The A Method

Some of the best advice to make building a remote team easier.

teamwork

Teamwork

remotework

Remote Work

Hiring Without an Office

A manager's guide to hiring the right employees introduces the practical and effective A Method for Hiring, which draws on the expertise of hundreds of high-level executives to present a simple, easy-to-follow program to guarantee hiring success. 50,000 first printing.

career

Career

humanresources

Human Resources

In this instant New York Times Bestseller, Geoff Smart and Randy Street provide a simple, practical, and effective solution to what The Economist calls “the single biggest problem in business today”: unsuccessful hiring. The average hiring mistake costs a company $1.5 million or more a year and countless wasted hours. This statistic becomes even more startling when you consider that the typical hiring success rate of managers is only 50 percent. The silver lining is that “who” problems are easily preventable. Based on more than 1,300 hours of interviews with more than 20 billionaires and 300 CEOs, Who presents Smart and Street’s A Method for Hiring. Refined through the largest research study of its kind ever undertaken, the A Method stresses fundamental elements that anyone can implement–and it has a 90 percent success rate. Whether you’re a member of a board of directors looking for a new CEO, the owner of a small business searching for the right people to make your company grow, or a parent in need of a new babysitter, it’s all about Who. Inside you’ll learn how to • avoid common “voodoo hiring” methods • define the outcomes you seek • generate a flow of A Players to your team–by implementing the #1 tactic used by successful businesspeople • ask the right interview questions to dramatically improve your ability to quickly distinguish an A Player from a B or C candidate • attract the person you want to hire, by emphasizing the points the candidate cares about most In business, you are who you hire. In Who, Geoff Smart and Randy Street offer simple, easy-to-follow steps that will put the right people in place for optimal success.

Dr. Geoff Smart is Chairman & Founder of ghSMART, a leadership consulting firm that serves Fortune 500 CEOs and boards, billionaire entrepreneurs, and heads of state. He is the New York Times bestselling author of Who, Leadocracy, and Power Score: Your Formula for Leadership Success.

Geoff Smart

Randy Street is an accomplished writer whose work has resonated with readers around the world. Randy Street has published several books that explore a range of topics, from fiction to nonfiction, and has received critical acclaim for the ability to weave compelling narratives. Randy Street's writing style is known for its depth and authenticity, and has earned quite a loyal following of readers.

Randy Street

Who: The A Method for Hiring

Find 100+ Business Facts, Ideas & Insights - Curated from inspiring sources like Books, Podcasts & Articles - Genuine Insights from like-minded Users like You!

Good Data Analysis | ML Universal Guides | Google Developers

4 Steps to Overcoming Sales Objections

Working Together

Who: The A Method for Hiring

5 Signs You Were Destined to Become a Billionaire (2022)

Ultimate Guide to Email Marketing Frequency - SharpSpring

Don’t tell me you miss me: 3 better approaches to user retention

How to Maintain Profitability in a Changing Market

The Simple Rupeee

Exactly What to Say

It's time to
Read like a Pro.

Good Data Analysis | ML Universal Guides | Google Developers

4 Steps to Overcoming Sales Objections

Working Together

Who: The A Method for Hiring

5 Signs You Were Destined to Become a Billionaire (2022)

Ultimate Guide to Email Marketing Frequency - SharpSpring

Don’t tell me you miss me: 3 better approaches to user retention

How to Maintain Profitability in a Changing Market

The Simple Rupeee

Exactly What to Say

It's time to Read like a Pro.

It's time to
Read like a Pro.