Tag: revenue intelligence

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What a Low Win Rate Really Means About Your Sales Team

00Blog: The Science of SalesTags: , , , October, 19

Few sales leaders would say they’re satisfied with their win rate. In fact, according to research, the average win rate per sales rep is just 47%. The question is, is your low win rate due to poor lead and opportunity qualification, below par closing skills, or a lack of product knowledge?

Searching for the root cause of declining or stagnant win rates can be an incredibly frustrating process. Most sales leaders will refer to their KPI dashboard in trying to solve this problem. However, looking at your sales KPIs alone will only surface the symptoms, not the root cause. 

The secret to improving your win rates is to get to the root of the problem. This article helps sales leaders address the issue of declining or stagnant win rates. 

 

What is Win Rate and Why Does it Matter?

A win rate is a key performance indicator commonly used by B2B sales teams to measure how good their account executives are at closing sales opportunities. This is typically calculated by looking at a rep’s total closed won opportunities divided by the number of closed opportunities for a given time period. 

When a company seeks to identify the root causes of lost deals, an important factor to examine is the ratio of closed won to closed lost deals. From this ratio, you can find the percentage of opportunities that the company actually won.

A win rate is often used interchangeably with the term ‘close rate’, although there is a small difference. While the close rate looks at all opportunities created, the win rate only takes into account closed opportunities.

Focus on close rates if your sales team has clear and consistent opportunity-qualification criteria and your reps consistently apply these criteria when creating opportunities. However, you should be looking at win rates if, like most sales leaders, you have weekly pipeline reviews with your team to flush out old opportunities or opportunities that are significantly longer than your average sales cycle.

 

Who is REALLY Closing the Most Deals?

A sales leader at a SaaS company with 22 account executives and 8 inside sales reps was frustrated with her team’s win rates. They had been hovering at 23-25% for the last 24 months. Was it a lead source, territory, or behavioral issue? But the quality of her CRM data was even more frustrating, so she turned to Cien to help her understand the factors that were driving her team’s win rate. 

At the end of the quarter, her best sales rep, Andy, had closed six deals and her second best rep, Melissa, had closed four. Looking at her standard sales dashboards, it was clear that Andy had a higher win rate than Melissa, yet she felt that his work ethic, communication skills and product knowledge were significantly lower.

With Cien’s Hidden Revenue Assessment, this sales leader learned that when measuring her team’s individual win rates, it was important to take into account that Andy was given 12 ‘easy’ opportunities and Melissa had been given five ‘hard’ opportunities. Given that Melissa’s opportunities were more qualified than Andy’s, and taking into account how much additional effort, time, and skill was required to close her deals, Melissa actually had a greater closing ability than Andy. 

Ensuring that each rep develops sufficient closing ability is essential to achieve the highest possible win rate.

For this sales team, their low win rates indicated that the company was not qualifying their opportunities well enough, not engaging effectively with their prospects, and not prioritizing their coaching efforts on their reps’ greatest weaknesses.

Get to the root cause of low win rates

What this sales leader learned was that working on your team’s closing skills is a huge factor when it comes to improving a team’s win rates, and not all the reps in her team needed the same coaching and support. In fact, other members of her team grew their win rates by addressing other causes or focusing on other skills and attributes. These include:

  • Poor lead and opportunity qualification
  • Poor prospecting or opportunity qualification
  • Poor engagement and communication skills
  • Insufficient stakeholder mapping and single threaded deals
  • Insufficient product knowledge

All of these potential causes can be overwhelming when trying to decipher which is impacting your team’s win rate. 

Not sure where to begin? Start by requesting Cien’s Free Hidden Revenue Assessment which applies 100+ AI models to your CRM data to identify the root causes behind your company’s low win rates.

 


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The Anatomy of a High Performing Sales Operations Team

00Blog: The Science of SalesTags: , , , , October, 19

Do you have a sales operations team? Do you have a revenue operations team? It’s what all the fashionable sales teams are wearing this season.

Think of a high-performing operations team as the “sales enabler,” according to Matt Cameron, Managing Partner at Sales Ops Central, and right-hand-person to a Chief Sales Officer (CSO).

“If the CSO is weaving the blanket of revenue that keeps us all warm, then sales ops provides them with the pattern and tools to get it done.”

No single person could accomplish all the tasks of sales operations (unless your company is extremely small). These duties include:

  • Sales process development and improvement 
  • Reporting and analytics
  • The CRM database
  • Sales efficiency tools 
  • The widgets that draw marketing content into sales functions 
  • Sales training and certification
  • A framework for strategy and planning

No single set of best practices is going to fit every sales organization, but there are some things that high-performing operations teams have in common:

 

Keep Your CRM Data Clean

In quality management, there’s something called the “1-10-100 rule,” and it goes like this: it costs $1 to verify the accuracy of data while it’s being entered, $10 to correct erroneous data in batch form, and $100 per record if the goof remains uncorrected. The latter amount represents costs associated with low customer retention and process inefficiencies that dent performance.

So you have a big incentive to keep your CRM data clean.

But let’s face it: everyone’s CRM database has errors. Sales staff aren’t perfect, and they are often rushing data entry on their mobile device.

While careful data entry is still important, Artificial Intelligence (AI) can help clean your CRM data automatically. In addition, AI-powered sales apps can identify lagging entries and adjust weekly activity levels to accurately reflect the entire week’s efforts. It also can identify erroneous and inconsistent entries and flag entries that are missing key data. 

 

Find the Right Number of Tools

Most large CRM platforms today support a variety of tools, apps, add-ons, widgets and gimmicks that (ostensibly) allow operations to use their data better. Individual salespeople may have their own personal favorites, or they might even be using tools unsanctioned by the company. 

The truth is that too many tools can kill performance rather than improve it. An Accenture study by Jason Angelos found that 59 percent of sales reps reported they are required to use too many tools. Furthermore, half of respondents felt that using too many sales tools was an obstacle rather than a facilitator to sales performance.

 

Assess Team Mood

Since your sales representatives aren’t robots, intangible, qualitative factors like team mood will have a direct impact on sales.

While nobody expects the operations manager to quiz each team member about how they feel each morning, it’s a mistake to discount team mood from a sales process development.

By analyzing a variety of activities, AI engines can accurately determine the team mood so it can be used as a factor for decision-making. Sentiment detection can analyze sales staff’s communications to determine the state of mind of individual reps and the sales team as a whole.

 

Determine How Much Value Each Person Contributes

“But I can’t quantify all my resources,” is a cry heard in sales departments around the world. It should be a core sales ops duty to find a way to do so.

For example, if you’ve just closed a big sale that came from a hot lead sent over by marketing, the lion’s share of the value of that sale should be attributed to marketing. If the sale came from a cold lead a sales team member carefully researched and nurtured into a sale, the value of that sale belongs exclusively to sales. 

AI-powered apps help companies understand who is contributing what to revenue by measuring the value generated by every person whose activities touch the sales pipeline (directly or indirectly). This makes factors such as closing ability, product knowledge and work ethic visible in your CRM. This way, managers can analyze each team member’s skills and attributes and hold them accountable for what they really contribute. 

 

High-Quality Forecasting

Accurate forecasting is a goal that all sales organizations chase, but some sales organizations do a better job than others. When a sales organization understands the value of every element of the sales pipeline, forecasting becomes much easier. 

Too much time spent on quantitative factors can produce lopsided and even wildly inaccurate sales forecasts. It’s operations’ job to ensure that qualitative factors are being appropriately weighed in sales forecasts. 

 

Solve Sales Performance Traps

Performance traps arise when human and business resources are not properly aligned. AI helps sales organizations understand the incremental value created by all sales and marketing leads, whereas managers can ensure that team members are avoiding activities that lead to little or no sales revenue. 

It’s not enough to estimate the average value of leads. For real sales performance, you need a way to determine how good a fit the lead is to your product or service, what that lead’s current level of interest is, and the true potential of the lead. When you know these factors, you can assign the lead a more precise value, and then rank leads on the to-do list. You can even understand which rep has the best chance of converting those leads to sales. 

 

Deconstruct and Rebuild Sales Processes

Your sales operations manager needs to be prepared to take all the data you have and find a meaningful way to look at and use that data. A new breed of sales performance apps such as Cien can help sales operations do all the hard work of deconstructing existing sales processes and rebuilding them in a way that drives maximum revenue. 

Sales operations teams are both useful and needed–but only when managed right, and with the right metrics. 

Get your free Hidden Revenue Assessment here

 


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The Problem with Sales Performance Programs

11Blog: The Science of Sales, FeaturedTags: , , , , , , , September, 19

Whether you’re a numbers person or not, advances in technology and data management are continuously creating new opportunities for transforming and improving an organization’s sales effectiveness.

To understand how sales managers can improve their teams’ sales performance, we spoke with Mike Kunkle, founder of Transforming Sales Results, LLC and Vice President of Sales Enablement Services for the recently-rebranded SPARXiQ (formerly SPA and SPASIGMA). Mike is an internationally recognized sales force transformation expert with over 20 years experience designing sales learning systems and guiding companies through all aspects of sales transformation.

The Problem with Performance Management Programs

Mike points out that sales managers often lack an analytical approach to management which prevents them from seeing the forest for the trees. This should be of particular concern to sales leaders of fast growing companies as often the need for sales analytics increases with the size of the team. Research suggests that poor tracking of individual and team metrics is often the primary cause for low sales performance.

To achieve its full potential, a sales team needs to have reliable data available for analysis. While simple in theory, this is often trickier in practice, particularly for teams in which CRM adoption is relatively low.

For those teams, Mike recommends they make the move from CRM to DRM, from “Could Really Matter” to “Does Really Matter,” by ensuring that the CRM implementation, policies, and usage benefits the sales reps, as well as management.

In addition, most B2B sales organizations track team performance by focusing on low level activities such as the number of calls made or emails sent by sales person. And while the quality of these activities is often hard to measure, they can have a direct impact on how sales managers assess their team’s performance.

To address declining team performance many organizations turn to performance management programs. Dealing with performance improvement initiatives can often be challenging for certain sales organizations. When presented with more scientific approaches to sales management, sales leaders tend to stick to their tried and tested ways.

Spend More Time Coaching and Less Time Reporting

To help a sales team achieve its full potential, sales managers need to spend more time coaching and less time reporting. This can be complex at a collective level when a company has a culture that values “managing up” more than getting things done. This can also be challenging at the individual level because sales managers are typically gifted at selling, but not necessarily at managing and coaching.

Part of this, explains Mike, involves getting to know your team members better by measuring not their results vs. objectives, their activities (what and how much), and their sales methodology (how they do the activities – or the quality of the activities). Mike calls this the ROAM method (Results, Objectives, Activities and Methodology), which is a key part of his sales coaching system.

To improve team performance, you need to identify who needs coaching and what type of coaching is needed for each person, to close key performance gaps.

A simple way to do this is to measure conversion rate across the different pipeline stages. Create a dashboard of the recent historical averages of top, middle, and bottom producers and compare the conversion ratios to a specific sales team or individual. This will allow you to see how each rep on a given team is doing at each stage of the buying process and identify the biggest opportunities for improvement, whether it’s help with generating leads, moving deals from stage 2 to 3, or closing.

Management Needs Coaching Too

Sales managers often get caught up focusing on ‘making the numbers’ when sometimes it’s their very own skills that need improvement. Management coaching often gets neglected because senior leaders tend to operate with a specific playbook in mind that was developed in the past. But the right thing for a sales team to do yesterday may not be the best way forward today.

The biggest risk in sales management is failing to recognize that contexts, people and businesses change.

Use AI to Identify What Works

Sales leaders need to rethink how they support and enable their teams. Supporting an effective learning system is one way sales leaders can improve their team’s productivity.  Another way is to implement AI and machine learning to help isolate each factor that influences the sales process.

While there are a lot of tools to help sales reps be more productive, few are geared at helping sales teams as a whole. Implementing new sales tools and training salespeople to use them is far from trivial, emphasizes Mike. The key is to incorporate the tools and training into a coherent and usable process that helps sales professionals rather than drags them down. If they can achieve this, sales managers can make better business decisions and their teams can enjoy greater levels of effectiveness and success.

About Mike Kunkle

Mike Kunkle is a respected sales transformation architect and internationally-recognized sales training and sales enablement expert.

Mike has spent 35 years in the sales profession and 25 years as a corporate leader or consultant, helping companies drive dramatic revenue growth through best-in-class training strategies and his proven-effective sales transformation methodologies. At one company, as a result of six projects, he and his team were credited with enabling an accretive $398MM in revenue, year-over-year. At another, within 9 months, newly-hired sales reps with 120 days on the job were outperforming incumbent reps with 5 years with the company. Mike is the founder of Transforming Sales Results, LLC, and today, works as the Vice President of Sales Enablement Services for SPARXiQ (formerly SPA & SPASIGMA), where he advises clients, writes, speaks at conferences, develops and leads webinars, designs sales training courses, delivers workshops, and designs sales enablement systems that get results.

You can connect with Mike on Linkedin and follow him on Twitter.

 


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Dealing With Imperfect CRM Data

02Blog: The Science of Sales, FeaturedTags: , , , , , , August, 18

Everyone has issues with their CRM data, but not everyone has to suffer from it. Instead of treating poor data hygiene as a fatality, forward-looking sales organizations can make use of artificial intelligence (AI) and natural language processing to make sense out of their data in ways that were previously impossible. Here are four of the most common issues that come up when dealing with imperfect data:

#1: Missing Updates

Sales people typically miss entering in activities and stage changes that would have reflected the true state of a specific deal or lead. When implementing sales productivity apps such as Cien into your sales organization, it can detect the reps propensity to do this and scores them so you can easily find those reps and coach to make better selling decisions.

#2: Incomplete and Untimely Updates

Sometimes the entry screens in your CRM are not conducive for complete entries of data records.  Some reps understand the importance of proper record keeping, but are too busy to update during the day. Instead they keep loose notes and enter it in later, usually on Friday afternoons. AI can fill in data to complete the record as well as detect these lagging entries and adjusts weekly activity levels to accurately reflect the entire week’s efforts.

#3: Inconsistent Entries

Many free form entry fields, like lead source, will quickly have hundreds of unique but very similar entries. This can make meaningful analysis difficult. By using Natural Language Processing (NLP), organizing data and making it consistent becomes a lot easier, so you still understand the true value of different leads.

#4: Changes in Data Methodology

Changes in data definitions are very common in organizations as they mature. That makes time based comparisons difficult to perform, and causes confusion in reporting. Data driven apps like Cien, automatically adjust and understand changes in your data methodology. So if you used your CRM’s lead module in 2017 and the account module in 2018 to prospect, Cien will detect and account for that. You can then have a consistent prospecting report for both periods.

For more information on dealing with imperfect data, download Cien’s Guide to Solving Performance Traps.

 


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