Data-Driven Sales Coaching: How to Use Data to Drive Coaching Conversations

As a revenue leader, you’re likely well aware of the potential impact of great sales coaching on team performance. In fact, the power of sales coaching is proven. A recent analysis found that top sellers receive significantly more sales coaching than average.

But generic, one-size-fits-all sales coaching doesn’t work. Furthermore, while deal coaching may improve the outcome of a single deal, it won’t have much of an impact on a rep’s long-term behaviors and sales performance.

Instead, sales managers must deliver tailored skill coaching that addresses the strengths and weaknesses of each of their sales reps. But first, they must have the right data to understand what those strengths and weaknesses are.

In this post, we’ll explore why your current approach to sales coaching isn’t working – and why data-driven sales coaching is the answer.

Why coaching conversations must change

Having conversations about performance can be hard, especially if a rep isn’t doing well. It can be especially difficult to call out areas for improvement when you don’t have the specifics to back it up. Relying on data makes these conversations easier.

For sales managers, being able to track and analyze seller performance all in one place using revenue enablement software provides concrete insight into every time you say “you’re doing a great job” or “there’s room for improvement.” Your feedback is rooted in actual performance metrics, rather than running the risk of being considered your opinion rather than fact.

Data also helps us to identify the exact ways each seller can improve. Perhaps one rep is weaker in their competitor knowledge while another may need more support when it comes to operating sales technology. By digging into the data, an effective sales coach can identify opportunities for improvement and, in turn, maximize each rep’s performance.

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What a data-driven approach looks like

In order to adopt a data-driven coaching approach, you should have a clear understanding of the key metrics you should be looking at. These will help you gauge the overall success of your sales force, but really allow you to dig into individual performance to fuel more impactful coaching conversations.

Below we’ve highlighted some key criteria you’ll want to evaluate—and how you can gather the data.

Product knowledge: Assess and certify sellers on their product knowledge with quizzes using different formats. You can create a proficiency threshold score for reps to show they’re knowledgeable experts on your product, which you can test through multiple-choice quizzes, checklists, and written tests.

Reps can also be assigned role-plays to see if they can demo the product, describe what it does and accurately express the value of the product. If you’re using sales readiness technology, all of this can be easily reviewed and scored through artificial intelligence (AI).

Selling behavior: Evaluate how articulate and enthusiastic a rep is on a call, voicemail or presentation as well as the tone. You can also keep track of how many filler words are used. This will gauge their overall confidence in selling your solution.

Selling skills: By tracking sellers’ progress in real-time, sales coaches can get data insights on each rep’s ability to demo, use a sales methodology (e.g. MEDDPIC) on a call, challenge competition, handle objection questions, and evaluate whether or not the correct terms are used to describe the product.

Message consistency: AI-powered keyword analysis is a great way for sales coaches to get better insight into individual competencies and needs based on live interactions during the selling process. When you analyze sales calls (and leave the hard work to AI), you can uncover key problems in deals that didn’t close and start training on common questions you’re finding reps are being asked in the field.

Technology skills: How well do your reps use sales tools? Assess sellers on their knowledge (and correct use) of sales stack tools like Outreach, Ring, Zoom, and Salesforce to see how effectively they’re being put into action.

Using a revenue enablement platform, reps can actually record themselves using the tools, which can then be evaluated and scored. This will give sales coaches visibility into any challenges with tech on the team.

Your new approach to coaching starts now

On top of using data to coach reps, this data-driven approach also empowers you to establish a “profile of excellence” on your sales team. This helps sales managers to identify their best performers and replicate their skillset amongst other reps through training and coaching.

While it certainly makes it easier to track and report on these metrics with revenue enablement technology in place, it’s not impossible to do without. However you gather the data, it’s important to start bringing it into coaching conversations in order to pinpoint areas for improvement, provide more specific feedback, and ultimately empower you as a sales coach to help your team succeed.

Start delivering effective, data-driven coaching at scale with Mindtickle

The impact of sales coaching can’t be denied. However, in order to be effective, sales coaching must be tailored to the needs of each sales rep.

With Mindtickle’s integrated revenue enablement platform, you can start delivering data-driven coaching that’ll improve reps’ behaviors and grow sales performance.

With Mindtickle, you can easily understand deal risk and buyer engagement. Then, you can use those insights to provide coaching to improve the outcome of deals.

Mindtickle also empowers you to go beyond deal coaching to deliver skill coaching that improves long-term results. With Mindtickle, you can understand how reps are performing in the field and what their strengths and weaknesses are. Then, you can use this intel to deliver coaching to improve lagging skills and long-term behaviors and performance.

Of course, delivering coaching and hoping for the best isn’t an effective approach. With Mindtickle, revenue leaders can actually understand the impact of their coaching efforts. Then, they can optimize their approach for even better results.

This post was originally published in June 2021 and updated in July 2024.

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AI in Learning and Development: How AI Enhances Your L&D Programs

The whole world is buzzing about artificial intelligence. Everywhere we go – from industry conferences and boardrooms to news publications and social media sites – we hear conversations about the large (and rapidly growing) impact of AI.

Already, the most innovative organizations are leveraging AI in learning and development initiatives. Those that embrace AI in learning and development are better equipped to build and deliver programs that grow skills and improve business outcomes. 

In fact, as an L&D leader, you simply can’t afford to ignore AI in learning and development.

If you’re looking to supercharge your learning and development efforts with AI, you’ve come to the right place. In this post, we’ll explore everything you need to know about AI in learning and development. We’ll cover topics including:

  • The role of AI in learning and development
  • Why AI in learning and development is important
  • Top challenges of implementing AI in learning and development programs 
  • How the right technology can help you capitalize on the opportunities of AI in learning and development while overcoming the key challenges.

The role of AI in learning and development

Chances are, you always strive to hire the strongest candidates for every open position. Most organizations do!

But every employee – from the lowest performers to the all-stars – need ongoing learning and development to be successful in their roles. With a strong learning and development program, employees have opportunities to grow their skills. When every employee has access to the right learning and development opportunities, performance improves across the entire organization. 

As an added bonus, a strong learning and development program increases engagement among employees. When you prove you’re invested in your employees’ learning and growth, it boosts employee satisfaction and retention. 

But where does AI fit in?

One-size-fits-all learning and development doesn’t work. Instead, the best L&D programs are tailored to the unique needs of each employee. Organizations must also be able to measure the impact of their learning and development program and make data-based adjustments as necessary.

Pulling this off requires a ton of time and effort – especially for enterprises with hundreds or even thousands of employees. In fact, it can feel downright impossible.

That’s where AI in learning and development comes in. 

When you strategically leverage AI, you’re able to develop and deliver effective, personalized training and learning that address the needs of each learner. That means every member of your team has the right opportunities to build the skills and behaviors they need to be as successful as possible.

Why do you need AI in learning and development?
 

AI isn’t just a buzzword or a passing fad. Instead, it’s becoming a critical component of any effective learning and development program.

Not sure why you need generative AI in learning and development? Let’s take a look at some of the key reasons innovative L&D teams are already incorporating AI in learning and development.

Faster creation of L&D programs

Strong learning and development programs can significantly improve performance. But building and launching impactful programs takes time. Chances are, you don’t have much to spare.

Today, winning organizations leverage AI to create learning modules quickly and easily. The sooner you launch a program, the sooner you’ll see an impact.

Personalized L&D programs that address the skill gaps of each employee

There was a time when learning and development was limited to onboarding, and programs were one-size-fits-all. That approach will no longer cut it. L&D needs to be personalized to the unique needs of each employee.

Organizations leverage AI in learning and development programs to recommend L&D that’s relevant to each employee. For example, AI for training can deliver recommended learning and development based on factors like:

  • Job title
  • Skill gaps
  • Learning preferences
  • Content they’ve already consumed

Self service learning opportunities

With AI in learning and development, employees don’t have to wait around for content or a training module to be assigned to them. Instead, they can access individualized training and other learning whenever and wherever they need it.

For example, a sales rep can type in a question they’ve received from a prospect. AI will analyze all the content that’s available to provide an answer. AI will also recommend additional, related resources (like microlearning and sales content) the sales rep can use to learn more.  

Immersive practice opportunities to perfect skills 

Ideally, employees would remember everything they learned in training and apply it in the real world. But that’s not reality. 

Ebbinghaus’s Forgetting Curve tells us employees forget half of all new information within a day; they forget 90% within a week of learning it. 

It’s important to provide employees reinforcement and practice opportunities. Role-plays are one example. However, traditional role-plays require a lot of a manager’s time.

That’s not the case with AI. 

Learning and development teams can create realistic scenarios and assign specific characteristics to an AI bot. Then, employees can engage with the bot to practice and hone their skills. 

Timely coaching and feedback

Employees depend on coaching and feedback from their managers to improve behaviors and performance. But delivering meaningful feedback takes time and effort, and employees can’t afford to sit around waiting.

AI for coaching is the answer. 

Today, sales enablement teams use AI to record and analyze sales reps’ calls. At the end of the call, the rep receives a score and real-time feedback for improvement. In addition, sales managers can look for trends (for example, a high volume of calls with low scores) to identify opportunities to deliver additional coaching to sales reps. 

Data analysis

Data is key to L&D success. Chances are, you have plenty of data available to you. For example, you likely have data related to how employees are engaging with different learning and development programs and how they’re performing on the job.

But it can be hard to make sense of the huge volume of data available to you.

Innovative teams use AI in learning and development to analyze data and provide summaries. AI can also make data-based recommendations for next steps.

Top challenges of implementing AI in learning and development

Leveraging AI can have a huge, positive impact on your L&D program. But implementing generative AI in learning and development also presents some unique challenges. 

Let’s take a look at some of the top challenges of implementing AI in development and learning.

Challenge #1: Integration with existing tools

Chances are, you already use certain tools to power your L&D program. You can’t bring a new tool or technology into your ecosystem and hope for the best. Instead, you must make sure it integrates with your existing tools.

Talk to your existing vendors to learn about the existing or upcoming AI capabilities of the learning and development tools you already use. If you’re searching for a new learning and development solution, look for an option that incorporates AI in innovative, impactful ways.

Challenge #2: Costs

Integrating AI in learning and development costs money. For example, you may need to purchase new learning and development tools. You must also consider the costs of training the members of your learning and development team.

In the best of times, it can be challenging to secure funds to invest in AI. This is especially true in today’s economy. 

Challenge #3: Training

Of course, you must train your L&D team on how to use your AI in learning and development solutions. You must also train employees who will be consuming your learning and development programs.

Developing and delivering training on AI requires time and effort. Employees have to set aside time that they would typically use for other tasks. 

Challenge #4: AI limitations

Make no mistake: incorporating AI in learning and development can dramatically impact the effectiveness of your programs. However, AI isn’t magic. There are limitations.

It’s crucial to understand those limitations. It’s also important to remember that AI outputs are only as good as the data behind them. 

Maximize the impact of your L&D initiatives with Mindtickle’s rich AI features 

Artificial intelligence is here to stay. Now is the time to determine how to incorporate AI in learning and development. Your competitors are already using AI in learning and development initiatives, and you can’t afford no to.

Today, some organizations continue to use a traditional learning management system (LMS) to deliver their learning and development programs. However, a growing number are trading their LMS for an integrated, AI-powered revenue enablement platform like Mindtickle.

The Mindtickle platform incorporates a number of powerful AI features that enable organizations to deliver engaging, effective learning and development at scale. 

For starters, Mindtickle leverages AI to allow training, development, and revenue enablement teams to spin up new learning experiences quickly and easily. The faster you develop programs, the sooner you’ll start seeing an impact. 

In addition, Mindtickle uses AI to help employees surface relevant learning resources, such as individualized training, content, and microlearning. That means employees can find the learning resources they need, when they need them. Mindtickle also offers AI-powered role-plays that ensure employees have opportunities to perfect their skills in realistic, dynamic scenarios. 

Finally, Mindtickle leverages AI to help organizational leaders make sense of the large volume of data available to them – and act on those insights in a way that improves sales performance and business outcomes.

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