Why Sales Coaching is So Hard and 6 Ways AI Is Making it Easier and Better


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Today, with product lines and competitive environments in constant flux and prospects more informed than ever, sellers must adapt their approach to match this new reality. While coaching has always been an important part of the training process, for sellers navigating today’s digital-first buying journey, coaching is now critical to helping reps learn how to control the sales process and win the customer’s confidence. Without carefully targeted coaching programs, reps can easily fall into a reactive mode with customers and their many requests. But while everyone agrees coaching is important, it remains a nagging worry for many sales execs. It’s a bit like a personal exercise regimen – everyone knows it’s crucial for good health, but getting it done consistently and effectively can be daunting.

Why is sales coaching so hard?

In my experience working with sales teams of various sizes and hundreds companies — from dozens to thousands of reps — and I’ve found that many coaching efforts often run up against the following hurdles:

Time: Finding the time to do coaching adequately is a big challenge. A recent survey by the Association for Talent Development found 43 percent of respondents identified scheduling conflicts and time constraints as their top barriers to sales coaching.

Feedback: The quality of feedback that managers provide is directly related to the amount of time they’re able to allocate to the review process. That can vary greatly across coaching sessions with the same learner and across learners, so consistency is hard to achieve. It’s very difficult to benchmark and measure these changes.

The objectivity of feedback is also often questionable. “Recency bias” can play a big role in the process – the most recent and most visible “win” or “loss” may have a disproportionate impact on the feedback. Confirmation bias is common too, where a manager’s established perception of the learner carries undue influence in the coaching session.

Visibility: While the benefits of coaching are very clear to sales managers who do it well, the value is not always directly visible to their peers, managers or sales reps. Anecdotal success stories are common and useful, but they have limited upside.

Personalization: To engage reps and change behaviors, a coaching program must highlight individual roles, tenure, performance, and strengths and weaknesses; a one-size-fits-all approach won’t cut it. In fact, a flexible coaching framework should accommodate different managerial styles and the individual needs of sales reps. The manager-coaches who achieve the best results often painstakingly keep notes on their team, get to know their individual backgrounds and track their progress closely. But that calls for an investment of time and effort that many managers just can’t afford without support.

Skills: Many managers have been promoted to their position after a successful run as a top producing sales rep but have never been given the proper training and tools to help them become great coaches. Unless a company develops a “coach the coach” program it’s difficult to expect anything but intuition-driven attempts to coach reps. The result is inconsistent, ad-hoc coaching that can negatively impact the performance and morale of the sales reps.

New sales coaching: the data-driven difference

But hope is not lost. AI (artificial intelligence) might hold the key to better coaching, but what may not be as obvious is that data is the real key to better coaching. The principle of “garbage in, garbage out” is just as valid for AI and machine learning as it is for any other kind of application.

Modern data-driven coaching solutions rely on a data store that centralizes and manages information provided by a sales enablement platform, including data about reps’ knowledge and proficiency, information on their learner backgrounds, and coaching performance data. The data store also pulls in data sets that are “external” to the enablement platform, such as company performance data and activity data (e.g. sales rep calls and emails).
This “enablement intelligence” data store provides the solid foundation that AI and ML engines need for accurate outputs and better coaching outcomes. This background intelligence is what enables text analysis, conversation intelligence, insight discovery, and all the other AI/ML engines to work their magic.

6 things next-gen sales coaching does better

So, what can a data-driven and AI-enriched sales coaching solution do for your organization?

1. Realistic practice: Next-gen solutions deliver video-based role-play capabilities that can significantly reduce the time burden on managers while extending the reach of coaching programs. You can have more realistic customer interaction simulations to help reps build their skills and confidence. Take objection handling, for example. You can build objection workflows into a video assignment, using either standardized objections based on real customer responses, or more dynamic and personalized ones based on the known strengths and weaknesses of the rep.

In the long term, coaching enablement will no doubt move towards augmented/virtual reality to present responses dynamically and in real-time based on the content and sentiment of the pitch. While AR/VR hold some promise, their actual implementation in most organizations is still in its infancy and long-term adoption remains to be seen. The good news is, companies don’t have to wait for AR/VR advances to build a safe, realistic environment where reps can hone their customer engagement skills. And the current technologies provide a clear path that could, in fact, enable an AR/VR future.

2. Machine-assisted reviews: ML/AI technology has advanced to where it can not only transcribe audio but also provide qualitative insights around topic coverage and speed of delivery; confidence and emotional tone; and conversational dynamics such as level of interactivity. The audio file can come from a practice session or from a recording of a live customer phone call.

Machine-assisted review automation saves managers time and improves the quality of their feedback. If the system indicates poor topic coverage or weak tone in some areas of the pitch, for example, the human reviewer can offer feedback at those specific points, providing information on what was missing and suggestions to help the rep improve. The technology also drives greater consistency, since managers can use the ML assessment as a baseline across a cohort of learners.

Machine-assisted reviews can also be deployed as a self-access learning option for reps, giving them instant feedback in a low-pressure practice environment. A similar approach can be employed for live customer calls providing a searchable transcript for reference, analysis for phone coaching and note-taking.

The technology has a lot to offer for product marketing, customer success, and sales enablement teams, too. It helps them keep track of broad trends in customer interactions – for example, which topics consistently elicit positive (or negative) responses; where reps are struggling to convey the messaging; and which topics generate mentions of competitors. They can use this information to create a powerful feedback loop that enables reps to shape better conversations.

3. Personalization: This is a foundational capability that should influence all stages of coaching. A key first step is simply to track each learner’s strengths and weaknesses across a range of competencies. This information should be made available, with full context, to managers, reviewers, and sales enablement administrators. In addition, with the right data, the ML algorithms can ensure that the entire coaching program – from identifying coaching topics to making remediation recommendations – is customized to individual learner needs.

4. Team-level remediation recommendations: In addition to personalized guidance for individual reps, next-gen sales coaching platforms can provide managers with actionable recommendations specific to their team – for example, by pointing out relevant “top submissions” to the video library which can be made available to those reps who could learn from these top performers.

5. Enhanced visibility and incentives to influence culture: Next-gen coaching platforms can provide a feedback quality index for both sellers as well as their managers to drive accountability and enable comparison of learning and coaching performance with peers. These solutions can even push the relevant outcomes to HR management systems like Workday to enhance year-end performance review and compensation plan discussions. These capabilities can help companies embed coaching into their culture.

6. Guided Coaching: Instead of leaving coaching up to the sales manager, guided coaching, with coaching guide forms from a sales enablement platform, can help guide managers on what to observe, evaluate, and look for. Coaching guides help to ensure managers are consistent in how they coach their teams and also, allow for a prescriptive, best-practice approach that can boost coaching effectiveness.

Though the concept of a data-driven, AI-enriched enablement solution may seem, at first glance, to reduce the human factor in coaching, but the opposite is true in fact. It extends and facilitates the personal attention and support that makes coaching so powerful. And at the same time, it goes a long way to lighten many of the burdens that traditional coaching approaches have placed on sales managers’ shoulders.

Himanshu Bari
Himanshu Bari is the Sr. Director of Product Management at MindTickle. He has over 15 years of enterprise software experience in a diverse set of roles spanning product management,software development, user experience for security, fraud, and data center products. Recently he’s focused on the big data space where he led the data platform and machine learning driven products for ThreatMetrix, a LexisNexis company. Himanshu holds a Masters in Computer Science and MBA from UC Berkeley.


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