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AI's Impact on Sales Compensation

While most of us think of applications like Alexa, Siri and Cortana when we think of artificial intelligence (AI), more than half of AI’s commercial impact is in sales and marketing. AI impacts how companies engage with their customers, a salesperson’s influence on the sale, and by extension, how these same salespeople should be paid. But let’s start with how AI is impacting the world of sales.

How AI Is Transforming Sales

Today, successful customer engagement requires a multi-channel model where sales plays just one of many roles. AI makes engagement with customers more seamless and continuous across many “touch points,” including email, social, chatbots, salesperson meetings, brick and mortar locations, etc. AI helps companies get the customer what they want, when and where they want it.

Below are a few examples of how AI is transforming sales:

  • Demand nurturing. Automated agents follow up on leads to ensure there is opportunity there. If so, they route them to the right sales channel (self-serve, partner, salesperson, etc). When the automated agent does not detect a current opportunity, agents can sustain engagement with tailored messaging and promotions to nurture leads until the time is right.
  • Demand scoring. Companies are using models to evaluate the potential of leads and opportunities. This model drives sales activity — which customers to target, which contacts to reach, methods of communication (email vs. phone), best times to reach them and the frequency and number of follow-ups.
  • Next best action. Orchestration engines optimize the next best customer-focused actions to take. These engines drive sales tactics, often including salespeople, to ensure both the customer and the company get what they need from the transaction.
  • Pricing. Models have been developed to optimize pricing with each customer and line item (when applicable) based on the customer demographic and their actions to date.

The question for sales leadership is not if, but how to leverage AI. The decision on how to leverage AI will impact a salesperson’s role in the sale, as well as how they are motivated and compensated.

How AI Will Impact Sales Compensation

Sales compensation is a critical part of translating a company’s growth strategy and sales process into actions by the salesforce. As a result, the AI-led transformation of sales will impact sales compensation in four key areas:

Pay Level & Mix

Pay level is based on employee skill/knowledge levels and demand for talent. Some argue that AI will drive a need for fewer salespeople by replacing some of their activities with machines or through higher salesperson productivity. If either of these happens, it would reduce demand for salespeople and, therefore, pay levels.

On the flip side, salespeople are becoming more sophisticated as a result of AI, requiring salespeople to have enhanced skills. Such a shift will put upward pressure on pay levels for salespeople with this skill set. Our hypothesis is that pay levels will go up for the most sophisticated salespeople based on the scarcity of this skill and the importance of the salesperson in this increasingly complex coordination role.

Pay mix is largely determined by the sales role’s influence and the selling environment. If AI results in certain roles having less of an impact on a sale, a case could be made for a lower percentage of pay at risk. Companies have been notoriously slow to reduce a salesperson’s pay at risk, even if justified, because they believe there will be a “first mover disadvantage” and their best performers will go elsewhere. While the net impact remains to be seen, what is clear is that AI will change the way organizations think about pay level and mix.

The question for sales leadership is not if, but how to leverage AI. The decision on how to leverage AI will impact a salesperson’s role in the sale, as well as how they are motivated and compensated.

Plan Metrics

AI will not replace sales or human judgment. In fact, the best applications are complementary to the sales role. For example, in situations where salespeople are empowered to act, they are given increasingly prescriptive direction on how to execute core job activities. This direction varies from customer targeting to solution development to pricing strategy. This is different than what it has been historically, where salespeople were required to deliver — and were paid on — results. How they got to the results was largely up to them.

But with their actions being increasingly directed by AI, should they be paid on their adherence to the prescribed path? What if they follow the prescribed path but don’t achieve their quota? What if the opposite happens?

Companies increasingly want salespeople to adhere to the AI recommendations. When those actions lead to results as expected, payouts based on activities and outcomes will be roughly the same. However, if there is a disconnect between the two (positive or negative), companies should consider carving out a portion of the plan to drive activities that adhere to the AI model. This will allow companies to continue to improve the model as more information on activities and their subsequent results is fed into the model.

Plan Design

This is where AI could have the greatest long-term impact. Just as AI has determined the best way to sell, it can also determine how to best motivate a salesperson. For example, while money may be the primary motivator for many, everyone is different. There are four ways in which individuals are motivated:

  • Control: Our need for choices and to be the master of our destiny.
  • Affiliation: Our need for social contact and cooperation.
  • Reward and recognition: Our need to be acknowledged and appreciated.
  • Excellence: Our need for accomplishment and growth.

AI could determine the optimal motivators for an individual based on a profile and recommend an optimized plan as part of a menu of options. AI could also recommend point-in-time incentives based on what has worked with salespeople of a similar profile. It could differentiate not only based on inferred preferences, but on any other factor proven to influence motivation and plan effectiveness (e.g. tenure, geography).

AI’s application could also be as simple as providing timely reminders based on salesperson preferences and current performance levels.

Forward-thinking sales compensation leaders are considering how these changes may impact the way that we motivate the sales organization and are likely to benefit significantly from AI.

AI will not replace sales or human judgment. In fact, the best applications are complementary to the sales role.

Quota Setting

The ability to forecast results (sales, revenue, etc.) and allocate that forecast down to salespeople in the form of quotas is one of the top challenges reported by sales leaders. But AI has significantly improved companies’ ability to forecast accurately, and to do it quicker than in the past. This helps organizations launch quotas on time, monitor trends and diagnose the health of quotas postlaunch. It can also significantly improve accuracy by more effectively navigating the following difficulties in the process:

  • Weighing outliers or noise that can lead to inaccuracy
  • Eliminating systemic biases which can lead to over- or under-forecasting
  • Determining the degree of desired accuracy (e.g., accuracy at national vs. territory level)
  • Understanding the stage of product lifecycle (e.g., accuracy for a launch vs. a mature product)
  • Learning from the quota refinement process feedback (e.g., last mile knowledge).

While setting more accurate quotas is always a good outcome, organizations need to diligently evaluate quota fairness and eliminate biases. The most accurate quotas may give your highest performer much more of a stretch than everyone else. While it may be more accurate, it is hardly fair and may result in turnover of your best performers. Finding the balance between accuracy and fairness is critical to this process and can be baked into the AI model.

The Journey Ahead

With sophisticated algorithms, more data and enhanced computing power, AI will continue to improve and transform how sales and sales compensation are done.

You should immediately seek to understand the extent to which AI is being used in your organization’s customer engagement process (CEP), and how it has changed the sales role, if at all. Based on the change in the sales role, assess how that change might impact the four key areas reviewed above.

Sales leaders are actively trying to incorporate AI into how they engage with their customers. You can be a critical part of helping your organization embrace AI with a forward-thinking assessment of how AI can enhance your sales compensation program.

Chad Albrecht Chad Albrecht is a principal in ZS Associates’ Chicago office and leads the firm’s Sales Compensation practice.

Kyle Heller Kyle Heller is an associate principal in ZS Associates’ San Francisco office and leads the firm’s high-tech industry vertical.

Arun Shastri Arun Shastri is a principal in ZS Associates’ New York office and leads the firm’s B2B Analytics practice.

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