What can take a lifetime for a human to figure out comes naturally to AI, saving time and accelerating opportunities across all business activities.
You don’t have to reinvent any wheels to begin adding AI into your sales process. It’s already available in software and on platforms. Not only does it use data to tell your salespeople where to dig, but it does so instantaneously. However, without the right people you are swimming upstream and fighting the inevitable, a reliance on too few high performers. We can invest in training, coaching and a host of ways to improve personnel performance, but if the people in the seats do not have a natural propensity to do well, at best we can expect only incremental improvement.
Digital transformation has been embraced by companies across a myriad of business activities and many forms, but application to Sales roles has been limited and suboptimal. Before we look at sales, lets take a brief view at some other revenue functions, forever impacted by AI.
Data and research, mined by scientists and statisticians on prospects behaviors is analyzed and run through sophisticated algorithms to predict consumer buying habits. They help determine channels to meet, engage, select topics and content to develop interest, and host of other insights from data to develop quality lead generation funnels and drive great leads for sales to close.
Templates can be effective, but not always. Receiving generic content can have a detrimental affect, rather than resonating with the audience. AI develops the narrative and process beyond A:B testing, rapidly delineating what sentence or word(s) increase impact, and capture the interest of a prospect to gently nudge them through the sales funnel. AI can develop unique messages, delivering account based marketing, at scale, to many leads and customers simultaneously and effortlessly.
The more detailed your messaging, the more customized they appear. Refining your sales campaigns with subtlety from sophisticated machine learning drives stronger connections with target prospects. Epsilon Email Institute noted it returned more than 70% higher open rates and 152% higher click-through rates than their generic counterparts. Collaboration between data and creative teams is crucial to making machine learning emails work enabling quick adjustments based on collective team insight and greater ROI.
No one ever really has enough qualified leads. Machine learning allows sales experts to focus in on prospects from collecting and synthesizing data. Leads are pulled, and converted using AI-discoverable deal patterns. Algorithms provide much needed help to move motivated prospects through sales funnels. Research published in Harvard Business Review found that when companies incorporated AI into their selling processes, their leads increased by 50%, and call time dropped, as much as 70%. It’s clear that AI can help sales leaders more intentionally direct resources and teams as well as prevent time and effort on poor prospects. Fostering prospects manually is unnecessary. With historical data, machine learning can manage a pipeline without intervention, saving sales representatives time, while mitigating human error. Streamlining and improving campaign management through AI and ML enables the capture, and curation of invaluable datasets - A treasure trove of data to accelerate opportunities - for greater win ratios and margins. The possibilities are immense, accomplished without the time and cost it takes to add a new hire.
Dynamically deliver leading based metrics, on the output of tasks, aligned to goals to meet strategic objectives and desired outcomes. Valuable digital solutions and advanced analysis are readily available covering almost all business drivers and system activities providing unbridled insight for informed, purposeful decision-making.
Behavioral and cognitive assessments, whether scientifically validated, or not - are intended to help determine performance propensity from an appreciation of personality type, traits and mental agility. In isolation human qualities tied to behaviors that drive performance have relevance; helpful in assessing role suitability, a candidates' potential to perform in a defined role and perhaps how, but they do not enable accurate prediction of how much a candidate, or existing team member will produce.
Multivariate regression analysis drives an algorithm with data that enables accurate prediction of personnel productivity in the KPIs that matter to each company. Mapping qualities of personnel to performance KPIs, and removing bias opportunity in the process.
Decisions in Sales personnel investments can be made with high confidence - backed by advanced data science designed to accurately predict future sales performance - down to the very specific metrics that matter most to each individual company. That means you can know how much an applicant will sell before you even hire them.
The value extends across an existing workforce, revealing the precise factors holding back employee performance enabling insight to develop custom training, and coaching or to make other measured personnel choices.
Hire, develop and foster more high performers than your competitors - and you win. Using human-centered data-driven AI models built on insights from your company’s human attributes and performance data, we accurately predict a team member's expected future performance in whatever KPI that matters to you: Revenue sold, units sold, appointments booked before you hire them. We don’t replace your role in the hiring process, we simply help you make better-informed decisions – pre-hire, post hire, coaching and career- pathing.
Until recently, hiring managers, recruiters and HR lacked the analytical tools and technology that have transformed other parts of contemporary corporations. Selecting, and coaching sales talent is challenging. Typically, up to 50% of sales people underperform with a similar % of core performers, leaving a minor number- around 10% of top producers, who drive sales effectiveness and company outcomes.
Hiring, developing and fostering high producers is largely a question of their“propensity” more so than history, or experience. Workplace data, and people science evidence illustrates why!
Most leaders recognize engagement, and discretionary effort drive productivity - underpinned by talent who fit in the right role with natural propensity to perform, with effective managers, team dynamics and cultural fit. What does right person right role really mean? and how do you know?
The progressive discipline of talent optimization requires the right tools to collect people data, best practices to analyze, and to take the appropriate actions to optimize.
The whole person shows up to work.
Here you can see a model person: head, heart and briefcase. When you think about evaluating someone to determine where they will be most effective in an organization, people are very quick to look at the knowledge, skills and experience someone brings to the job. This model represents that as the briefcase. We typically look at someone’s resume to find out about these things, and we dig a bit further in an in-person interview.
The heart represents things like values and interests that someone has. What are they passionate about? Will they be a good fit for our company culture? We attempt to uncover these things during interviews.
The heart and the briefcase change over time. Someone’ skills and knowledge grow with experience, and values and interests can also change over time. But at the top you see the head, which in this model represents a person’s innate drives and cognitive abilities. These things tend to remain stable once someone matures. Your behavioral drives and the rate at which you learn new things stay constant. And this is where data helps you evaluate these critical aspects of a person.
You can find out about these things using validated assessments - Behavioral assessments measure behavioral drives, and cognitive assessments measure cognitive ability. The combination of the two provides valuable data and insight that can help to predict someone’s behavior and performance. When used together, these two assessments can be powerful indicators of job success, but are just part of the whole - trying to understand how productive someone will be at work, and helpful data, but certainly not the whole story. The reality is the whole person turns up - head, heart and briefcase.
The hiring and career-pathing process in many companies miss the insight gained from validated assessments. By omission, and unwittingly they lean a lot more on intuition, gut instinct and subjectivity. In fact there's a further step we can take to significantly increase the probability for personnel performance
All combine to drive personnel investment decisions, likely to have some level of unconscious bias.
Intuition, and subjectivity are powerful qualities, which can be difficult to reason with even in the face of science.
"People trust that the complex characteristics of applicants can be best assessed by a sensitive, equally complex human being. This does not stand up to scientific scrutiny"
"The traditional unstructured interview has remained the most popular and widely used selection procedure for over 100 years(Buckley, Norris, & Wiese, 2000). This is despite the fact that, during this same period, there have been significant advancements in the development of selection decision aids"
When we look at key data, the numbers tell the story. Hiring and developing top performers -even at companies with best practices in talent optimization - can be a widespread economically inefficient pursuit, and very costly. However, it doesn't have to be.
A further step, leveraging untapped proprietary data allows us to gain a true statistical confidence in talent investments. By mapping people data to KPI workplace data, we can identify the specific group of human qualities that drive the highest productivity. In doing so, instead of answering the questions - Can they do it? and or How they do it? - We can confidently answer the question thats really on leaderships' mind - How much will they do it by?
Our decision support engine is a strategic tool that goes beyond people science assessments.
From talent selection, to coaching and retention, company performance is based on its personnel decisions and ability to optimize talent. And yet quality of personnel and correlation to performance is critical data that's too rarely fed back into the talent acquisition and development functions. This results in hard, soft and opportunity costs from sales personnel decisions that are hidden and poorly understood.
Poor sales personnel, hiring, coaching and career pathing-decisions create high early- tenure attrition, slow ramp, lost leads and revenue opportunities, lost customers and uninspiring customer experiences.
The emergence of Performance Profiles now provides sales leadership with the technical capabilities to quantify and predict the sales and revenue impact of any new recruit on the business, before they're hired, but also a road-map for coaching and development!
Companies have enormous amounts of proprietary data including KPIs - sales performance, calls made, quota met that is untapped and wasted!
The trait analysis example below presents 2 out of a possible 400 factors that can be measured and can contribute to performance. Emotional insecurity, which in this company represents a strong negative correlation to their measured KPI, revenue production. The 2nd, work ethic, not surprisingly shows a strong positive correlation to the production of revenue.
All performance is contextual; and any holistic model of performance must include multiple work performance domains. To achieve a more accurate prediction of job performance, a People Intelligence model identifies latent characteristics that are critical to contextual and adaptive work performance. Employees who exhibit voluntary effort and discretionary, innovative behavior are not only high performers but collectively, their performance contributes to customer satisfaction, loyalty, economic value creation, and ultimately an organization’s competitive advantage. Accurate personnel production predictions are the results of multivariate regression analysis across untapped proprietary datasets.
Performance profiles enable companies to ;
1. better track the impact of poor hiring decisions
2. treat each new hire as an investment decision, with analytical data predicting the sales performance impact pre-hire
3. increase accountability and the consequences for making the right hiring (investment) decision
4. accelerate the hiring process, and make it more agile, by enabling recruiters and hiring managers to prioritize their focus on higher value recruitment prospects
5. stop turnover being viewed as an unavoidable cost of doing business
6. empower sales leadership with data, tools and intelligence to make better hiring decisions
7. develop tailored coaching and training to improve individual weaknesses.
Performance Profiles are not a one-time process - Implementing role-based profiles empowers a company with new tools to engineer performance outcomes.
Created from your own company’s attributes and sales data allows you to predict sales performance in your KPI’s
Instantly see a highly accurate performance prediction of all candidates. Know which ones to prioritize at a glance.
You’re able to look deep under the hood to very specific information about your existing team as well as any potential hire.
Delivers a ‘deep’ picture of your sales talent, what to develop and coach, and who you need to recruit - Delivers a ‘deep’ picture of sales talent, what to develop and coach, and who you need to recruit – The process is blind eliminating the opportunity for bias elegantly supporting diversity and inclusion policies.
1. Company Psychographics - measure existing company DNA
2. Performance Data - add detailed team performance data
3. Performance Profile- run predictive modeling to create a role-specific performance profile
Talent and data science analytics leverage company proprietary datasets. Workforce and performance data- sociodemographic, psychographic, and biographic - all tied to specific individuals and correlated to performance outcomes.
Combining detailed psychographic data at scale with role-specific sales performance data creates a unique predictive performance profile through which sales leaders can make informed decisions with a high degree of confidence (up to 90%) in achieving budget objectives.
1. Future annualized sales revenue or other leading relevant KPI for existing salespeople and candidates for lifetime sales value by individual and teams
2. Candidate tenure
Clients leverage our team of data scientists, and simply deployed software, to increase the number of top performers and overall company productivity. Both privately held and publicly listed companies are accelerating sales performance - through the application of multivariate regression analysis, and data science.
Blind recruitment is the best practice to remove unconscious bias and ensure equal employment opportunity and workplace diversity, equity, and inclusion. When human-centric science and data-driven analysis is rigorously applied to the hiring process decisions are faster better and consistently more accurate; with less effort, frustration, and failure than conventional methods. Developing a performance fingerprint algorithm that's custom validated, using incumbent team data across the spectrum from under-performers to top performers delivers a unique understanding of performance drivers.
See how your company data and this process can help drive personnel investment choices to predictably increase revenue while mitigating defensibility risks in your diversity and inclusion policies