8 of the Most Important Sales Terms Defined

The world of B2B sales technology is evolving so rapidly–in many ways thanks to advances in data science and Big Data–that it’s sometimes hard to keep up. Things that meant one thing even 5 years ago, mean something completely different now. At the very least, it’s enough to make someone’s head spin, and at worst, it means that sales leaders and C-level executives aren’t speaking the same language. When it comes to things like opportunities, forecasts, and revenue–and the decisions that need to be made based on that information everyday–miscommunication introduces unnecessary risk, danger, and increases the possibility of missing a number.

This is especially true if the economic recovery is going to experience a slowdown. Aside from the issues in China, the stock downturn, and the uncertainty surrounding the Presidential election, a few signs are starting to appear that it’s hitting the tech sector. Recent news about the Optimizely layoffs and rumors about Intel selling part of its Intel Capital venture are potentially the canaries in the coal mine.

That’s why now is the time to define today’s terminology for everyone involved. There are 8 important sales terms that have been redefined over the last few years that will help bridge the gap between what was previously well known to sales pro’s and what forward-thinking organizations need to understood now.

  1. Sales Forecast Management: This phrase goes way beyond what you consider to be a straight-forward sales forecast such as a static set of numbers or report. It includes an embedded workflow to help sales leadership automate the business process of gathering, managing, and presenting a sales forecast. That process includes activities such as the forecast call, the weekly and monthly roll-ups, team coaching, overrides, quarterly business reviews, war rooms, and what-if scenarios that help identify previously unknown risks and potential opportunities.
  2. Forecast Science: Forecast science is the data-driven and predictive analytics technology that gives everyone involved in the sales process increased and real-time clarity on changes in the pipeline, movements in the forecast, and a view of where the quarter will end. It is the technology that drives early warning systems and the most accurate forecasts available today.
  3. Data Science for Sales Forecasting: Using the most advanced data science techniques such as machine learning algorithms, cohort analysis, and predictive analytics, data science for sales forecasting provides data-driven insights into every opportunity in a sales pipeline–giving each a probability about their likelihood to close, when, and for how much. The information produced at this level ladders up to the rolled-up numbers at the forecast science level.
  4. Opportunity Management: Opportunity management is the process of tracking opportunities in a CRM, including advancing the opportunity stage, updating activity, adding notes or attachments, etc. This is usually where the raw sales data is housed and maintained and where advanced sales forecasting tools pull from, among other places, to analyze opportunities and produce a data-driven forecast.
  5. Opportunity Scoring: Many CRMs have given opportunity scores for years, but in today’s context, advanced sales tools perform a multidimensional cohort analysis on each one, rather than analyzing the pipeline as a whole or at an aggregated level, leading to a much more accurate opportunity score.
  6. Machine Learning: According to Wikipedia, in 1959 machine learning was defined as a, “field of study that gives computers the ability to learn without being explicitly programmed.” Sometimes referred to as self-healing, machine learning algorithms detect changes in patterns and adjust their models to deliver accurate results as the data evolves. This is one of the most vital technologies that power data science for sales forecasting.
  7. Data Sources: These are the data streams that facilitate accurate sales forecast management and opportunity scoring. They include data streams from a CRM system, email and calendar applications, and other business sources.
  8. Cohort Analysis: Cohort analysis doesn’t look at data as one unit. Instead, it breaks data into related groups for analysis–called cohorts. These cohorts usually share common characteristics and enable companies to see patterns clearly across a defined life cycle and enables pinpoint decision-making about each individual cohort grouping. In terms of sales technologies, cohort analysis is critical to inform accurate predictions about the likelihood of a deal closing and the close amount.

The words that we use matter and that has never been more true today. We must all evolve our understanding of sales technology terminology as fast as the technology itself moves–if not faster–if we are to stay one step ahead, optimize business results, stay in control during a potential economic slowdown, and power the next wave of growth.

Originally published at www.aviso.com on March 16, 2016.