Sales Analytics and Data-Driven Selling Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Sales Managers, Account Managers and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-S-045

Before you join: Participants should have practical experience in sales, account management, business development, sales operations or commercial analysis.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Sales teams often hold large volumes of CRM, activity, account and revenue data but struggle to turn it into better territory choices, opportunity actions and reliable forecasts. Poor field discipline, inconsistent sales stages and dashboard overload can hide pipeline risk until late in the quarter. Managers need to distinguish meaningful leading indicators from vanity metrics, while sellers need account-specific evidence that improves prioritisation and customer conversations.

This course teaches participants to build a practical sales analytics operating model from CRM data through to action. Participants define sales metrics, assess data quality, segment accounts, score opportunities, analyse conversion and velocity, forecast revenue, and identify pipeline risk. The course covers descriptive, diagnostic and predictive methods appropriate for sales teams, including cohort analysis, funnel analysis, weighted pipeline forecasting, regression concepts, propensity scoring and scenario planning. Participants also learn how to translate analysis into territory plans, account priorities, manager coaching and sales actions.

Instruction combines short briefings with CRM-style case data, dashboard interpretation, group analysis, forecast reviews and selling simulations. Participants work with realistic cases involving enterprise account pipelines, bank relationship portfolios, public-sector tenders and subscription renewals. They leave with a reusable Sales Analytics Action Pack containing a metric dictionary, pipeline-health scorecard, opportunity scoring model, forecast template, dashboard specification and 90-day application plan.

The course is suited to sales professionals and managers who already use CRM reports or spreadsheets and need a disciplined method for making commercial decisions from sales data.

What you will be able to do

  • Define a sales metric dictionary with formulas, owners, data sources and refresh cycles
  • Assess CRM data quality using completeness, validity, uniqueness and timeliness checks
  • Build account segments and opportunity priorities using value, fit, intent and risk criteria
  • Analyse sales funnels, conversion rates, stage ageing and pipeline velocity
  • Create weighted pipeline, run-rate and scenario-based revenue forecasts
  • Apply regression and propensity-scoring concepts to sales prioritisation decisions
  • Design executive and manager dashboards that connect metrics to sales actions
  • Produce a 90-day sales analytics application plan with governance routines

Benefits of attending

For you

  • Improved confidence challenging weak pipeline assumptions and forecast submissions
  • A repeatable method for selecting priority accounts and next-best sales actions
  • Stronger capability to explain conversion, velocity and coverage metrics to leaders
  • Practical experience translating CRM data into customer-facing account priorities
  • A structured 90-day plan for improving personal sales analytics practice

For your organisation

  • More reliable revenue forecasts through standardised assumptions and pipeline controls
  • Earlier identification of stalled opportunities, coverage gaps and close-date risk
  • Better allocation of seller time across accounts, territories and renewal portfolios
  • Higher CRM reporting credibility through clear metric definitions and data-quality routines
  • More focused manager coaching based on observable sales behaviours and evidence

Course modules

10 modules over 5 days

Participants can establish reliable sales data definitions, quality controls and metric ownership.

  • Revenue architecture: accounts, opportunities, activities, products, territories and sales stages
  • CRM field definitions, mandatory values and controlled picklists for analytical consistency
  • Data-quality dimensions: completeness, validity, uniqueness, timeliness and referential integrity
  • Audit missing close dates, duplicate accounts and inconsistent opportunity amounts
  • Define leading, lagging, diagnostic and outcome sales metrics
  • Data governance ownership, refresh cadence and exception-resolution workflow

Participants can convert commercial priorities into measurable sales questions and KPIs.

  • Translate growth targets into measurable sales questions and decision criteria
  • KPI trees linking revenue, pipeline, conversion, capacity and activity drivers
  • Distinguish activity metrics from productivity, effectiveness and outcome measures
  • Set metric baselines, targets, tolerances and escalation thresholds
  • Apply SMART criteria to sales performance measures and reporting commitments
  • Avoid vanity metrics, double counting and misleading percentage comparisons

Participants can segment account portfolios and prioritise sales coverage using evidence.

  • Segment accounts by current value, potential value, fit and relationship status
  • Ideal Customer Profile criteria derived from profitable customer patterns
  • RFM analysis for renewal, cross-sell and reactivation account portfolios
  • Construct territory potential estimates from installed base and market indicators
  • Prioritisation matrices combining account value, propensity, access and sales effort
  • Allocate coverage tiers and contact cadence using portfolio economics

Participants can diagnose where sales opportunities slow, convert or fail.

  • Map opportunity stages, entry criteria, exit criteria and stage probabilities
  • Calculate stage-to-stage conversion rates using cohort-based funnel analysis
  • Measure pipeline velocity from opportunity value, win rate and cycle time
  • Ageing analysis identifies stalled opportunities and inaccurate close-date commitments
  • Win-loss analysis separates controllable sales causes from market conditions
  • Compare seller, territory, segment and product performance without misleading averages

Participants can create transparent opportunity scores that guide sales attention.

  • MEDDICC evidence mapped to opportunity qualification and forecast confidence
  • Build weighted scoring models from fit, intent, engagement and deal risk
  • Propensity scoring concepts: target variable, predictors, training data and validation
  • Interpret correlation, causation, false positives and false negatives
  • Regression outputs translated into practical opportunity prioritisation rules
  • Monitor model drift, seller adoption and score-performance calibration

Participants can prepare defensible sales forecasts using multiple forecasting methods.

  • Weighted pipeline forecasts using stage probabilities and opportunity-specific confidence
  • Commit, best-case and pipeline categories governed by evidence standards
  • Run-rate forecasting from bookings, seasonality, renewal dates and capacity
  • Scenario analysis tests upside, downside and base-case revenue assumptions
  • Forecast accuracy measured through bias, variance and mean absolute percentage error
  • Reconcile seller forecasts with historical conversion and pipeline coverage

Participants can specify dashboards that support sales decisions at different management levels.

  • Dashboard audiences: executives, sales managers, sellers and sales operations
  • Select decision-oriented visuals for trends, funnels, cohorts and distributions
  • Design drill-down paths from revenue variance to opportunity-level action
  • Use benchmark lines, alert thresholds and annotations to focus attention
  • Report pipeline coverage, conversion, velocity, forecast and activity together
  • Apply data-visualisation principles to prevent chart clutter and misinterpretation

Participants can turn customer and pipeline signals into targeted sales actions.

  • Intent signals from engagement, usage, buying events and stakeholder activity
  • Next-best-action rules connecting signals to seller tasks and customer messages
  • Account plans structured around whitespace, risk, influence and value hypotheses
  • Buying-committee mapping identifies economic buyers, champions, blockers and users
  • Trigger-event selling linked to funding, leadership, compliance and renewal changes
  • Sequence outreach using account priority, contact role and engagement evidence

Participants can run evidence-based sales reviews and coaching conversations.

  • Pipeline review agendas focused on evidence, risks, decisions and commitments
  • Coach to leading indicators: qualification quality, stage movement and stakeholder coverage
  • Use deal inspection questions to test assumptions and next actions
  • Weekly, monthly and quarterly reporting cadences linked to sales decisions
  • Exception reporting highlights forecast changes, ageing deals and coverage shortfalls
  • Create accountability routines for CRM hygiene, actions and forecast updates

Participants can implement a practical sales analytics plan with appropriate controls.

  • Sales analytics roadmap sequencing data fixes, metrics, dashboards and adoption
  • Define minimum viable dashboards before expanding reporting scope
  • Change-management actions for seller trust, manager use and leadership sponsorship
  • Privacy, consent and purpose limitation in customer and prospect data
  • Bias checks for territory assignment, lead scoring and performance comparisons
  • Ninety-day application plan with milestones, owners, measures and review dates

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Sales metric dictionary template
  2. CRM data-quality audit checklist
  3. Account segmentation and prioritisation model
  4. Pipeline-health scorecard
  5. Revenue forecast and scenario template
  6. 90-day sales analytics application plan

Who should attend

  • Sales Managers — improve coaching, pipeline reviews and forecast accountability
  • Account Managers — prioritise accounts and opportunities using customer and CRM evidence
  • Business Development Managers — focus prospecting effort on higher-value, higher-probability pursuits
  • Sales Operations Professionals — establish trusted metrics, reporting definitions and data controls
  • Commercial Analysts — convert sales data into decision-ready analysis and reporting
  • Revenue and Customer Success Managers — improve renewal, expansion and churn-risk decisions

Skills and tools covered

Sales data governancePipeline analysisRevenue forecastingOpportunity scoringDashboard designCommercial decision-making

Tools and standards

Salesforce, Microsoft Power BI, MEDDICC, RFM analysis

How the course runs

Training methodology

The course uses short instructor briefings to establish methods, followed by case work using realistic CRM extracts, account portfolios and pipeline reports. Participants interpret dashboards, diagnose conversion problems, test forecast assumptions and make prioritisation choices in facilitated groups. Sales-manager and account-team simulations connect analysis to pipeline reviews, coaching conversations and customer actions. Individual application planning translates course methods into each participant’s reporting cadence, sales process and commercial responsibilities. Examples can be adapted for B2B, financial services, public-sector, technology and subscription sales contexts.

Requirements and prerequisites

Participants should have practical experience in sales, account management, business development, sales operations or commercial analysis. They should be familiar with common CRM concepts such as accounts, contacts, opportunities, sales stages, activities and pipeline value. Basic comfort reading tables, percentages and charts is expected. Participants may bring examples of current reports, dashboards or forecast processes if organisational policy permits. Prior training in statistics, data science, SQL, Power BI, Tableau or predictive modelling is not required. The course explains analytical methods in commercial terms before applying them.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should understand basic sales processes and CRM terms such as accounts, opportunities, stages and pipeline. No prior statistics, coding, data science or business intelligence software training is required.

Bring a laptop if possible, plus examples of your current sales reports, dashboard screenshots or forecast templates where policy permits. You do not need to provide confidential customer data; course cases use representative data.

It is designed for sales managers, account managers, business development professionals, sales operations teams and commercial analysts. It is particularly useful for people responsible for pipeline quality, account priorities or revenue forecasts.

This course focuses on commercial decisions made from sales data rather than system configuration or generic analytics theory. It connects CRM data, pipeline analysis, forecasting, account prioritisation and manager coaching.

Participants can apply the metric dictionary, pipeline-health scorecard, opportunity scoring model and forecast template to existing CRM reporting. The final application plan identifies immediate data, reporting and sales-management actions for the next 90 days.

The course runs for five instructor-led days from 08:30 to 16:30, providing approximately 6.5 contact hours daily. Time is divided across ten modules covering data foundations, pipeline analysis, predictive prioritisation, forecasting, dashboards and implementation planning.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

More courses in Sales

Sales Professional Sales Management Training Course 10 days · from USD 2,200 · next 16 Nov Sales Professional Sales Skills Training Course 10 days · from USD 2,200 · next 16 Nov Sales Professional Sales Training Course 10 days · from USD 2,200 · next 16 Nov Sales Retail Banking Sales Training Course 10 days · from USD 2,200 · next 16 Nov Sales Sales Analytics and Excel Dashboards Training Course 5 days · from USD 1,100 · next 16 Nov Sales Sales and Closing Skills Training Course 10 days · from USD 2,200 · next 16 Nov Sales Sales and Customer Service Training Course 5 days · from USD 1,100 · next 16 Nov Sales Sales and Marketing Alignment Training Course 10 days · from USD 2,200 · next 16 Nov