Growth in the Flow of Work
We help teams provide employees with career growth, employee engagement through skills development.
Affordable cohort-based learning in teams
We combine mentorship and projects in the flow of work.
Capability Academies
Complementing existing efforts and tailoring to updated business outcomes.
Retention in Hybrid Workplaces
Engaging hybrid teams through cohort-based learning as benefit.
Recession-Ready Reskilling
Empowering teams to become more agile and leverage tools.
How learning sprints work
Learning Sprints make effective digital learning possible at work to deliver the critical skills your teams need without ineffective video lectures.
Case Study
We helped HR professionals at SAP create personalized learning roadmaps and upskill with HR analytics
Other Sprints
Learning and Development
Quantifying RoI
How much impact is L&D having on business outcomes? Use handy data techniques to find out!
Learning Project Output
Presentation report showing results of a decomposition analysis of business results and the impact of contributing variables including L&D initiatives.
Why would this be useful?
One of L&D’s most crucial tasks is to demonstrate the value of initiatives to business outcomes. This is one of the key ways in which teams can evaluate their impact, identify high-value initiatives, and justify further resources to enhance L&D at the firm. Join a virtual coached and facilitated Learning Sprint where you apply simple regression and attribution techniques to assess ROI.
who is this for?
Anyone working in L&D, particularly those familiar with the Kirkpatrick or LTEM models.You do NOT need to have prior experience in statistics, but you MUST be familiar with Excel or another spreadsheet program. This will not require any tools other than Excel.
Program Duration
6 hours spread out over 2 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out a regression analysis to identify explanatory variables
- Learners will understand how to structure an analysis to eliminate confounding variables
- Learners will understand the core aspects of attribution in causality and correlation
- Learners will be able to conceptualize hypothesis tests to feed into attribution analysis.
Behavioural Outcomes
- Learners will be able to transition their reporting from sharing basic engagement statistics about L&D (# of participants, attendance, satisfaction) and to more assessing impact on business outcomes and training RoI.
- Learners will be able to evaluate vendors and trainings based on RoI.
- Learners will be able to reframe their communication to business teams and executives to focus on business outcomes rather than just training needs.
- Learners will be able to share with their peers how data skills can enhance their work as L&D professionals.
- Learners will be able to communicate in a credible and understandable manner with data storytelling.
- Learners will be able to improve their critical thinking and creativity skills with techniques from the worlds of analytics and design.
Training Needs and Capacity Gap Analysis
Master one of the most important and complex analytical needs for an L&D professional to map capacity gaps.
Learning Project Output
Annual planning presentation showcasing training and capacity gap analysis along with recommendations for future L&D initiatives.
Why would this be useful?
A great L&D team provides recommendations on a recurring basis to executives for how to best uplift a company’s greatest resource - its people. A high-quality training needs and capacity gap analysis will allow you to stand out as a contributor to one of the most crucial and complex deliverables required of the L&D team. Join a virtual coached and facilitated Learning Sprint where we apply data analytics and design techniques to map gaps and produce unique and actionable L&D recommendations.
who is this for?
Anyone working in L&D. You do NOT need to have prior experience in statistics, but you MUST be familiar with Excel or another spreadsheet program. This will not require any tools other than Excel.
Program Duration
9 hours spread out over 3 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to create heatmaps and waterfall charts to visualize training gaps.
- Learners will be able to compare and conceptualize inputs into a training needs analysis.
- Learners will be able to use benchmarking analysis for comparative analysis.
- Learners will be able to use design techniques to ideate creative and user-centred L&D initiatives.
Behavioural Outcomes
- Learners will be able to begin all L&D planning with a rigorous gap analysis.
- Learners will be able to build effective data collection into all L&D initiatives.
- Learners will be able to bring both data and design mindsets into initiative planning and prescriptive analytics.
- Learners will be able to move from gap analysis to lift analysis in order to better support assessing training impact.
- Learners will be able to share with their peers how data skills can enhance their work as L&D professionals.
- Learners will be able to communicate in a credible and understandable manner with data storytelling.
- Learners will be able to improve their critical thinking and creativity skills with techniques from the worlds of analytics and design.
Designing Amazing Experiential Learning
Learn the backwards design approach to create unforgettable experiential learning for your teams and clients.
Learning Project Output
User journey for an end-to-end experiential learning experience with consideration of user personas, intended outcomes & metrics, and activities derived from the backwards design approach.
Why would this be useful?
Experiential learning is one of the most effective ways to learn, providing not just memorable hands-on experience for learners, but also a way for learners to ensure they build concrete deliverables that can be used at work or shared with their teams. Join a virtual coached and facilitated Learning Sprint where Eskwelabs will show you how to combine project management and design techniques with instructional design to create an effective experiential training for almost any subject or audience.
who is this for?
Anyone working in L&D, training, or education. Prior experience with alternative learning approaches such as ADDIE or SAM, or instructional design more generally is beneficial but not required. The primary tool for this sprint will be Miro.
Program Duration
6 hours spread out over 2 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to use the backwards design approach with an Eskwelabs-provided framework to develop learning experiences.
- Learners will be able to use Miro as a design and ideation tool.
- Learners will understand the key tradeoffs involved in experiential learning.
Behavioural Outcomes
- Learners will be able to design effective experiential trainings, workshops, courses, programs, projects, onboardings, or journeys for their stakeholders.
- Learners will be able to authoritatively communicate about the benefits and disadvantages of experiential learning and the situations in which it is worthwhile.
Talent Acquisition
Headcount Planning Forecast
Learn to accurately predict your headcount and recruitment needs with effective forecasting.
Learning Project Output
Presentation of forecasted numbers with multiple scenarios and sensitivity analysis using Excel regression model.
Why would this be useful?
Forecasting is one of the more complex tasks involved in human resources, yet vital to the success of the team. Stand out by being able to use historical data, scenario planning, and sensitivity analysis to plan out the recruitment needs of your firm.
who is this for?
Anyone responsible to plan out hiring or onboarding needs for a company, including but not limited to HR professionals. No prior statistics experience is required, although prior experience in Excel or Python is necessary. This sprint can be carried out with either Excel or Python.
Program Duration
12 hours spread out over 4 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out a simple regression or time series-based forecasting based on historical data.
- Learners will be able to create scenarios that affect key variables involved in the forecast.
- Learners will be able to use sensitivity analysis to provide nuance to their forecast and plan for variance.
Behavioural Outcomes
- Learners will be able to use historical data and best techniques in forecasting to provide informed evidence for decision-makers of the recruitment needs for the team.
- Learners will be able to provide inputs into capacity planning, KPIs, OKRs, and team target-setting for HR in the form of future headcount estimates.
Capacity Planning
Plan your team’s staffing needs based on organizational goals, including scenarios for variance in team attrition and performance.
Learning Project Output
Presentation of staffing needs with multiple scenarios and sensitivity analysis using Excel regression model.
Why would this be useful?
Capacity planning is a key skill for managers in HR, People Operations, and Talent Acquisition. Combining forecasting with sensitivity analysis allows for a team to use historical data to predict their future team needs and staff accordingly in order to meet business goals.
who is this for?
Anyone responsible to plan out staffing needs for a company, including but not limited to HR professionals. No prior statistics experience is required, although prior experience in Excel or Python is necessary. This sprint can be carried out with either Excel or Python.
Program Duration
12 hours spread out over 4 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- How to select and design beautiful data visualisations for powerful stories
- How to derive insights from data that speak to client needs
- How to structure data narratives for storytelling
- The core statistics used in data storytelling for executive communication
- How to present and communicate data to stakeholders with actionable recommendations for data-driven decision-making
Behavioural Outcomes
- Teams can produce higher quality insights that go beyond pure descriptive analytics and instead provide high-quality insights that generate actionable recommendations
- Teams can report and communicate results to stakeholders in a more persuasive and effective manner using data storytelling
Attrition Modelling
Provide detailed employee churn analysis for your teams using simple but effective cohort analysis techniques.
Learning Project Output
Presentation of employee attrition and behavior based on descriptive and cohort-based analysis.
Why would this be useful?
Reducing employee attrition is often a key focus of talent management inside a company, due to the consequent impact on hiring, onboarding & training, employee morale, succession planning, and culture. Calculating employee attrition is also a crucial input into headcount planning and capacity planning for talent acquisition and recruitment teams. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out retention analysis and prescriptive analytics to improve customer retention.
who is this for?
Anyone working in teams focused on People or Human Resources, including talent acquisition (for hiring needs planning), L&D (for onboarding planning), and management would benefit from being able to carry out quantitative employee attrition analytics. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
9 hours spread out over 3 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners are able to calculate employee attrition and retention rates with different metrics quickly and effectively over any time period.
- Learners are able to use cohort analysis to calculate individual churn rates per employee cohort.
- Learners are able to connect cohort analysis to prescriptive analytics to identify explanatory variables for employee churn and hypotheses for exploration.
Behavioural Outcomes
- Learners are able to provide recommendations to reduce churn based on quantitative analysis of attrition and the factors involved in it.
- Learners are able to calculate retention metrics for reporting and as inputs into headcount and capacity forecasting plans.
- Learners describe attrition and retention with authority and with an understanding of the limitations and insights present in the underlying calculations.
Quality of Hire Analytics
Provide effective analytic inputs to quantify and compare quality of hire based on diverse inputs.
Learning Project Output
Presentation of quality of hire metrics based on descriptive analysis with decomposition of relevant factors.
Why would this be useful?
It’s one thing to bring in new hires, but it’s another thing to bring in the right hires. Quality of hire analysis is about quantifying the relevance and fit of new hires, allowing you to identify the contribution to team performance of the recruitment function of your organization. Quality of hire analytics is a complex but important analysis technique that involves segregating the roles played by a variety of different factors, including the quality of the hire themselves, the fit with the team they join, the background effects of team cultures and managers, and the inherent challenges associated with the role.
who is this for?
Any team that is involved in recruitment and wants to quantify the quality of hire as well as identify and assess the key factors at their organization involved in new hire quality and performance. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
9 hours spread out over 3 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners are able to set up quality of hire calculations and explore them to create descriptive and comparative trend analysis.
- Learners are able to conceptualize and separate out the variables involved in quality of hire including candidate quality, team fit, manager quality, and more, and model them in order to assess the relative importance and value of each.
Behavioural Outcomes
- Learners are able to provide recommendations to improve and track quality of hire based on quantitative analysis, including assigning rankings and coefficient weights to success factors.
- Learners are able to input quality of hire metrics and explanatory variables into other workstreams including capacity planning, forecasting, or attrition modeling.
Recruitment Funnel Optimization
Optimize your recruitment funnel to maximize conversion at every stage.
Learning Project Output
Presentation of optimized recruitment funnel with recommendations to improve conversion based on pipeline stage benchmarks.
Why would this be useful?
An effective recruitment funnel is necessary for the survival and growth of a modern organization, but how many teams are familiar with the pipeline analytics and statistics required to quantify, assess, and optimize a recruitment funnel? Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out pipeline and funnel analytics that can be applied in your industry.
who is this for?
Recruitment and HR professionals who want to optimize their recruitment funnel. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
9 hours spread out over 3 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out funnel analysis, including conversion rates, cumulative losses, and funnel decomposition.
- Learners will be able to use benchmarking analysis to compare conversion rates at each step of the funnel to expected industry standards and calculate lift from possible recommendations.
- Learners will be able to provide recommendations and insights to improve conversion rates and optimize the funnel.
Behavioural Outcomes
- Learners will be able to systematically track and analyze their recruitment funnel in order to tweak it and optimize it over time.
Sales
Sales Projections and Forecasting
Learn to use Excel to effectively estimate projected sales under different scenarios.
Learning Project Output
Presentation of forecasted numbers with multiple scenarios and sensitivity analysis using Excel regression model.
Why would this be useful?
Predicting sales volumes allows organizations to anticipate future revenue, plan around it, and proactively address resource gaps. Teams that can carry this out can adapt their forecasts to market conditions, allowing the company to be more informed and nimble. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out sales projections that can be applied in your industry.
who is this for?
Sales or BD managers and analysts that need to forecast their expected future sales. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
12 hours spread out over 4 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out a simple market factors forecast based on historical data.
- Learners will be able to create scenarios that affect key variables involved in the forecast.
- Learners will be able to use sensitivity analysis to provide nuance to their forecast and plan for variance.
Behavioural Outcomes
- Learners will be able to use historical data and best techniques in forecasting to provide informed evidence for decision-makers of incoming revenue generation.
- Learners will be able to provide inputs into capacity planning, KPIs, OKRs, and team target-setting for sales or business development teams about expected sales under baseline and edge scenarios.
Sales Workforce Analysis and Capacity Planning
Plan your sales team’s staffing needs based on organizational goals, including scenarios for variance in team attrition and performance.
Learning Project Output
Presentation of staffing needs with multiple scenarios and sensitivity analysis using Excel regression model.
Why would this be useful?
Capacity planning is a key skill for sales managers. Combining forecasting with sensitivity analysis allows for a team to use historical data to predict their future team needs and staff accordingly in order to meet sales targets. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out workforce planning analysis that can be applied in your industry.
who is this for?
Anyone responsible to plan out staffing needs for a company, including but not limited to sales team managers and staffing specialists. No prior statistics experience is required, although prior experience in Excel or Python is necessary. This sprint can be carried out with either Excel or Python.
Program Duration
12 hours spread out over 4 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out a simple regression or time series-based forecasting based on historical data.
- Learners will be able to create scenarios that affect key variables involved in the forecast.
- Learners will be able to use sensitivity analysis to provide nuance to their forecast and plan for variance.
Behavioural Outcomes
- Learners will be able to use historical data and best techniques in forecasting to provide informed evidence for decision-makers of the staffing needs for the team.
- Learners will be able to integrate concepts of variance into capacity planning.
Pipeline and Conversion Analytics
Optimize your sales pipeline to maximize conversion at every stage.
Learning Project Output
Presentation of sales funnel with recommendations to improve conversion based on pipeline stage benchmarks.
Why would this be useful?
Revenue-generating teams such as sales and business development rely on their sales pipeline to move prospects from leads to deals. Sales analytics can allow a team to optimize their pipeline in order to identify and troubleshoot problematic steps as well as identify lift from comparison to benchmarks. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out pipeline and funnel analytics that can be applied in your industry.
who is this for?
Sales managers and professionals who want to optimize their sales funnel. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
9 hours spread out over 3 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out funnel analysis, including conversion rates, cumulative losses, and funnel decomposition.
- Learners will be able to use benchmarking analysis to compare conversion rates at each step of the funnel to expected industry standards and calculate lift from possible recommendations.
- Learners will be able to provide recommendations and insights to improve conversion rates and optimize the funnel.
Behavioural Outcomes
- Learners will be able to systematically track and analyze their sales pipeline in order to tweak it and optimize it over time.
Analyzing Outreach Email A/B Test Results
Develop and analyze A/B tests to optimize your outreach emails.
Learning Project Output
Presentation of A/B test results with statistical significance with recommendations for which emails to use and future tests to run.
Why would this be useful?
Your outreach materials are a key driver of success in sales and revenue operations, but getting the most out of them entails understanding how to use statistical analysis to run and interpret A/B tests. Use existing tools to calculate required sample sizes, structure effective A/B tests, and interpret results to integrate best practices in research into your day-to-day work. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out A/B testing for sales & BD outreach that can be applied in your industry.
who is this for?
Anyone who finds themselves sending outreach emails for sales or business development purposes. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
6 hours spread out over 2 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to interpret A/B test results, including statistical significance, rejection of the null hypothesis, and p-values.
- Learners will be able to structure A/B tests for email comparison, including calculating required sample size and ensuring sound experimental design.
Behavioural Outcomes
- Learners will be able to systematically improve their email outreach by using A/B testing to identify high-performing variants under appropriate testing conditions.
Data Storytelling for Technical Sales
Enhance your client credibility with data storytelling and integration of quantitative insights into your sales communication.
Learning Project Output
Pitch presentation that integrates data storytelling to effectively build interest and handle objections from a quantitatively-inclined audience.
Why would this be useful?
Human beings associate well-articulated statistical insights with credibility, expertise, and objectivity. As sales professionals, it behoves us to do what we can to enhance our credibility towards clients by being able to speak authoritatively about the facts and the evidence base, and to use statistical insights and data storytelling techniques to reinforce our ability to inform others. Join a virtual coached and facilitated Learning Sprint where we apply data storytelling techniques to help you build better pitches, objection-handling, and general client-facing communication.
who is this for?
Sales executives and managers that find themselves selling technical products and want to enhance their technical credibility towards potential clients through the strategic use of data storytelling and quantitative insights.
Program Duration
6 hours spread out over 2 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners are able to translate key metrics and data concepts into layman’s terms in order to use them for objection handling and sales calls.
- Learners are able to identify aspects of their pitch and potential objections that can be improved with data storytelling.
Behavioural Outcomes
- Learners are able to enhance their communication and critical thinking skills through the use of data analysis and data storytelling
Marketing
Customer User Journey Analytics
Optimize your customer user journey to maximize conversion at every stage.
Learning Project Output
Presentation of customer user funnel with recommendations to improve conversion based on pipeline stage benchmarks.
Why would this be useful?
Marketing relies on customer user journeys to move prospects from awareness to revenue (and beyond!). Funnel analytics can allow a team to optimize their user journey in order to identify and troubleshoot problematic steps as well as identify lift from comparison to benchmarks. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out user journey analytics that can be applied in your industry.
who is this for?
Marketing managers and analysts who are charged with optimizing user journeys, whether from marketing campaigns, websites, brand events, or any other instance of marketing that involves concrete steps for a user to go through to reach conversion into revenue. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
9 hours spread out over 3 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out funnel analysis, including conversion rates, cumulative losses, and funnel decomposition.
- Learners will be able to use benchmarking analysis to compare conversion rates at each step of the funnel to expected industry standards and calculate lift from possible recommendations.
- Learners will be able to provide recommendations and insights to improve conversion rates and optimize the funnel.
Behavioural Outcomes
- Learners will be able to systematically track and analyze their customer user journeys in order to tweak and optimize them over time.
Churn Analytics
Provide detailed customer churn analysis for your teams using simple but effective cohort analysis techniques.
Learning Project Output
Presentation of churn & customer behavior based on descriptive and cohort-based analysis.
Why would this be useful?
Reducing churn is one of the best ways to increase business value, and reducing churn starts with being able to analyze it. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out churn analysis and prescriptive analytics to improve customer retention.
who is this for?
Anyone working in product or marketing would benefit from being able to carry out quantitative churn or retention analytics. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
9 hours spread out over 3 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners improve the user journey of their customers through integrating the results of churn and retention analysis, and use it to improve marketing materials and communication.
- Learners are able to think about churn in the context of cohorts, feature iterations, and Agile rather than as a single metric
- Learners describe churn and retention with authority and with an understanding of the limitations and insights present in the underlying calculations.
Behavioural Outcomes
- Teams can produce higher quality insights that go beyond pure descriptive analytics and instead provide high-quality insights that generate actionable recommendations
- Teams can report and communicate results to stakeholders in a more persuasive and effective manner using data storytelling
Customer Targeting and Segmentation Analysis
Create insightful user segments for marketing purposes based on quantitative and qualitative data inputs that can be used for effective user targeting with product features and services.
Learning Project Output
Presentation of user target segments along with defining characteristics and recommended GTM strategy per segment.
Why would this be useful?
User segments are a core part of targeting for marketing campaigns and outreach, but many professionals are unaware of effective quantitative techniques for segmentation. Learn the skills that allow you to use modern tools and understand statistical results of user segmentation in order to build and interpret your own or existing user segments for targeting. Join a virtual coached and facilitated Learning Sprint where we apply data analytics or data science techniques to carry out user segmentation that can be applied in your industry.
who is this for?
Marketing analysts and managers who need to deal with targeting for marketing campaigns. No prior statistics experience is required, although prior experience in Excel is necessary. This sprint can be carried out with Excel without any machine learning. An alternative version of this sprint can be carried out using machine learning - for this, prior familiarity with Python or R is required.
Program Duration
6 hours spread out over 2 sessions (no machine learning)
12 hours spread out over 4 sessions (with machine learning)
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to identify the most relevant characteristics for user segmentation and use them to define segments with both measures of central tendency and variance.
- Learners will be able to identify the strengths and limitations of user segments.
- Learners will be able to integrate user segments into prescriptive analytics.
- [Optional] Learners will be able to use classical unsupervised machine learning for algorithmically-created user segments.
- [Optional] Learners will be able to interpret algorithmically-created user segments.
Behavioural Outcomes
- Learners will provide quantitative evidence for user segments and personas, rather than intuition.
- Learners will add value-added insights through analyzing user metrics through the lens of user segments.
Structuring and Analyzing Campaign A/B Tests
Develop and analyze A/B tests to optimize your marketing campaigns.
Learning Project Output
Presentation of A/B test results with statistical significance with recommendations for which campaign variants to use and future tests to run.
Why would this be useful?
A/B testing is a core feature of setting up successful marketing campaigns, by allowing marketers to identify which versions of a campaign will be more successful with potential customers. Use existing tools to calculate required sample sizes, structure effective A/B tests, and interpret results to integrate best practices in research into your day-to-day work. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out A/B testing for campaign selection that can be applied in your industry.
who is this for?
Anyone who finds themselves analyzing marketing campaigns. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
6 hours spread out over 2 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to interpret A/B test results, including statistical significance, rejection of the null hypothesis, and p-values.
- Learners will be able to structure A/B tests for email comparison, including calculating required sample size and ensuring sound experimental design.
Behavioural Outcomes
- Learners will be able to systematically improve their marketing campaign analysis by using A/B testing to identify high-performing variants under appropriate testing conditions.
LTV Analysis
Optimize your customer’s user journey to maximize retention and lifetime value.
Learning Project Output
Presentation of customer user journey with recommendations to improve lifetime value based on user journey stage benchmarks.
Why would this be useful?
The user journey is one of the most important aspects of optimizing customer lifetime value, and user journey analytics provides a useful tool to quantify and assess how this journey can be improved and refined. User journey analytics analytics can allow a team to identify upselling and cross-selling opportunities as well as identify lift from comparison to benchmarks for LTV extension opportunities. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out user journey analytics that can be applied in your industry.
who is this for?
Marketing managers, analysts, customer loyalty teams, and product teams tasked with improving the lifetime value and revenue per customer. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
9 hours spread out over 3 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out funnel analysis and identify product upselling and cross-selling opportunities at each stage of the funnel.
- Learners will be able to use benchmarking analysis to compare conversion rates at each step of the funnel to expected industry standards and calculate lift from possible recommendations.
- Learners will be able to provide recommendations and insights to improve retention rates and optimize the funnel.
Behavioural Outcomes
- Learners will be able to systematically track and analyze their customer lifetime user journeys in order to tweak and optimize them over time.
User Growth Forecasting and Analysis
Learn to use Excel to effectively estimate projected user growth under different scenarios.
Learning Project Output
Presentation of forecasted numbers with multiple scenarios and sensitivity analysis using Excel regression model.
Why would this be useful?
User growth is a key metric for most companies to optimize, and the capacity of the team to provide predictive analytics for user growth modelling is an important input for business planning and strategy. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out user growth projections that can be applied in your industry.
who is this for?
Marketing managers and analysts that need to forecast their expected user growth numbers. No prior statistics experience is required, although prior experience in Excel or Python is necessary. This sprint can be carried out with either Excel or Python.
Program Duration
12 hours spread out over 4 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out a simple time series analysis forecast based on historical data.
- Learners will be able to create scenarios that affect key variables involved in the forecast.
- Learners will be able to use sensitivity analysis to provide nuance to their forecast and plan for variance.
Behavioural Outcomes
- Learners will be able to use historical data and best techniques in forecasting to provide informed evidence for decision-makers of expected growth in new users.
- Learners will be able to provide inputs into capacity planning, KPIs, OKRs, and team target-setting for sales or business development teams about expected sales under baseline and edge scenarios.
Product
User Segmentation Analysis
Create insightful user segments for product development purposes based on quantitative and qualitative data inputs that can be used for effective user targeting with product features and services.
Learning Project Output
Presentation of user target segments along with defining characteristics and user behavior, and proposed segment-relevant features.
Why would this be useful?
User segmentation allows you to move beyond treating your users as one homogenous mass and instead identify the defining characteristics that set users apart from one another and allow you to prioritize segments and identify what drives engagement, retention, satisfaction, and revenue on a per-segment basis. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out user segmentation that can be applied in your industry.
who is this for?
Product analysts and managers, customer loyalty teams, or customer satisfaction teams who deal with user data and want to produce more effective recommendations for user engagement or revenue based on targeted segmentation. No prior statistics experience is required, although prior experience in Excel is necessary. This sprint can be carried out with Excel without any machine learning. An alternative version of this sprint can be carried out using machine learning - for this, prior familiarity with Python or R is required.
Program Duration
6 hours spread out over 2 sessions (no machine learning)
12 hours spread out over 4 sessions (with machine learning)
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to identify the most relevant characteristics for user segmentation and use them to define segments with both measures of central tendency and variance.
- Learners will be able to identify the strengths and limitations of user segments.
- Learners will be able to integrate user segments into prescriptive analytics.
- [Optional] Learners will be able to use classical unsupervised machine learning for algorithmically-created user segments.
- [Optional] Learners will be able to interpret algorithmically-created user segments.
Behavioural Outcomes
- Learners will provide quantitative evidence for user segments and personas, rather than intuition.
- Learners will add value-added insights through analyzing user metrics through the lens of user segments.
Customer Lifetime Journey Analytics
Optimize your customer’s lifetime user journey to maximize retention at every stage.
Learning Project Output
Presentation of customer user journey with recommendations to improve retention based on pipeline stage benchmarks.
Why would this be useful?
The user journey is one of the most important aspects of optimizing retention and revenue from users, and user journey analytics provides a useful tool to quantify and assess how this journey can be improved and refined. Funnel analytics can allow a team to optimize their user journey in order to identify and troubleshoot problematic steps as well as identify lift from comparison to benchmarks. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out user journey analytics that can be applied in your industry.
who is this for?
Product managers and analysts who wish to improve user retention and have visibility over distinct stages of a user journey with their product. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
9 hours spread out over 3 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out funnel analysis, including conversion rates, cumulative losses, and funnel decomposition.
- Learners will be able to use benchmarking analysis to compare conversion rates at each step of the funnel to expected industry standards and calculate lift from possible recommendations.
- Learners will be able to provide recommendations and insights to improve conversion rates and optimize the funnel.
Behavioural Outcomes
- Learners will be able to systematically track and analyze their customer user journeys in order to tweak and optimize them over time.
Customer Cohort and Churn Analysis
Provide detailed customer churn analysis for your teams using simple but effective cohort analysis techniques.
Learning Project Output
Presentation of churn & customer behavior based on descriptive and cohort-based analysis.
Why would this be useful?
Reducing churn is one of the best ways to increase business value, and reducing churn starts with being able to analyze it. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out churn analysis and prescriptive analytics to improve customer retention.
who is this for?
Anyone working in product or marketing would benefit from being able to carry out quantitative churn or retention analytics. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
9 hours spread out over 3 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners are able to calculate churn and retention rates with different metrics quickly and effectively over any time period.
- Learners are able to use cohort analysis to calculate individual churn rates per customer cohort.
- Learners are able to connect cohort analysis to prescriptive analytics to identify explanatory variables for churn and hypotheses for exploration.
Behavioural Outcomes
- Learners improve the user journey of their customers through integrating the results of churn and retention analysis, and use it to improve client communication.
- Learners are able to think about churn in the context of cohorts, feature iterations, and Agile rather than as a single metric.
- Learners describe churn and retention with authority and with an understanding of the limitations and insights present in the underlying calculations.
Structuring Product Feature A/B Tests
Develop and analyze A/B tests to analyze the impact of new product features.
Learning Project Output
Presentation of A/B test results with statistical significance with recommendations for which product feature variants to use and future tests to run.
Why would this be useful?
Products require new features in order to meet user needs and maintain growth and retention, but which features have the most impact? Which color is the most effective for a new gamified progress bar? Which email copy would be more effective to announce our new version launch? These are all questions that can be answered with A/B testing. Use existing tools to calculate required sample sizes, structure effective A/B tests, and interpret results to integrate best practices in research into your day-to-day work. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out product feature analysis that can be applied in your industry.
who is this for?
Product managers and analysts who find themselves proposing and implementing new features, or teams that have to communicate such changes. Prior experience in statistics is not required, but learners should already be comfortable with Excel.
Program Duration
6 hours spread out over 2 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to interpret A/B test results, including statistical significance, rejection of the null hypothesis, and p-values.
- Learners will be able to structure A/B tests for email comparison, including calculating required sample size and ensuring sound experimental design.
Behavioural Outcomes
- Learners will be able to systematically measure and improve which product features are being rolled out by using A/B testing to identify high-performing variants under appropriate testing conditions.
Design Thinking for Product Feature Development
Adopt the core tools and processs of human-centered design in a design sprint in order to improve your ability to plan out product feature needs and development.
Learning Project Output
Design sprint results for a new feature proposal with prototype pilot implementation plan
Why would this be useful?
Design thinking is an entire discipline, but key aspects of design thinking can be used by any professional to help develop creative and effective product feature ideas that are rooted in the science of user design. Evolve away from haphazard feature ideation and towards the structured process of a design sprint in order to better address your user needs. Join a virtual coached and facilitated Learning Sprint where Eskwelabs will show you how to combine project management and design techniques to carry out a design sprint and propose an effective feature idea and prototype pilot plan for almost any subject or audience.
who is this for?
Anyone working in product development who wants to add key design skills to their toolkit. Prior experience with product management is beneficial but not required. The primary tool for this sprint will be Miro.
Program Duration
8 hours spread out over 4 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to use the design sprint framework to develop new feature ideas and prototyping plan.
- Learners will be able to use Miro as a design and ideation tool.
- Learners will understand the key concepts involved in effective user research, ideation, and prototyping.
Behavioural Outcomes
- Learners will be able to design effective feature ideas and pilot plans for their stakeholders.
- Learners will be able to support other team members to integrate design thinking into their workflows.
User Growth Forecasting and Analysis
Learn to use Excel to effectively estimate projected user growth under different scenarios.
Learning Project Output
Presentation of forecasted numbers with multiple scenarios and sensitivity analysis using Excel regression model.
Why would this be useful?
User growth is a key metric for most companies to optimize, and the capacity of the team to provide predictive analytics for user growth modelling is an important input for business planning and strategy. Join a virtual coached and facilitated Learning Sprint where we apply data analytics techniques to carry out user growth projections that can be applied in your industry.
who is this for?
Product managers and analysts that need to forecast their expected user growth numbers. No prior statistics experience is required, although prior experience in Excel or Python is necessary. This sprint can be carried out with either Excel or Python.
Program Duration
12 hours spread out over 4 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners will be able to carry out a simple time series analysis forecast based on historical data.
- Learners will be able to create scenarios that affect key variables involved in the forecast.
- Learners will be able to use sensitivity analysis to provide nuance to their forecast and plan for variance.
Behavioural Outcomes
- Learners will be able to use historical data and best techniques in forecasting to provide informed evidence for decision-makers of expected growth in new users.
- Learners will be able to provide inputs into capacity planning, KPIs, OKRs, and team target-setting for sales or business development teams about expected sales under baseline and edge scenarios.
Management
Decision Science and Data-Driven Leadership
Learn from business leaders and data scientists how to use data analytics techniques to improve your decision-making and communication skills and to move your team towards a higher level of data maturity.
Learning Project Output
Presentation of a personalized multi-criteria decision model framework for a key business target (e.g. revenue) that can be communicated to stakeholders.
Why would this be useful?
Making good decisions is the single most important task entrusted to any form of leader. The world of data has enabled the emergence of a new domain - decision science - which uses a combination of business strategy frameworks and data analytics techniques in order to inform better decisions grounded in the kinds of insights that only your data can tell you. Join a virtual coached and facilitated Learning Sprint where you will familiarize yourself with the value, limitations, requirements, and success factors of decision science techniques, and build your own multi-critera decision model to help you in your most important decisions.
who is this for?
Managers or business leaders who want to be more informed and convincing through the use of decision science techniques that inform data-driven decision-making. No prior experience in programming or statistical analysis are required, although prior familiarity with Excel is recommended.
Program Duration
4 hours spread out over 4 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners can draw on aspects of decision science and data translation in order to improve their effectiveness as a leader.
- Learners understand the core components of data maturity, including governance, strategy, culture, processes, tools, and data quality, and how they can be applied in the context of your organization.
- Learners can structure simple multiple-criteria decision analysis models to help make complex data-driven decisions.
Behavioural Outcomes
- Learners have a data-driven mindset by which to set and evaluate decisions.
- Learners feel connected to their fellow participants in this Learning Sprint as future supporters and peers in your learning journey as a data-driven leader.
Data Science Project Leadership for Non-Data Scientist Managers
Learn from business leaders and data scientists how to translate between the world of AI and the word of business in order to conceptualize and oversee successful data projects.
Learning Project Output
Presentation of an innovative application of data science and artificial intelligence for a business need at your organization including success criteria and requirements.
Why would this be useful?
Data science is one of the key drivers of the 4th Industrial Revolution, but most organizations struggle to obtain real value from this expensive and complex domain. Challenges include a lack of familiarity with data by those closest to the business problems, but also a lack of connection to the business priorities by the engineers and scientists most fluent in data science & analytics. The modern manager must learn this new language in order to effectively act as a translator between business priorities and data teams. Join a virtual coached and facilitated Learning Sprint where you will familiarize yourself with the value, limitations, requirements, and success factors of different data science and AI use cases,and practice conceptualizing a data science project with clear success criteria in the context of your work.
who is this for?
Managers or business leaders who lead or interact with data science or data analytics teams but do not themselves have a data science or machine learning background. No prior experience in programming or statistical analysis are required. This class will focus on project leadership for a manager working with data scientists or data analysts; it will not involve hands-on data engineering, modelling, or other coding or statistical analysis in class - for such skills please look into our other sprints intended for data scientists and analysts.
Program Duration
4 hours spread out over 4 sessions
Mode of Delivery
Virtual Instructor-Led with Virtual Small Group Coaching
Learning Outcomes
- Learners can differentiate between useful data, noise, and data gaps, and understand the impact of features and quantity for data project success.
- Learners understand the use cases, requirements, value, and constraints for all four forms of data analytics, classical machine learning, and deep learning applications, and can effectively vet when and which are most likely to bring value to your team.
Behavioural Outcomes
- Learners can use data translation to switch between communicating with business stakeholders and data science stakeholders.
- Learners can conceptualize realistic project ideas and build persuasive proposal plans for data projects.
- Learners are excited to help their company reach their full potential with data science and AI.
- You feel empowered to discuss data needs and projects with both non-data teams and data teams.
- Learners feel connected to their fellow participants in this Learning Sprint as future supporters and peers in your learning journey as a data-driven leader.
How teams were transformed
Eskwelabs designed structured and effective cohort-based courses that offered career mobility to our customer support staff serving tech giants to move into digital roles.
Ryan Bobos
Kumu is building a rocketship and Eskwelabs has been a key cog to make this happen. Eskwelabs created the right formula to enable success for our teams.