Top 5-reasons why organizations fail to leverage Analytics. Aryng's Analytics workshop directly addresses these issues. For more information: www.Aryng.com/blog
Is there alignment between analysts, product managers, marketing managers, QA, Dev?? Are the right people in the room when analytics road map is being planned? Is one able to build consensus and influence?
Good analytics need clean accurate data, i.e customer behaviors are well instrumented to capture clean meaningful information. Additionally, the access to data needs to be easy. Some of the big analytics failures I have seen is where relevant data was not being captured, or not sanitized and/or standardized, or the data sat in disparate sources making the access impossible.
Due diligence to understand the real business question…
Chaotic unstructured approach to analytics, incorrect techniques and methodologies,
We recognize, great analytics is a result of Good technical skills married to good soft skills
So we designed, an Easy to follow, 5-step structured Framework to get one from Data to decisions, which incorporates examples, excises, and role plays to facilitate learning of both technical skills and soft skills.5-step framework, starts from understanding the real business question, laying out a hypothesis-driven analysis plan, data collection and audit, analysis using right techniques and Clear-crisp presentation customized to the audience.
We cover 3- most commonly used techniques, which covers 70-80% of business cases- Trend Analysis: used for Analyzing trends of sales, revenues and identify breaks in trend and segments/ driversSizing and estimation: used for Business case analysis with limited internal data; dependence on external data/ assumptionsCorrelation Analysis: used to study Relationship of an attribute with another attribute useful in pre-post, test- control, dashboard analysis
Predictive Analytics: covering what is it? Why is it useful, case studies from Fortune 500 companies and live demoTest and Learn: covering the 3-steps of effecting experimental design also known as A/B testing or multivariate Testing.