Analyze

 Lesson 1

  1. DMBOK2 it's not bad but it's difficult;
  2. Knowledge at the analytics get old fast;
  3. MS Exel and analog it's for simple job;
  4. Business analyze - consultations; Important variables for the business - credit percent for the banks, sells coefficients for seasonal business; e.t.c.; business process is important and tech-ability is so on the second place; More about peoples and communications and less about numeric and tech; Important ability - check interesting but hidden information from the data what is may be profitability; Searching all what must be profitability;
  5. Business analyze - system analyze; They are helpful for the difficult IT systems; They are analyze tech-process and do conclusion about really task what is possible there to get result what we want; Example: how to choose Data Base for the web shop?
  6. Data analyze / BI analyze; It's about visualization; Provide data to do business conclusions but on the simple presents what easily to understanding at the any place by people who don't understand difficult statistics methods; It provide communications also and it's system to show information for all people who need it fastest;
  7. Digital analyze / Web analyze / Web Marketing - about traffic and how to get it effectively and usage; How many and where to pay - how many we shell getting and where from;
  8. Product analyze; It's previous analytic but with high tech-ability;
  9. Finance analyze; It's about finance and about Exel; But some BI instruments and tech-ability will not bad there;

Lesson 2

  1. Engineer, Data Science, Finance - ... 
  2. They are haven't term for it ... it will change ... It's terms without famous measure for our now, also ... Each employer set all what he or she can or want into the bid of the job, also ... 
  3. If watch it as list without guaranties -  possible what complete chance to get job is 0.5 x 0.5 x 0.5 x 0.5 x 0.5 x 0.5 x 0.5 x 0.5 = 0.390625% with each employer it they are not independent or ... What is alternative here?
  4. If they are independent, it's 12.5% per each term x 8 = 100% total;
  5. Finance, product for me ... ;
  6. English - read manual, no communications / government, medicine
  7. Low-Code development, it's platform to create applications with GUI interface what is really equal to code drawing as alternative to write it ... Result will changes? Hm ... It's more on the SaaS, cloud and also isn't cheap;
  8. Limits at 2022 year, politics;

Lesson 3

  1. Analyze, hypothesis and metrics;
  2. Case: what in marketing is better then other, car model what is better selling in Internet, completed profit and margin, car model what is profitability to sell in Internet;
  3. Data: limited data by BMW and Mercedes;
  4. What is metrics? High level: costs, income, clear profit;
  5. Possible to decompose each. Example: income = count of sells + average pay + LVT
  6. All what we do for the high level metrics;
  7. We want points where business have problems to find way exclude them;
  8. Tool where we can find data: web analyze systems, ERP (1C), CRM;
  9. We want metrics what correlate with important business targets/results;
  10. Popular metrics to show off, not for business conclusion. Examples: users per day, pages on the site;
  11. Metrics of effective for conclusions, planning. Example: CTR, ARPU;
  12. Important to each metric:
    1. Sensitive to changes of the product;
    2. Easy to understanding;
    3. Correlation: if it's down - bad, if it's up - good;
    4. Possible to compare to itself or with equal metrics of another products on the market;
    5. Possible to calculate without problem and control this with the management;

  1. Example of key metrics for e-commerce: Revenue, ARPU, ARPPU, Average Margin, LVT, Retention, Repurchase;
  2. Example of key metrics for entertainment sites: MAU, CAC, view per session, Steakness, advertising amount, cash from adv. per user;
  3. Example of key metrics for XaaS: DAU, MAU, CAC, LTV, Steakness, Average subscribe payment, Retention;
  4. Important for the Case: total clear profit and per client, income, deal loop, CJM, unit-economy, CAC, ROMI, Retention, repeatable sells;
  5. CPO (cost per order) = costs to get users / total orders on the site;
  6. CPS (cost per sale) = costs to get users / count of payments on the site;
  7. CPL (costs per lead) = costs to get users / count of leads;
  8. Lead - if we have his contacts;
  9. CPA (costs per action) = costs to get users / count of actions on the site;

  1. Hypothesis = Action [X] provide to increase metric [N] on [Y] because [Z];
  2. Example: If exclude input field to Index, it increase conversion from 12% to 10%, because it will be more easily to customer;
  3. Ask to test hypothesis, before do it:
    1. What is problem? How it's large? Maybe not need to do it? Problem => Solution => Result;
    2. What are we doing? What will customer look here?
    3. Who are our customers? Why are they?
    4. What is key metrics here to measure result?
  4. Source of hypothesis:
    1. Data analyze of the product;
    2. Quality research;
    3. Market research;
    4. Psychology;
  5. Methods to formalize hypothesis:
    1. Interview with customers/users/experts;
    2. Relevant market' cases;
    3. UX-research;
    4. Polling;
    5. Customers/users session analyze;
    6. Compare with competitors;
    7. Check sales funnel;
  6. How to set priority if we have many of them? ICE score method;
    1. Impact - how many positive effect we will get [0 - 10 points];
    2. Confidence - chance to successful result [0 - 10 points];
    3. Ease - about costs to release [0 - 10 points];
  7. Task. Important metrics to service "Auto per subscribe":
    1. ... 
    2. ... 
    3. ... 

Lesson 4




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