How to analyze data - 11 tips for marketers

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There are certain ways that may help marketers in data analytics. Here we present 11 tips that should be remember and used if you want to do you ecommerce business successfully.

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How to analyze data - 11 tips for marketers

  1. 1. Problem
  2. 2. Internet grows annually by 40%
  3. 3. Equally rapidly is growing amount of data about our customers, about what do they click, what do they like, what sites they visit, etc.
  4. 4. None marketer is able to analyze this data set itself... Equally rapidly is growing amount of data about our customers, about what do they click, what do they like, what sites they visit, etc.
  5. 5. Solution
  6. 6. It is a science dealing with artificial intelligence, and its purpose is to allow to automate knowledge enrichment, reasoning and decision-making by modern software. Machine Learning
  7. 7. It allows us to put together all the data we have about the customer and his behavior and get from this information relevant solutions for business development and sales. Machine Learning
  8. 8. Benefits
  9. 9. Advanced mechanism of data mining can help you better understand the customer in any industry and increase sales.+
  10. 10. Advanced mechanism of data mining can help you better understand the customer in any industry and increase sales.+ +It will help you with reports preparation and even recommend the changes.
  11. 11. Advanced mechanism of data mining can help you better understand the customer in any industry and increase sales.+ + + It will save your time because data will be collected and processed automatically. It will help you with reports preparation and even recommend the changes.
  12. 12. Advanced mechanism of data mining can help you better understand the customer in any industry and increase sales.+ + + It will help you with reports preparation and even recommend the changes. You will get relevant solutions and be able to make the best decision for your business and sales. + It will save your time because data will be collected and processed automatically.
  13. 13. To consider
  14. 14. Remember about human factor !
  15. 15. Remember about human factor Complex analytical system would be useful in this process, but also the man who will be able to draw valuable conclusions from these data which can help in creating more effective strategy and provides bigger profits and sale. !
  16. 16. According to the survey “25 Best Jobs in America” created by the Glassdor Survey’s, most wanted profession this year in the US will be data scientist.
  17. 17. What skills should have the ideal analyst? ● analytical thinking, the ability to draw conclusions, ● creativity, ● ability to ask the right questions, ● combining data and facts from various sources.
  18. 18. There are two ways to gain data for the analysis purposes:
  19. 19. There are two ways to gain data for the analysis purposes: Acquired from filled forms, surveys, mailing databases, loyalty programs – data declared by the user. These are mainly information such as e-mail, name, address, age, education. DECLARATIVE DATA
  20. 20. There are two ways to gain data for the analysis purposes: DECLARATIVE DATA BEHAVIORAL DATA Related to user behavior on the site – in which places on the website someone clicks, what is watching, what is buying. We receive them with pre-installed code tracking on the website. Acquired from filled forms, surveys, mailing databases, loyalty programs – data declared by the user. These are mainly information such as e-mail, name, address, age, education.
  21. 21. The system not only collects all the data for us, he also processes it. However, marketer should know what to do with them later.
  22. 22. How to analyze the data properly? 11 tips for marketers
  23. 23. Do not focus on the analysis of individual cases. Depend on the search based on the analysis of the entire data set. 1.From the general to the particular
  24. 24. Correlate collection of data about your customers with other collections and search for dependencies. Check if the exchange rate, month, day, weather, world events etc. affect significantly change the buying behaviour of customers. 2.The strength of the correlation
  25. 25. Pay attention to the fact that the products are linked to each other – a person who is buying a laptop may be interested in a laptop bag, but probably will not buy the keyboard. So analyse shopping carts. 3.Search for links
  26. 26. Consider that sometimes the least important data gives us some information about the client: ● operating system – informs us what kind of income can a customer have ● browser – you’ll be able to properly show up dynamic blocks, etc. ● devices – people who use more devices spend more time on the Internet – it is easier to hit them with on-line advertising 4.The devil is in the details
  27. 27. Analyze data about customers who have recently purchased your products. Pay attention, what devices they used (or perhaps make a purchase in the local store?) what time, in which days. Once you know the answers to these questions it will be easier for you to select the appropriate promotion model. 5.Learn all about those who buy
  28. 28. Check the ways in which customers come to the purchasing process. With such data you can find the patterns and figure out what influences their purchasing decisions. 6.Analyze the entire path purchase
  29. 29. Automatic segmentation allows you to better analyze data and see which results increase or decrease your sale. Eg. if you notice the sharp decline in sales, while a large number of users were on the site, you can ask yourself a question – why they did not buy anything? You should analyze it on the basis of separate customer segments. 7. Build segments
  30. 30. Conversion is often used by marketers in their reports. But the conversion of 20% will achieve when 10 users visit the site and 2 makes a purchase, but also when 10 000 visitors will be on the site and 2 000 users will buy the service. Therefore it is important to take into account in your reports the number of page visits and number of transactions 8. Do not judge by the conversion
  31. 31. It concerns industries selling seasonal products eg. skis, christmas trees, bathing suits, etc. Real conversions should be count for a particular season of the year. Sales of bikini decrease in winter, but people usually look for swimsuits before the summer season. You should then take care of the page optimization, launch a new promotions, etc. 9. Pay attention to seasonality
  32. 32. Propoper analysis of data allows you not only to find the accuracy and better personalize promotional activities, but also to quickly detect anomalies. 10. Search for errors
  33. 33. Eg. Walmart has been long monitoring all the data from sales of their products. On Halloween, they monitored the sale of special Christmas cookies. According to statistics, in many shops total sale of these products were high, but in some of them it was zero. It turned out that in some shops they have not been laid on the shelves. 10. Search for errors
  34. 34. Much data correlated with each other, gives the illusion that both variables actually have an impact on each other but its just coincidence. Eg. Forbes reported that according to studies there is a correlation between the murders in the US and the market shares of Internet Explorer. 11. Watch real
  35. 35. But even if you can see a certain mathematical relationship between the abstract data, it does not mean that in fact there is a causal relationship between them. As you can see – the attendance of a human in the process of data analyse is still necessary. 11. Watch real
  36. 36. Synerise solution
  37. 37. With Synerise you can collect behavioral and declarative data from various data points (online&offline) and gather them in one place directly in the Synerise CRM.
  38. 38. Synerise automatically creates segments in terms of demographic and behavioral issues and segmentation is updated in the real time.
  39. 39. Synerise correlates various data and check dependencies. Eg. Synerise proposes the comparison of transactional clients’ data with the current weather.
  40. 40. www.synerise.com Podole 60/3.30, 30-394 Cracow hello@synerise.com Have a question?

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