Everyone uses data to make their point or prove something. Often this is done by interpreting the data the way it suits you as a writer without taking into account a number of factors. Think of: Do the figures give the right picture? A good example of this is described by Renée Romkes in her July 2013 blog about the limitation of the data collected and the conclusions that are attached to it. She rightly points out that the figures on violence against women do not indicate the extent of the violence, but only the number of women who dared to report. Yet we read this number everywhere as THE NUMBER that indicates violence against women.
Is the collected data correct?
A good example of distorted images is the measurement of new visits in Google Analytics. According to the graph, a large proportion of the visitors to my website in the month of August 2013 were new visitors (read = Never visited my site). It would be nice if this were true, but I know that some visitors to my website always disable their cookies. They are therefore always seen as new visitors by Google. So just drawing the conclusion based on this graph that my website only attracts new visitors is not a correct conclusion.
In addition to collecting the wrong data and collecting data incorrectly, it can also go wrong when interpreting data. Data only measures and we connect this data on the basis of our conclusions.
Another example from Google Analytics. Based on statistics, it is possible to look on your website at which places there is a lot of clicking. This is very interesting for web stores, for example bol.com. With these statistics you can see through the color variation what visitors click on the most. By following this data over a longer period you can make statements. For example, that the products in the upper right corner are always clicked on. This means that they go to a deeper layer of the website more often. In this case the explanation of, for example, a book. Is this important yes? The visitor stays on your website, but do they buy the product? Mostly not. So you have a lower conversion from click behavior to revenue.
A better measurement would be that like Amazon and also bol.com use to classify the website according to your interests. Which products did you last buy or view that come on top. Quite annoying if you’ve already bought them before and don’t want to buy the same dress again. But when it comes to books in the same genre you can be tempted to make a purchase if there is money left over. Read larger sales.
Buy now ecourse Big Data with ExcelIs this a good predictor of sales?
For web stores, but there are more variables that count. Another big one is customer satisfaction, service. This is measurable by putting a survey at the end that the customer fills out, but this is a snapshot. This is only mass data and at large companies BiG Data. But do not forget to occasionally hold an interview or a focus group to find out more about your client. Data alone never gives the answer to the needs of your customers and therefore your revenue growth, because they are just figures to which we attach a statement or conclusion. With this you miss the individual explanation of the explanation behind the data.
Yet, again business cards
Varied vegetarian food
2nd quarter and charity 3rd quarter turnover
hip, Instagram cooking class
My after Corona Bucket List
Chocolate fair
Not one but two walking related conferences just before spring starts
No I am not ….
No reason Retroprints
1 percent turnover to Wegloophuis Utrecht
1st quarter 2020
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