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You are in luck! - Segments, niches, micro-niches…and value! Up to this point we have seen ways to segment and classify our potential customers based on their web behavior or particular characteristics. What's more, all the questions that I have just thrown at you en masse respond to possible candidates for segments to analyze in a comparative way between one another. “Web analytics is a game of questions and answers” This point in the guide will be the true trigger for your SEO strategies and, applied to the rest of the marketing channels, your overall online marketing strategy.
I am not going to answer all the possible types Special Database of segmentation because we would never finish and you can reach the level of detail you want, even if we end up with a report of users with so many filters applied, but what if they are our best users? Therefore, starting from the idea of quantifying the unquantifiable, we must be able to put figures to certain micro-objectives in order to make better business decisions. With this we will answer many other questions: What marketing channels generate the greatest value to the website or blog at an absolute level? And on a relative level? How much do we earn for each visit? In the case of an ecommerce it will be much simpler, because we can assign value based on purchases, and from there begin to distribute weights and values.
How much concepts such as a tweet from the page, watching a video or chatting with our operators are worth. The key will be to collect that information through events, once that is done the possibilities are endless. Let's cross the users who have chatted against those who have not, what share of the proportional income does each have? Therefore, what does a visitor who does not chat and one who chats contribute on average? And between someone who looks at the catalog and someone who doesn't? Or between someone who watches the corporate video or one specific of our services? With this information we can assign numerical values to not so numerical objectives such as attended chats, average waiting time, chatted time, resolved tickets, etc.
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