More interest (they responded to the email or called him to better understand the conditions) and were even grateful, while Anthony improved his productivity by focusing on closing the sale of a much more qualified usa phone list for the product already. offered. Basically, Anthony managed to get his list of clients to receive the offer of his credit product, at the -supposedly- most usa phone list moment . And this gave rise to the first CRM on the market that had Event Based Marketing (EBM) functionalities. Most of us rely on the averages of the metrics we get related to timing. For example, we understand that Tuesday and Wednesday between 12:00 and 4:00 p.m. is a good time to launch online product sales campaigns via emails or push notifications in mobile applications because, on average, this is when openings, clickability, reading, response, or sales are best. generate. This approach is useful and effective, and as seen in the pyramid, it is the 3rd most important factor.
But, what if we knew when each user, client or record in our database is more likely to give us that YES? This, of course, is better than using an average and therefore increases the impact of the timing variable. To a certain extent we usa phone list do it when we buy ads in search engines such as Google Ads for users that we do not know or do not have registered usa phone list our database. We know that the one who will receive the ad will be a potential interested party (the right list), usa phone list a product, service or topic of interest (the right offer) and will do so at the TIME that this person chooses as appropriate (the timing appropriate). The combination of the above has proven to be highly effective in contrast to other digital sales channels. This is one of the main characteristics of Pull-type advertising channels. The user asks for information that is served to him at the TIME he requests it, that is why it is so effective. Because it implies the right target, relevant content, but above all... the perfect Timing .
Now, we can also do this with registered users in our database. In other words, we can know when someone shows special interest in something we offer and that is in the digital ecosystem that we "control" (content on the web, in email, in social profiles, etc). Once that MOMENT has been identified, the triggering of immediate actions can be programmed to multiply the usa phone list of generating a response. In other words, we can know when user ID 168353 in our database is most likely to open, read, interact with or buy something… and usa phone list act accordingly. This is possible thanks to the computerized reading that some software does on the digital path that a user leaves in his navigation. This is what is called " Digital Body Language " (DBL). There are already many solutions on the market that -with and without AI- give us the possibility to know this, and to program actions (trigger emails, notifications, calls, etc.) accordingly.