Every marketing and web management professional should know and use these data science techniques
Currently, the Smart marketing It is being key in the growth of many companies with a clear and solid digital strategy. One of the central factors of this strategy is the creation of intelligent recommendation engines based on what is known as machine learning, which are making a difference in sectors such as tourism and travel, commerce online and, above all, entertainment and stocking.
The key point is have customer data that allow a direct communication with the same one, fine online, offline or through traditional channels, such as the telephone or even the mail. In addition, it is essential to have Data from a first transaction, since, from here on, we already have work material to focus on.
Subsequently, clustering and segmentation techniques they help to group them according to certain common traits; and they analyze the shopping features that customers have associated with each of the products or services to be marketed.
With all this information, the next step is to choose a series of algorithms that are good for Anticipate possible future elections of customers and, therefore, allow us recommend those products or services with a high probability of being chosen.
Of all the models, we finally choose the one that better behavior It has with Regarding a random choice. The necessary adjustments are made and tested with a first campaign. Once finished, we check the “delta”, that is, the deviation, and adjust again.
This exercise is done repeatedly until the algorithm It fits completely adjusted to the level of effectiveness we want to achieve. In the future, it will be necessary to make the adjustment every so often, three or six months, depending on the customer base, products, frequency and effectiveness of the campaigns, etc.
For example, a hotel chain use recommendation engines for Suggest their next trip to their customers with a special family promotion because you know what time of year you take family trips, as well as your favorite destinations and areas, choosing hotels according to your preferences.
Likewise, the Airlines they use them to launch exclusive promotions to gold or platinum customers to leisure destinations that are totally different from your usual business destinations; and they propose as dates for travel just those free periods between trips and business trips.
Portals of leisure or multimedia content, on the other hand, they perform Rankings of Contents adjusted to Profile and history of each customer's consumption, facilitating the customer's next purchase. Meanwhile, on the portals of online commerce, the Articles are related to each other and in parallel they adjust to the customer's profile. In this way, the purchase of a camera entails the subsequent purchase of a backpack, cap and sunglasses, for example, according to the algorithm, but, of course, only for a certain segment of customers.
The power of these intelligent recommendation engines It can also go beyond sales success and even reach transform one's own business model. Imagine, for example, the case of a company like Amazon, whose natural process is “sell and ship”, the purchase decision and the financial transaction precede the shipment of the product.
Well, it is conceivable that some of the recommendation engines achieve such a level of precision that in Sometime Start to be more cost-effective to ship the product first, in anticipation of the customer's “safe” buying decision (“ship and sell” model).
And the thing is that a higher number of transactions, both global and individual, we achieve a higher chance of engine success, even to the point where the business model itself changes.
Currently, the Smart marketing It is being key in the growth of many companies with a clear and solid digital strategy. One of the central factors of this strategy is the creation of intelligent recommendation engines based on what is known as machine learning, which are making a difference in sectors such as tourism and travel, commerce online and, above all, entertainment and stocking.
The key point is have customer data that allow a direct communication with the same one, fine online, offline or through traditional channels, such as the telephone or even the mail. In addition, it is essential to have Data from a first transaction, since, from here on, we already have work material to focus on.
Subsequently, clustering and segmentation techniques they help to group them according to certain common traits; and they analyze the shopping features that customers have associated with each of the products or services to be marketed.
With all this information, the next step is to choose a series of algorithms that are good for Anticipate possible future elections of customers and, therefore, allow us recommend those products or services with a high probability of being chosen.
Of all the models, we finally choose the one that better behavior It has with Regarding a random choice. The necessary adjustments are made and tested with a first campaign. Once finished, we check the “delta”, that is, the deviation, and adjust again.
This exercise is done repeatedly until the algorithm It fits completely adjusted to the level of effectiveness we want to achieve. In the future, it will be necessary to make the adjustment every so often, three or six months, depending on the customer base, products, frequency and effectiveness of the campaigns, etc.
For example, a hotel chain use recommendation engines for Suggest their next trip to their customers with a special family promotion because you know what time of year you take family trips, as well as your favorite destinations and areas, choosing hotels according to your preferences.
Likewise, the Airlines they use them to launch exclusive promotions to gold or platinum customers to leisure destinations that are totally different from your usual business destinations; and they propose as dates for travel just those free periods between trips and business trips.
Portals of leisure or multimedia content, on the other hand, they perform Rankings of Contents adjusted to Profile and history of each customer's consumption, facilitating the customer's next purchase. Meanwhile, on the portals of online commerce, the Articles are related to each other and in parallel they adjust to the customer's profile. In this way, the purchase of a camera entails the subsequent purchase of a backpack, cap and sunglasses, for example, according to the algorithm, but, of course, only for a certain segment of customers.
The power of these intelligent recommendation engines It can also go beyond sales success and even reach transform one's own business model. Imagine, for example, the case of a company like Amazon, whose natural process is “sell and ship”, the purchase decision and the financial transaction precede the shipment of the product.
Well, it is conceivable that some of the recommendation engines achieve such a level of precision that in Sometime Start to be more cost-effective to ship the product first, in anticipation of the customer's “safe” buying decision (“ship and sell” model).
And the thing is that a higher number of transactions, both global and individual, we achieve a higher chance of engine success, even to the point where the business model itself changes.
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