Over the past 7 years, we have conducted over 23,000 comprehensive website audits and I have learned that all of us as leaders need clear and working algorithms for our marketing and sales.
Today we will share with you 6 of the most valuable documents that we have developed for our clients.
Download for free and implement today:
Step-by-step guide to creating marketing KPIs
Template for calculating KPIs for a marketer
9 Examples of Universal homeowner database Selling Commercial Proposals
Upgrade your CPs to close more deals
How to make KPI for the sales department so that profits grow by 20% or more?
Step-by-step template for calculating KPIs for OP managers
Checklist of 12 main indicators for website promotion
Find out what metrics are needed to properly optimize your website
40 Services for Working with Blog Content
We have collected the best services for working with content
How to define your target audience without mistakes?
A proven guide to defining a company's target audience
Download the collection for free
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Types of recommender systems
There are four smart recommendation systems. Let's look at each of them in more detail.
Collaborative filtering
Recommendations for the user are formed based on both his own assessments and the experience of other users with similar preferences.
Let's look at a simple example. Ivan and Vasily are fans of the Dynamo hockey club. They also have another common hobby - active tourist recreation. And they both prefer hard rock music. There is also a user named Semyon, he also closely follows Dynamo and loves heavy music. There is no information about how he spends his free time, but since two of his three interests coincide with the interests of Ivan and Vasily, the algorithm will offer tourist products to all three.
Collaborative filtering
This approach is implemented in many web services, for example, Last.fm, Imhonet. This way, you can determine the preferences of users who share a certain interest with a high degree of probability. However, the disadvantage of this method of recommendations is the lack of initial data on new users, so it will be difficult to create relevant recommendations for them, which will cause a considerable percentage of refusals.
Content-based
This is also a widely used approach. It is based on using any available information about the client: preferred brand, genre, size, product features, etc. The stage of getting to know the user is skipped here. This algorithm is often used in online cinemas and stores. Thus, the IVI service generates recommendations based on information about genres, countries where the films were shot, cast, etc.
An important advantage of this algorithm is the ability to offer the user more or less suitable content in most cases. However, at the first time of using the service, there may be errors, since information about the client is minimal.
Case: VT-metall
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