Comparing Content Recommendation Tools & Engines

Content Recommendation Software: Our Comparison Reveals the Best Content Recommendation Tools - Here you’ll find more useful information

A Content Recommendation Software automatically offers context-specific and target audience-specific content in certain parts of a website. The results of using a Content Recommendation Tool are often found in areas marked "Recommended for you" or "You might be interested in this". A Content Recommendation Software, also known as a Content Recommendation Engine or Content Recommendation System, collects and analyzes data, which is broadly based on the behavior of (potential) customers. This data is then used to offer personalized and relevant content or product recommendations. Predicting the preferences of users ideally provides them with a better or more effortless customer journey. Global corporations like Spotify or Amazon are really good at this form of content marketing, but there are also many opportunities for small businesses to effectively engage their (potential) customers. The big players often have their very own recommendation systems, which is not an option for smaller businesses of course. However, the latter can currently choose from a fairly large market of universally applicable Content Recommendation Software.

Good Content Recommendation Software often comes with the following features or benefits:

• It works with customer data from all major online channels of the customer journey, thus enabling very efficient content delivery.

• Content Recommendation Software greatly assists in creating personalized, advantageous customer experiences across multiple channels.

• It allows users to create individual personalizations.

• It often uses machine learning and enables largely automated segmentations and A/B tests for creating customer profiles.

What types of Content Recommendation Software are there?

Not all Content Recommendation Engines are the same! Basically, there are three different types. Some tools follow users on the web and directly analyze what they do on websites. They can only suggest content that is purely based on their online location or previous interactions with a website. Content Recommendation Software can also work purely on the basis of manual entries, personal information, or predefined rules. These types, however, carry increased risk that recommendations can become quickly irrelevant or outdated if data accuracy is not meticulously maintained. Often, users can't do anything (anymore) with the suggested content because it is inaccurate or misleading. Nevertheless, such systems are often the best choice for companies with comparatively few (potential) customers. With Content Recommendation Software that always uses up-to-date data from all available sources, the risk of outdated recommendations is significantly lower. Visitors are almost automatically served exactly according to the status of their concerns or conditions. This can not only strongly favor direct sales, but also greatly benefit the image of the brand serving the content.

How do Content Recommendation Tools work?

Content Recommendation Software, which analyzes data from different sources and uses it for distributing content, typically proceeds in three steps as follows.

1. Data collection: Data is the fuel needed by the Content Recommendation Software for its work. The engine collects implicit and explicit data.

   • Implicit data are pieces of information that result more or less automatically from the use of online channels and can be captured, among other things, by tracking. Examples include search history, clicks, and previous orders.
   • Explicit data is information created by direct input from users, such as ratings and/or personally communicated preferences or dislikes.

2. Data storage: The more data that is stored, the better the recommendations that can be made. Companies should therefore collect as much information as possible. This data can be used to accurately analyze (potential) customers and segment them.

3. Data analysis and recommendation: Content Recommendation Software analyzes data by filtering it to extract relevant insights for the final recommendations.

Who needs Content Recommendation Software

Almost every company can benefit from using a Content Recommendation Software. There are two important factors that determine how much a business benefits.

• The breadth of data: A company that serves only a handful of customers, who behave differently, will not benefit much from a fully automated recommendation system. When it comes to analyzing fewer pieces of information, people are still the first choice. In such cases, employees should use their qualitative and quantitative understanding of the target audience to deliver accurate recommendations. This can, however, also be done with the help of special Content Recommendation Software.

• Depth of data: A single or only a few data points about (potential) customers are not particularly helpful for a Content Recommendation Software. Accurate recommendations can only be achieved through deep data about respective online activities, and - if possible - offline behaviors as well. Under these circumstances, Content Recommendation Tools tend to be particularly helpful in the following industries.

• E-Commerce: The first industry where recommendation systems were used on a large scale is e-commerce. With often millions of customers and data about their online behavior, e-commerce companies are perfectly positioned to generate accurate recommendations.

• Retail: Shopping data is very valuable information since it provides direct insights into the needs and desires of customers. Retailers with many customers and correspondingly large data pools are at the forefront of companies that can and should deliver accurate recommendations.

• Media: Similar to e-commerce, media companies are among the absolute beneficiaries of Content Recommendation Software. For instance, it's hard to imagine a news website today without a recommendation system. When such a system is used to its maximum utility, readers can be accurately guided through offers all the way to advertising partners.

• Banking: All kinds of banking businesses are conducted online very frequently today – and in the process a lot of useful data is generated, which companies in the financial sector can use effectively for recommendations leading to new deals.

• Telecommunications: Telecommunication has a similar dynamic to banking. Telecommunication companies often have access to millions of (potential) customers, each interaction of whom is recorded. Corresponding product ranges are limited compared to other industries, making recommendations in telecommunications relatively manageable.

• Other companies with large amounts of data: Basically, all companies that have a large amount of data and whose information is sufficiently deep are benefiting. Playing out the right recommendation in the appropriate context is almost always appreciated by recipients.

What specific benefits can Content Recommendation Software provide?

As competition is increasing in all industries, customer retention is an important goal for every company. Content Recommendation Software allows for a massive increase in sales through very efficient up-selling or cross-selling to existing customers. The following figures from some world corporations on content recommendations leave no doubt that such tools can be extremely valuable conversion helpers for businesses:

• 35 percent of Amazon.com's revenue is said to be generated by its recommendation engine.

• 75 percent of Netflix users choose movies/TV series based on suggestions. The Netflix executive team states that recommendations reduce the churn rate by several percentage points. This increases the lifetime value of existing customers, which is why they assume that recommendations save Netflix more than one trillion US dollars per year.

• Spotify first released Discover Weekly Playlist recommendations in 2015 and was able to record an 80 percent increase in sales in 2016 with 40 million Discover Weekly users (40 percent of total users at that time).

These are of course absolutely outstanding cases, but they also demonstrate to smaller companies what can be achieved with Content Recommendation Software or their appropriate use. Specifically, Content Recommendation Software can provide the following key benefits.

• Increased sales/conversions: There are very few ways to increase sales without increased marketing effort. And when a Content Recommendation Software is used, recurring sales can actually be achieved with almost no additional marketing effort. The tool just plays existing content at exactly the right time or the ideal phase of the customer journey and thus convinces very efficiently.

• Increased user satisfaction: Of course, the goal is always to provide the shortest possible path to sales, as this offers both companies and customers - if the product really fits - the major advantage of greatly reduced effort. With Content Recommendation Software, such a short path can be created by providing a suitable product or information that motivates to buy a suitable product or to use a certain brand in the right context. Sometimes this can even happen before target customers start specifically searching for the respective product.

• Increased loyalty: Content Recommendation Software can persuade (potential) customers to spend more time on a website and to use its content intensively. This automatically strengthens the standing of the respective brand, which in turn increases the likelihood that users will return again and again - in the end, even without recommendations - and maybe even become real brand ambassadors.

• Lower churn: Content Recommendation Software offers many opportunities to convince and engage (potential) customers very directly. This way, discounts or vouchers can also be targeted or coupled with recommendations.