Most businesses are putting their digital marketing budgets into data analysis, social media, and, above all, content. The problem with the content, though, is the same as with advertising. If no one sees your content, then it’s not going to build your brand or drive conversions. You can drive traffic to your site via SEO, social media, and other tactics, but most likely, you’re still missing a large part of your target audience.
That’s why so many content marketers are turning to native recommendation engines to drive traffic and with impressive results. Now, let me define Recommendation Engines to you, recommendation engine rely on the desire of the users in order to compute a similarity index between users and Ads, then recommend items to users accordingly.
The principle is simple: When a user clicks on a particular site or piece of content when he or she reaches to the content, there are recommendations for additional content that is relevant to that particular user. This is what Amazon or Netflix when you buy the product they recommend you the suggestions. As the ads are Now we are talking about native ads, so Ads should follow the natural form and function of the user experience in which it is placed. So, Native Recommendation Engine defines ads in the natural form and function of the website according to the desire of the users by computing a similarity index without disturbing the website real-estate. This concept arises after experiencing the content blindness and banner blindness.
Image: Native Ads blue box powered by Trackier
Why Native Recommendation Engine?
In the immortal words of Steve Jobs – “a lot of times, people don’t know what they want until you show it to them.”
The statistics related to traditional online advertising are scaring most of the marketers. 54% of users don’t click banner ads because they don’t trust them.- BannerSnack 33% of internet users find display ads completely intolerable.-The average user sees 1,903 ads a month, while just 2.8% of the users find these ads relevant.-Infolinks After going through these results marketers would probably fund in traditional online advertising. Native recommendation engines are in winning proposition for everyone. Get the data right and you can shape the overall customer experience by applying data science and machine learning. Recommendation engines are very powerful personalization tools because it’s a great way to do “discovery” – showing people items they will like but are unlikely to discover by themselves. They improve a visitor’s experience by offering relevant items at the right time and on the right page.
Advantages of Native Recommendation Engine:
No More “Banner Blindness”. The tendency of Web visitors to ignore display ads when consuming content online, leading to lower click rates, visibility for advertisers, and revenue for publishers. The recommended engine helps overcome this problem since it’s not usually packaged with attention-getters like banner ads. It’s subtle, quiet, and trustworthy.
More Targeted Traffic. Instead of placing ads on low-quality websites and hoping that people will see them, native recommendation engine brings the content to the right people. Different users will receive different recommendations because the ads shown to the users is driven by the recommendation engine.
Increased Engagement. Native recommendation engine works are based on complex algorithms that use data on not only what the user is currently reading or watching, but also what he or she read or watched in the past to recommend additional content. When users see the favourable advertisement on sites they trust, they are more likely to click on it. The click-through-rates is around 6%-8%, much higher than the typical online advertisement.
Branding. Brand awareness among the visitors is always being the issuer for online marketers. Now sharing your brand-related content via native recommendation engine helps build more awareness. And when you share high quality, useful content, you support your brand-building efforts, and in turn, your conversions.
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