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What brands gain from embedding YouTube videos on their websites

Research suggests YouTube videos are a preferred tool for marketers who want to drive user engagement and purchases. Indeed, since video enables users to visualize the brand story better than text or images, it has a higher likelihood of driving active user engagement.

Embedding videos on a website also makes brand discovery more enjoyable. Video compels brands to communicate concisely and effectively, helping visitors get the information they need faster than through text.

Video also helps conversion. It makes it easier for people to remember the brand message and connect with their story, increasing the chances for them to come back and turn into paying customers.

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Video can tip the balance when it comes to the bottom line

Video is a powerful way to build trust and position the brand, and adoption of video for marketing is rising among brands and marketers. According to Unbounce, including a video on a landing page can increase conversion rates by 80%.

87% of online marketers already use video content and the number of businesses using video as a marketing tool has gone up by 35% in 2018 compared to the previous year.

A diversified offer of video content is proposed by marketers, ranging from product promotion to instructional or brand development video content. According to Curata, the top three most effective types of video are customer testimonials (51%), tutorials (50%), and demos (49%).

Embedded website videos have a positive and measurable impact on marketing results.

SEO results : Companies that use video get 40% more web traffic from search than companies that don’t. A 2017 Cisco study forecasts that video will represent 80% of all internet traffic by 2021, and video content is 50 times more likely to drive organic search results than plain text, according to Omnicore.

ROI optimization : 87% of marketers feel video drives positive ROI, and companies that include videos in their marketing report 34% higher conversion rates. Consumers reportedly spend 88% more time on websites that show video content, and Google reports that nearly 50% of users look for videos about a product or service before visiting a store.

Putting Machine Learning in marketers’ hands

1. Personalized content

Showing your audience the right video at the right time is critical to delivering a personalized and relevant brand experience.

Machine learning techniques can analyze massive amounts of data – from explicit, demographic customer information to inferring tone or sentiment – in order to find underlying patterns and correctly detect customer preferences and personal interests. Learning what is most effective for each visitor enables marketers to deliver video content that is truly relevant for their prospects.

2. Enhanced segmenting

Customers are not all the same, so different engagement techniques or incentives should be used. Machine learning technology can interpret data from social activity or shopping activity to identify complex behavioral patterns.

Marketers can correlate this information with customer journeys for more precise segmenting that ensures each user is shown the appropriate content depending on their profile, stage, and customer journey objectives.

3. Better content performance

You can’t optimize what you don’t measure. Marketers typically rely on insights such as watch time or view-through rates.

With machine learning, such data can be analyzed together with audience profile information, contextual information, and website behavior to enable brands to automatically pull the best YouTube content into their website. Feedback from this dynamic optimization allows marketers to create and serve video content that is ever more relevant and generates the best results.

4. Manual tasks automation

A/B testing optimization, manual segmentation, or video content scheduling can be enhanced when audience data can be interpreted and acted upon in real time. Relying on machine learning, these tasks can be automated and better performed so that only the most relevant videos are shown to users. This improves productivity by eliminating the time and manual effort for marketers to extract and read through the data, make decisions, and then implement them.

A few examples

Image Source : IKEA

IKEA uses YouTube embedded videos on their website to promote their holiday product collection. The video tells a short, memorable story that associates an emotional connection to the brand while enabling an interactive product discovery. This inspires and engages the viewer more than traditional product category browsing.

Image Source : Gillette

Gillette is another great example of a brand that relies on YouTube embedded videos to provide educational content. Short videos are used to explain the technology behind the brand’s products, making it easy for consumers to understand what makes the company’s products superior.

Unlike promotional content, Gillette’s instructional videos are mostly used to build the brand image and customer trust, rather than push for sales.

About Knexus

Knexus platform automatically transforms UGC, influencer & brand owned assets into hyper personalized, shoppable content for ecommerce. Move visitors through inspiration to buying!

Rise of Machine Learning

Arthur Samuel, who coined the term machine learning in 1959, said that “rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves”.

In recent years, the rapid advancements in technology & infrastructure in Data science has made machine-learning one of the leading vehicles in development of intelligent applications & systems. Machine learning now provides a way for programming language based applications to scale in a manner that was not possible previously because all programming languages are essentially a set of predefined rules.

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Machine learning is now manifesting its power in a myriad of applications such as: Digital (web & mobile applications), healthcare, surveillance & robotics, etc. Industries such as retail & finance are at the forefront of leveraging machine learning, given they generate/possess vast amounts of customer data, which is the lifeblood of machine-learning. This allows these industries to connect with their customers in a much more scalable way by serving intelligent & personalized experiences.

How Machine Learning Impacts Customer Experience

Below are a few important applications of machine learning to improve customer experiences:

1. Analytics

Predictive analytics allows brands to understand intent of their customers. It allows brands to predict future trends & imminent customer behaviour in order to fine tune marketing strategies. An insight that was previously unavailable.

2. Personalize Customer Experiences

Forbes recently said that most of the personalization available today is essentially items you’ve searched for that follow you around. With machine learning, companies can actually personalize the customer experience & serve the right content or product at the right time in their journey. This aspect will play an even greater role in maintaining customer loyalty & acquiring new customers.

“By providing better search results, Netflix estimates that it is avoiding cancelled subscriptions that would reduce its revenue by $1B annually.”

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3. Tag Your Content

Companies are able to save costs by tagging their content & other data using machine learning. Machine learning can understand your content automatically by either reading the text, image recognition & video processing capabilities. Once you automatically understand the content, then it becomes easy for companies to serve the right content to internal and external stakeholders such as sales teams – to help them engage better with prospects & customers.

4. Just In Time Engagement

Forrester research found that 77% of consumers in the United States suggested that valuing their time is the most important aspect of the brands interaction with them. Machine learning allows you to understand the user/customer intent, and serve personalized actions at the precise time your customer expects. This creates a distinct experience (for your audience) that values customer’s time and thereby increasing loyalty & engagement.

5. Customer Service

Natural Language Processing (NLP) is a subset of machine learning that enables systems to understand language. Today digital assistants that augment customer service help companies in not only giving better service but also saving significant human resources costs. Currently the Financial services industry is at forefront of using these applications.

Take an example of a bank’s customer who has repeatedly waited until the last moment to make a minimum payments. Applications can learn about this behaviour and anticipate that this customer might be facing a cash flow situation. Intelligent applications can then send this person personalized loan offers!!

Customer Experiences Powered by Machine Learning – An Option No More

No Marketer can afford to ignore the importance & power of machine learning in their marketing strategy. Today, every company is engaging with their customers through multiple digital & offline channels. This is generating unimaginable quantities of data, and growing rapidly – the digital ecosystem is expected to grow from a mere 130 exabytes in 2005 to 40,000 by 2020.

Frank Palermo, executive vice president of global digital solutions at VirtusaPolaris, “Responsive retail has peaked – the next step for the industry is predictive commerce”. Brands have no choice but to include machine learning in their marketing strategies in order to serve personalized customer experiences that keep customers loyalty & engagement. This is no longer an early mover field. Studies show that about half of consumers in US & Europe will interact with applications and services based on machine learning by 2021.

In future more and more companies will have fully cognitive websites such as Amazon. The Brand marketing & Brand identity will lose some of its power, when coldly rational machines are making decisions. Therefore brands will have to move away from a “traditional” marketing approach and they will have to rearrange their marketing investments to invest more in technologies that support their brand’s identity.

About Knexus

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Companies must keep new technologies on their radar. Indeed, it is crucial to anticipate how those can support to deliver a superior customer experience. If business leaders don’t pay attention closely enough, it won’t be long before more agile competitors take the lead, erode their market share, and transform them into industry laggards. But the tech landscape is vast and complex. And not every innovation may influence the bottom line of your business and gaining popularity like machine learning and predictive analytics-enabled applications do – growing 65% faster than apps without such functionalities.

Image Source: rapidvaluesolutions.com

A Growing Link Between Customer Engagement and Machine Learning

Artificial intelligence (AI) will go mainstream in the future. And this may happen faster than you think. IDC predicts that over 50% of consumers will regularly interact with AI applications by the end of next year. This might sound unrealistically soon, but there is nothing disruptive that has not been brought to the table already.

Some companies, especially e-commerce platforms, have gathered data to anticipate online behaviors for over two decades – sometimes with enough accuracy to predict a purchase even before a visitor consciously begins his or her customer decision-making process. That’s why today’s technological advances are only the next logical step to an improved customer journey mapping with machine learning.

In the words of Amazon CEO Jeff Bezos: “We’re on the edge of the golden age (with AI). There is so much more to come. It’s just the tip of the iceberg.” And this is highlighted by Gartner’s prediction that the majority of money invested in analytics for the three years to come will be on making sense of customer journeys.

The Benefits of Artificial Intelligence for Your Organization

At this stage, you might be wondering what the use cases of machine learning are for your organization and whether any tool is already available on the market for you to implement quickly. You can start benefiting from the technology today in two ways: adding relevance and making operations more efficient.

Content personalization tools can help you offer automated and individualized experiences to your consumers and followers with highly targeted and meaningful text, images, and videos. This, in turn, can increase your close rate and return on marketing investment.

However, like all technologies, machine learning and artificial intelligence also have a dark side. And Rachel Barton, Managing Director at Accenture Strategy tell that brands “need to be careful not to cross the ‘creepy’ line when it comes to customer privacy.”

Knexus platform automatically transforms UGC, influencer & brand owned assets into hyper personalized, shoppable content for ecommerce. Move visitors through inspiration to buying!