IMAGE ANALYTICS AND AI IN SOCIAL MEDIA MARKETING

The advent of social media has made us a visually driven generation, with both the camera and the internet being available in a single touch. This increasingly image based sector can be a powerful marketing tool if used efficiently. Adweek says that over 3.2 billion images are shared online every day. The visual content has become an imperative part of the messages shared on the web. Using AI and image analytics, this rich source of information can be tapped into, in order to gain consumer insights and make the best decisions for the brand. Artificial intelligence and image recognition are also making it easier for marketers to identify visuals in social media for better metrics and customer service.

Watching out for the brand

AI and image recognition can be used to understand the need for a visual perception of the logos, placement of the products and how products are actually being used in real life. Social media is a massive open database. It has a huge open source framework and large worldwide collaboration. An analytics tool will help in keeping track of the number of times a certain brand has featured in the billions of photos that are posted every day. This can be used as a tool to measure the marketing effectiveness and gauge the ROI. A photograph containing a logo might reveal more about the context in which the products are consumed. These image mentions are often passive and unfiltered but analysts can learn a lot more from them. Insights gleaned from images can help inform decisions surrounding the context in which the product is advertised and how different groups use your product differently. This information will enable the brand to integrate the data-driven visual imagery into brand storytelling.

Gaining consumer insights

As mentioned before, analyzing the data could provide insights into how products are being used, providing a way to track brand displays online hidden within pictures, and allowing the brand to find out when influencers are using their products. Social media has turned into a rich resource that provides consumer and customer insights, by letting the brand analyze nuanced metrics like customer opinion, emotion analysis, audience interests and demographics, which can in turn help understand and better predict consumer behavior.

Brands can identify the target segment discussing relevant topics and the sentiment or perception they associate with the brand or product. As the brand keeps track of their audience, they would easily be able to analyze the change in trends and act upon it. Such data can help the marketers to come up with the right and most effective product strategy.

Improving customer service

Social media has paved a direct and public way of contacting the company or a brand. People can tag the brand both under circumstances of duress or of celebration. Keeping track of the brand mentions, enabled by AI can help the brand to respond to the customer in the most appropriate manner. Research shows that the speed of a response in social customer care is a far stronger cause of customer happiness than even solving the customer’s issues.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.

DATA SCIENCE AND HOW NETFLIX DOES IT!

Companies of this age invest heavily on data to stay ahead of the competition. Data analysts and data science experts are hired to analyze the vast expanse of data available, an understanding of which will render them capable of making informed and educated choices, optimizing marketing strategies and putting off projects that have a lower rate of success. Once the initial goal of securing customers is achieved, the next step for a successful company is to keep the customers entranced with their working, thereby building the company on their loyalty. For example, recommendation services that are high quality and geared towards recommending new products to customers has the potential to dramatically improve sales per customer and price point per order. Data collected from the customer base can be analyzed to give the customers what they need. Some potential issues can also be solved in the same manner, before they even materialize. Optimizing product locations is another way to provide customer services in a personalized manner.

An excellent example of how effective use of data can result in success is that of Netflix, which is presently valued over 164$ billion, having overtaken Disney’s place as the world’s most valuable media company.

Some guidelines to optimize the company’s profits by using data include:

  1. Setting clear goals

Laying out well defined goals and expectations of the company in order to make the action plan is important as this will give data scientists the opportunity to adopt the right methodology. Netflix is an online streaming channel, with over 130 million users. Their success rates depend on their customer satisfaction, on whether the subscribers like what they are watching. Here Netflix uses predictive analytics to put forth options which the user will find favorable, as it is based on the data collected from the user’s experience. When the channel succeeds to strike a chord with a viewer, then the credibility increases.

  1. Identifying available data

The available data has to be analyzed brought to the right format, so that it can be used to bring about the goals of the company. With the large number of subscribers come tremendous amounts of data that Netflix can use. During the onset of subscription, the customer has to give information about his/her interest in specific genres. They are also asked to rate the movies which they have already seen. Such information is used by Netflix to help them to discover new movies and T.V shows, something that is integral towards its success. Data is also collected from events like the customer’s searches, the date the show was watched, the device on which it was watched, when the program was paused, when the program is re-watched etc. The vast data collected from such events helps Netflix to understand their subscriber’s choices and preferences. This will in turn be used for the user, providing them with a much more personalized experience.

3. Adopting the right methodology and being data driven.

With an in-depth understanding of the data at hand, the right tool has to be chosen so as to use the information most effectively. Netflix uses algorithms for predicting the user’s choice based on his previous ratings. It is said that the recommendation system used by Netflix influences 80% of the content the subscribers watch on Netflix. Recommendation systems are simple algorithms which aim to provide the most relevant and accurate items to the user by filtering useful stuff from of a huge pool of information base. Content based systems, recommends item based on a similarity comparison between the content of the items and a user’s profile.

Collaborative Filtering algorithm considers “User Behaviour” for recommending items. Other users’ behavior and preferences over the items are used to recommend items to the new users.

A personalized video ranker orders the entire Netflix collection for each member profile in a personalized way, keeping in mind their interests, habits and choices. Jenny McCabe, Director of Global Media Relations says “We always use our in depth knowledge (aka analytics and data) about what our members love to watch to decide what’s available on Netflix….If you keep watching, we’ll keep adding more of what you love.”

An action plan based on the available data would enable the company to take the right decisions. In 2011 Netflix outbid top television channels like HBO and AMC to earn rights for a U.S version of ‘House of Cards.’ At a cost of $4 million to $6 million an episode, this 2-season series cost over $100 million. Such a big decision was made on the data that they already had.

Using methods like clustering analysis, sets with similar attributes are studied. A lot of users watched the David Fincher directed movie The Social Network from beginning to end. The British version of “House of Cards” has also been well watched. Those who watched the British version “House of Cards” also watched Kevin Spacey films and/or films directed by David Fincher. Such factors gave them the confidence to make the $100 million investment, which turned profitable in the end. Here, using association mining of customer information with similar behavior is targeted to make a decision that satisfies that particular set.

  1. Testing regularly

Data driven models should be constantly checked as demographics have a way of changing gradually. The algorithms that Netflix uses are constantly revised in order to achieve maximum optimization. Bill Franks, Chief Analytics Officer, International Institute for Analytics says that “ I can say that no changes in Netflix products are not tested and validated and we do not just test to test. If we do not believe it will not improve, it will not be tested. We have 300 major tests of products and dozens of variations within”

Data science practices should be implemented wisely. Netflix has successfully shown us that machine learning can been used to convert the user’s cravings into the company’s business goal. Quantitative data is always a good basis on which better and cost effective decisions can be taken. Data can predict whether certain innovations or experimental projects can take off.

While discussing analytics, Netflix co-founder Mitch Lowe says “He [Reed Hastings] taught me how to use analytics to make decisions. I always thought you needed a clear answer before you made a decision and the thing that he taught me was [that] you’ve got to use analytics directionally…and never worry whether they are 100% sure. Just try to get them to point you in the right direction. ”

Intelligent use of data can reap benefits, but it should be done responsibly. Data should be protected from others and used carefully. Data science should be seen as a solution to solving problems as well as a way to greater rewards; it should be given due importance.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.