June 11, 2019

AI Driven Personalization

AI Driven Personalization

In the connected world of today, the role of a user has changed from a passive spectator to an active participant. 41% of U.S. consumers abandon a brand when there is no personalization or trust factor. With millennials spending close to 9.6 hours per day and Gen X spending 7.9 hours per day, on average, consuming digital content, attention has become a precious commodity in this digital age.

This is happening because we are surrounded with personalized interactions, personalized campaigns, personalized news feeds and personalized deliveries due to AI-powered data driven tools.

Artificial Intelligence (AI) has altered the basic idea of how consumers perceive a product and the way marketers perceive the purchase journey of customers. AI-driven personalization technology is able to accurately determine the consumer journey and can anticipate the next step along that journey. It has also added a cognitive component to the otherwise traditionally human-powered and automated tasks.

AI marketing tools can make companies perceive the needs of customers in a better way and push sales efforts to the next level. However, they are not a panacea on every take of personalization.

There is a growing need to understand user profiles while personalizing content for them. The content and amount of the information within a user profile can vary depending on the application. The accuracy of the user profile is based on how user information is gathered and organized, and how accurately this information reflects the user needs. In other words, it depends on the user profiling process in which the information is gathered, organized and interpreted to create the summarization and description of users to provide a personalized experience.

Today’s User Profile

Creating intelligent user profile implies the application of intelligent techniques and tools, coming from the areas of Machine Learning, Data Mining and Natural Language Processing. These tools make us contextualize and improve results.

Capturing and representing user interest is the key component for Personalized Services. A user profile is composed of concept and relations that ensures the user’s interests. Data can be collected in three ways — by directly asking customers, by indirectly tracking user data and by appending other sources of data. This makes it easier for enterprises to understand and engage their audience effectively.

Seeking the activity & journey of users is a clear process to understand their requirements in a very clear manner. This data gives an opportunity to deliver relevant content and experiences based on location and intent of users. Thus generating brand loyalty, helping in refining the marketing strategies and giving users a tailored shopping experience.

Let us look at the different user profiles that can be captured:

  • Explicit User Profiles are created when the user answers different questionnaires and provides ratings. This kind of gathered information is usually of high quality. But it requires user effort to update the profile information.

  • Implicit User Profiles are system generated. They are created from the digital footprints of users. This requires minimal user effort. However, many interactions happen between users and content before an accurate user profile is created.

  • Hybrid User Profiles are a combination of both explicit and implicit user profiles. They are created using AI tools and does not require much effort from the end user.

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How are the Millennial User Profiles created?

Millennials are connected and content creating generation. Every minute we have Facebook users share 2,460,000 pieces of content, Twitter users tweet 347,222 times, WhatsApp users share 347,222 photos and Blog writers post 1400 new blog posts.

With so many avenues available, companies are leveraging and harvesting this data big time. However, not many are aware on the different methods, this content / user profiles are being generated. Listed below are the primary processes adopted by the AI tools to generate content.

  • Content-Based Method is used to predict user current behaviour using his past behavior. In this method, user profiles are represented with user search queries and then the system selects the activities that have a high content correlation with the user profile. Hence, this method tends to perform badly if the users’ content is limited.

  • Collaborative Method assumes that the users who belong to the same group (e.g. same age, gender or social class) behave similarly, and therefore have similar profiles and is based on the rating patterns. These groups are referred to as Like-Minded People. Unlike the content-based method, collaborative method recommends the activities or products based only on the similar users’ ratings.

  • Hybrid Method is used to overcome the issues with content-based and collaborative methods. This method guarantees the immediate availability of a profile for each user. The system that employs the hybrid method provides a more accurate description of the user interests and preferences, as it continuously monitors and retrieves the user related information through the user-system interaction.

Typically, the hybrid method assigns a new user a default profile with the use of the collaborative method and further enhances the profile using the content-based method.

Fast forward to 2019, most successful digital companies are building their product offerings around these concepts and working on the AI based systems accordingly.

AI-Boosted Personalization — Some Awesome Use Cases

  • Volvo is trying to leverage technology big time. Its machine can now analyse one Million events to discern their relevance to breakdown and failure rates

  • American Express is using AI to crunch through its mass data and make quicker, smarter decisions, detect fraudulent cases in pretty much real time.

  • Thread is using AI to provide personalized clothing recommendations to its customers. Customers take style quizzes which provides data about their personal style.

  • The first AI-powered jeweler, Rare Carat uses AI to compare prices of diamonds across numerous retailers to find the best deal for its customers.

  • Macy’s uses AI technology to enhance the shopping experience. Macy’s On Call, its smartphone-based assistant chats with the customers when they enter the store. The chat bot asks questions to make personalized shopping, provides recommendations and directions around the store. It can also sense when the customer is not able to find what he needs, sends out an alert to human associates to intervene appropriately.

Shaping Data into Knowledge

As machine learning algorithms and other forms of AI proliferate, data analytics become more powerful in breaking it down into manageable and actionable insights. Some AI programs flag anomalies or offer recommendations to decision-makers within an organization based on the contextualized data.

The smartest and quickest way to mine user data is with AI and machine learning. Machine learning is a subset of AI, wherein machines, over time, learn for themselves by responding to different types of data and eventually creating repeatable patterns. AI super computers like Oxford, Watson and DeepMind can be programmed to recognize every behavioral trait of customers over time that they are remembered instead of treated as just another voice in the crowd. The good news is that AI and machine learning can also help companies deliver personalized promotions that cultivate engagement and build customer relationships.

Machine Learning schemes are one of the most effective applications for the brand name looking to modify their digital revision strategy. Through using numerous patterns to track down the one that fits best for each purpose, machines can modify testing and provide working plasticity. While machine learning excels at pattern recognition, Artificial Intelligence is well-suited for creating recommendation engines. Joined together, these two technologies can bring a scale previously unimaginable for all marketplaces. Major companies like Google, Amazon, Facebook, Apple are collecting personalized data to improve their customer base across the world.

Data Protection and AI-driven Personalization

In the new GDPR world, every enterprise needs permission to use customer data. Enterprises should concentrate on tracking the right data and must make the right decisions to make sure of a clear and consistent vision for the organization’s data landscape. To effectively leverage AI, they need to use tools that connect, structure, tag, and organize data with machine learning algorithms.

Thus, with more content generation, came in the data privacy regulations which are changing the way business captures, stores and analyses consumer data. Governments are crafting strict data privacy regulations for users to control how data is being used. Example — European Union’s General Data Protection Regulation (GDPR) lays out strict rules on capturing, storing, using and distributing the data. The California Consumer Privacy Act (CCPA) of USA also made data privacy policies like GDPR.

Though, it’s unlikely for the private companies to stop data collection, they might just adapt different laws and regulations.

Final Thoughts

The amount of data exchange has become significantly larger. Audio, video and images will become the default medium of interaction than text for most of the use cases. AI has become one of the most efficient data-driven tools, helping in blueprinting the consumer needs.

Obviously, the biggest advantage of AI enabled machines are, we never really have to program them. Though we do a lot of tinkering and improve on how the systems process data and how to ingest it, we are essentially not telling them what to look for. This means, the machine in the future will not just spot patterns that humans miss but they are going to influence people.

And eventually, influence- seeking algorithms might go beyond their specified goals which can result in another breakthrough in machine intelligence or an unrecoverable catastrophe. Of course, as of now, the latter use case is theoretical but examples like Google Maps, predictive searches, recommendations, daily commute apps show our level of reliance on these machines. Such examples do make us think otherwise!

Yet again, we must wait and watch this space!