Retail Financial Services Telecom Ecommerce/Social Media Education
Behavioral Segmentation Scorecard Development Market Basket Analysis Personalized Recommendations Churn Management Text Mining Campaign Management Social Network Analysis Loyalty Measurement
Applied Data Mining Techniques Statistics Essentials Statistical Model Development Text Mining Analytics for Marketing Managers Analytics for Risk Managers Analytics for Collections Managers Mechanics of Financial Products Emerging Trends in Analytics
Free Pricing Piracy or Free Promotion Impact of Color Promotion thru' Spokes-characters Chief Customer Officer Speech Analytics Number Portability Cause Marketing Fear Marketing Global Recipe with Local Spices Facebook: Social Media Marketing
Management Team Partners Spotlight
Popular Quotes Do You Know? Glossary Ten Things in Retail Best Practices - Amazon Best Practices - Singapore Airlines Best Practices - Shopper's Stop Best Practices - Tesco



Personalization: The Key To Success In The Online Era

Think about a sensor that catches information of your eye contact with the newspaper articles you have read or surfed and then infers areas of your interest based on the amount of time you spent on each article. The data captured by this sensor gets transmitted to the publisher and the next day, you get a personalized newspaper that delivers articles suiting your taste. This kind of sensor may still be far from development but it’s definitely a possibility in e-world. It’s not difficult for an online news site to track the articles that you read and then provide a customized menu of articles the very next time you login to the site.

Why Personalization?

Most people are looking for a way to add a personal touch to their gifts; most customer service companies are planning to provide unique customer interaction; most banks providing customized products (e.g. loans, cards) and investment plans; and most retailers are recommending products that are close to customer’s preference.

It’s a competitive world – moving from supplier centric to customer centric products. From a monopolistic market, the environment forced companies to provide products for customer segments and now they are putting strategies to proactively recommend products to individuals.

In the last few years, the companies that have grown and dominated the market place have also been the early adopters of personalization. Dell, Amazon, Netflix, etc. are just a few names that come high on the mind. For example, Amazon would recommend books, apparels, electronic items, etc. based on your past purchases, clickstream data, etc.

How Internet Is Helping Personalization?

With the advent of Internet, the web has grown exponentially – but also started creating a problem of information overload. With the concept of personalization getting popular, Internet stands at an advantage to provide more personalized solution as compared to Brick and Mortar business and help user to reach to his/her target easier. The typical “Design of Experiments” used by Market Research companies can be done much faster and efficiently in an online environment. Theoretically, you can consider each user as a test which isn’t possible otherwise.

With proper analysis of past Click-stream data, it is possible to understand browsing and product search patterns. This could be used for upfront personalized recommendations.

It is also important to remember that creating a successful personalization plan is challenging. However, effective implementation can yield substantial benefits. Just as any advertising plan relies on repetition for success, so, too, does personalization. When developing your personalization plan, it is important to implement features that will keep people coming back on a regular basis.

How Is Personalization Achieved?

The personalization process typically track customers' likes and dislikes and look for patterns similar to other customers. This concept is designed to simulate a "word-of-mouth" campaign. The traditional approach would have typically looked at one person's behavior but personalization approach would compare behavior of like people and would predict one person’s behavior based on popular behavior of that group. The process studies at affinity of products/services at transaction level, demographic profile of the customer, past purchases and click-stream data, and provide recommendations to the user.

Some of the common techniques include collaborative filtering, similarity analysis, path analysis, market basket analysis, etc. Collaborative filtering aims at predicting the user interest for a given item based on a collection of user profiles and can broadly be of two types – memory-based or model-based. Apart from development of algorithms to mine complex interactions, efficiencies are required in integration of data from disparate systems, and usage of Natural Language Processing techniques to decipher unstructured text.

From a user perspective, important factors are accuracy of the response, speed of response and how many iterations it took to get desired results. For example, a search engine should be able to recommend ‘wild spiders’ to an animal enthusiast, ‘spider-man’ to a kid interested in comic books and ‘web spiders’ to technologist if ‘spider’ is used by various users and the results customized on past searches. The speed of response is dependent on efficiency of queries, database structure and the search algorithm. Most of the personalization tools refine their recommendations with incremental data.

What Does Future Hold For Personalization?

There’s already an effort started towards integrating business model with online social networking – get continuous feedback from online networks, quickly integrate that with personalized messaging and customized product design. Future lies in integrating personalization features with business model. The challenge would be to modularize the business that caters to niche requirements of an individual. Opportunities lie in making supply chain systems more proactive, pricing more dynamic, marketing more agile and systems like datamarts, payment gateways, etc. more integrated. Time has come to create ‘profile bureaus’ in line with ‘credit bureaus’ that incorporates users’ interests, passion, online history apart from demographic and credit data.

 

You may also like to read:

Behavioral Segmentation
Market Basket Analysis
Text Mining
Amazing Amazon: The Largest Online Mall