Product recommendations in Digital Age

By 1994 the web has come to our doors bringing the power of online world at our doorsteps. Suddenly there was a way to buy things directly and efficiently online.

Then came eBay and Amazon in 1995....... Amazon started as bookstore and eBay as marketplace for sale of goods.

Since then, as Digital tsunami flooded, there are tons of websites selling everything on web but these two are still going great because of their product recommendations.

We as customers, love that personal touch and feeling special, whether it’s being greeted by name when we walk into the store, a shop owner remembering our birthday, helping us personally to bays where products are kept, or being able to customize a website to our needs. It can make us feel like we are single most important customer. But in an online world, there is no Bob or Sandra to guide you through the product you may like. This is where recommendation engines do a fantastic job.

With personalized product recommendations, you can suggest highly relevant products to your customers at multiple touch points of the shopping process. Intuitive recommendations will make every customer feel like your shop was created just for them.

Product recommendation engines can be implemented by collaborative filtering, content-

based filtering, or with the use of hybrid recommender systems.

There are various types of product recommendations:

           ·        Customers who bought this also bought - like Amazon

           ·        Best sellers in store – like HomeDepot

           ·        Latest products or arriving soon – like GAP

           ·        Items usually bought together – like Amazon

           ·        Recently views based on history – like Asos

           ·        Also buy at checkout – like Lego

There are many benefits that a product recommendation engine can do for digital marketing and it can go a long way in making your customers love your website and making it their favorite eCommerce site to shop for.

Advantages of product recommendations:

·        Increased conversion rate

·        Increased order value due to cross-sell

·        Better customer loyalty

·        Increased customer retention rates

·        Improved customer experience

Application of Data Science to analyze the behavior of customers to make predictions about what future customers will like. Big Data along with machine learning and artificial intelligence are the key to product recommendations.

Understanding the shopper’s behavior on different channels is also a must in personalizing the experience. Physical retail, mobile, desktop and e-mails are the main sources of information for the personalization engines

Amazon was the first player in eCommerce to invest heavily on product recommendations. Its recommendation system is based on a number of simple elements: what a user has bought in the past, which items they have in their virtual shopping cart, items they’ve rated and liked, and what other customers have viewed and purchased. Amazon has used this algorithm to customize the browsing experience & pull returning customers. This has increased their sale by over 30%.

Yahoo, Netflix, Yahoo, YouTube, Tripadvisor, and Spotify are other famous sites taking advantage of the recommender systems. Netflix ran a famous 1 million dollars competition from 2006 till 2009 to improve their recommendation engine.

Many commercial product recommendation engines are available today such as Monetate, SoftCube, Barilliance, Strands etc.

Ultimately most important goal for any eCommerce platform is to convert visitors into paying customers. Today the customer segmentation era as gone and its hyper- personalization. 

Product recommendations are extremely important in digital age !!

What is Deep Learning ?

Remember how you started recognizing fruits, animals, cars and for that matter any other object by looking at them from our childhood? 

Our brain gets trained over the years to recognize these images and then further classify them as apple, orange, banana, cat, dog, horse, Toyota, Honda, BMW and so on.

Inspired by these biological processes of human brain, artificial neural networks (ANN) were developed.  Deep learning refers to these artificial neural networks that are composed of many layers. It is the fastest-growing field in machine learning. It uses many-layered Deep Neural Networks (DNNs) to learn levels of representation and abstraction that make sense of data such as images, sound, and text

Why ‘Deep Learning’ is called deep? It is because of the structure of ANNs. Earlier few decades back, neural networks were only 2 layers deep as it was not feasible to build larger networks. Now with big data platforms we can have neural networks with 10+ layers.

Using multiple levels of neural networks in Deep Learning, computers now have the capacity to see, learn, and react to complex situations as well or better than humans.

Normally data scientists spend lot of time in data preparation – feature extraction or selecting variables which are actually useful to predictive analytics. Deep learning does this job automatically and make life easier.

Many technology companies have made their deep learning libraries as open source:

• Google’s Tensorflow
• Facebook open source modules for Torch
• Amazon released DSSTNE on GitHub
• Microsoft released CNTK, its open source deep learning toolkit, on GitHub

Today we see lot of examples of Deep learning around:

• Google Translate is using deep learning and image recognition to translate not only voice but written languages as well. 
• With CamFind app, simply take a picture of any object and it uses mobile visual search technology to tell you what it is. It provides fast, accurate results with no typing necessary. Snap a picture, learn more. That’s it.
• All digital assistants like Siri, Cortana, Alexa & Google Now are using deep learning for natural language processing and speech recognition
• Amazon, Netflix & Spotify are using recommendation engines using deep learning for next best offer, movies and music
• Google PlaNet can look at the photo and tell where it was taken
• DCGAN is used for enhancing and completing the human faces
• DeepStereo: Turns images from Street View into a 3D space that shows unseen views from different angles by figuring out the depth and color of each pixel
• DeepMind’s WaveNet is able to generate speech which mimics any human voice that sounds more natural than the best existing Text-to-Speech systems
• Paypal is using H2O based deep learning to prevent fraud in payments

Till now, Deep Learning has aided image classification, language translation, speech recognition and it can be used to solve any pattern recognition problem, and all of it is happening without human intervention.

Deep learning is a disruptive Digital technology that is being used by more and more companies to create new business models.

Digital Transformation – Age of Instant Gratification!!

Remember the scenario of 1990s office environment:

• We had our family photos pined on the board,
• Our contacts were written by our hands and arranged in alphabetical order for easy retrieval,
• For calling anyone we used to have one black dialing phone at the end of the hall
• Most of the time outside dialing was allowed to only select few privileged seniors,
• We used yellow post-it stickers to put our thoughts on the bulletin board,
• Any software delivery to customer was copied on the 8 inch floppy disk and shipped across continents to be hand delivered.

Now fast forward to 2016 – we have Twitter and blogs to post our thoughts, Pinterest and Instagram to post our photos, no more wait for calling anyone, Facebook to talk to friends, smartphone to store our contacts, we can do not only audio but video calls via Skype or Face Time and software deliveries are instant via email.

Today we live in the world of instant gratification and digital transformation is making it happen.

Our smartphones have become more important than our spouses. We can’t live without them. They can do the jobs of alarm clock, camera, radio, torch, music systems, maps, books, news channels, credit cards, language translators & play games. We can do anything and everything from anywhere at any time. They are no more just a communication device, but has become our life’s remote control.

Here are some examples of Instant gratification – here and now!!

UberRUSH – Delivery service by Uber with ability to directly talk to/ chat with couriers to track the package in real time instead of notification or sms alerts.

Click and collect your merchandise, multi-channel easy returns, free WiFi access while shopping, the ability to check stock online, update customer via beacon technology… these all can enhance the high street experience, bringing it more real time to customers.

An experiment of customer experience started at LaGuardia Airport, where food and Beverage Company OTG had set up 300 tablet kiosks located in the terminal. As a traveler, you can use the tablet to check flight status, order food, play games or shop at airport stores. When you order food or purchase products, they can be delivered to you at your gate. While improving the travel experience, this is also creating more revenue for the restaurants and shops. This new approach has become so successful that it is being rolled out at other airports. This is instant happiness to customers.

Digital transformation is helping to reduce customer information gaps, wait times and frustrations.

"We will revert immediately" is not fast enough. Customer wants the service NOW!!

Why Culture change is essential for Digital Transformation

Gone are the days, when companies used to decide strategy and then execute it for next five years as planned. 

Today company’s life on Fortune 500 or S&P 500 is just 15 years. Digital businesses like Uber, Airbnb did not exist before 2008 but now they are multi-billion dollar poster children for digital disruption.

Today due to digital, every business has to change how to operate, interact with their customers every day. Long term strategies are no longer valid or sustainable and change is constant feature.

Culture is a key determinant of this successful digital transformation. We can change our technologies, our infrastructure, and our processes. But without addressing the human element, lasting change will not happen. Culture is the operating system of the organization. It is like air, it is there but you can’t see it.

It's important for leaders to understand the business's current culture to map the right solution and timeline that will work for that business. No two organizational cultures are the same. Executives underestimate the importance of culture in an era of digital.  Most cultures are risk averse at a time, when taking risks is the most direct path to innovation.

But we have to remember that without the involvement, cooperation and feedback of the workforce, any digital transformation will struggle to maintain momentum.

Building an organizational culture for a successful adoption of digital technologies like IoT, Big Data Analytics, Mobility requires everyone in the organization, from leaders to front-line employees, to be prepared to work in an open and transparent way. It’s hard for an organization to undergo digital transformation if the culture is one built around silos. In cases like these, cultural change would need to be addressed before the transformation process could begin

Culture leads the adoption of technology. The ability to innovate depends on the impatience of the organizational culture. Organizations have to build the culture and community, making the time for people to share experiences, test and learn what works, brainstorm and collaborate.

It takes time to develop a digital culture; the sooner a company acts, the more quickly it will be in a position to compete in this fast-paced, digitized, multichannel world.

Southwest Airlines, in operation for more than 40 years, brought in culture change and empowered employees to go Digital and help customers.

Imagine how GE, which is more than 130 years old and operating in more than 175 countries now, has a quest for cultural change to be leader in Digital and Industrial Internet of Things.

Coca Cola has reinvented itself with culture change by focusing on digital natives while offering more than 100 flavored drinks.

For Digital Transformation Culture is top most enabler. Without people, tools won’t make any difference!!