Role of Personalisation in online shopping

Role of Personalisation in online shopping

“Let’s go shopping” Nah. Who wants to step out in this congested traffic and go to a store with such limited and impersonal options? Few prefer the brick-and-mortar stores for shopping today. The tech-savvy world has shifted to online shopping, some time ago. Reasons are many. More options, ease of shopping, discounts, etc. Although, one thing that is not on the top of the customer’s list of reasons and yet is a powerful motivator for online shopping is “personalized shopping experience”.

According to a report by Total Retail 2016 by PricewaterhouseCoopers, retailers who do not meet the evolving customer experience witness significant drop in sales. Customers expect the retailers to understand their ever-changing needs and plate up a personalized experience. As attention spans of the customers go lower and the diversity of customer preferences go higher, personalisation becomes the need of the hour.

Role of Data Intelligence in e-commerce

Larger e-commerce companies set up their own internal teams of data scientists. These teams help analyse and understand the end-user behaviour. Data intelligence, though shouldn’t be confined to just confined to the big players but to every e-commerce company irrespective of the size. When companies do not have the luxury of time and money to set up their own teams, our expertise is sought after. We work with such companies to design and personalize the purchase experience of end users on their e-commerce portal.

According to MyBuy’s 2015 Personalization Consumer Survey, consumers purchase more from brands that instil the following in their marketing efforts:

• Recommend products based on browsing or purchase behaviour (53%)
• Personalized content in online ads that promote offers and products (49%)
• Personalized emails based on the shopping history and browsing patterns (48%)
• Personalized the shopping experience across multiple channels (48%)

The data that revolves around the customer’s behaviour online is spread across:
Google analytics
Social Media platforms
CRM’s
Websites
Email/SMS
Mobile apps

Our high-end data management platform acts as a one-stop storehouse for data from the above sources. Primary information like the name of the customer, purchase history, phone number and other data are aggregated from these sources.

Natural Language Processing (NLP)

When we need advice on some specific shopping tips we turn to our friends and family. Similarly how about seeking advice from our “machine friend?” Sounds cool, right? This is where Natural Language Processing(NLP) comes into picture.

Through NLP, the machine understands the context of the content. It may be overwhelming to understand the changing human needs and be proactive to fulfil them, but through NLP the nuances of every item a user interacts with is understood. Ultimately, the uniqueness of NLP is providing a better understanding of the customer’s needs and behaviours to the retailers.

How NLP helps collect customer behaviour data is mesmerizing. Customers are on social media and on various forums expressing their views and comments about products and brands. Computers are programmed in such a way that they can categorize the frequently used words by customers into different topics. For instance, if the computer spots words like “quality”, “performance”, “durability” it is categorized under a topic “Product attributes”. These topics give an idea on what that customer has on mind. Machines can never compete with human intelligence. So, the search terms fed into the program should be very flexible rather than immutable words.

E-commerce portals and retailers should have access to efficient personnel who can handle the NLP efficiently. The better the companies are at doing this the better they can understand the customer behaviour. Various applications are being developed to answer the customer queries using natural language.

As the world is turning to artificial intelligence it is high time e-commerce companies become proactive. The customers should not miss the experience of being attended to by a salesperson like in traditional shopping. E-commerce websites should be ready to play that role and satisfy them, unless they want to lose customers to a competitor who does. Our platform uses Natural Language processing and Machine learning to understand user preferences through the content semantics.