E-commerce is the bastion of Big Data.
There are many sources of data in E-commerce like Orders, Baskets, Campaigns, referrals and social sources like Facebook, Google marketing, Cookies and more.
The data for e-commerce companies is is huge in its value on four dimensions :
Volume: Terabytes of data! More data generated in last 2 years than in rest of history
Variety: Structured data in tables, unstructured like tweets, and videos and pics
Veracity: How to make sure the data can be trusted and used for business decisions
Velocity: How to use fast streaming data on youtube, facebook etc. to take real time decision.
Also here is what's going on every minute on the web
- YouTube users upload 300 hours of video, an increase from 72 hours a year ago.
- 80,000 hours of video streamed on Netflix alone.
- Instagram has likes on 1.7 million photos per minute.
- 300,000 Shares on Snapchat.
- Pinterest pinners pin nearly 10,000 images.
- Applying Big data solution helps to analyse this data real - time in E-commerce.
- Close to 70% users on Social media buy bases recommendations from others and another statistic says that upwards of 40% of users who give a positive review on social media purchase a product. So purchase data on enterprise and social media data can be linked using big data.
- E-commerce Recommendation Engine make real time recommendations to customer's e.g. Suggest a Taxi service and Hotel in Singapore to a person who just booked a ticket on http://via.com Suggest on the spot (to someone buying a wireless mouse) that customer's who bought wireless mouse also bought a wireless keyboard The Amazon recommendation engine is very useful.
- Real-time offers Make real time offers to consumers based on their spending profile or analytics's around what segment they fall in.
- Regression Use it to predict or model how popular a product will be on launch or how much response a promotion or campaign will receive.
- Communication channel Customise the communication channel on the basis of the user profile or preference of a customer. A major retail chain in Seattle does this. It is called Omni channel.
- Returns analysis To see what clothes returns imply. For example if a return a particular size by different brands what is my correct size per brand.
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| Image Courtesy Wipro |
Big data analytics is the process of examining large data sets containing a variety of data types. Big data - to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.
In this way, companies planning to use Big Data have to change their approach, as data flowing into the company becomes constant rather than periodic. This requires a lot of work. So many entrepreneurs prefer using a single platform that can manage the entire operations of an online business.
For this reason, I would recommend you to try ShopingCartElite. It is good solution with features for all the requirements. It has tools for solving all e-commerce problems like web data analytics, website hosting, ranking monitor, selling on multiple marketplaces, SEO, and more. They aren’t the biggest platform around in terms of client base, but that reflects in their reasonable pricing. I find them highly customizable and easily scalable whereas other platforms tend to stay within a business type, like only small/medium businesses or only enterprise. It is better to use hosted platforms as a single team will be taking care of your business.
Big data analytics is the process of examining large data sets containing a variety of data types. Big data - to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.
In this way, companies planning to use Big Data have to change their approach, as data flowing into the company becomes constant rather than periodic. This requires a lot of work. So many entrepreneurs prefer using a single platform that can manage the entire operations of an online business.
For this reason, I would recommend you to try ShopingCartElite. It is good solution with features for all the requirements. It has tools for solving all e-commerce problems like web data analytics, website hosting, ranking monitor, selling on multiple marketplaces, SEO, and more. They aren’t the biggest platform around in terms of client base, but that reflects in their reasonable pricing. I find them highly customizable and easily scalable whereas other platforms tend to stay within a business type, like only small/medium businesses or only enterprise. It is better to use hosted platforms as a single team will be taking care of your business.
The main issue that is addressed by analytics to aid online retailers is reducing the hassle of traditional shopping experience. The whole process of spending time for identifying good deals for specific products is considerably reduced.
With the use of analytics, online retailers are able to speculate the spending and behaviour patterns of online customers and are in a great position to capture their attention by providing exactly what the end consumers need.
Segmentation, personalization and customer satisfaction are easily achieved by online retailers with data analytics. This simultaneously improves sales , loyalty and brand recognition.
One big data analytics use case we see frequently in the context of online retailing is competitive intelligence. Finding out what you are selling and what your competitors are selling and at what price. Also catalog enrichment by matching your own product data with other feeds. Fraud analytics by linking blacklisted sellers on marketplaces with existing sellers. Logistic planning by fuzzy matching route addresses.
In very simple words, it is usually structured data that would not fit well in a traditional relational database.
Relational databases is what people used for storing most large structured data in the world until like 5-7 years ago.
Structured data is data that has some structure to it (an excel spreadsheet is an example)
Most online retailers are unable to make complete use of the data they have and continue to present the same products to each customer when they visit their website. Each customer is unique and is looking to buy products from an online store of his/her choice, have different buying behaviour and different paying capacity. But still the e-commerce websites and online retailers are showing the same products to each customer.To offer the customers the most personalised experience that caters to customers needs, online retailers can use omni channel personalization solution offered by big data analytics firm Retail Automata Analytics where it offers the following solutions:1)
1)E-commerce Personalization: The online retailer is being offered with complete onsite personalization through product recommendations based on browsing, purchase history. The onsite personalization provides the product recommendations in different slots such as Similar produtcs (Upsell), recommended products,products you may like,bestsellers across category, recently viewed,etc which can be featured on home page, cart page, product page, my account page of the website. The onsite personalization shows the most relevant products to the customer which in turn results in more sales conversion for the online retailers. The use of historical data from various sources helps the online retailers to make the best use of the same and maximize revenues.
2) Shopping Cart Retargeting:The cart abandonment percentage in online retail is close to 68% as no additional efforts are being done by the online retailer to do cart recovery. The shopping cart retargeting solution helps the online retailer to send personalized emails to the user post shopping cart abandonment. This personalized email is being triggered and sent to the user in various instances so that the user can complete the purchase action. The shopping cart retargeting solution can be most useful for online retailer to do shopping cart recovery and get the sales conversions.The online retailers can also send customers personalized emails based on the products they have browsed in case they haven't visited the online retailers website for quite some time.Also Miss you emails can be sent to the users to bring them to the online website. The personalized emails along with discount coupons can make the sales conversion for the online retailer.
3) Predictive Campaigning & Emails:The online retailer can do customer segmentation based on the available data of the consumers such as the number of purchases made, amount of money spend. Based on these customer segmentation,the online retailer can send out personalized emails to the customer group. The customer segmentation can be done on the basis of the number of previous purchases, the purchase order value, the customers who have bought during discount or those who bought without discounts. Once the customer segmentation is done, the recommended products can be then sent out via email, or onsite or via Push notification. Result is the most relevant shopping experience for every customer that builds brand loyalty. Resulting into higher conversion rates, higher average order values, and increased profits for the online retailer.
By using big data analytics for Onsite personalization, Shopping cart re-targeting and personalized predictive campaigning and emails can certainly enhance the sales for online retailers.
Companies make very intelligent use of big-data, meaning - they would consider most of the patterns or parameters a consumer uses while searching a product for ex- Mobile phone companies capture mobile product search with a price range, screen size, processor etc.. and does data mining to match these search elements with other products and give the options to the user not only in their own website but also on the other famous websites (such as social network, other online retail websites, or frequently visited websites etc) that they post adds in or have business tie-ups and again that goes with the kind of email that you use to access all these websites. They can act intelligent here by matching if that is the same consumer for ex- if the consumer clicks the options provided in the website B for the similar mobiles he/she was searching in the Website A --> after clicking he would land again on to the website A and he/she logs-in with the same email ID over a period of time, this may help companies to give even more relevant options to the consumer.
It goes the same from the the products perspective. Companies very well capture the search information or patterns Ex- if the consumer searches the products A, B, C belonging to a company and then searches products D,E,F belonging to another company and so on ---- now the company matches and lists out the similar products seen by many other consumers and there you always see the products listed saying " for Ex- Flipkart says - the customers who viewed this product also viewed these products" this obviously will help the consumer to know more similar options as others search, to understand the pulse of other consumers with similar product options and that boils down to understanding how the consumer market is treating those newly released products.
Data mining and intelligent use of Big-Data Analytical approach is being widely used by most of the online companies for ex- BharatMatrimony gives the profile options based on the search history and YouTube shows similar video tiles on screen based on the search history etc.
I am reluctant to rule out the possibility for the companies to trace and track the IP addresses which makes it even more easier for them to post and give the product options where ever one logs-in based on the search history.
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