Market Survey and Research about Recommendation Engine

According to the detailed research done and lots of surveys for recommendation engines created in collaboration with renowned companies, it came out with few surprises. B2B is a strong and driving marketplace, and almost half of the suspects devise to deploy recommendations by the last year. These days, big data recommendation engines are emerging and dawning as the top notch marketing as well as advertising tools for all kinds of businesses. The technology is being used to personalize and tailor made communications, improvise website search, drive conversion and lift productivity. A recommendation engine advises and suggests people items including products, services and articles including personalized content that can be viewed by online customers. Making use of big data, hadoop and other platforms, recommendation engines are used for multiple purposes. Some of them are even mentioned in brief below.

1.       Build a personalized experience for a specific segment or a customer.

2.       Advise and commend people additional products based on their buying preferences and patterns from the recommended community.

3.       Help to showcase and present banners and ads that are more likely to be specific and apt for a particular group involving bunch of people based on their profiles and information provided.

4.       Draft a personalized e-commerce website customized according to every customer relationship covering queries like sports played, machines managed or car owned and more.

5.       Deliver a personalized and customized view of the corporate intranet.

6.       Build a tailored support online portal to the professional help desk.

7.       Customized content featured and addressed in e-mails or displayed on the website.

The purpose and motto of the survey was to do some research and detailed study on the goals, potential, capabilities and achievements for recommendation engines. In last month, there was a survey conducted for recommendation engines in which there were about 100 respondents to three versions of survey. About 10% of them were already using such systems, many of them have plans to deploy them in next few months and few of them are not even aware of the same. As a conclusion, about half of the respondents are pretty sure and know about recommendations. Retailers and vendors unanimously chose such frameworks for deployment.

In a zest, the top three purposes for which respondents and appellants chose big data recommendation engines are either for content personalization, personalizing ads or up-selling to be done for an e-commerce website or online portal. Moreover, the three arenas that could be addresses as separate market platforms used by recommendation engine vendors and third parties are revenue, acquisition and last but not least user satisfaction. We used to ask people all across the world a lot about what recommendations are for. And unsurprisingly, answers are all same that is along with increasing effectiveness and performance, taking more care of customer acquisition, revenue generation and client satisfaction. Even in few countries, employee productivity and potency was an important and vital factor for many of the appellants of small, medium and large sized companies. Adding more, to stay ahead and keep up with the globalized and fast paced industry, technical geeks must keep track of big trends and find ways to inculcate the significant ones in their respective enterprise’s technology portfolio. And use of recommendation engines will drive users to leverage more about the offerings from your business or websites.

Big Data Recommendation Engine in Retail Industry

Each e-commerce industry is focusing on more use of big data these days to improvise operational efficiency and performance especially in the retail industry even before the term big data existed. Walmart is moving on the same path and understood that by reaping the power of data, it could really streamline as well as consolidate its critical supply chain management to take benefit of economies of scale and robustness, making a restriction on extra inventory costs and its related costs to be incurred upon. It basically passed some of these enabled savings on big data to users in the form of lower prices that in few cases excavated and undermined the retailer’s competition. Well, this was the scenario of early 2000s. After this, retailers have started making use of innovation and creativity in data to deliver not only value added solutions in their offerings but also that could be advantageous for both customers as well as the bottom line users. As compared to other advanced and innovative retailers available at online platform helping users in buying and selling and suggesting them recommendations to them, Amazon is the one that in mid 2000s started using what it actually knew about its users buying preferences and behavior to recommend similar stuff and related things to clients at the checkout point of time.

In today’s challenging and competitive world, big data recommendation engines and data driven supply chain optimization are like the table perils and sticks for most of the retailers. And in last few years, forward thinking and moving retailers endeavor to holocaust an innovative path especially in big data. Based on the chats and conversations conducted with number of retailers and members of Wikibon community and other users, the following big data recommendation applications have been identified amongst the more promising, successful and innovative methods must used mentioned in below.

1.       Dynamic price optimization – Retailers are making use of big data backend techniques and approaches to dynamically price up goods and services at both online as well as offline stores. In its most advanced form, dynamic optimization keeps into consideration umpteen numbers of data streams including supply chain, competitor pricing, inventory data, consumer behavior data and market data to fix and compensate on prices to optimize sales and profits, enhance profit margin along with meeting up with other strategic aims and objectives.

2.       Video enabled product placement analysis and store layout – In order to drive high conversion rates, a bunch of retailers have started examining and thinking about video data, not only the associated metadata with videos but also the content of the video to enhance and make it better in terms of store layout, promotional displays and product displacement criteria’s. In fact, according to a survey, the retailers who are using video to analyze and understand the video data are actually trying to grab attention of a large base of customers not affecting the actual significant sales.

3.       Decision support and staffing analysis – Both national as well as multinational retailers with diversified and geographically spread and scattered workforces usually have long struggled and optimized in-store staffing services. There are many factors that affect staffing prerequisites and needs including promotional campaigns, weather forecasts and time of a particular month, year, week or day. These days, retailers are examining and evaluating data associated with other factors to assure stores are optimally staffed and casted.

Adding more to the point, retailers are using a wide variety of technologies and methods to support big data applications involving usually Hadoop, enterprise data warehouses, immensely parallel analytic databases, data visualization tools and many others. As a conclusion, bigger retailers who have started using 

big data recommendation engine technology to consolidate and streamline operations, examine marketing campaigns, improvise and enhance customer experience, boost sales and optimize profitability to put plans immediately. As stated, the retail industry is like the early innovative users as well as adopters of big data driving those vendors who haven’t even started harnessing data for their own benefits are farther behind dawdlers and slow starters in other industries. In all, retail CIOs at this peak of time should not at all waste their time in bringing together business stakeholders and IT people to lay out a bigger big data vision for the practical and enterprise plans to deploy them. And, the few reckoned leaders offering in the same arena are 

IB Technology, Wipro, Persistent, Polaris, Nucleus, R Systems, Global Logic, Infosys, TCS and Cognizant.

Big Data Solutions: Building a Recommendation Engine using Hadoop, Pig and Mahout

With time and over the years, recommendations have become expected and important as an integral part of the user experience worldwide. From a consumer’s point of view, marketing interactions could be favorable and useful and even time saving, rather than just being generic, annoying and out of the context. If you shop from leading and pioneering vendors and retailers like Amazon or Flipkart or Jabong or Bluefly, you might think that somehow they have gotten inside our mind while presenting and showing recommended items relevant to your search results. It is a consequential improvisation of the conventional psych demographic profiling as well as targeting of the old world. It has always been a point of discussion how Mahout can be used to build a recommendation engine with minimal coding and programming needs. Moreover, it has also been discussed a lot how the machine learning capabilities and open search potential of Mahout and Apache Solr can be blended to empower huge scale data driven applications that efficiently combine real time access with global scale discovery, enhancement and enrichment.

Recommendation engines are everywhere assisting firms and enterprises all across the world to provide a kind of ‘artificial intelligence’ to help put down filters through numerous options to pick handful of selections most apt and authentic for you. For example, when Netflix advises you movies based on the reviews and your preferences, after thoroughly searching history of you and zillions of other subscribers, you are actually benefitting from their recommendation engines. Many other big internet marketing companies makes use recommendation engines to boost their online services powered by big data solutions. In fact, for your record and knowledge, Datameer Company is going to have a seminar on the same specialized in offering data analytics solutions by making use of Hadoop technology.

Business problems being targeted and focused – A new suite of business issues were difficult to think and handle before including customer churn analysis, modeling true risk, recommendation engines, ad targeting, loyalty pricing, threat analysis, search quality fine tuning, trade surveillance etc. To address and solve above mentioned issues, a flexible infrastructure for data warehousing and big data analytics emerged heading to below mentioned endeavors and support public and private cloud deployments.

·         Capability to determine structured, unstructured and transactional data at a single platform

·         Solid state or lower latency in-memory devices for high chunks of web and real time applications

·         Slice out low cost commodity software, workloads and distributed processing

The interesting part is that the fundamental things of big data have been drastically changed from the core endeavors of BI and analytics to the ability that end users perform while analyzing, reporting and handling tasks over consistently growing chunks of structured as well as unstructured information like sensor data, log files, sales transactions, streaming data, emails, research data and images collectively known as ‘big data’.

Big data recommendation engine for e-commerce retailing is helpful in multiple things. Quite favorable and beneficial in raising average order size by recommending complementary items based on the predictive and foreboding analysis for up selling and cross selling of products, the cross channel analytics cover different attributes like average order value, sales attribution, lifetime value and event analytics cover a vast series of steps induced to a desirable outcome including registration and purchase.

Big data Existing and Emerging Firms to Watch –

·         Pioneering Leaders - Few of the leading firms catering big data solutions to medium and large-sized enterprises all over the world include HP Vertica, IBM, Microsoft, Teradata, Oracle, Netezza etc.   

·         Evolving Players – Few well known emerging leaders catering big data recommendation engine solutions include IB Technology, Splunk, Cloudera, Hortonworks, Datameer, DataStax etc.

All the enterprises are following two distinct approaches in general, including serving big data solutions in the public cloud or in the private cloud. It is becoming clear with time that there is going to be a tremendous and growing push towards an adaptive and flexible infrastructure for big data, master data management, data warehousing, and data analytics. All in all, with the increasing and consistent focus on mobility and better decision making, the businesses are going to push faster and quicker than corporate IT can ever react and respond.