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.
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