Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

Chapter 20: Marketers profit by using
data science
 
The marketing industry has been involved in gathering trade statistics for a
long time and the amount of data collected to date is very substantial. This
data has not only increased in capacity but also in range, quality, and
accuracy. While there is an acute awareness of the significance of this data
by retailers, they are not so clear about its many applications and how to get
the best usage from the system. Most retailers are unable to handle this data
correctly. Basically, they do not know how to collect, analyze, and apply it
appropriately because they are not familiar with the science behind data
gathering and usage, and the various interactions that make it invaluable for
higher profitability in their areas of business.
 
Specialists in data science can convert haphazard, unstructured data clutter
into a well-organized system which the marketer can operate with ease.
Because of the inability to apply data correctly there has been a rising
demand for data scientists. The retail industry has recognized the
advantages of employing individuals who are skilled in data analytics, and
have seen their profits rise exponentially through expertise in data handling.
This has convinced retailers to work with scientists whether it is internally
or externally. A huge competitive advantage could be gained by the smart
use of data analytics in the retail business. Retailers who are able to
organize, assimilate, and apply their data will maximize their marketing
tactics and optimize their chances of success.  
 

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Reducing costs to increasing revenue 
 
Sales are the most vital aspect of any business. Pricing must be realistic and
yet profitable, and appeal to consumer requirements. It is always a priority
to improve customer experience online or offline and this can be done
accurately by using predictive analytics. Retailer requirements should be
adapted to suit market demands and insightful data from social sites, call
centers, product reviews, surveys, and any other means of customer
feedback will be helpful in outlining best retail practices. Improved displays
of promotional presentations and other marketing resources can be
accomplished through the use of heat sensors or image analysis for retailers
to obtain a greater appreciation of consumer behaviors. Scrutiny of video
data can also be used to ascertain shopper tendencies and this analytical
data will help identify cross-selling possibilities.
 
Data from internal and external sources can create consistent profits for
marketers. Information may include raw material costs and competitor’s
prices, economic, weather, and traffic reports, seasonal products and high or
low purchasing periods. Sales must be founded on the conception of a
systematized, planned strategy and the detailed statistics of this data guides
retailer’s to create a perfect marketing plan. Without sufficient, accurate
data there can be no foundation for determining and evaluating supply and
demand.  Revenues can be improved quicker due to detailed investigative
market reports, and by making use of product sensor devices to convey
significant statistics regarding product and purchase in real time.
 
Marketing practices may cause retailers to cut costs by making special
offers through mobile messaging. They can deliver these deals which is
more economical than general sales.  Since retailers are able to
communicate in real time they are able to modify pricing structures which
change from second to second. Retailers can maintain an edge by
comparing competitor pricing data which is available to them through
sensors. Being aware of how a product compares, positions the seller and

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buyer to make an advantageous deal. Segment consumers markets can be
spotted by looking at analytics data on any number of information systems.
Making use of behavior analytics helps to improve retailer’s return on
investment policy and shape effective marketing proposals as a result.
 
Data science helps when retailers need to track and manage inventory in
real time. Supply and logistics sectors also profit from data science which
permits retailers to improve distribution means through GPS enabled big
data. This allows the choice of the most inexpensive, safest, and swiftest
routes. Supply chain logistics also benefit from structured and unstructured
data as dealers are able to prefigure prospective consumer needs well in
time. The analysis of accessible market information on big data creates a
chance for retailers to bargain with suppliers for better deals. Gathering and
applying big data for insightful retail practice will meaningfully define the
bottom line in all transactions.
 

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