The Rise and importance of Prescriptive Analytics.

1. The Rise and Importance of Prescriptive Analytics
Submitted By,
Julie Elizabeth George
Digital Marketing Analytics

2. In the Digital Analytics platform, trends keep changing. In 2016, we saw that the
focus was on descriptive analytics. However, in 2017 the focus was on Prescriptive analytics.
Descriptive analytics provides insights about what has happened and further Predictive
analytics provides insights into what could happen. This two scenarios focused on digging
information from the past to know what happened and understanding the future and
predicting what could happen. At such a fast-paced business environment marketers do not
have time to just sit with data and ponder upon it. They need to be able to take the optimal
action for it as well. Therefore, Prescriptive analytics is everything about providing the best
solutions as to what we should do. It provides several possible actions to help users to take
full advantage of opportunities and reduce all risks. It uses optimization and simulation
algorithms to find solutions for every situation. There is a huge demand for prescriptive
analytics as accumulating loads of data without being able to take quick actions on it is not
effective and it would leave us far behind in the present competition.
The present analytics industry does not fully exploit the opportunities and knowledge
available in the data they collect due to certain limitations. Firstly, they do not take
prescriptive techniques for evaluating and transforming the obtained data to improve the
actions. Secondly, they do not integrate enterprise resource planning systems and other
operational and process data to get a better understanding of the process. Process data consist
of the data obtained for process execution whereas operational data provides more
information on the process subjects like the machines or the employees. Thirdly any sort of
optimization is conducted after the process and not during the process execution. Considering
the business perspective, the earlier the deviations could be detected, the easier it is to avoid
them.

3. Therefore, the ability to provide real-time recommendation and prediction of deviation for
any business or departments enables them to take optimal decisions on time. This process is
able to reduce costs, improve the efficiency, create new opportunities for revenue,
In digital marketing analytics, the complexity of prescriptive analytics can be
understood by the simple question “Why do we do what we do?” In the case of a food
delivery service, the business may notice that they have most delivery orders during
weekends. Therefore, they can recommend steps which would increase delivery even during
weekdays like providing discounts or coupons. Setting up specific theme for week days,
happy hours or any such promotional activities. Prescriptive analytics helps to notice
behavioral pattern, and take meaningful decision out of it. This trend is shaping digital
marketing analytics and it serves as a recommendation engine. Another case scenario wherein
prescriptive analytics is shaping digital analytics is that it studies each metrics obtained from
digital analytics. We can obtain in depth information as to which location brings in the most
customers for us and which location brings in the least customers. Knowing this information
is not enough unless appropriate strategies are not taken to take important decisions.
Prescriptive analytics studies this data and makes sure they enable managers in taking
decisions as to which is the ideal location as to where they should expand their business.
Which location they should increase their marketing campaigns and promotional activities
etc. thus the information obtained for these digital analytics when passed through the
prescriptive analytics radar, will be able to help business in finding the ideal solution to either
expand their business or to take improved strategies to improve business in certain locations.
A plethora of data without the ability to make or comprehend meaningful information
out of it to take decisions in a timely manner is of no use for any business. That is where
prescriptive analytics come into play. As mentioned in the above case, if businesses take a lot
of time to find data, analyze it and decide what actions are to be taken as a step by step

4. process, we would be left far behind in comparison to our competitors. Only those businesses
which are able to comprehend these data and make the most out of them will be able to
survive in the long run. No management would want their business to stay behind and thus
spending a huge amount of money due to the inability to take timely decisions. The whole
point of obtaining data from digital analytics would be of no use unless the strategies of
prescriptive analytics is not practiced.
Various industries have implemented prescriptive analytics. Some of the examples includes
the Oil and Gas industry. In the previous years “fracking” which is a process of extracting oil
or gas has been on the rise. In 2013, approximately $ 31 billion was the amount spent on
fracking 26,100 wells in U.S. in order to make the fracking process safer, where to frack and
also to optimize the whole process, a very large data set is required. Various data such as
sounds of the drilling, images, videos and text documents have to be analyzed in real-time in
order to find out the most optimal fracking location and also related decisions to have the best
results. Prescriptive analytics enables this industry to determine where they should drill or
frack and also how to complete and stimulate wells to maximize their production and further
minimize any sort of environmental disruption. Another industry which uses prescriptive
analytics extensively is the HealthCare industry. They maintain huge sets of data like the
medicine information, the patient records, the health trends and other hospital data. Using
prescriptive analytics several data could be combined to provide improved healthcare
facilities to people. They could also provide better infrastructure and hospital equipments
thus improving the goodwill and efficiency of these hospitals. Aurora Health Care Centre
was a hospital that was able to improve their healthcare using prescriptive analytics by reduce
re-admission rates by 10% thus saving $6 million annually.

5. In a phone interview with Janeesh Uthuppan, Director- Digital Strategy of Webdura
Technologies a Digital Marketing Agency I was able to know how prescriptive data has
become a very important part of business that in order to lead the race being efficient in the
same is absolutely essential. They have clients in the real- estate and construction industry
who uses prescriptive analytics to reduce their costs and find opportunities to improve their
business. They are able to conduct analysis before construction, use the sites more efficiently,
improve their safety training, predict any sort of risk and ensure they are able to finish their
project on time. Prescriptive analytics certainly helps a lot in the process of project
management.
By gaining more knowledge of prescriptive analytics I was able to know how important this
trend is in this extremely competitive business world. Every time we lag behind in making a
timely information, we are already losing the race. Every business is thinking way ahead into
the future and investing millions in research activities. With predictive analytics, business
would be able to reduce risks and uncertainties, reduce their expense, use available resources
judiciously and increase profits. Furthermore, when it comes to customers, by analyzing their
behavioral pattern businesses can provide services that customer wants and not what is forced
upon them by the companies. It can help them in their decision journey by giving them the
best options to choose from and follow an inbound method of attracting audience.
The best part about prescriptive analytics is that it relatively new trend and a lot of companies
are just getting into the process of implementing it. Certain ways to make the best use of
prescriptive analytics and staying ahead of competitors is by testing limits and assumptions
and by developing robust solutions that would perform efficiently under different scenarios.
Decision making should be automated. That way the speed of response can be increased.
Automating several tasks can enable managers to focus on complex issues than on the regular
or routine issues. Business should not focus on just unstructured data. A combination of

6. structured and unstructured data which is known as hybrid data should be incorporated for
decision making. Without hybrid data, we can say that decision makers are using only 20% of
the available data. Hybrid data consist of computer vision, image processing, signal
processing, speech recognition etc. Lastly another factor that business should keep in mind is
the fact that predictions and prescriptions should work synergistically in order to gain the
benefits of incorporating prescriptive analytics.
The information regarding this trend is certainly supported by the course content. Five points
to support the same includes:
1. Prescriptive analytics help to take informed decisions. As this process focuses on the
future of business, it enables managers to evaluate the data available to extract timely
and meaningful information out of it and use this to make informed decision. By
studying customer behavioral pattern and analyzing the data obtained from digital
analytics, businesses can make the most of the data we have obtained.
2. Prescriptive analytics helps to reduce costs. Proper planning and efficient response to
issues can help us to reduce the expense that could be incurred with the inability to
take quick actions when needed. It is always better to prepared for what could
possibly happen than to wait for it to happen and find solutions for it.
3. Prescriptive analytics uses the data obtained from sources like digital analytics to
make predictions and analyze the issues related to the functioning of the business.
4. Prescriptive analytics uses various testing methods like A/B testing, multivariate
testing and longitudinal tests to test various hypotheses for decision making. The
course specifies clearly how the three testing methods mentioned above is absolutely
essential for digital analytics therefore the trend supports the digital analytics process.

7. 5. Prescriptive analytics is a continuous process. It is not a one-time process which when
done gets over. Data has to be continuously evaluated, tested and multiple alternatives
should be extracted from it. Digital marketing analytics also is a continuous process
wherein various data, tools, strategies etc. are evaluated continuously to bring out the
best results.
Therefore the importance of prescriptive analytics is on the rise and companies are
integrating this process to ensure they do not fall behind in their decision making and are able
to take timely decisions.

8. REFERENCES:
Gualtieri, M. (2017, July 13). What Exactly The Heck Are Prescriptive Analytics?
Retrieved from https://go.forrester.com/blogs/17-02-20-
what_exactly_the_heck_are_prescriptive_analytics/
Basu, A. (2013, March). Executive Edge: Five pillars of prescriptive analytics
success. Retreived from http://analytics-magazine.org/executive-edge-five-pillars-of-
prescriptive-analytics-success/
Morey, J. (2015,Sep 02). How Prescriptive Analytics Influences the Way We Do
Buisness. Retrieved from https://www.experfy.com/blog/the-effects-of-prescriptive-
analytics-on-the-way-we-do-business
Rosi, B. (2015, Feb 05). From insight to action: why prescriptive analytics is the next
big step for big data. Retrieved from http://www.information-age.com/insight-action-why-
prescriptive-analytics-next-big-step-big-data-123458977/
Rajeck, J. (2017, August 9). Analytics approaches every marketer should know #4:
Prescriptive analytics. Retrieved from https://econsultancy.com/blog/69319-analytics-
approaches-every-marketer-should-know-4-prescriptive-analytics/
Thurai, A.(2014, March 22) Prescriptive analytics: an adaptive crystal ball. Retrieved
from https://gigaom.com/2014/03/22/prescriptive-analytics-an-adaptive-crystal-ball/
Uthupan, Janeesh. Phone Interview. 24. Feb. 2018.