Download Decision: To implement CRM project or not XYZ Global Pvt. Ltd

Decision: To implement CRM project or not
XYZ Global Pvt. Ltd decided to implement the CRM project using their high end product of
project management methodology PRINCE2. However, due to complexity and budget of the
project requires in-depth analysis of critical factors for CRM implementation and support of tip
management. Through my research and compliance with industry constraints, the project
management, change management and sponsorship is crucial in getting green signal for the
implementation of CRM project.
User adoption is generally highlighted as key challenge in success of CRM project. Market
analysis shows that 47% of the company finds that inadaptability of the end-user with CRM
applications put the project in jeopardy. If the project is cancelled, it results in loss of million
dollars.
As a business analyst of the XYZ Global Pvt. Ltd, I have to provide statistical interpretation to
the senior management whether CRM project is worth pursuing given the strength of firm’s
project management capability along with market evaluation of CRM implementation. Risk is
huge if project goes on wrong note.
Research
To make an accurate decision; I researched market analysis regarding implementation of CRM
through different vendors for companies of all range from less than $750K to over $10M. My
researched data set was from the CRM LANDMARK and consisted of implementation statistics
over the past 10 years. More importantly, my research specified the success and failure of
implementation along with factors associated to the result of success. Since my assessment was
to get the prediction of CRM project in the firm in next 2 year, my research primarily
concentrated on this time frame.
Once the research was gathered, I focused on accurately interpreting my data to make an
accurate decision.
Interpretation of Data (using Bayes’ Theorem)
To interpret my data, I chose to use Bayes’ theorem as the probability model that was the most
applicable to implementation of CRM project. Bayes’ theorem emulates the process of logical
inference by determining the degree of confidence in possible conclusions based on the available
evidence. This evidence is best stated in terms of quantitative as well as qualitative probability,
where the probability is based on evaluating opinions and information, then estimating this data
and finally assigning probability to the outcomes. Therefore, Bayes’ theorem is best used for the
purposes of predicting confidence levels for implementing CRM project, predicting the success
of an implementation, and or predicting a project’s failure if there is said lapses in project
management methodology.
While there are other effective analytical tools used to derive the probability of data, i.e.
hypothesis testing, Bayes’ theorem is more appropriate for this situation based on the subjective
nature of the evidence. Part of statistical modeling is using the right analytical tool for the
appropriate situation. Hypothesis testing is more effective for a scenario with an observed
difference. In other words, hypothesis testing is used to determine the probability when a given
hypothesis is true. In our CRM project scenario, for example, hypothesis testing could be used if
one professional body said the probability of a PRINCE2 project management methodology
being success is .5, while another body stated that the probability of a PRINCE2 project
management methodology being success is .35. Since our variables are not defined, it is more
effective to use Bayes’ theorem.
With the appropriate theorem chosen, I continued to gather my data.
After contacting the CRM user, consultant and others, I found out that 47 % of CRM projects fail
due to poor project management.
I then set up the variables to examine the probability of.
Cruise 1 = C1 = failed due to project management methodology
Cruise 2 = C2 = not failed due to project management methodology
In turn, this helped me decide whether I should suggest the implementation of CRM.
This is what I knew:
• the dates of the project implementation within next 2 years
• the probability of project being failed by a project management methodology is 47%; (P (C1) =
being failed by a project management methodology = .47).
So, prior probability of a project not being affected is P(C2) = .53
The CRM LANDMARK historical evidence cross-referenced showed that if there is established
project management methodology in the firm there was a 16% chance the project would fail. In
other words, B = established project management methodology is present (as indicated by the
market research). This is written as P (B|C1) = .16.
The CRM research analyst additionally stated that even though a project management is not
adequate, failure is not always imminent. Failure also depends on the subjective nature of the
organization adoption of new technology.
The probability of the project being not being failed due to lack of project management
methodology is .35, or written as P (B|C2) is .35.
Posterior probability according to Bayes’ shows us:
P(C1/B) = P(C1)P(B|C1)
_________________________
P(C1)P(B|C1) + P(C2)P(B|C2)
= (.47)(.16)/( .47*.16 + .35*.53) = 0.0752/0.2607 = 0.2884
Based on the calculations, the data illustrates the probability that the CRM implementation will
fail is .47. Now, if project management methodology is adequate provided that the market
research positively identifies a status of project management methodology in the firm, then the
probability of CRM implementation failure decreases from 0.47 to 0.2884.
It is important to the data into a table to check for accuracy and help determine what decision to
make.
Event Prior Probability
P(Ci) Conditional Probability
P(B|Ci) Joint Probability
P(Ai and B) Posterior Probability
P(Ai|B)
CRM implementation failure = .2884
CRM implementation success = .71166
Based on the above calculations, there is a .2884 probability that the CRM implementation will
and a .7116 probability that the CRM implementation will succeed.
Decision
All of firm’s decisions involve risk and its tolerance for risk. Statistical tools for analyzing data
give us a means of minimizing that risk. Based on the above analysis of available data, what
would company do? Would the organization go ahead with the CRM project, not worrying about
project management methodology, and thus risk losing firm’s capital? Or, would they proceed
with project and compensate the revenue with inflow other successful project, which
subsequently adds more pressure for the allocated budget for the project?
Reference:
Coltman T. and Devinney, T. M. (2007): Customer Relationship Management, Market
Orientation and Firm Performance, AGSM working paper, Centre for Corporate Change,
University of New South Wales.
http://www.crmlandmark.com/survey_crmfailures.htm
http://www.crmlandmark.com/crmlabsindustrytrends.htm