Mergers of time before eventually returning to the

Mergers are an exciting time in the
history of an organization. There is excitement about the possibilities of two
organizations coming together as one, to expand the products and services
delivered to your customers. 

The true test of a merger’s success is often not seen until after several
months of post-merger trends are analyzed. Customer retention during the months
leading up to and immediately following a merger is critical or you risk losing
the loan and deposit base originally projected to make the new organization
profitable.

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After announcing your merger, the real question is do you have a plan for
retaining your existing customers? As you go through the merger and carry on
for the first 12 – 18 months, your most valuable customers will be making a
decision to either stay with the new organization or look for services
elsewhere. 

History is littered with examples of companies that
have experienced mass customer defection and other kinds of losses when
undergoing a merger. For example,
a large bank merger went awry when customers began taking low-interest loans
from the acquired bank and investing them back in higher-earning fixed asset
products of the acquiring bank’s investment division. The acquiring bank
discovered and addressed the issue more than one year later and after millions
of dollars were lost. The average customer attrition rate for banks,
measured at around 15 percent, can double following a merger, and remain that
high for an extended period of time before eventually returning to the
“standard” rate.

Another example can be
of the
merger of two major telecommunications companies. The acquiring company
incurred billions of dollars of expense integrating the acquisition while
losing millions of newly dissatisfied customers. In the first quarter of the
year, the acquirer lost about 1.1 million “post-paid” subscribers, or customers
who pay a monthly bill for service. Meanwhile, one of the acquirer’s major
competitors added 1.3 million wireless subscribers during the same quarter, and
another gained 1.5 million customers.

To avoid degradation of the assets they just acquired,
companies must do everything they can to retain existing customers while, at
the same time, adding new customers during the integration process and this can
be achieved with the help of big data and predictive analytics.

 Predictive analytics is the branch
of the advanced analytics which is used to make predictions about unknown
future events. Predictive analytics uses many techniques from data mining,
statistics, ratings modelling, machine learning, and artificial intelligence to
analyze current data to make predictions about future.

 Predictive analytics
allows organizations to become proactive, forward looking, anticipating
outcomes and behaviors based upon the data and not on a hunch or assumptions.

At
the most basic level, predictive analytics adds value by protecting or growing
revenue. If a bank is being acquired, you can imagine that many of the bank’s
current customers may consider leaving rather than stay on with the new,
combined bank. You can use predictive modeling to understand which customers,
based on their past relationship, account history, etc., may have a higher
probability of leaving than others. Once a model is in place, you can use it to
proactively target these at-risk customers with targeted customer care to
retain their business. Likewise, if the merging companies have complementary
products, you can use predictive modeling to identify the best target customers
for each firm’s products. In this case, you treat the new company’s customers
as a prospect universe and use predictive modeling to mine that data for the
best sales opportunities.

The bottom-line is
that predictive analytics can be used to improve the overall financial
performance of the new, combined company and help ensure that the acquisition
adds value as quickly as possible.

Mergers are an exciting time in the
history of an organization. There is excitement about the possibilities of two
organizations coming together as one, to expand the products and services
delivered to your customers. 

The true test of a merger’s success is often not seen until after several
months of post-merger trends are analyzed. Customer retention during the months
leading up to and immediately following a merger is critical or you risk losing
the loan and deposit base originally projected to make the new organization
profitable.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

After announcing your merger, the real question is do you have a plan for
retaining your existing customers? As you go through the merger and carry on
for the first 12 – 18 months, your most valuable customers will be making a
decision to either stay with the new organization or look for services
elsewhere. 

History is littered with examples of companies that
have experienced mass customer defection and other kinds of losses when
undergoing a merger. For example,
a large bank merger went awry when customers began taking low-interest loans
from the acquired bank and investing them back in higher-earning fixed asset
products of the acquiring bank’s investment division. The acquiring bank
discovered and addressed the issue more than one year later and after millions
of dollars were lost. The average customer attrition rate for banks,
measured at around 15 percent, can double following a merger, and remain that
high for an extended period of time before eventually returning to the
“standard” rate.

Another example can be
of the
merger of two major telecommunications companies. The acquiring company
incurred billions of dollars of expense integrating the acquisition while
losing millions of newly dissatisfied customers. In the first quarter of the
year, the acquirer lost about 1.1 million “post-paid” subscribers, or customers
who pay a monthly bill for service. Meanwhile, one of the acquirer’s major
competitors added 1.3 million wireless subscribers during the same quarter, and
another gained 1.5 million customers.

To avoid degradation of the assets they just acquired,
companies must do everything they can to retain existing customers while, at
the same time, adding new customers during the integration process and this can
be achieved with the help of big data and predictive analytics.

 Predictive analytics is the branch
of the advanced analytics which is used to make predictions about unknown
future events. Predictive analytics uses many techniques from data mining,
statistics, ratings modelling, machine learning, and artificial intelligence to
analyze current data to make predictions about future.

 Predictive analytics
allows organizations to become proactive, forward looking, anticipating
outcomes and behaviors based upon the data and not on a hunch or assumptions.

At
the most basic level, predictive analytics adds value by protecting or growing
revenue. If a bank is being acquired, you can imagine that many of the bank’s
current customers may consider leaving rather than stay on with the new,
combined bank. You can use predictive modeling to understand which customers,
based on their past relationship, account history, etc., may have a higher
probability of leaving than others. Once a model is in place, you can use it to
proactively target these at-risk customers with targeted customer care to
retain their business. Likewise, if the merging companies have complementary
products, you can use predictive modeling to identify the best target customers
for each firm’s products. In this case, you treat the new company’s customers
as a prospect universe and use predictive modeling to mine that data for the
best sales opportunities.

The bottom-line is
that predictive analytics can be used to improve the overall financial
performance of the new, combined company and help ensure that the acquisition
adds value as quickly as possible.

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