brands cannot do without data analytics. Without data analysis, companies would
be blind and deaf, and would just roam on the web without a specific direction.
One of the most important competitive advantages in the world is the ability to
combine data and analytics. Data and analytics help to significantly improve
business results. This is why a lot of companies are rushing to deploy armies
of analysts that are capable of creating predictive models that could boost
their revenue and earnings to new heights.
combining behavioral data and appended data with machine learning and logistic
regression, it becomes easy to engage the right customers, with the right
messages, and also at the appropriate time, through the digital channels. However,
even though data and analytics have become a gold rush. There is a critical
flaw that can lead to marketing failures and the loss of money. This is dirty
data. No company is immune to dirty data. Studies and audits carried out over
the last 2 decades have shown severe data problems. In addition to flawed data,
every customer database suffers from missing or incomplete data. In cases where
the incomplete data is used for analytics and direct marketing, significant
financial problems and risks may occur. Examples of situations that could
develop due to the use of incomplete data or dirty data include wasted
marketing budgets, missed revenue and profit targets, frustrated customers and
prospects, lawsuits and costly fines, and the loss of reputation and brand
equity. Overall, problems never stop developing, when dirty or inaccurate data
is used as the foundation of your analytics and marketing. This piece would
focus on some of the ways of making your analytics stronger, cleaner and also
yield better marketing results:
and standardize customer address: The physical address of your customer matters
a lot. It’s one of the information you need for many critical data elements and
updates. However, studies have shown that about 20% of addresses can be
incorrect on many customer databases. You should ensure that you have the
latest algorithms to clean and also update your customer addresses. While
carrying out data cleaning, you should deploy up to 16 different processes,
including DPV, NCOA, CASS, PCOA, deceased suppressions, zip and city name
corrections and so on. After carrying out these processes, your correct
addresses would improve the following downstream efforts:
appending and enhancement match rates
and language preference coding.
appending match rates
media ID appending match rates.
matching and correction
around proximity to locations
identify unique individuals and households: It’s important for you to be able
to identify unique individuals and households from any source of data that
contains a name and an address. This is referred to as “pinning” by the advanced
algorithms that are involved in the inning process should go well beyond the
use of simple match codes. Your households should be assigned a unique
household identifier, and each individual should be assigned a unique
individual identifier. These identifiers are invaluable for linking all sources
of data together and providing a single view of your customer across all
channels. In other words, your pinning system would become the foundation of
your entire data structure.
your data with consumer profiles, behaviors, and propensities: After precisely identifying
unique individuals and households, you’re now in a position to accurately
improve your data with a wealth of consumer information. Appending your data
would help correct the errors and also fill in missing data. This data hugely
improves your predictive models, and also your marketing results. Some of the
most common categories that should be evaluated and appended to your customer
and prospect databases include the following:
and retail preferences and so on.
omnichannel contact information and preferences: Your analytics would be
useless, no matter how good they are, if you don’t know how to reach your
customers and prospects. You should ensure you accurately and frequently append
the following omnichannel data:
media ID: This includes the popular ones such as Instagram Twitter, Facebook
your data inputs, processes, and outputs: The next thing to do is to make use
of your data consistently. This involves standardizing how you receive,
transform, store, analyze, report, score and act on your data.
the right people: Asides from the technical aspects of improving your analytics;
it’s important to have the right people on your team. There are two types of
people you need to consider if you want to achieve analytics success. These are
the analytics team and analytics users. You must bear in mind that analytics is
a multi-disciplinary profession that combines both business and technical
skills. You should avoid hiring the so-called full-stack analyst or data
scientist. They are not only expensive and rare but are more like a jack of all
trades, master of none. Your team should be structured and positioned like a
group of consultants, that partner with the business to support decision making
rather than a team of technologists who simply build systems.
use of evolutionary development: You should apply development methods that
would allow your team to deliver content quickly and also respond to rapidly
changing requirements. The reason behind this is because of requirements for
analytics change continuously and quickly. Overall, you must ensure that your
analytics system is flexible and can deliver changes rapidly.
the right technology: It’s important to choose the appropriate technology for
your data analytics. You should go for technology products that are flexible
and adapt to changing user needs.
appropriate resources: Analytics is a process, and not a project, which is why
it’s important to ensure that the resourcing model for your analytics
initiative reflects that it is processed. Overall, analytics should be viewed
as an ongoing business function, and not a technology project.
- Understand decision support requirements:
Although some of the objectives of analytics include solving business problems,
generating insights and so on. However, the ultimate purpose of analytics is to
support and/or automate business decision making.
Web Analytics Tools & 10 Reasons for Failure. (2019). Retrieved from
Analytics || Why getting your company to the first page is important. (2019).
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