Why Your Analytics Are Failing And 10 Tools to Help

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:

  1. Correct
    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:
  2. Data
    appending and enhancement match rates
  3. Ethnicity
    and language preference coding.
  4. Email
    appending match rates
  5. Social
    media ID appending match rates.
  6. Phone
    matching and correction
  7. Calculations
    around proximity to locations

  • Precisely
    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
    analytics team.

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.

  • Enhance
    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:
  • Demographics
  • Financial
  • New
  • Purchase
  • Lifestyles
  • Brand
    and retail preferences and so on.
  • Append
    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




  • Standardize
    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.
  • Have
    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.
  • Make
    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.
  • Choose
    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.
  • Have
    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).
Retrieved from https://www.youtube.com/watch?v=DDPLPG7Jps8

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