Why Your Analytics Are Failing And 10 Tools to Help

Companies, 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.

By 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.

The 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 data
  • New movers
  • Purchase triggers
  • 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:

Email address

Social media ID: This includes the popular ones such as Instagram Twitter, Facebook

Phone numbers

IP address

Channel preferences

  • 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.

References

13 Web Analytics Tools & 10 Reasons for Failure. (2019). Retrieved from https://www.toprankblog.com/2010/06/your-analytics-are-failing/

Google 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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