Peeling away the layers of Marketing Analytics

Peeling away the layers of Marketing Analytics
Marketing Analytics has traditionally been associated with online analytics and most strategies have revolved around SEO, website traffic management, hits to conversion ratios etc. but this field has been constantly evolving and growing. What we need today is a MarTech solution that not only measures your online efficiency but also looks at your company’s ability to engage customers effectively across all channels. Some of the key questions a MarTech Analytics solution can offer answers to are:

  • How are your marketing initiatives performing today across all channels? What can you do to improve them?
  • Where are your customers spending their time and money?
  • Are you aware of all your channels’ effectiveness or are you only concentrating on online channel?
  • Does your Loyalty program identify top paying customers and are you monitoring the effectiveness of the offerings being made?
  • How many customers are shopping with you off-line at stores while researching you online?


Omni-channel is selling to your customers both online and offline and being able to figure out what is working well and what is not. To get to that one must understand the four layers of analytics:

Data Source: At the heart of analytics are data sources. If you are not collecting data, there isn’t much to analyze. An old adage goes like this “We become what we measure” and if you aren’t measuring your KPI’s you are probably not sure where your company is headed. I would rephrase this as “We measure what we want to become”, so create and capture all possible data sources, measure them towards your definition of success.

Data Quality ensures that your always dealing with the right data. All the analyses time is completely wasted if data quality cannot be guaranteed. Some of the most common recurring problems due to data quality issues are:

Data Quality ensures that your always dealing with the right data. All the analyses time is completely wasted if data quality cannot be guaranteed. Some of the most common recurring problems due to data quality issues are:

  • Missing data – You have a customer name but no contact details or you are or entirely missing potential channel data.
  • Duplicate data – Multiple leads pointing to the same source which creates an illusion of a bigger sales pipeline.
  • Irrelevant data – Data that is not being used for business improvement. Companies are spending millions of dollars collecting data that has no potential use. Companies have to keep investing in IT infrastructure, teams to keep the data sources running without realizing that most of the data captured is not actionable or un-utilized!
  • Incorrect data – Wrong revenue numbers, incorrect address, incorrect names, multiple spellings for same data point (city names, client names)
  • Stale data – Like everything else in the world, data ages too and loses relevance. A sales conversion cycle going on for more than a year is potentially a lost opportunity and spending time tracking this data has less economical value with the passage of time.
Now that you understand the nuances of difficulties of Data Quality (DQ), what can you really do to ensure data quality? DQ is a journey that keeps evolving and improving over cycles and isn’t something you can do once and forget. There are essentially six steps to DQ as given below and it is quite important for companies to keep working on this regularly. I would not be stretching the truth by boldly stating that without data quality you might as well operate without data!
Data Model/Semantics: A data model or Semantic soon emerges once you start collating data and analyzing it. Common business logic is created for recurring calculations and themes and this drastically reduces the over head on the reporting layer. It provides a common language for business intelligence that your teams can understand. Data model tends evolves over a period of time with enough accumulation of data points but some of the easier business logic can be incorporated from the beginning.

Reporting & Analytics: This is the final consumption layer that most users of data see. Any report, dashboard, chart that you may have used is created in this layer. Although this is the most visible layer, please remember its utility is dependent on the effectiveness of the layers below. Most companies spend a lot of time and investment in this layer without thinking about the layers mentioned before. To get this right, your data journey has to take care of all the layers before it.

To summarize, the right analytics strategy for your company will not focus on your reports and cool dashboards alone but will transform the way you capture, manage, store, clean and analyze your data. Such a layered approach to managing analytics you will not only unleash your Marketing strategy but also effectively monitor your progress and be able to predict outcomes with more surety than ever before!

Why not start today and talk to us at Mobilise – we’ll help you understand your systems and data and how you can get the most value in the shortest of timescales. Do drop me an email at [email protected] to have a discussion! Cheers.

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