The ROI of Data

“Carl, why are you so obssessed with data?”

“Because data tells you stories. Behind these stories, you will find actionable insights.”

Dalphine would always tease me on how data obsessive I am.

I certainly don’t think I’m being obsessive, I just feel that we’re not using data enough.

Let me ask you this, how many of you can actually find out:

  • How many meetings did you meet on average to close a sale?
  • What was the trigger point that lead to the sale?
  • What was the items they viewed before proceeding to check out?
  • After understanding all of the above, how can you improve?

If you’ve did telemarketing before (which I did), you would know that 8 out of 10 customers that you called would raise similar questions.

With these data, you would be to understand your customers better to craft out a proper response to certain questions which is helping you in closing sale or setting appointment.

This is what I call being data-driven.

Data-Driven is using data to validate your assumptions

Many companies have been sold on the concept such as big data and predictive analysis. I even got this:

“Look Carl, we understand why is it important, but we don’t really know how to do it.”

The telemarketing example I raised earlier is one simplified data-driven execution.

All we have was a spreadsheet and being consistent in documenting it.

Using salesforce doesn’t make naturally make you data-driven.

It’s the approach to data that counts. Being data-driven is a mindset that you validate your assumptions with data.

In essence, data helps you to identify few things:

 All you need to do is to collect the data, and analyse them.

What should I track for data?

Everything.

From the number of offline meetings to closing of the sales. Everything in the process.

Find out the how your top 20% customers are behaving and study them carefully.

Think along the lines of:

  • How did we get them to this stage?
  • Any significant event that triggered certain action?
  • What pushed them to take action?

Consider giving your customers a quick visit to understand how it worked and why it worked too (that is if you want a deeper insight).

Understand this – the ROI of data is highly correlated to your level of understanding. The more you understand about your customers, the better you will be able to make proper responses to your prospect.

2 comments

  • Hey Carl! Have been a subscriber of your blog for the past few months and enjoy reading your posts.

    In a world where there is an abundance of data, sometimes its up to how we “slice” the data/ ask the questions that leads to different conclusions, don’t you think? How do we prevent incidents like confirmation bias, wording bias etc? Ie I am in the opinion that conclusions is hugely dependent on the person running the study.

    Sorry if its a silly question/comment!

    • Hey Yvonne,

      First of all thanks for following my blog! I hope I’ve inspired you somehow someway.

      Btw it is a valid question, definitely not a silly one 🙂

      I agree. It is true that slicing data or segmenting them into clusters may lead us into potential blind spot. I don’t think we can fully prevent that even with softwares.

      That is exactly why it is important to test our assumptions a.k.a data findings with sample groups. Validation can be done in the form of focus groups for qualitative data or even A/B testing. It is the best way to validate your data findings.

      Let me give you an example of email testing:
      1. Data is telling me that 24 year old male are generally into tech stuff. I think this is true (note: think).
      2. To validate this, I send 2 email templates with same layout but different content (fashion & gadget) to sample A & B.
      3. Find out if the assumption holds true, if not, study the data and understand what went wrong.
      4. Rinse and repeat.

      In a non-technical way, speaking with your customers and colleagues can be one way of validating your assumptions too.

      The idea here is to always validate assumptions against data through execution. Rinse & repeat.

      I hope that clarifies!

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