Lights, data, action! What do TV Production & Analytics have in common?

Categories Insights

Back in 2007, I decided to change my path, leaving behind a successful career in TV as a producer. I was able to use my project management experience as the entry point to digital marketing and analytics. I also joined Market Motive, in a crash course to learn about marketing online, and obtained their “Master Certified in Digital Analytics” certificate. Nine years later, I had to undergo Domo partner training, which focused more than 40% of the time on how to define a customer’s need and formulate an actionable business question.

Both of these training opportunities centered on understanding business needs and translating them to meaningful business questions.

The two industries I worked in – TV Production & analytics –  start with the same step: “requirement gathering”, AKA “discovery”. This is the moment when you need to understand the business you are consulting or working for, and help your client focus on the 20% of the questions that will draw 80% of the returns.

This means spending your energy on educating your client as the subject matter expert and helping them formulate business questions that are:

  1.     Actionable
  2.     Operational
  3.     Timely
  4.     And above all, meaningful to the business

Imagine this conversation:

Sales Executive: I would like to know which sales person is performing by area.

Consultant: What action will this data enable you to take? And how often?

Sales Executive: Reward them on a quarterly basis.

Well, that is a nice manager, would you agree?

But, how will this enhance the company’s performance?

How can I provide an operational value?  

This request requires combining multiple data sources before you start the data visualization process. This is a labor-intensive process. I did not see the ROI for this initiative.

After a few conversations, we landed on this question:

How can I increase my sales, and which opportunities should I focus on first?

We provided a bubble chart with the opportunity advancement in the sales cycle on the Y axis and overtime on the X axis. The net revenue of the opportunity is basically the size of the deal. Now, I can see at a glance which opportunities I should focus on to maximize my profit. If we also add the region as a segment or filter, then we can do the same analysis on a regional level.

Now, as a sales executive, I would come back to this dashboard on a daily or weekly basis. The changes we made allowed us to create a dashboard that is actionable, timely, operational and meaningful to the business.

Here is another conversation from a mobile analytics client using Adobe Analytics:

Manager: I need to know the number of installs compared to last year’s benchmark.

Consultant: What action will this data enable you to take? And how often?

Manager: This will help me analyze the top of funnel conversion.

Consultant: What is the main purpose of your application?

Manager: Sales.

Let’s pause on this conversation. Do you think this is a meaningful use of our time?

We know this will be a time-intensive initiative. Will this meet our main criteria of a meaningful, actionable, operational and timely dashboard?

I think this is a good question if I am building a funnel analysis. And based on the target audience, it might be helpful if we change it to:

How do I increase the number of installs derived from a given campaign?

Similar to the sales dashboard, we can create a bubble chart visualization of the number of installs – Y axis, overtime – X axis and the cost per install as the bubble size. Use the campaign and device as segments/filters and you have an actionable graph that a marketing manager can use daily, if not hourly, to optimize their marketing campaign.

Notice that we are thinking about data collection from the end user’s perspective. We are not just thinking what data you need, but what business questions you need answered and why. If the data requested will not serve a purpose and increase conversion, then it would only be noise that would distract your attention from what matters the most.

Avinash Kaushik explains the need for a macro conversion and some micro conversions in this blog.

Long story short, your business and its online presence have one primary conversion, such as purchase, which we call “macro conversion”. It also has many small micro conversions, that will add up to the main conversion, such as product view, add to cart, etc.

While we need to focus on the macro conversion, we also need to have data that will answer the critical business questions related to micro conversions. Remember that these micro conversions will build up to the Macro conversion and assist your team in testing and optimizing the user experience leading to the macro conversion.

The main point I am making here is that you have to ensure you agree with your client on a few business questions that are truly actionable, timely, operational and beneficial to the business. This is the base you will use to design your data collection, dashboards, testing and optimization hypothesis and plan, and final analysis.

During the DAA Raleigh symposium 2017, somebody from the audience asked how to collect requirements for a large corporation that has many teams. My response was:

  • Use a Google Sheet, which allows the main stakeholders from each team the opportunity to present their need for data collection
  • Hold stakeholder interviews to clarify the questions and educate them
  • Compile a shortlist of the common question – do not forget to apply the four main principles around business questions
  • Provide a training session

This process may take a long time or it may be as quick as an one hour meeting. Regardless of how long it will take, I believe it is an essential part of your client’s success and the analytics initiative success.

To recap, the requirement gathering process is all about educating your client and getting to the few meaningful, actionable, operational and timely business questions. This is the seed to a growing and effective digital analytics practice, with all its branches.  

I am a seasoned Digital Marketing Analytics professional with over ten years of experience in the digital marketing analytics, data collection, attribution modeling and marketing automation. I've taken a leading role in building the analytics services practice for various organizations in the past few years, through which I lead the project/practice team of digital analysts and engineers in servicing major brands such as Target, Granger, AAA, Shire Pharmaceuticals, Otsuka Pharmaceuticals, J&J, Box, Red Hat, GOP & Discount Tires. Successfully designed, engineered and lead the implementation of multi-solution strategies and cross devices utilising various tools such as Adobe Analytics (SiteCatalyst), Google Analytics, Coremetrics, Adometry, PointInside, MediaMath, Target, Optimizely, Audience Manager, DTM, Signal, Domo and many others.

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