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Managing marketing analytics for the largest companies in the world: Meet Conrado Abreu, Market Intelligence Manager at UnitedHealth Group with more than 6 years of experience in healthcare. Conrado is leading the market intelligence area under Marketing and Communications. Responsible for analyzing the business environment, competitors, and customers. All in order to identify potential threats and seize opportunities to drive sustainable growth.

How did you get into Marketing Mixed Modeling (MMM)?

I joined UnitedHealth Group (UHG) in 2013. At that time I was working with digital marketing, PR and different sports projects.

I had started to search for new opportunities and for new things to learn. And that was when the opportunity to work with a marketing team at UHG came up. The timing could not have been more perfect. Prior to this I never came in contact with data, analytics or MMM.

How did you start working with MMM at UHG?

I was curious about market intelligence and marketing performance and learned that this was somewhat of an unexplored territory.

We started working with marketing intelligence sales inside the digital marketing team. We knew we had to prove it was worth investing in. So we started with something that we knew the company needed. How to measure marketing performance.

When we started our first projects around measuring marketing performance. I could instantly notice challenges in the sales and marketing process. It was impossible to tell precisely what the contribution of marketing was since we didn’t have any direct sales. That’s why I thought about solving this problem with Marketing Mixed Modeling (MMM).

With the help of MMM we started to be able to optimize our budget and understand our ROI. Together with MMM we could finally get a clear picture. A clear picture of how marketing was generating revenue and contributing to the company.

“With MMM we could finally get a clear picture of how marketing was generating revenue and contributing to the company.”

How are you working with marketing mix modeling today?

At this point, we are planning to get back with this kind of project in 2020. As we had different investment priorities in the last couple of years. For us, MMM can really help us understand how we can optimize our marketing efforts. But also deliver better results for the company.

We have a big challenge to develop how we work with MMM. Considering the complexity and size of our industry, and the current modeling approaches.

What is your best success story using MMM?

The biggest win for us was being able to see if the investments were in the right place or not. Check the effectiveness of our investments and make the right changes.

We could balance the spending between different activities after analyzing the ROI. If one brand was not generating the expected results, we could either redirect or increase the investment to get the targeted return.

With MMM, we got an ally to prove we are generating x dollars in profit. Due to one activity and that we are delivering value to the company. This is a great way to get people from the organization on board with our strategies and engage them.

“MMM is great to get people from the organization onboard with our strategies and engage them.”

What is your best tip to others that want to start using MMM?

I think, if you want to start using marketing mix modeling, instead of asking for more budget, you start by taking a small part of the media budget. Many large enterprises have big media budgets.

By using MMM you can argue that you will be able to optimize the media investments. You will actually do better with less money. So start to finance your marketing mix modeling with a small part of the media budget, that’s my best tip.

“Start by taking a small part of the media budget, and optimize the outcome”

How does UHC plan to work with MMM in the future? 

We will definitely get back to MMM this year. It’s something that we really want to do but we need to figure out the right approach on how to use it.

“Most important, analytics will allow us to deliver more value for the end user, which leads to more success for our company. It’s all connected.”

We are involved in several different markets and different products. Using MMM for each market and product is going to be both time consuming and highly expensive. So, we will focus on specific opportunities to obtain the best results.

Most important, analytics will allow us to deliver more value for the end-user, which leads to more success for our company. It’s all connected.

In order to do that, I believe modeling if one of the best approaches to provide actionable insights around marketing effectiveness. Again, our insights should help us to deliver more value to the people we serve, generating sustainable results for the company.

Written by: Maziar Nodehi, Proof Analytics

Gajendra Jangid is the Co-founder and CMO of CARS24 and is in-charge of developing the strategy for advertising and branding, as well as customer outreach.

He spearheads the initiatives to accelerate business growth by building the brand, taking new innovations to the market and enhancing customer experience.

Prior to CARS24, he took care of operations for Schlumberger, the world’s leading provider of technology for reservoir characterization, drilling, production, and processing to the oil and gas industry.

What are the core advantages that MMM has brought to your business in terms of marketing efficiencies?

Our approach, towards Marketing Mix Modeling, is to generate key insights in terms of the marketing channels we deploy and determine each channel’s contribution to the overall business by closely analyzing historical channel performance and channel spend data.

Our marketing channels involve offline media such as television, radio and OOH as well as online channels such as affiliates, OTT, search, display and programmatic to name a few.

The exercise, overall, has helped us identify which markets are most responsive to individual, or a collection, of marketing channels. For example, we were able to identify that Pune and Hyderabad as markets where our prospective audience could be targeted aggressively via Facebook. Similarly, insights such as increased investment in Google Search Non-brand in markets such as Mumbai proved effective.

“The exercise, overall, has helped us identify which markets are most responsive to individual, or a collection, of marketing channels.”

How has MMM helped improve company revenues?

Cross media effects, wherein several marketing channels have been deployed in succession, basis a cumulative overlap could be accurately measured via Marketing Mix Modeling. This, in turn, has helped us determine the media that performs best in individual markets, thereby allowing us to optimize our overall marketing spend.

Optimizing our spend, and reducing spillage, has helped enhance revenues by diverting budgets to channels, as well as regions, more efficiently. Similarly, spreading budgets in accordance with this approach has enabled to achieve an incremental increase in our business KPIs, increases clearly measurable by improvements in our top, middle and bottom funnel.

Furthermore, the generation of responsive via this modeling exercise has also been able to help us determine the head room available to deploy marketing investment across media channels, region-wise, not only to achieve incremental growth but also to ensure that such growth is achieved without deploying a medium that is either decaying or plateauing.

What is your biggest success case with MMM and can you share some details on that in terms of improved efficiencies in your business?

Marketing Mix Modeling has simplified the budget planning process for us, to a great extent.

Undertaking this exercise has enabled us to move from the initial ‘hit and trial’ approach to a more methodical and statistical approach, thereby optimizing the spends allocated to each medium across every region and identify areas where transactional ROI was less.

One such success case was the initiation of a television deep dive study wherein BARC data, provided at granular levels measured the effectiveness of television media planning across genres, dayparts and ad duration. This, in turn, helped us identify an optimal television channel mix, outline the best performing genres in accordance with our target audience and reduce wasteful expenditures often incurred while planning television on the basis of sampled insights.

“Similarly, spreading budgets in accordance with this approach has enabled to achieve an incremental increase in our business KPIs, increases clearly measurable by improvements in our top, middle and bottom funnel.”

By: Proof Analytics

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