'We aim to provide minimum 20% optimisation on revenues & budgets through our insights'

Christopher Engman, Global CMO of Proof Analytics & Harshil Karia, CEO, Proof Analytics India, spoke about factors that differentiate them from other analytics tools, challenges and more

e4m by Neethu Mohan
Updated: Nov 14, 2019 9:53 AM


Christopher Engman Harshil Karia

Proof Analytics is a Swedish-American company at the forefront of automated Marketing Mix Modelling. Proof Analytics India has been set up as a subsidiary of Proof Analytics International in partnership with Schbang- an integrated marketing agency. Harshil Karia, Founder and MD of Schbang will be the CEO of Proof Analytics' business in India.

They have put together an AI engine that draws correlations between marketing spends and sales using Regression Analysis. While such a solution already exists in the market, they are confident that Proof is significantly better because it automates the Model creation process, thereby making it significantly faster as well as cheaper.

In conversation with exchange4media, Christopher Engman- Global CMO - Proof Analytics and Harshil Karia, CEO, Proof Analytics India, spoke about Proof Analytics, factors that differentiate them from other analytics tools, challenges faced while pitching the business model etc.


What exactly does Proof do?

Most companies today struggle with balancing the spends between marketing, PR and sales. It is also a challenge for CMOs to be able to justify their budgets and prove the value of marketing to the rest of the stakeholders like the CFO and CEO.

To resolve this challenge, many of the biggest enterprises are using Marketing Mix Modelling. MMM gives you detailed insights like ROI, time lag and volatility when it comes to marketing efforts to be able to provide real analytical acumen when it comes to marketing-led decision making.

Proof Analytics is the golden standard in Marketing Mix Modelling and the world’s first fully automated MMM engine. It uses powerful statistical modelling and regression analysis to derive insights and prove Marketing value.

How is it different from other data analysis tools? Can you please elaborate on its accuracy?

While there are multiple tools used for measurement, visualisation and multi-touch attribution, there is only a handful of solutions for MMM as a whole. These use a combination of human intervention and analytics and therefore are an expensive investment with very long turnaround cycle times. The resource pool for mathematicians qualified to do multivariate regression is a small one and most global companies today indulge in MMM in a fraction of their top markets and only once in a year due to these hurdles.

With Proof’s revolutionary fully automated AI, it provides a monthly reporting structure at a fraction of the cost. It is also the only MMM tool to be able to work with dynamic data and give you results within minutes. A good example to understand this would be to think of traditional MMM like a paper map vs Proof as a live GPS which gives you live real-time insights.

Why India? And what is the team like?

The Indian advertising industry is now the fastest-growing in the world and estimated to grow by a further 16.4% next year. With a consistent rise in all channels including TV, Print, Outdoor and Digital, we are looking at an all-time high for the industry at more than Rs 70,000 crore. This rise also begs the question of how all this marketing is providing actual value, which is where a key solution like Proof comes in.

Proof Analytics was founded by Mark Stouse, who has a rich history in MMM solutions during his role as CMO in companies like Honeywell and BMC. It was co-founded by Christopher Engman, serial entrepreneur and author of the book Megadeals Our India team is led by Harshil Karia, Founder and MD of Schbang. With over twelve years of experience in the advertising space, Harshil has worked with some of the biggest brands in India including Amazon, Godrej and Castrol. He is now splitting his time equally between Schbang and building Proof Analytics in India.

We also have Sharmeen Indorewala, an entrepreneur with over ten years of experience in product development and market penetration and strategy, onboard as head of business along with Yash Awasthi, our data scientist who is a gold medalist in Mathematics from ACTU, India.

India is predicted to be leading advertising spends by over 15%. What is your take on this and what should marketers keep in mind with such high spends?

In the Indian advertising industry today, the math men have been relegated to focussing their talents on negotiation and price discounting rather than being able to lay emphasis on rebalancing and reallocating the marketing portfolio in order to ensure that the focus is on areas which deliver growth.

While advertising spends will continue to grow, it’s also important for marketers to understand which of these channels are actually able to deliver value. Statistically proven methods like multivariate regression are used across industries and markets including climate change, actuarial sciences, banking and finance and medicine, etc. so as we increase spends, it is vital that we also work towards making marketing more mathematical and accurate and make it deliver more Proof.

In Daniel Kahneman’s book, ‘Thinking Fast and Slow’, he states that the human brain is capable of handling no more than four dimensions. Marketing typically has anywhere between 15-50 variables and while marketers have sharp instincts, we’re adding a little more data to enhance the cerebral element to the gut that drives decisions in order to add a mathematical element to the science.

What challenges do you face when pitching the platform to another business? Are these the same across countries? Or something specific to India?

One of the common challenges we face from CMOs is the questions on the methodology behind multivariate regression and in order to tackle that we consult with companies and do one on one sessions on data science and will soon be rolling it out as the data science for CMOs workshop which is in the works.

The other challenge is data collection. Questions on how much data will the company be able to give and how they organise that data. We have noticed that India is ahead of the curve when it comes to data organisation but it is still a WIP so we’re also consulting with companies to organize that data and build their data collection portfolio.

How do you expect businesses to change once the Proof model is adopted? Any specific case study to recall?

Proof is an endeavour in optimisation. We work towards providing a minimum of 20% optimisation on revenues and budgets by using our insights. There are multiple benefits to Proof which not only include revenue surges but also creating efficiencies towards data initiatives and forecasting.

We have one such case study involving a $2 billion software company. Their challenges were long sales cycles and limited product sales per each deal. With Proof’s analysis of reducing sales spends and increase marketing spend on branding and PR, the company was able to increase sales velocity by 8%, profits by 24% and revenues by 34%.

While the assessment will vary depending on the type of business and trade, whether it’s B2B or B2C, Proof’s innovative automated MMM can span across industry landscapes.

Which brands are you currently working with?

Proof has a varied client portfolio which includes brands such as Intel, Oracle, Johnson Controls, Daniel Wellington.

What’s the next step for Proof in India?

While MMM is an age-old science, thanks to automation, Proof is faster, cheaper and better. With Proof’s groundbreaking AI, we hope to grow its access and application across Indian industries and revolutionize the marketing mix. We are working towards building Proof in India to take it’s potential to large and mid-cap Indian companies in order to drive efficiencies and provide marketing value-based insights. While building our marquee customers, the aim is to become a de facto part of the CMOs report and an inherent science when it comes to marketing-led decision making.

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