An Interview with:

·     Marc Vollenweider, Co-founder and Chief Strategist

·     Ashutosh Gupta, Co-CEO and Global Business Unit Head for Financial Services

·     Ravi Mehrotra, Co-CEO and Global Business Unit Head for Corporates and Professional Services

 Founded as a start-up in 2001, Evalueserve is a global professional services provider offering research, analytics, and data management services. The company is powered by mind+machine – a unique combination of human expertise and best-in-class technologies that use smart algorithms to simplify and automate key tasks. This approach enables Evalueserve to design and manage processes that can generate and harness insights on a large scale, significantly cutting costs and timescales and helping businesses to overtake the competition. The company works with clients across a wide range of industries and business functions, helping them to make better decisions faster, reach new levels of efficiency and effectiveness, and see a tangible impact on their top and bottom line.

This month, Finance Monthly had the privilege of speaking to Marc Vollenweider - Co-founder and Chief Strategist, as well as the company’s new Co-CEOs - Ashutosh Gupta and Ravi Mehrotra, who tell us all about the mind+machine concept and Evalueserve’s mission.

Marc, you have recently shifted your role from the being CEO to becoming Evalueserve’s Chief Strategist, can you tell us a bit more about this transition?

Marc: After spending about 16 years as Evalueserve’s CEO, I decided that it was time for the next generation to get involved in running the business, from an operational point of view. We decided to go with a Co-CEO structure, by splitting the role between Ashutosh and Ravi, while I shifted to a full-time board role, which allows me to concentrate on innovation-related projects. I am still strongly involved in the company, but instead of dealing with the day-to-day operations, I focus on proposing strategies and examining our next steps for the future.

Tell us about the experience of writing a book while running a global business? How have mind+machine and the book influenced Evalueserve and its business?

Marc: As soon as the idea about the book came about, it was clear that I wouldn’t be able to combine writing a book with my full-time CEO role. Thus, I took some time off, between March and June 2016, and wrote the book, while Ashutosh and Ravi got to practice running the business on their own. The experience of authoring a book on its own was extremely rewarding and I am proud of the final result.

The mind+machine concept is something that we started working towards 5 years ago, realising that a people-only approach was becoming too slow and too costly. We saw an opportunity for automating tasks and processes, and using machines to run repetitive tasks. We started coming up with workflow platforms, productivity tools, analytic engines, better knowledge management, etc. and fully rebranded the business. If we compare Evalueserve today to the company it was 5 years ago, I’d say that we have reached a productivity increase of about 30-40% on a per head basis. Mind+machine makes us faster - it makes the quality of our work better, and it gives our clients new capabilities to work with.

In today’s world, clients want to see innovation, so developing mind+machine has been essential for Evalueserve.

What are the benefits of a dual CEO-structure?

Ashutosh: At Evalueserve, we are very client-centric and also serve a variety of different industries. The fact that I have previously worked within financial services, while Ravi comes from a more corporate and consulting-oriented background, allows us to focus on these very different client segments. It also provides us with two different points of view and diverse ideas when it comes to dealing with common areas like HR, policies, marketing, etc. So the Co-CEO structure actually works really well for us.

Ravi:  Given the scope and scale of Evalueserve, being a very large and complex business, we want to make sure that we divide the different areas of the business and responsibilities between each other.

Additionally, the dual CEO structure, although not new, is still very rare and unusual. One of the reasons why it works for us is because, before embarking on this, we already had a very strong working relationship, with both of us having spent over 6 years in Evalueserve.

What are the main challenges that decision makers are facing today? How can mind+machine help overcome these challenges?

Marc: There are millions of decisions that need to be made in a company on a daily basis. And I’m not talking only about the decisions that the executive board makes; at Evalueserve we’re looking at decision makers at various levels of the company - from the CEO to the service technician. So for these millions of decisions, naturally, there’s a very large number of analytic use cases.

The problem of decision-making includes the logistics of having the right data at the right location, at the right time.  Then, these decisions must be transferred into a workflow where they get converted into actions and create impact. How does mind+machine help with all of this?

Unfortunately, when you look at how many decisions are being made within an organisation today, you’ll still see a lot of manual work. There are a few workflows, which make the life of decision-makers significantly easier. Mind+machine provides the client with the ability to crack the analytic use cases and then put a system or a machine in place to get them done on a recurring basis, as well as a platform that has all the necessary data feeds and analytics, and most importantly, links multiple end users and decision-makers in a collaborative way. This results in fast and efficient decision-making processes, with the knowledge management being done within the platform, so it doesn’t have to be redone every time.

Why do analytics matter for almost all types of businesses?

Marc: In today’s world, analytics are critical, solely because a lot of decisions within an organisation depend on diverse and complex data, which wasn’t so much the case up until 15 years ago. When it comes to decision-making, we need data that has been prepared, analysed and converted into insights and decision-ready output.

Today, every business needs an increasing amount of analytics because they improve the return on investment of many processes – it’s as simple as that.

How can one set up the use-case thinking in the company?

Marc:  Use cases have a number of implications for the whole company. Currently organisations tend to mingle everything together in a big pot - they set up central data scientist teams and large data lakes from where the teams try to come up with analytic output. However, this approach frequently leads to White Elephants not serving the end users’ needs well, often with negative Return on Investment (RoI). Companies should move to a culture of individual-focused analytic use cases.

A use case is not just an analysis; it comprises the data flow, the analytic engine, the UX (User Experience), the knowledge management for improving re-use, and the link to the overall workflow. Only in this way can the end users get what they need and achieve positive RoI.

It is my belief that this cultural change needs to be driven by the C-Suite, including the Chief Data Officer, who should jointly agree on putting this philosophy in place. Setting up the use-case thinking in a company should start with an agreement between the highest level executives, who should then drive it into individual units, so everyone gets into this mode of thinking over time.

How does Evalueserve use use cases to help its clients with decision-making?

Ashutosh: At Evalueserve, we have a whole collection of analytic use cases that are well-documented. Thus, when a new client comes to us with a business problem, we can easily leverage our database where we’ve cracked similar use cases before and come up with a specific solution, while saving our client a lot of time and money.

Ravi: Additionally, our use case hub is also helpful when it comes to showing our clients what mind+machine means. We’ve gathered all of these concepts that we are developing into a product, and have created a collection of use cases that we’re able to demonstrate to our clients at all times. The use case hub also enables the Chief Data and Chief Analytics officers to scale up their analytics capabilities by harnessing the knowledge, ensuring consistency and carefully selecting use cases for wider deployment and investment, based on the RoI.

What is your advice for successful leaders in the modern tech-focused world?

Marc: If we look at this from a ‘what lies beyond the horizon’ perspective, my advice is to get your feet wet - look into potential trends and then take well-informed, but still risky decisions. People nowadays are myopic when it comes to considering competition and future client needs and often get stuck in their current views. Being open to change and innovation and having a portfolio of new initiatives to play with is a critical element of strategy in today’s tech-focused world.

Ashutosh: Nowadays, it can be very difficult to stay on top of and respond to the changing technology trends, while running the business. So while it’s important to have a good technology strategy, it is also very important to communicate that strategy to your clients and throughout your organisation, so everyone shares the same goals and feels they can contribute to its success.

Ravi: The two things that I’d like to add are to firstly, be comfortable with uncertainty – we live in a very dynamic world and things change all the time. Leaders need to be flexible and always prepared for change.

Secondly, nowadays, it’s very easy to get so fascinated by the technology aspect; so my piece of advice is to not forget about the basics when it comes to leadership. i.e., technology and analytics are enablers to serve the broader business goals, and not the other way around.

Read more about mind+machine at: http://blog.evalueserve.com/ 

Vishal Chhibbar is the CFO of EXL, as well as the executive sponsor of the company’s Finance and Accounting Business, based in New York. EXL is an operations management and analytics company that designs and enables agile, customer-centric operating models that allow the firm’s clients to improve revenue and profitability. EXL delivers market-leading business outcomes by integrating cutting-edge analytics, digital transformation and domain expertise into their proprietary Business EXLerator Framework.  The company predominately serves the insurance, healthcare, banking, utilities, travel, and logistics industries, among others.

 Due to having had the opportunity to work and live around the world, whilst capitalizing on diverse thinking and multicultural environments, Vishal has the global mindset to truly understand the challenges of a global company today. Here he shares some of his experiences and plans for the future with Finance Monthly.

You joined EXL in 2009 – how would you evaluate your role and its impact over the last 7 years? What have been your major achievements?

I joined EXL from a great, large corporate environment with the goal to play a role in generating a bigger impact at a smaller company - by creating value for shareholders, value for the company itself and greater value for the clients.

And we’ve done exciting things exactly on these fronts. An example that illustrates this perfectly is the value created for our shareholders. Our revenue guidance this year is $750 million, compared to $180 million when I first joined. Our market cap today is up to $1.8 billion, in comparison to approximately $300 million in 2009. Back then, we had $100 million in cash and had made no acquisitions. In the past 8 years, we’ve made 11 acquisitions, deploying $275 million. We’ve also launched share buy-back programs, which benefit shareholders through a healthy capital allocation strategy.

Operationally, we’ve expanded to 40 delivery locations across 10 countries (compared to 13 delivery locations in three countries in 2009) and we’ve invested heavily in our capability development to keep up with market demand. Our heritage was transactional BPO, but today we are leading players in robotics and analytics, so we’ve been able to invest to increase the value we deliver to our clients.

What further goals are you currently working towards with the company?

Our current goal is very much focused on making sure we have a profitable growth model. This can be challenging because, like many of our clients’ industries, ours is going through transformation and disruption, with companies like EXL moving from traditional “lift and shift” BPO to a focus on value creation and building new operating models for our clients using digital interventions, advanced analytics and domain expertise. We focus on leading this transition in our industry, delivering significant impact for our clients, while also driving both top and bottom line growth for our shareholders. This means charting sustainable double-digital growth at the top and bottom lines.

Another aspect that we are currently focusing on is connected to driving an aggressive M&A strategy that adds capabilities to our growing solution set, while maximizing the value of those acquisitions.

On top of this, we continue to deploy approximately 4% of revenue back into the business, in order to drive new innovative solutions such as AI, robotics and other digital solutions for our clients.

What challenges would you say you and the firm encounter on a regular basis? How are these resolved?

As a company our challenges are twofold. First, we’re focused on how we transform our existing business, which was traditionally a people-intensive business. Second, as we transform, we are cannibalizing revenue through technology, such as automation, so we need to balance that with the ability to grow and add greater value to our clients. We’ve done a good job at this thus far.

Another focus for us, as we go forward, relates to addressing a bigger share of wallet and moving towards becoming a strategic digital transformation partner, rather than simply an offshore solution provider.

How are these challenges set to change, in conjunction with the advent of technologies and the potential future needs of clients?

In the digital age, technology is crucial. Today, due to the rapid pace of technological change, leaders need to be broadly knowledgeable, but at the same time - technology agnostic. There are so many tools and platforms, web services, automation and other digital technology, our own platforms, so the key for EXL is to really understand their strengths, what works best for us, what works best for our clients and combine those with our own domain expertise to come up with the right solutions. What differentiates us is the fact that we blend the analytics with our industry and domain experience, which when combined with technology, allows us to find the solution that works best to solve specific business problems.

Technology is bringing the finance industries one step closer to fighting money laundering thanks to the special identification of irregularities in trends and patterns of data, thus creating more 'hits' and fewer 'false negatives.' Aashu Virmani, CMO at Fuzzy Logix here talks to Finance Monthly about the potential impact data analytics can have on fighting money laundering and changing your business for the better.

As long ago as November 2009, Forrester published a research report entitled 'In-Database Analytics: The heart of the predictive enterprise'.  The report argued that progressive organisations 'are adopting an emerging practice known as 'in-database analytics' which supports more pervasive embedding of predictive models in business processes and mission-critical applications.’ And the reason for doing so?  'In-database analytics can help enterprises cut costs, speed development, and tighten governance on advanced analytics initiatives'.  Fast forward to today and you'd imagine that in-database analytics had cleaned up in the enterprise?  Well, while the market is definitely 'hot' it appears that many organisations have still to see the need to make a shift.

And that's despite the volumes of data increasing exponentially since Forrester wrote its report meaning that the potential rewards for implementing in-database analytics are now even higher.

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Given we can deliver our customers with analysis speeds of between 10 - 100 times faster than if they were to remove the data to a separate application outside of the database, we have a 'hard metric' that is very compelling in helping us convince prospects of the value of in-database analytics.  It's what gives us confidence that the shift to in-database analytics as the standard for data analysis is a question of time rather than choice.  Quite simply, the volumes of data that are increasingly being created mean that the only way to process the data and find analytical value is by doing so within the database.  But, as ever, real world examples are the best way to illustrate a point so let's take an unusual one; money laundering.

Banks have a vested interest in ensuring they stay compliant with the regulations in place for catching and reporting anti money laundering (AML).  The regulations have been in place for several years, and it is likely that most large banks have systems/processes in place to track and catch money-laundering activity.  Despite this, we still hear about cases where the authorities have fined reputable banks for their failure to implement proper AML solutions.  Not too long ago, in 2012, HSBC was fined $1.9 Billion by the US Department of Justice for “blatant failure” to implement AML controls related to drug trafficking money and, as recently as 2017, Deutsche bank was fined $650m by British and US authorities for allowing wealthy clients to move $10 billion out of Russia.  So why are current implementations/best practices not keeping up?

Let’s look at 3 big factors that contribute to compliance failure in the realm of anti-money laundering:

• The number of non-cash transactions around the world is exploding. Think about applications like Uber, Venmo, Paypal, Xoom and various international money transfer services (almost every large retailer is now in the Moneygram business) –  the number of non-cash transactions in the world has exploded.  The number crossed $426 billion in 2015, and is growing at over 10% annually.  Conclusion:  There’s just a lot more hay to find the needle in.
• Legacy solutions were largely rules-based. A program would run through the list of rules, and if any transaction matched a rule, a STR (suspicious transaction report) would be raised, meaning the transaction needed deeper analysis – often with a human in the loop.  These rules were good at some point in time, but there are several issues with any rules-based system:  (a) Rules are static, and require constant periodic refreshes.  For instance, is a $4000 deposit to a rural bank typical of a crop cycle, or an anomaly?  (b) Rules do not often transcend cultural/economic/geographical boundaries – e.g., a $400 hotel room in Washington D.C. may be normal, but in rural India it might warrant a second look.  Finally (c) Rules present an inherent bias and don’t look at the data to determine what is ‘normal’ and therefore what pops out as ‘abnormal.’
• The analysis of AML is mathematically complex. Incidence of fraud is a rare event, compared to the data that needs sifting through, and catching AML transactions involves looking deep into the history of the account.   Each time the system flags a transaction for a deeper look, a human has to investigate further.  False positives mean a lot of resource and time investment for the bank.  Couple this with the explosion in the number of transactions and the multiple channels through which non-cash transactions occur today, and you can see why systems that were put in place over a decade ago are in serious need of an upgrade.

With the money at stake for money launderers (according to the UN, $2 trillion is moved illegally each year), the efforts taken by criminals to avoid detection have become incredibly sophisticated.  Organised crime is continually seeking ways to ensure that the process of money laundering is lost within the huge amounts of financial data that are now being processed on a daily, hourly and even by-the-minute basis.  Their hope is that, because so much data is being processed, it is impossible to spot where illegal money laundering activity is happening.  And they'd be right, if you had to take the data out of the database for analysis.

Achieving a good degree of accuracy in a typical large bank means having to analyse billions of data points from multiple years of transactions in order to identify irregularities in trends and patterns. A traditional approach would require moving the data to a dedicated analytical engine, a process that could take hours or days or more depending on the volume of data. This makes it impossible to perform the analysis in a manner that can provide any real value to the organization. With in-database analytics, there is no need to move the data to a separate analytical engine, and the analysis can be performed on the entire dataset, ensuring the greatest possible coverage and accuracy.

One of our largest customers is a leading retail bank in India.  It was experiencing a rapid growth in data volumes that challenged its then-current AML processes.  By not needing to move the data for analysis, we were able to analyse billions of data points over a number of years (3+) of historical data to identify possible irregularities in trends/patterns, and do so in under 15 minutes – faster than any other method.  By not working to a pre-defined set of analytical rules and by letting the data 'speak for itself', it is possible to uncover patterns which occur naturally in the data. As a result, the bank is seeing an improvement of over 40% in terms of incremental identifications of suspicious activity and a 75% reduction in the incidence of 'false positives'.  In short, good guys 1, bad guys 0 because in-database analytics is having a very real impact on the bank's ability to spot where money laundering is happening.

I'm pretty sure that when Forrester published its report into in-database analytics towards the end of the last decade, it didn't envisage the fight to combat money laundering being a perfect case study for why in-database analytics is a no brainer when handling large volumes of data.  But in today's world, with ever increasing data volumes and the requirement to understand trends and insight from this data ever more urgent, in-database analytics has now come of age.  It's time for every organization to jump on board and make the shift; after all, if it can help defeat organized crime, imagine what it could do for the enterprise?

The first individual that we had the privilege of interviewing for our March Executive Insight section is Rex Briggs – the founder and CEO of the NYC-based Marketing Evolution, which brings together advanced analytics and cloud-based software to support message exposure at the person-level, across all media, and in-campaign. Their software represents a new generation of analytics using big data and artificial intelligence. Within the last year, Marketing Evolution has been named “a leader” by the independent research firm Forrester in their latest global “Measurement and Optimization Wave” report, won the “Gold Medal” from the Advertising Research Foundation, and has been the subject of a Best Practice report by the CEB Marketing Leadership Council. Their software increases profits by analysing advertising effectiveness of each and every message in near real-time. Here Rex tells us more about Marketing Evolution’s recent accomplishments and sheds some light on the way that the company operates.

As an award-winning marketing ROI researcher who has been helping Fortune 500 marketers improve marketing ROI by applying analytics for more than two decades – how has the sector evolved over the past 20 years?

Marketers talk about getting the right message to the right person at the right time at the right price, but until very recently, it been more talk than reality. Now, with the latest generation of analytic technology, we’ve made a major breakthrough. Today, every single message to nearly every single person can be analysed and optimized by our software.

We affectionately call the software the ROI Brain because it uses Artificial Intelligence (AI) to find patterns. The patterns the ROI Brain is searching for are which messages are influencing which people. The ROI Brain does this analysis at a scale and speed that is breath-taking. Most marketers are blown away when they see the ROI Brain in action. The biggest change in the past twenty years is the one-two punch of Big Data and AI. The implications to business are as far-reaching as the impact of the Internet.

You’ve written a couple bestselling books. Your most recent book was about how software and algorithms are changing marketing. Can you explain how Big Data and AI can be used to improve marketing results and what you are doing to educate business leaders? 

When you hear the terms Big Data and AI, is it a little intimidating? I want to demystify the terms Big Data and AI by giving business people a look under the hood to see the specific data and analytics at work. At Marketing Evolution, we connect data from every media exposure, online and offline, to sales data, brand perception data, social data, digital profile data, location data and more. By connecting all this data at the person level, we can see which exposures to advertisements contribute to a sale.

This level of analysis wasn’t possible a few years ago. Most marketers and finance leaders aren’t aware of how this technology has advanced recently, or how it can improve their business. Therefore, we see a huge education need. We run a no cost private executive briefing onsite at Fortune 500 companies. We speak at a lot of conferences and do webinars to demonstrate Big Data and AI. Of course, we hope to meet like-minded marketers and finance leaders that want to become our customers, but the broader goal is education. Businesses spend over half a trillion dollars a year on advertising globally. Marketing and finance people are looking for advances in data analysis to help them improve the productivity of their spending, and reduce waste. We want business people to know what savvy marketers are doing today with Big Data and AI to improve business results.

Your company, Marketing Evolution, has been called a new generation of marketing analytics. How is what you are doing different from what came before?  

The old ways of measuring advertising use brand tracking surveys and econometric mix models. These old tools provided a high-level view of how advertising increased sales overall, or improved brand awareness and preference. These tools were innovations in the 1970s, and grew in popularity in the 80s and 90s.

The old analysis was pretty superficial in its recommendations – only giving a marketer a budget recommendation, a high-level guidance of how much to put in TV versus digital, versus price promotion, and a few tactical suggestions such as the best TV dayparts to advertise on. These reports were backward looking. They came along with the caveat that differences in message quality may change the results. The reality is that one message might work five times better for a certain group of people than another message. The concept of the right message to the right people is well-understood, but the old analytic systems did nothing to help a marketer measure which message was working with which people while the campaign was live.

How helpful is it to get a report that comes out weeks after the campaign is over, giving a rear-view mirror view, and warning that the road ahead is likely to be different because you are running different advertisements?

Not helpful at all is what most marketers say.

Enter Big Data, software and algorithms. Advertising ROI analysis used to take months. With automation in our software, results come in while the campaign is still live. In the past, ROI analysis wasn’t very detailed. Now, we can take the ROI analysis all the way down to each and every impression that is delivered to each and every person. The biggest wildcard in advertising performance is the message itself. Now, real-time analysis of which messages are influencing which people lets a marketer adjust the message targeting and media mix in real-time. The software does the work, and eliminates wasted time and money. The problem is, most marketers and finance people are still using the old analysis systems. They aren’t aware of how Big Data and AI have changed marketing ROI analysis. That means a lot of marketers are missing out.

You mention waste in advertising. The Association of National Advertisers (ANA) released a report after an eight-month investigation that found advertising agencies systemically padding their profits by using non-transparent practices such as taking rebates from media companies and not disclosing them to clients. Does your software help address these issues of agency transparency?

The ANA report was a bombshell in the advertising industry. Every senior marketer and finance leader should take a read so they can protect themselves. In light of the revelations in the ANA report, we looked at this situation and said, “There has to be a better way.”

We developed a two-track approach. One track is to take our technology directly to the marketer. With our software, marketers can build their own media plans, or check the media plans their agency has proposed to them. This gives marketers and finance total visibility and control.

The second track is to certify advertising agencies to use our technology to ensure transparency. The agency builds the media plan in our software. The agency adds their expert human judgement on top of the data driven plans generated by our software. Any changes the agency makes are logged in our software so there is an audit trail. Our software creates complete transparency, and works directly with auditors.

As we started working with select advertising agencies, we saw that many of the people we met want to do right by their marketing clients. However, their approach to media planning and buying is manual, inefficent and messy – it isn’t a shock that there are problems. These agencies like the automation and labour savings from using our software. For example, if the software detects that rainy weather decreases sales by 10%, the ROI Brain automatically reads the weather forecast for the next week, and makes adjustment to media based on the weather patterns. If the software detects that one of the advertisements is working better with women who like exercising, the software has detailed profiling of every media placement, and will automatically adjust the message targeting accordingly. Can you imagine how labour intensive it would be to make all these changes manually? Agencies old approaches to media planning and buying simply aren’t fast enough to gain the benefits of real-time marketing ROI analysis.

The better agencies respect marketer’s right to have direct visibility into media planning, buying and optimization. They love the automation. There are some agencies that don’t like the power our software gives to marketers – but I can tell you, marketers love it.

You mention that better agencies respect the right of marketers to use your software to have direct visibility. What differentiates the good actors from the bad actors in the agency world?

If you are wondering how to spot a potential bad situation with an advertising agency, a tell-tale sign is if the agency is trying to sell you on having them do their own attribution or marketing mix models. The dirty little secret about the old generation of mix and attribution models used by agencies is the extent of human decision-making and interpretation in the models. This leaves room to manipulate the model recommendations. The new generation of analytics we apply is much more detailed and therefore removes the risk of an agency steering dollars toward media where the agency is getting rebates or so called kickbacks.

Other signs of a problem with the agency include a resistance to sharing all their buying data in a consistent machine-readable format with an independent measurement company or auditor. Or, contracts that give the agency ownership of the marketer’s data.

The ANA report gave us a renewed sense of purpose around giving marketers and finance the right tools for independent ROI measurement. Checks and balances are important. The old saying, strong fences make good neighbours applies to the marketer/agency relationship. A good agency can be an amazing partner for marketers to achieve their business goals – but it is important to have some well-defined boundaries and independent review. I think it is a good sign for the industry that several agencies have asked to be trained and certified on our software.

You’ve won several new retail customers in the past year based on your use of location analysis. Can you tell us more about location analysis?

Our analysis is at the person-level. That means we factor in how close a person lives to a store, their local weather, the options to purchase from a competitor, even the specific billboards the person will pass on their commute to work.

Our customers wanted to measure the effect of advertising down to the specific store. When we worked with Walmart, we learned that the top three deciles of most profitable customers could largely be explained by how close they lived to Walmart. Perhaps that’s not surprising, because the old saying about retails is, “What is the three most important factors in retail? Location. Location. Location.”

Yet, the old approaches of attribution and mix modelling couldn’t tell a marketer when and where to activate a mobile geo-fence offer, or to buy a specific billboard, or which TV placement will disproportionately reach people that live close to your stores, or any of the other decisions that are location specific. Our new generation of analysis, which is location aware, showed the specific incremental contribution of geo-targeted mobile advertising, and every other form of advertising. Walmart shared the results publicly, and showed how we helped increase their ROI using our Big Data analysis. This location analysis capability has been a winner and was a factor in several retailers becoming customers in the last year.

It was announced that you’ve teamed up with Joel Rubinson, the former Chief Research Officer for the Advertising Research Foundation. He says he wants to disrupt the New Product Forecasting industry. Can you tell us more?

Joel is an expert in new product forecasting. When he read about our person-level analysis, he had an epiphany. The old generation of new product forecasting that is still used by many companies today isn’t linked to detailed media planning software. He saw an opportunity to re-invent new product forecasting.

The product, MoreCastR, has an eight factor scoring system to identify the people with the highest propensity to try a new product. It scores everyone in the country with a look-alike model and then concentrates the advertising on these people. This approach dramatically reduces wasted impressions on people unlikely to try the product. It boosts sales results by ensuring those most likely to try the product have sufficient level of advertising exposure. A leading consumer package goods company has successfully completed the beta test. We will unveil the capability at our customer roundtable in Los Angeles, in April.

Tell us more about the customer roundtable. What do you hope to accomplish at the event?

I have a passion for making marketers smarter and giving them a competitive edge. The roundtable will feature many of our customers sharing their success stories, and what they hope to accomplish in the future with our software. It is a great opportunity for our customers to share ideas and network with one another. Several of our data and technology partners will present how marketers can use their capabilities to get more value from the ROI Brain.

In addition to the customer roundtable in Los Angeles, we are hosting a European tour designed to bring some of the best content from the roundtable to Europe. We are doing two-hour briefing sessions for marketers upon request. Both events are free education events because we love to share what is possible with marketers. We hope the events will open up business people’s minds and inspire marketers to evolve faster than their competitors.

What do you find most enjoyable about your work and why?

I enjoy onboarding new customers. Most marketers hear what we are doing, and it sounds difficult. But, in reality, we have automated so much of the data and analysis that it takes less than six weeks for a marketer to get up and running. There is something about that moment when a marketing team gets around the table together, and realizes that it is actually easier to benefit from the new generation of analytics than it was for them to use the older approaches. I get a lot of satisfaction from helping marketers take this step into the future. I enjoy the bond that forms between our team at Marketing Evolution and our customer’s team. I’ve made lifelong friends through our work with our customers. I appreciate the opportunity to help marketers to evolve.

You successfully grew Marketing Evolution for more than a decade without any outside capital. What made you decide to go from bootstrapping the business to taking Venture Capital now?

I love the fact that over the last decade, our growth was entirely fuelled by providing great software and analytics to major marketers all over the world. According to Pacific Crest, less than 5 % of SaaS businesses achieve our scale without Venture Capital or significant debt. We have over 50 enterprise customers, including marquee brands like Amgen, Best Buy, Citibank, Timberland, Warner Brothers and more. We grew entirely by word of mouth from one CMO to another. It is a great way to grow, but here’s the thing: Most marketers have not heard of Marketing Evolution – and therefore these marketers are missing out.

I want to see marketing evolve to be more data-driven. I want to see waste and inefficiency removed from the advertising system. I want transparency for marketers. I want to see marketing become more personally relevant to consumers. We are using additional capital to get the word out so more marketers can benefit from Big Data and AI.

We’ve been approached by a lot of VCs, but it wasn’t until I met Mark Gorenberg, founder of Zetta partners, that I found the right fit. He understood how AI is changing business. He was the first investor in Omniture, DOMO, and Inside Sales – each achieved phenomenal growth and over a billion-dollar valuation. With Mark’s help, we are applying the same playbook that’s helped propel his other investments to great success.

What lies on the horizon for you and Marketing Evolution in 2017?

We have lots of hiring to do. We are recruiting for a VP of Talent & Recruiting, a Chief Revenue Officer, a Chief Marketing Officer, and building out our sales team. We’ve got a great product team and product – we will continue to advance it. We are opening EMEA operations with a team in London. We want every major advertiser to know about Marketing Evolution and how we are changing marketing analytics.