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Interview With Chandar Pattabhiram, CMO, Coupa Software

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Chandar Pattabhiram

Chandar is a results-driven senior executive with more than 23 years of experience in building category leaders. Proven leadership and success in strategy, digital marketing, & management consulting. Proven Chief Marketing Officer (CMO) with in-depth know-how and proven success in the main pillars of marketing: Go-to-market strategy, product marketing, demand generation, brand building, event marketing and corporate communications.. Focus on SaaS, Cloud, Digital Marketing, Ad Tech and Mobile areas.


Founded in 2006, Coupa Software is the leading provider of cloud-based applications for finance. Only Coupa provides a true suite of cloud applications for finance, including procurement, expense management and accounts payable that enables customers to launch the solution immediately and quickly realize significant savings.

Tell us about your role at Coupa, and how you got here? What lead you to join a spend management SaaS company?

As the Chief Marketing Officer at Coupa, I drive all aspects of go-to-market strategy, product marketing, demand generation, and corporate and brand marketing.

After I left Marketo, I wanted to join a company that had a combination of three things:

  • It could lead the market in a category that was mandatory;
  • It had a unique competitive advantage,
  • It had a vibrant, employee-centric, authentic culture.

Coupa fulfilled all three requirements. CRM is a mandatory category on the revenue generating side of the business, and Salesforce is the undisputed leader there. Spending money is mandatory in an organization, and Coupa’s comprehensive cloud platform offers a competitive advantage that will allow us to dominate that category. What Salesforce is to CRM, Coupa is to Business Spend Management (BSM).

Is it time that CMOs finally take AI-as-a-Service seriously, and start deploying them?

In B2B marketing, when we are talking about  ‘AI-as-a-Service,’ we’re mainly talking about machine learning. As Geoffrey Moore says, AI seeks to emulate human intelligence, whereas machine learning tries to simulate it with brute mathematical force.

CMOs can start using machine learning seriously across a number of use cases: Predictive lead scoring; tracking customer pulse across social channels; determining propensity to buy based on behavioral signals; helping orchestrate multi-channel campaigns, and predicting and offering the right content asset to the buyer at each stage of the buying cycle.

The impact is pretty substantial because now that we have the ability to better understand our buyer based on real-time data, we’re able to develop stronger relationships and have tighter alignment with sales than ever before.

Define your ideal customer profile. What AI-powered solutions does Coupa offer to ideal customers?

We have two market segments: Enterprises with more than $1 Billion in revenue, and midmarket companies below a billion in revenue.

We brought in a partner that uses machine learning algorithms to help identify customers with the highest propensity to buy in each segment, and then within verticals and micro-verticals within the segment. For example, some of the verticals we’re targeting are healthcare, financial services, pharma, high-tech, and manufacturing. The AI-powered solutions we offer every customer in every segment are based on community intelligence that helps them make better spending decisions.

Because we have a true multi-tenant cloud platform, we’ve amassed data on more than $570 Billion in cumulative spending transactions from customers.

We operate on the guiding principle that none of us are as smart as all of us.

So, for example, if you’re trying to choose a particular supplier or buy a particular item, our AI-powered solution will give you prescriptive insights about that supplier that are drawn from the entire Coupa community, including overall ratings, information about on-time delivery, shortages and overages, and the price you should be paying.

What is Coupa AI Classification, and how does it enable a business to gain actionable insights from available data?

Coupa AI Classification is both a product that we sell and a capability that we are using on our platform to classify the spending data in our warehouse so we can use it to deliver community intelligence. In order to get insights from our data, it has to be normalized and classified. The way spend classification has been traditionally done is with “if this, then that” rules-based systems. This doesn’t work on large, disparate data sets such as ours, so we’re using machine learning to solve that data classification challenge.

Once the data is normalized and classified, we are using AI to find the patterns and relationships across all of our customers so that we can make prescriptive recommendations tailored to each customer.

Now, customers don’t always spend every dollar through Coupa. For example, large, global organizations that are doing a phased rollout may run Coupa side by side with other systems for a period of time. So, we offer Coupa AI Classification so that they can do the same thing within their company — take large data sets from disparate sources and normalize and classify them in order to identify opportunities for spend or risk reduction.

Read More: Interview With Peter Cahill, CEO, Voysis

What industries and departments are most likely to benefit from utilizing Coupa’s AI-powered business spend management platform?

Coupa is a horizontal solution in a mandatory category. Just like every company across every industry that wants to make money, every company across every industry spends money. The need to maximize the value of every dollar the company spends spans all industries, including the government sector.

The finance and procurement departments are power users of Coupa for visibility and reporting and such, but Coupa is really the shopping tool for the entire company. Almost every employee inside a company uses it. User-centricity is a huge focus for us, because if employees go outside the system, you don’t get all of your spends under management, and you don’t get the data you need to make better spending decisions.

How do customers leverage more value from Coupa Open Buy and Amazon Business partnership?

In many organizations, employees are required to buy from catalogs maintained by the company or its suppliers, but they may not always find what they are looking for in these catalogs. So, they buy outside of approved channels, resulting in spending without proper budget checks or compliance approvals.

Coupa Open Buy with Amazon Business expands an organization’s buying options by giving its employees access to the Amazon Business marketplace while maintaining visibility and control through Coupa.

What are the fundamental tenets of Coupa’s AI enabling customers to reduce fraud through Coupa’s community intelligence?

It is difficult to catch fraud today because there are too many potential fraud indicators for a human to detect them all. Most auditors selectively audit transactions, resulting in a lot of false positives, wasted effort, and actual fraud escaping detection. Because our platform encompasses spending through procurement, invoiced spending, and expenses spending, we have a holistic view of people’s spending patterns within their company. Using community intelligence, we build behavior profiles based on title, function, and industry, and apply AI to detect behaviors that are abnormal for that profile and flag them for review.

This reduces false positives and equips auditors to catch fraud with greater efficiency. With this approach, one in two audits catches actual fraud, compared to the industry standard of one in twenty audits. As our data set grows and we continue to learn from the data, our fraud detection capability will get more accurate over time. AI will replace most of the manual tasks in auditing over the next two to three years so that auditors can focus on handling exceptions and high-ticket items.

What are your predictions for AI and business intelligence technology industries in 2018?

Like most functions, marketing has three kinds of jobs — “do” jobs, “think” jobs, and “feel” jobs. Automation is taking over many of the do jobs, and machine learning is supporting the think jobs with predictive insights, but AI will never replace the feel jobs. Marketing is always going to be the art and science of storytelling and building emotional connections. Marketers should look at AI as an augmenting technology that helps us to be more scientific about what stories we tell to whom, and in what channels.

Read More: Interview With Tomer Kashi, Co-Founder & CEO, SkyWatch

What startups in the tech industry are you keenly following? Who would be your ideal community builder for promoting AI as a Service?

I follow a bunch of startups that are applying machine learning technology to all stages of the customer journey. I’m on the advisory board of one very interesting company — Blueshift — that is using AI to orchestrate multi-channel campaigns so as to deliver the right personalized experience in every channel.

I’m also following blockchain startups such as Ethereum, which is one of the early leaders with this technology. We’re very early with the blockchain, and it’s fascinating to watch as that industry gets more mature.

When I think about building a community around a new technology like AI, it’s a whole host of things. It is publications, such as yours. It is conferences like TiEcon, which emphasizes AI. It is influencers talking on social media. The conversation, and therefore the community, is multi-channel.

In 2018, what marketing and business demographics are you planning to expand into?

Now that we have defined our ideal customer profile through machine learning, we will be programmatically building campaigns and relationships in those segments. Strengthening our presence in Europe and expanding our presence in APAC and Japan is important for us.

Thank you Chandar! That was fun and hope to see you back on AIThority soon.

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