Measuring Thought Leadership Value with Arwinder Kaur

Measuring the impact of thought leadership has never been easy. 

It’s more complex now as consumers linger across channels, skim through text, and prefer visual forms more. But this content type can still transform into your revenue engine. But how?

We might have the answer right here.

“Companies need cohesive content strategies covering different service pillars. Especially if a service related to AI, thought leadership can fuel sales,” believes Arwinder Kaur, Vice President of Marketing at Grazitti Interactive. 

In a chat with me, she shares tips on measuring thought leadership and the essentials of becoming a data-driven marketer. She also outlines a unique approach to marketing AI products. Let’s dive in.

This interview is part of G2’s Professional Spotlight series. For more content like this, subscribe to G2 Tea, a newsletter with SaaS-y news and entertainment.

Deep dives with Arwinder Kaur

Let’s start with your story — how did your career path unfold, and what led you to Grazitti Interactive?

In 1995,  after I had completed an MBA in marketing and a second MBA in personal management, I started my career as a market researcher.

Later, I moved to the telecom industry as a business analyst before moving to a direct marketing role. This was a customer-focused job, where I worked with sales more closely and learned about direct and indirect sales channels and marketing.

Later, I worked with Sun Microsystems and Intel before landing a role at IBM, where I worked for 17 years. I performed a variety of roles there, from marketing to channels to event specialists. My last role was that of a global strategist for account-based marketing (ABM) for the finance and insurance industries. 

I had to leave IBM because I needed to relocate to another city. So then, I joined Capgemini. There, I was the global director of marketing and communication services at a GCC for Capgemini in India. We had a 300-member team. Later, I had to relocate to the tricity of Chandigarh, where I started working for my present company Grazitti Interactive. 

You've led go-to-market (GTM) strategies for many products in the past. SearchUnify, the current focus of your efforts, is an AI-powered solution. How does GTM for AI products differ from others? And could you suggest a few successful approaches? 

For companies like ours, where AI is the product's core, there’s a different way you do GTM. There are four aspects to our strategy: 

First, a lot of our effort has an educational focus. 

This is about educating the target segment about available technologies, features, benefits, adaptation models, and implementation challenges. 

Second, this requires thought leadership content. So, instead of purely developing tactical content that generates leads, we focus a lot of our effort on thought leadership content. At the same time, we want to use the derivatives of such content to create demand-generation content. 

Third, we rely heavily on product demonstrations and trials. These demonstrate a product's use cases through videos or in-person demos.

“Customers today are overwhelmed with information on AI. So how do you stand out from your competitors? We use customer marketing to generate references and reviews and bank on them for the next opportunity.”

Arwinder Kaur
Vice President of Marketing, Grazitti Interactive

Fourth, we depend on customer success and reviews. That’s where reviews on platforms like G2 help.

So, you’ve combined thought leadership and demand-generation tactics for GTM campaigns. What’s the key to creating this link firmly?

I think large enterprises with an established brand can still survive simply by producing a great deal of thought leadership content. People would listen to them because of their brand equity and the popularity of their subject matter experts (SME). 

But, for growing organizations, thought leadership alone is not sufficient. You must also use the derivatives of thought leadership, which include demand generation and revenue acceleration. 

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How do we do this? 

We focus on a cohesive content strategy encompassing different service pillars. The derivatives of this strategy will define the campaigns and online or offline activities we do. 

Then, we try our best to maintain consistent messaging throughout our content and use it not only to generate demand but also to enable sales. So we do 30% thought leadership, 40% demand generation and the rest 30% sales enablement content. I also have to ensure that my sales engine is firing with the same velocity as the marketing one. 

And the last aspect is using customer-based content. For example, we’d cut case studies into 10 different ways for the market. These formats include videos, social media posts, blogs, co-authored articles, interviews, sales decks, and demo material. 

So, we use thought leadership content to establish brand trust and visibility in target segments and, at the same time, use the same content in sales cycles. 

In your experience as a B2B marketing leader, have you encountered any effective frameworks for measuring the impact of thought leadership?

Some of my lessons come from my previous role in ABM. Our job was to positively impact relationships with customers, partners, and prospects. 

You could measure these by gauging the number of repeat business opportunities, the incremental value you attach to a contract, and cross-sales. Executive connects can also be measured. 

Second, you could measure brand reputation. We used to measure it in terms of request for information (RFI). Typically, sellers target managers, but we coached them to sell at the C-level. There, marketing comes into play to support the relationship. Brand reputation also plays an important role here for the executive to make a favorable decision. 

You can use different metrics to measure this using customer relationship management (CRM) data. Tools like Tableau and BI help you correlate data. You just need to use the right logic as a thinker, and the tools will give you the trends accordingly. 

Speaking of data, you've also called yourself a data-driven marketer. Many organizations want to engage in data-driven marketing and stack multiple tools. Yet, they struggle with using data and proving an ROI. Where do you think most organizations go wrong?

Marketers could fall into several traps that make a data-driven approach difficult. 

First, they may lack clear KPIs for their function or their project at the beginning. As a result, the outcomes can’t be benchmarked against anything, and the data is of no use.

“People often make great plans, and they jump to execution straightaway. But many times they don’t have documented KPIs to measure outcomes against. Those KPIs could be vague and undocumented. ”

Arwinder Kaur
Vice President of Marketing, Grazitti Interactive

Second, it’s data quality and silo issues. This is most prevalent among organizations and teams that work with data. Structured and unstructured data continue to reside in silos, and thus, it’s difficult to achieve accuracy while correlating and comprehending if the data overlaps. 

Third, it is the sample you use. The sample needs to be representative of the segment or the market to which you’re selling. 

Fourth is the lack of appropriate skills to use data. Sometimes, people even fail to correlate data from different tables. 

As for AI in marketing, G2 research suggests that the marketing department is leading the adoption of AI across organizations. Where do you think the primary opportunity areas for marketers lie regarding AI use?

There are obvious ones, such as content curation and generation. 

Then, you have applications in multi-channel, iterative campaign formulation. Thanks to AI, what would take days is now possible in seconds. AI can pull data from multiple sources and even predict the response rates of your campaigns across channels. It’s like being a train engine driver; I could pull different levers to secure relevant insight. 

This is helping marketers create integrated marketing campaigns in a shorter time frame and back them up with data. As a result, marketers are able to present their cases with more conviction to the leadership. 

Another application is predictive analytics in customer experience. We can predict customers at the risk of churning. This helps you diagnose churn factors and take correct measures preemptively. 

AI is also helping manage social media more efficiently. Using AI-captured metrics, you can take corrective measures quickly and minimize damage to brand reputation. 

You’ve also led events teams. The COVID-19 pandemic made virtual events more popular. What’s the key to building a lead-sourcing pipeline for virtual events?

Although virtual became popular after COVID-19, my opinion is that physical events are making a comeback. 

Your focus time on a video is not more than a few minutes. Your mind is multitasking and diverted most of the time. In offline events, you’re present to a greater degree and have captive attention.

“Virtual events have been replaced by podcasts and audio casts. People now want crisp content that they can tune into while being on the go.”

Arwinder Kaur
Vice President of Marketing, Grazitti Interactive

You can tune into a podcast while driving or doing a chore. Short-form content, including LinkedIn shorts, has become more popular. However, most of them remain communication and thought leadership assets, whereas virtual events have the purpose of generating demand. 

As for measuring the impact of virtual events, companies could start by gauging the impact across online channels, including impressions, traffic, mentions, and the number of SMEs engaged. You could also spot people who attend not just live virtual events but also on-demand virtual events. 

Capturing lead generation starts with simply calculating the number of registrations, returnees, and those who engage in chats and discussions. Then, you reach out to them to nurture them with further opportunities. 

What marketing trends do you foresee in B2B in the next three years?

The first is the rising dependence on search optimization and actionable insights. Marketers use these approaches to use the right content, information, and customer data for campaigns. And AI is proving helpful here. 

Marketers are also leaning towards hyper-personalization in an omnichannel environment. Our marketing communication strategy hinges on content, so we personalize content to suit the tastes of the industries, companies, and personas we’re targeting. 

The third trend is the popularity of ABM. Today, we have a larger addressable market in almost every industry and a greater number of vendors, so you can’t market in conventional ways. That’s why ABM is gaining prominence alongside personalization. 

We’re also moving towards consumable, more visual information. Video marketing statistics prove that carousels, videos, and GIFs get much better engagement. Similarly, skimmable content is more popular now. 

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Follow Arwinder Kaur on LinkedIn to learn more about emerging trends in digital marketing and thought leadership content.