MarTech Interview with Shoel Perelman, VP Product Management at Pegasystems

Shoel Perelman, VP Product Management at Pegasystems chats about evolving customer engagement trends and what marketers of today need to do more of to drive deeper customer interactions:

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Welcome to this MarTech Series chat Shoel, we’d love to know more about your time at Pega and the key highlights through your years in the B2B / Tech industry?

For the past decade, I’ve been drawn to building products that apply data science and machine learning to real-world data, and packaging it up to help business people be wildly successful in their day jobs. I started my professional software career in the 90’s, building products that made sense out of a fire hose of streaming IT events to pinpoint the real problem from a barrage of symptoms.  

From there, I pivoted into martech in the early 2010’s, when I ran engineering for IBM’s Watson Customer Engagement.  We did some pioneering work in event triggered marketing to help marketers tap into streams of customer behavior events.  That’s where I became acutely aware of how two silos in martech that crept up across two distinct sets of companies desperately needed to come together.  We had a set of products that focused on outbound campaigns majoring on blasting messages out, and another set that focused on choosing the right conversation to have when a customer approaches a brand to engage, either via the web, or by calling a call center. 

When I first heard Pega’s CEO, Alan Trefler talk on stage at PegaWorld about “Frankenstacks,” referring to the common practice of buying a slew of products and attempt to glue them together, it really hit a nerve.  I could see how even with the best glue, marketers – and their customers – would still be stuck living in these silos until we could build the next generation of martech software based on the core principles of communicating only with relevance and empathy for a customer’s situation, in a channel-less way.  

The highlight of my time since joining Pega in 2018 has been having the opportunity to work with brilliant machine learning PhDs and product leaders who built industry leading marketing automation products to marry these experiences into a unified offering – and getting to see this new product make heroes out of visionary marketers in the real world.  It’s required a lot of teeth gnashing, as we work to find common ground between marketers’ natural inclination to control their customers’ experience, while giving AI enough room to learn and add value.  But it’s been thrilling to see our clients embrace it.

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Over this time in the market, how have you been observing key fundamentals in customer engagement processes and tactics evolve?

Even as recently as the beginning of 2020, I’d often see a disconnect between how marketing executives would say they want their tactics to embrace AI and how strongly their teams on the ground would push back against giving up control over exactly how they manually targeted segments of customers with content they thought was best.  

With the rapid shifts in everyone’s lives that have occurred faster than manual targeting can adjust to, I’ve seen a real shift in the past 18 months toward marketers accepting and embracing that – even for outbound communications – it is now practical, feasible, and preferable to let machine learning at scale make the final decision individually for every single customer, right at the moment they’re being engaged with. Now, marketers can spend more time creating and activating fresh content.  

Customers’ expectations have changed as well, fueling this shift.  Because our experiences with brands have shifted to being entirely digital, we expect seamlessly connected experiences that speak to us as people, not as segments.  When the person we’re speaking with on the phone, or an email we receive is tone deaf to the interaction we just had, that brand gets laughed at (not in a good way!).

What in your view should marketing teams be focusing more on when driving their customer engagement initiatives? What are a few marketing tech features and tools they should be optimizing more when doing this?

Marketing teams have historically viewed next-best-action as competing with their work to script out customer journeys. But these elements can – and should – reinforce each other. The key to trusting AI to make the final decision for each customer is making sure the AI has the right constraints so it isn’t experimenting with the wrong conversations for that moment. For example, it would be off-putting to initiate a conversation about spending more money to trigger a credit card reward with a customer who hasn’t even activated their card yet.   

Marketers have put a tremendous amount of valuable work into identifying the distinct stages that their customers go through with each of their products.  With Pega’s latest enhancements to marry customer journeys with next-best-action, we’re letting marketers list the actions the AI can consider in each of those journey stages without compromising the principles of next-best-action; the customer’s propensity at that moment will still drive what should be said to them in that moment. AI just helps to achieve this at scale, acknowledging that customers are often simultaneously on several journeys at once.  

I think marketing teams should strive to focus most of their efforts on generating relevant content to initiate and advance their customers’ journeys, while spending just enough time on logic to protect their customers’ experiences.

We’d love to hear more about Pegasystems’s new AI driven capability meant to drive omnichannel marketing efforts, what can marketers look forward to with this?

Now that marketing teams no longer need to build one-size-fits-all generic content, they can focus more on creating a long tail of interesting content inspired by specific situations their customers find themselves in. That means marketers need to make sure they have the tools to listen and hear what their customers are experiencing in the places where they interact.

They’ll want to identify those emerging situations where they may have nothing relevant to say and use their empathetic creativity to think about what a few relevant conversations for that situation would be, and a few ways to express each conversation on each channel.  The AI will then learn which of those resonates with a person in that situation.

This is why Pega recently released an offering we call Next-Best-Action Customer Journeys. It’s a new AI-powered capability that provides intelligent decisioning and propensity modeling for optimal customer interactions. This differs from conventional marketing because it factors in the changing nature of human behavior. Instead of forcing customers down pre-determined paths, this capability helps marketers sense customers’ unique context and needs at any given moment, adjust and change their outreach approach in real time, and proactively deliver personalized messages on their channel of choice.

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What does it take for marketers today to effectively drive their omnichannel marketing strategies while not losing brand image in today’s crowded market?

Marketers have two tough jobs to balance. On one hand, they want their brand to speak with relevance in the eyes of their customers, while at the same time, they have to drive specific product-oriented goals their company has set for them.

Customers don’t want static experiences and don’t care about a company’s product goals. They want experiences that are tailored to their unique situation. Traditional marketing tools often pigeonhole customers into one linear journey at a time which may have been relevant yesterday, but doesn’t reflect their actual human behavior today. This can result in frustrating experiences that are heavily sales-focused when that person needs to have a service-oriented conversation.

Even though we, at Pega, are huge proponents of transparent AI, we also recognize it’s unrealistic to expect marketers to just put their trust in a “black box” – because they are still accountable for those product-oriented results.  In order to help marketers who may not be trained as data scientists get comfortable trusting AI, the technology has to explain how it’s making its decisions in a way that makes sense to them – without having to ask their data science buddies to jump on a Zoom to explain.  They also need to be able to influence the AI to meet those product-centric goals.

For example, a marketer responsible for credit cards is still expected to generate a certain number of leads. So, they need an easy-to-understand projection of how many customers are likely to see and accept credit card offers. They can then make conscious trade-offs. We respect that these trade offs aren’t black and white.  

Brands know that they need to meet their customers where they are. They need to make sure every experience takes a customers’ context into consideration in real time to bring relevance to each message and interaction. We know this approach ultimately drives more value for their customers and results for the business. To make this approach practical, Pega is paving the path from the world marketers are coming from (and largely still operating in) to where they want to go in raising the relevancy of their brand. 

What are some of the basic best practices that you feel today’s marketers need to follow to drive better marketing output?

Great question! Let’s break this down into a few concrete best practices that I think make the biggest difference in helping marketers drive better marketing output:

1. Focus on creating and activating fresh, high-quality content that caters to specific customer situations.  
2. Rather than thinking about targeting people with that content, think about the minimal amount of static filtering necessary to protect the customer experience.
3. Forget about running A/B tests that get stuck on a single ‘winner.’ Instead, adopt a culture of continuously introducing new content that challenges your incumbent content.
4. Keep striving for new ways of listening to the experiences and behaviors of your customers in the channels they interact with your brand. Those behaviors are the fuel that feeds the brain.

We’d love to hear more about some of the top martech that have always been your go-to platforms through the years?

I’ve long been a fan of technology that lets brands listen to how their customers are interacting with their brand, even when no human can be directly in the loop.  In face to face, or even telephone interactions, a human is still there and part of their job is to read the moment.  We can do that digitally now as well.  I first became a fan after seeing the impact in e-Commerce of detecting when customers struggle to check out.  As we work with companies who are adopting this approach, we consistently see behavioral data is the most predictive of customer’s propensity.

The talk in our industry about data privacy is rooted in how consumers clearly don’t want brands snooping on their behaviors that are none of their business.  At the same time, there is so much to be learned from listening carefully to interactions that are directly with their business.

Next, technology that embeds data science in a practical way, without forcing it in your face in a technical way, has fascinated me.  We experience this every day in consumer apps (how long will it take for a car to come if I click the button right now?), but it’s taken longer for data science to become ubiquitous in enterprise systems.  That probably has a lot to do with the need for explainability for an enterprise (we don’t ask WHY the app says our car should arrive in 7 minutes).  I’m always impressed when I see data science elegantly baked into enterprise software in a way that business people find immediately useful.  You can expect to see more from us in this area!

Some last thoughts, takeaways, digital marketing and martech tips and best practices before we wrap up! 

This is an invigorating time to be working in martech. The rapid change in customer behavior and expectations, bolstered by some bold regulatory moves, is pushing the industry in a healthier direction.  Outdated approaches can now die out more quickly, giving a push to later stage adopters to get on board with approaches that will better serve their customers, and surprisingly, their own brand as well.  We didn’t speak much about the impending end of 3rd party cookies, but I think that’s a great example of how the shutdown of technology from the “You’ve got mail!” era makes this a great time to focus energy on speaking with relevance based on the data that consumers expect brands to listen and respond to in context.

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