Technology makes promises, but you need data to realize value

Learn more about People.ai’s approach to increasing personalized, actionable insights via data

I recently spoke with Oleg Rogynskyy, CEO & Founder of People.ai, a company that helps sales, marketing, and customer success teams uncover every revenue opportunity from every customer. His startup is using data science, machine learning, and text analytics to solve the age-old problem of measuring and optimizing human productivity through sales automation tools. I knew Oleg has a front row seat to data issues with a variety of customers, and there is a lot to learn from his innovative problem solving approach. Oleg did not disappoint, his responses were as insightful as ever!

Venkat: Hi Oleg, thank you for speaking with me. Can you please tell us something about yourself and how you became the founder of People.ai? 

Oleg: After graduating Cum Laude with a degree in Political Science from Boston University, I joined a company called Nstein Technologies as the first Inside Sales Rep. One day our COO grounded the entire sales team for a week to clean up every record in Salesforce because our data was, to put it mildly, garbage. I hated every minute of it, and it felt like a complete waste to take us away from revenue-generating activities for an entire week. 

In 2011 I started my own company, Semantria. Ironically, I also had to ground my sales team for a week to clean up our CRM data. I sold Semantria in 2014 and became head of sales at a company called H2O.ai, I experienced the same issue again. That meant that in nearly a decade, nothing had changed in the CRM world. People still had to manually enter data into Salesforce, and it was a mess. I knew there had to be a better way, which is why I decided to start People.ai.

I had been at the intersection of complex AI technology and selling in every stage of my career, which gave me an appreciation of AI technology and led me to understand the pain points of CRM. I started People.ai because I believe technology has matured and is ready. I learned lessons in each of my previous roles and companies that I apply to making People.ai a success. 

Venkat: When it comes to selecting technology and solutions to enable the business, sales and marketing leaders today have a choice. The challenge arises in selecting and integrating them to gain business benefits. What is the progression you’d recommend based on your experience with implementing technology that depends on data? 

Oleg: According to McKinsey, only 20% of Business Activity Data is captured today. We chose to start by collecting that kind of data to see what is there. It turns out that this data is very valuable for many different reasons. Next we are then able to compare the data and solve for productivity by understanding what activities are sucking time away from activities that add value to the business. Once we understand that, we are able to incorporate intelligence in the form of the best way to act on that data and solve those problems. 

My advice is very simple: first, collect data to diagnose what is wrong. Secondly, see if data can be used for better productivity because in the current scenario, people are spending lots of time manually entering data. Once you solve for productivity, see if data can add intelligence for you by making you smarter about what you are going to do today, how the campaign went, what is wrong with a particular deal, or how to make an account engage with you more. 

Venkat: They say "sunshine is the best disinfectant". While no one likes to solve “data problems”, they love to solve “business problems”. Can you give me some examples of how your customers have surfaced the urgency of data issues and are solving for them?

Oleg: Solving data problems is not the right place to start, because good data is a means to an end. By focusing on business problems, our customers are creating a value case for solving data problems. Here are some examples from our experience.

The first problem we address is to increase the productivity of the sales team by automating workflows. Sales reps currently spend 1-2 days on data entry. Remember the days when taxis had to radio into a dispatcher to understand where to go - submitting activity data via analog format? Now, Uber has a platform that removes this by automating it. Therefore, you are increasing the productivity of a sales team by automating manual workflows. 

The second problem to solve is prioritization. How do you make your people spend their time more wisely? Find the data to make your people more productive and more effective and intelligent. 

Third, with all of this data, you essentially have “medical records” to run diagnostics on your business. You can dive into this data to compare to other businesses and benchmark yourself. 

Finally, make it easy to consume the data. E.g. Use APIs to flow this information back into your other systems to build an end to end solution. 

Venkat: Can you share examples of your customers who have gained a competitive advantage by managing data as the fuel to deploying technology and automation? 

Oleg: We helped Zoom seamlessly bring all their sales reps’ activity into one place so they could see what was working and why and what wasn’t. We helped them get started by surfacing data hygiene issues. They recognized very quickly that clean data is a prerequisite. After Zoom saw the impact that this data could have on their sales team, they implemented a comprehensive coaching strategy for their teams to review activities, results, and which deals are and aren’t ready Now start with the activities (born from the data) every time. What was amazing for Zoom is that their sales activities level increased by 43% and average meetings per rep increased by 37%. 

We’ve also seen this in customer success with Red Hat’ where we looked at Engagement Activity Scoring (Implementation/Renewal Activities) to identify customer challenges and upsell opportunities. We were able to provide a 360 view of customer engagement. 

Okta also implemented our solutions for marketing, to identify true campaign ROI, while reducing pipeline wastage, and increasing marketing attribution. 

Venkat: While GDPR and other privacy legislation require companies to manage their data better. How can executives connect the dots between data management and compliance?

Oleg: For GDPR, Article 6 states that businesses must be able to prove valid business interest to store contact information. Companies can ensure GDPR compliance by scanning business activity in their systems, current and historical, validate bi-directional activities and recover all missing or deleted contacts for pre-existing business relationships.

GDPR says you cannot spam or reach out to people who have not given you permission unless you have a pre-existing business relationship with them, which GDPR defines as consent without proof of activity. People.ai helps to make you more compliant by telling you where all of this data came from before you engage with customers and prospects. 

Venkat: What trends have you seen in the industry that builds the business case for data being treated as a strategic asset?

Oleg: The trend I see is that manual data collection is being automated more and more. This data is combined with existing customer data and there is a network effect in learning and insights. 

One example is the Uber one I mentioned above, where Uber has automated the vehicle summoning process. Now, they are able to take that data a step further and pick the next rider or driver. 

More and more companies are realizing that data will fuel the effectiveness of your sales and marketing teams, by offering personalized, actionable insights when and where you need them.

This is very real for us personally. People.ai needs data to fulfill our mission to fulfill our mission to be the AI platform for all business activities across the organization. This technology will run on steroids with good data!

Crowdstrike also uses their threat graph for all customers, which automates not only the data collection from endpoints, threat analysis at scale, but also the next best actions for security experts.

Venkat: Thank you for speaking with me, Oleg! As always, this has been a very insightful conversation! 

Oleg: Thank you!

Venkat Narayanan is a Business Architect in Cisco's Data & Analytics organization. He believes data is a strategic asset. Almost all enterprises (big and small) have a “data problem”, but they may not be aware that bad data is just a symptom of the real business issues. He hopes to demystify business, compliance, and operational issues in managing data by harvesting ideas from practitioners with real life experience in the trenches, and combine ideas in interesting ways to engage his audience and drive change. Venkat led the onboarding team at People.ai during its early years, and had the privilege to implement People.ai’s amazing technology for new customers.