Bridging Product Gaps: Hightouch’s Adam Greco on Data & Mentorship

1. What are the biggest challenges in bridging the gap between product capabilities and user adoption?

Most users only utilize about 40% of a product's features. Product analytics tools are helpful in identifying which features are being underused. Typically, the lack of adoption stems from users being unaware of the features available or not understanding how to apply them. For example, during my time at Omniture, we had a powerful but complex product. I noticed many users weren't fully leveraging it, so I began writing blog posts about each feature, illustrating how they could bring real-world value. The blog gained popularity and helped significantly boost user adoption. Educational content like this, along with customer success teams showing real-world use cases, can play a crucial role in increasing adoption.

I believe the best way to bridge the gap is to identify underused features, educate users about their value, and continuously monitor analytics to gauge if education efforts are driving better adoption.

2. How do you approach educating the market about a new product category?

Creating a new category is one of the most difficult challenges in marketing. Humans naturally group things into familiar categories, so introducing something entirely new requires a lot of work. When Hightouch first introduced the concept of building customer audiences and activating them directly from cloud data warehouses, it was hard for people to grasp because the concept didn’t fit into any existing category. Initially, we referred to it as "Revense ETL" because consumers were familiar with ETL, and our product was essentially the opposite. As we expanded the functionality, we built a new category, the "Composable CDP," which was different from traditional Customer Data Platforms (CDPs). However, the creation of this category took time, effort, and many customer conversations before the industry accepted it. Throughout this process, we had to work around consumers' existing mental models to help them understand this new concept.

If you're trying to create a new category, it's crucial to spend a lot of time educating the market about its need, benefits, and how it connects to existing product categories. Though it’s tempting to dominate the category without competition, it’s often more advantageous to have competitors emerge, as it validates the existence of the category.

3. What role does data play in driving innovation and competitive advantage?

In today’s digital age, most user interactions take place on digital platforms, making behavioral data an invaluable resource for understanding what works and what doesn’t in your products. Data is the new way to “listen” to users, and the better you are at listening, the more likely you’ll be able to turn those insights into innovation.

Since most companies use similar platforms to build their products, the key to gaining a competitive advantage is learning faster through behavioral data. However, it's not as simple as it sounds. You need to gather the right data at the right moment, have the right people to analyze it, and then act on the insights to innovate. Those innovations, when implemented effectively, can help you outpace competitors.

4. What common mistakes do companies make when implementing data-driven decision-making?

One of the biggest mistakes companies make is failing to first define the questions they want their data to answer. Many jump into data collection without considering the broader questions that can have a real impact on revenue or user experience. Another mistake is letting data drive decisions too much. Data alone can't reveal the reasons behind customer struggles; you also need to understand the customer journey through tools like surveys or session replays. Data is most useful when it quantifies known problems, but identifying those problems based solely on data can be a misstep.

5. How do you approach mentorship and knowledge sharing within your industry?

The data industry, despite its prominence today, is still relatively young. Many people in data roles today didn't study it in school and instead fell into the field as the world digitized. Given the industry's youth, mentorship and knowledge sharing are essential to help everyone grow. When I started in the data space, few people had written or spoken about data best practices, so I began sharing what I learned through blog posts. I continued this trend at Salesforce and in consulting, always eager to pass on what I had learned. At Salesforce, my team and I would document what we did in blog posts so other companies could benefit from our experiences.

Mentorship is also a key part of my approach. I regularly schedule calls with people in the data industry to discuss trends and help guide their careers. It’s incredibly rewarding to hear that my blogs and books have helped people launch or grow their careers. I believe that knowledge sharing and mentorship are mutually beneficial: the more you give, the more you get.