Russell Goldman was most recently Head of Platform & Innovation Product Management at LG Electronics North America. He started his career in marketing, working for Bertelsmann Media Group and American Express, before transitioning to management consulting at Accenture. An interest in product management spurred his move into innovation roles at companies like Mindshare, JetBlue, and LG.
In our conversation, Russell talks about the possibilities and challenges associated with bringing generative AI into a combined retail and software space, such as transitioning toward digitization and having different stakeholders managing each specific customer journey. He also shares how his earlier stint in management consulting has influenced the way he develops innovative products.
There are several. Particularly for large retail companies, they’re going through a long digital transformation process. There’s a lot that accompanies going through that, such as organizational, technology, and software challenges. For instance, companies that are retail-focused and started a digitalization journey may have in-store retail vendor management. They might have ecommerce divisions or may be thinking about new technologies and their applications. They don’t necessarily have an integrated way of thinking about technology that runs across the company.
There are also different stakeholders who manage the different parts of customer journeys for different customers. Unless you’re working at a company where it has transformed into an entirely product-led business, you have to pick and choose the customer you’re focused on. You have to deeply understand that customer journey and think about the products you create and how their technology backbone or different feature sets can apply to other types of customers later.
One of the biggest opportunities today in terms of enhancing user experience is using LLMs and generative AI to facilitate helpful, human-like interactions for customers. Vectorizing all types of data to make it accessible to users through conversations should be a top priority. This has multiple benefits across the customer journey, from helping someone with the shopping experience to helping them extract specific information for support use cases to searching for things on home devices and allowing agents to retrieve specific information.
If you think about generative AI in a retail context, there are multiple customer touchpoints, whether it’s consumer products or online and in-store retail. To actually enable omnichannel experiences, you have to make sure your data is accessible, controllable, understandable, and usable. Otherwise, you’re never going to be able to create that 360 experience.
Using technologies, thinking about how you’re collecting the data and making those things happen, and using modern CDPs so that you have one standardized set of data protocols is important. Bringing together different parts of the organization, like software or IoT businesses, .com businesses, and call center divisions, and making sure they’re all actually collecting information in the same way is really critical. And when you’re building generative experiences and thinking about personalizing them, making sure that you have that common data foundation is the only way to do that efficiently. That’s true whether it’s a generative experience or anything omnichannel.
I use both a press release FAQ (PR FAQ), which Jeff Bezos made popular, and a financial model. I tend to do the PR FAQ after the strategy.
For strategic planning, I tend to use a PowerPoint that goes through all of those things and sets the stage. I use it as a way of doing a checklist. Have I covered the strategic landscape? Have I thought about my customer needs? Have I planned out the hierarchy of what those needs are? Have I gone through the process of prototyping so that I know what questions or gaps a user might have?
The financial model helps you think through the operational components of what the risks would be. So in that financial model, I always think about not just the mechanics of how a business works but also, in a very detailed way, the risks or gaps in my financial model today that I need to plug going forward.
A PR FAQ serves the same function: it helps you think about the end goal that you have in mind. What’s the message that you want to be able to lead with if you were to launch the product today? And what are all the different types of questions that you need to ask?
The two groupings that I think about, particularly with new products that aren’t released yet, are pre-launch metrics and post-launch metrics. I also think about the metrics in terms of performance metrics, user metrics, and business metrics.
In terms of post-launch and pre-launch, pre-launch is much more difficult because some of these metrics don’t exist yet. In pre-launch, it’s really important to be using both qualitative and quantitative metrics that come from other types of testing. I recommend doing a lot of user research. In terms of measuring the impact pre-launch, I make sure to ask the same types of quantitative questions in a usability test or any type of unmoderated study so that I can map progress as they move toward launch.
Performance metrics are common to any type of product. If it’s software, it could be things like the uptime of the software or the rate of errors. You could divide that into the rate of errors or the severity of different errors that are happening.
And then there are also user metrics, which relate more, to me, to actual broad product goals or user-specific goals. In this section, I think about things like general user happiness. Surveys or tools like Qualtrics or NPS can be helpful for understanding how users are receiving the product. It is also important to think specifically about goal metrics for the user that indicate that the user need was actually met.
From a business perspective, you’ll want to understand things like whether the product exists for purposes of making money or reducing costs, and the ability to track that information. I think about revenue or cost in terms of direct revenue as well as attributed revenue. You may not get an exact measure of the product’s impact on sales, but you can do an attribution for most products.
I also think about broad product or feature-related goals. That’s related to the customer journey and conversion metrics. Was the user aware? What percentage of users were aware of the product and engaged in the product? I break that down into very specific steps that I take to get to the actual end goal. In the case of shopping, that may be checkout, so being able to manage that funnel or having funnel-based thinking is important.
I always ask at the end of a study, “Did you find this experience useful? Did this experience give you what you need?” If I’m able to run a study at scale, I also ask, “Would you use this tool or product again?” By measuring that over time, you get a general understanding of whether you’re moving toward success based on the ranking. If you do it on a five-point scale, the more people who are rating it at a four and a five, at some point you’ll know that the product overall should be well-received. It may not tell you exactly what your post-launch business metrics will be, but you’ll get an idea of reception. I think that that’s an important part of the process as well.
One of the things that’s important for product managers (that also comes from my marketing background) is not just being funnel-focused, but really understanding the broader consumer journey and the capabilities of artificial intelligence. I really believe that AI is the new UI. Where older interfaces, like web or app-based technologies, limit your users with visual cues and applications to communicate their needs, generative AI enables companies to develop features that are almost at every stage of that retail journey and beyond what these static interfaces can provide.
An example is being able to mimic the way an in-store salesperson might help a customer who’s very early on in the selection process think about their lifestyle and their broad needs. They help them whittle that down into a specific set of products that they might want to consider. This process can also help people compare products by coupling language and visuals together to mimic the experience you might have on a normal website but would present it in a way that makes sense for conversational interfaces. Users can go through a full journey of actually selecting products or receiving recommendations through natural language.
A company’s conversational engine sets a foundation to be used for many other things. That experience could be made available in a variety of different places or added in new types of product catalogs and things like that. It is not taking the place of a .com experience, but it’s a comprehensive complement to what a user today might be familiar with experiencing in an ecommerce environment.
What I find fascinating about building products in this space for generative conversational experiences is that the only limit to what a generative experience can do is based on feature sets of how a conversation interacts with you. As I was saying, with those features of consultative sales or comparing products, the information is just the information. The only limit is the information that’s actually housed in that database.
You could potentially put IoT data into that database or content from an entertainment experience. You could even put information from partners. Whatever you feed into that experience can be made available to your customers. The engine itself is what, to me, is so innovative. I think any company that’s thinking about it today should be thinking about it that way. The expertise you’re building is actually the facilitation of the conversation.
The reason why I’ve been able to move from marketing to consulting to entrepreneurship and then back to product management has a lot to do with how I think about work and my life, as well as the different things that I’m interested in. At my core, I’ve always been focused on a couple of things. One is that I’m innately very curious. I’m, by personality, someone who likes to explore and someone who doesn’t like to be limited by any type of constraints. I’ve taken that approach in my career to learn different things and explore different ways of solving problems.
The importance of having a customer understanding, and even the analytical perspective that I have as a product manager, actually comes from my marketing background in campaign management — thinking about the outcome that a marketer needs to have when they’re designing campaigns. I apply that in a different way as a product manager. I think the work that I did as a management consultant was specific to growth strategy and innovation. In that part of my career, I spent a lot of time thinking about the cultural alignment that needs to happen in the organization to bring about innovation or new products.
Now, one of the things that I look for and try to influence is thinking about not just how you tactically build something, but how to actually evolve your larger business so that innovation or product management can thrive. There’s a lot that goes into that in terms of how products are funded and how teams operate cross-functionally.
One of the things that management consultants do really well is have a clear understanding of the competitive or market landscape. They come up with ideas and use tactical skills, like financial modeling, to understand the problem space. Then, when you actually go and build something, you have a clear understanding of the main triggers of a business to make it successful long-term.
Something I try to implement in product now is that strategic rigor — making sure that people have those tactical skills. I think it also makes for a better product manager so that you have the end in sight, even if the journey to get there changes or you need to insert more user research as you go. That really helps direct a product team to know where they’re going.
The other way is thinking in terms of product innovation. It’s one thing to be a product manager and be focused on a specific existing product. It’s another to think about what’s coming next and how those things come together. That’s something that I’ve attempted to influence — thinking about the life cycle of a suite of products. How do you move from the ideation or inception of the idea through the process of conceptualization, prototyping, launching and scaling, and being able to plan out that life cycle? In terms of consulting, I learned to have a wider lens — to take a portfolio approach and play it out operationally. This is something that I would always recommend for someone working in product innovation.
LogRocket identifies friction points in the user experience so you can make informed decisions about product and design changes that must happen to hit your goals.
With LogRocket, you can understand the scope of the issues affecting your product and prioritize the changes that need to be made. LogRocket simplifies workflows by allowing Engineering, Product, UX, and Design teams to work from the same data as you, eliminating any confusion about what needs to be done.
Get your teams on the same page — try LogRocket today.
A happy customer is an enthusiastic advocate for your brand, a loyal repeat customer, and a source of positive word-of-mouth.
Dakota Keyser talks about how he helped implement a data-driven culture in teams throughout the company in his past roles.
Galileo Highlights eliminates the need to watch hours of sessions by automatically summarizing each user’s experience and identifying patterns, friction points, and behavioral trends across groups of sessions.
A/B testing is a method of comparative analysis where two versions of a product change (A and B) are tested against each other.
Learn more →
