5 Questions with Dave Rogers and Sameer Karim, Product Strategy & Analytics Experts at Slalom

Slalom is a modern consulting firm focused on technology, strategy and business transformation. Slalom believes that product analytics is a critical centerpiece at every stage of product development. Their product-focused mindset has driven them to wins including building a mobile app that increased Hyatt’s booking revenue by 80%, as well as revamping REI Adventure’s digital experience to improve the earliest stages of the customer journey. To learn more about Slalom’s perspective on product analytics, we interviewed Sameer Karim from their Products and Innovation team and Dave Rogers, an analytics and optimization strategist.

Tai Rattigan: What is your role at Slalom and how did you end up there?

Sameer Karim:

I’m one of the leaders in our Products and Innovation practice. I work with our clients to identify the next areas of customer opportunities and also help them improve their approach to innovation and product development.

I have experience in entrepreneurship, start-up acceleration, finance, and working with product teams at small and large companies. I bring that all together for our clients in this role at Slalom.

Dave Rogers:

I’m a data analytics strategist and specialize in analytics and optimization. I spent the first part of my career in academics, mainly studying social psychology and human behavior. About 15 years ago, I moved over to the business side. Initially, I built analytics functions for large technology organizations and start-ups, then ran my own digital analytics agency for seven years. About a year and a half ago, I joined Slalom. You could say that I still study human behavior, but now mostly online behavior.

TR: Why is Slalom the choice for so many large, traditional, and digital-first companies?

SK:

We have a really broad mix of deep capabilities – everything from business strategy and operations to software engineering – to bring the product to life for our customers.

We also have a strong bias towards delivery and iterative learning. So for us, the differentiator is bringing that strategy and execution together in delivery and meeting our clients where they are with a tailored approach. We achieve that by understanding where our clients are right now to identify the best next step for them.

DR:

We are a one-stop-shop that can handle all of it. We’re not just a digital creator shop, we’re not just a strategy shop, we’re not just an implementation shop. We’ve got the resources and expertise to handle all of our clients’ demands including strategy, execution, delivery, and building for the next iteration.

TR: What are the biggest challenges companies face when it comes to product analytics?

SK:

One of the biggest difficulties is drawing actionable insights from the flurry of data that is being captured and turning those insights into a product roadmap.

Put differently, you need to view that data within the business context and then interpret that real-world data to figure out the next areas of product growth. It’s a hard thing to pull together. There are lots of techniques that can be used, and those techniques need to be applied to the right problems at the right point in time.

These techniques vary by where you are in the product development life cycle. So, for example, when you’re in the very early phases of product growth, you’re really exploring all the possibilities out there. At that point, qualitative techniques are most useful, though quantitative data can give you some additional direction here.

A little further down the line, when you get into a cycle of building, releasing, testing, and learning, then you can use product data as your primary source for insights, and qualitative data becomes a secondary, supporting technique.

DR:

I’d say the biggest challenge is drawing knowledge from the data and applying those learnings. You need to get to market fast with your best foot forward and in many organizations I support, the investment has not been made in process and strategy. This leaves product teams less equipped to move quickly with certainty. As a result, they’re taking three times as long to get the right feature set to their users because they haven’t invested or put the time into building a strong case for these needs.

I believe that more data sources and more types of data formats from the beginning sets us up for building the best product. And, I believe it’s important to use data across different parts of the product journey. It’s not enough to just look at user behavior. We should learn about users in general – their experiences, what they like and don’t like, how they’re different from other cohorts, and usage patterns by segment. In addition, to develop the best products, product managers need to understand the landscape – general market forces, competitive intelligence and positioning, and timing for new product development.

Using a robust collection of different data sources while focusing specifically on the decision-making and ideation process, is a best-in-class approach when testing data sources against your hypothesis to move forward with a few concepts to build.

TR: What do product innovation and data analytics mean at Slalom and why is it a core of your strategy?

SK:

For us, it means balancing big bets, enhancements, and incremental improvements. It’s about taking an existing product and making it better as you go while using analytics and insights to identify those areas of breakthrough product growth.

At the core, we take a portfolio approach to building a product roadmap, whether that’s identifying new customer segments, new experiences that would address unmet needs, or gaps that we’re seeing in the user journey. Then, we apply product analytics and other insights to drive the opportunity discussion.

DR:

For us, a defined data analytics perspective is not just about tools and reporting. We feel most successful analytics capabilities are developed on a strong data strategy foundation. That critical foundation is required for the best analyses and decision-making within an organization, unlocks the most successful product innovation and is a core business strategy component for all organizations.

What do you look for in technology when you make a recommendation to a client?

SK:

We start by working with the client to identify what they need in the near term and also what they are looking for in the longer term to make sure any recommendation will fit their current and future needs. Along with their needs, we take the flexibility of the technology into account.

A large part of what we then look for, especially when the technology is user or employee-facing, is how easy it is to use. We’ll look at ease of onboarding, the flexibility of the tool – for example can new and more advanced users get something out of it – and finally, training and support offered. All of this is viewed in terms of how the client is planning to use the technology, and who the users will be.

DR:

Selecting which analytics technologies to bring to the data platform requires balancing current needs against anticipated future ones. I tend to short-list those technologies that are leaders within their specific domain and have been providing value across industries for a while. An integration and architecture strategy across tools is important to me, realizing any given technology is just one component of an analytics solution. I also look for technologies that have a track record of addressing user concerns.