This week Product School hosted Jess Bahr, Senior Director of Growth at NS1, for an #AskMeAnything session. Jess gave insight into managing the growth of a product. She used her extensive background collaborating with brands like BBC, Conde Nast, Spotify and plenty more.
Meet Jessica Bahr
Jess is a data-driven and award-winning marketer, currently thinking about the convergence of data and marketing to build better consumer experiences. She works with startups to drive growth through a holistic growth strategy rooted in social and inbound initiatives. Jess considers herself a social media scientist: always testing the latest theories and technique then sharing those findings with the world.
In her previous positions, Jess has worked with some of the world’s largest media companies and publishers to drive revenue and meet business goals using paid and organic social tactics. The brands she has collaborated with include BBC, The Economist, Hulu, AMC, IFC, Viacom, Conde Nast, Bloomberg, Time Inc, Spotify for Brands, Microsoft, Madewell, Barnes & Noble, to name a few. Today, she is a Senior Director at NS1, leading the company’s efforts to provide precision control over application delivery for the data-driven traffic management platform.
What tools do you use to keep track of KPIs for startups?
Honestly, spreadsheets are my go-to tool to build a dashboard for tracking metrics, and then I’ll often move over to a tool like databox, Google Data Studio, etc.
How do you start to help a business move from being customer request led to being data driven?
It often comes down to where your data is being sourced from. Your data should be reflective of customer feedback, demands, and user experience. Customer metrics should be reported next to revenue and growth metrics.
How do you cater a huge amount of data? In terms of processing and management.
Collecting data without a purpose is just hoarding numbers, so first take a step back and evaluate what data you need to be collecting. Then architect what you’ll need to be doing with it. Do you need to collect data to go look at historically, or do you need real-time metrics that may be changing quickly? A great stack is some data storage layer, an ETL like Fivetran, and then a visualization tool like Tableau.
How do you discover the best channels to generate demand for your product or features? Today’s customer is omni channel.
The first step is to make sure you’re set up to discover which channels are driving the strongest results, and what the key, important results are. There are some cross-platform attribution tools like Branch.io, Demandbase, Kochava, Spps flyer, etc. If you’re running ads from specific platforms you should always tie these into your conversion pixels. Using proper UTM tags on URLs can let google help guide that discovery.
I want to know how to identify the right set of metrics that will help you measure sustainable growth of the product?
It’s really easy to get attracted to shiny, top-level big metrics like site visitors or social mentions, but you always want to use metrics that tie back to actual business goals. If you’re launching a recipe app, don’t just look at unique signups but pay attention to actual engaged users and what people are doing in the app.
Is there a trend in the number of receipts viewed per user per day? Or, are the clicks out from the app to affiliated sites increasing? Think about the metrics that matter to the business, and then tie them back to your growth efforts.
How do you usually approach a growth strategy problem from a marketing standpoint? Can you give a high-level overview?
In a previous role, we had an issue where customers would buy the product and then never use it or would rely on our customer success team to execute what they had brought the product to do. Through talking to customers we found there were two main issues:
- The complexity of steps involved in using the platform was not properly reflected during the sales cycle, in the marketing and onboarding materials.
- Some customers bought the product thinking their teams would use it but did not actually include the end-user in the buying decision, so those end-users didn’t believe there was an actual value to be had.
We deployed two projects, one to create better marketing messaging that better reflected the complexity of the platform and one that helped quantify the impact the platform could have on end-users in terms of better performance but also ease of use. Testing different messaging showed us that end-users cared more about their job being easier than doing a better job.
What is your advise to someone who wants to switch from a Data Scientist role to a Product Management. Where do you start when all jobs ask prior experience of having done product management?
- Find ways to get product management experience without being in a product management role. Maybe you have a side project where you’re leveraging the skills? Or, by taking courses through product school.
- Find a product management role at a data science or data-focused company where your experience in the field will give you a unique perspective into what the customer actually wants
- Apply at startups where you may wear a lot of hats, one of which may be product management and one may be data science, and then as the company grows, focus on growing into the PM role
- Keep applying, it’s hard to find the first role when you’re changing discipline or industry, but you’ll get it!!
Do you have a business case model/exercise you use to convince stakeholders and other teams to scale results from a test?
Optimizely has the most comprehensive set of resources around testing and includes lots of use cases on brans that have leveraged testing to scale their product and organization.
Could you give us a breakdown of a typical day in your role?
I generally check my dashboards in the morning to see how my team is tracking on our business-level KPIs that I have to report on, and if projects are on track.
I spend about 1/3 of my day actively managing employees and their career growth, 1/3 of my time interfacing with other teams for high-level planning and alignment, and then 1/3 of the day working on actual company problems be it trying to pull platform using stats around a specific feature set to see what they care about or talking to customers to get feedback on positioning.
Did you miss this event? Check out our events page to sign up for the next #AskMeAnything session!