[Editor's Note] Amplero's newly launched editorial series focuses on the people who are rethinking the way brands and consumers interact.
It’s a place where core conversations are taking place on the evolution of AI and the modern enterprise and how it impacts our communities.
For our second installment of Conversations at the Core, we sat down with Amplero's VP, Customer Analytics Julie Penzotti, PhD. Julie specializes in data mining and analytics. She has 20 years experience in multiple industries including drug discovery with CombiChem, DuPont, Cerep, and Pfizer, modeling software with Rational Discovery and marketing technology with Amplero. Julie earned her PhD in Bioengineering and MS in Physical Chemistry from the University of Washington.
Julie shares her insight on everything from marketing strategy and business goal alignment, developing a diverse and inclusive mindset, and having a data science career with real business impact.
Q: How did you make the transition from biochemistry to marketing technology? What would you tell someone looking to make a similar transition?
I worked in pharmaceutical R&D for more than 12 years before changing to marketing technology, which seems like an unlikely transition, because it’s such a different industry vertical. But many of the projects I worked on to support drug discovery are in essence the same optimization problems that apply to marketing – learning what works and what doesn’t and for who, quickly so you can fail fast and optimize for better results the next round. The same AI and ML algorithms are applicable, you’re just optimizing for different metrics. If you have a solid background in statistics, scientific method and fundamental data science approaches along with practical experience applying machine learning and data analytics to solving real world business problems, you can make this transition. Before you start job hunting, it’s important to first to spend some time learning about the domain so that you can discuss how your skill set and specific experiences can help them solve relevant problems. Set up some informational interviews, attend relevant conferences and/or local professional events.The other area that is important is networking wherever you go network, network, network! I suggest talking to different types of people.
One of the biggest challenges I faced was making this career change was my resume. My untraditional biochemistry background often meant that my resume was not usually at the top of the stack of resumes. That’s when my network helped. It's important to connect with people that can help you get your foot in the door. This will go a long way in every stage of your career.
Q: As a data scientist, how do you see AI/ML in martech impacting today’s customer experience?
While personalization of customer experiences has come a long way, marketers are still challenged by how to deliver truly individualized experiences that are optimized for relevance, timeliness and value delivered. And do so at the scale of millions to hundreds of millions of customers. Today’s customers are very fickle, and the competitive landscape can also change very quickly, overwhelming marketing teams to try to keep up. This is where AI/ML has the greatest impact – it automates the heavy lifting of learning and experimentation needed for personalization at scale, enabling brands to deliver meaningful customer-centric experiences across all channels and touch points, and continuously adapt the delivery of marketing strategies to produce the best possible outcomes. This frees the marketer to focus on strategy and developing impactful marketing experiences to that can be personalized to build a brand that speaks directly to its customers at an individual level.
Q: What are some of the biggest challenges and/or opportunities that you see amongst enterprises adopting an AI decisioning layer into their marketing stack?
Data can be a challenge since it’s the most important input to an AI decisioning layer. Without good data that captures customers’ behaviors, it is useless. And a feedback loop to drive optimized results. The best way to get started is to pick 1 or 2 use cases and 1 or 2 channels where AI can execute marketing. You don’t have to think about how to integrate your data right away. AI Marketing allows enterprises to deliver optimized customer interactions at a scale that’s virtually impossible for humans to do. It’s about improving response time and creating more efficiency. It supports customer onboarding and retention, increases cross-sell/upsell opportunities and reduces churns.
Q: What advice would you give to an analyst or data scientists struggling to make an impact at their organization?
They need to understand their company’s top business challenges and goals. You need to know how to connect the data that you have available so that you can tell a compelling story. Specific domain expertise is often important to define interesting use cases, so if you don’t already have it, link up with a business team that can help you with this. And most importantly, deliver insights that can be turned into actions that can generate impact. For example, delivering a churn model that predicts which customers are most likely to discontinue their subscription or service. But to prevent churn you have to dig into the insights and drivers of churn operationalizing the model so it can be acted on in a timely manner.
Q: As a female leader in a traditionally male-dominated industry, what advice would you give to data scientists looking to support industry change in terms of inclusivity of diverse voices and talents?
First and foremost, take ownership and pride in your work. If you are working on something interesting share your learnings internally and externally. Work hard and be knowledgeable and prepared. This is how you become a leader and a role model within your organization and industry. Your influence can promote change. Second, don’t wait for opportunities and change to come to you – be proactive, discuss your goals with your bosses and leaders. Don’t be afraid to lead new initiatives.
Diversity and inclusion is not an initiative it’s really a mindset. It should be at the core of everything from hiring, building products, business strategies, etc. Data scientists should tap into communities and resources online and offline. There is so much value into supporting organizations and projects that focus on underserved groups like women, people of color, people with disablilites. These groups and or initiatives will help you create a diverse network while helping you learn skills that you need to become successful.
Q: What is the biggest misconception that you hear about AI technologies from marketing executives?
It’s important to realize that AI is an enabling technology, not a magic silver bullet that drives ROI with just a push of a button. AI must be implemented thoughtfully in marketing. You must know your goals and what metrics truly matter to your business. At the core, AI systems are just mathematics, signal detection and iterations, designed to optimize for outcomes. So before you put AI in the driver’s seat for marketing, it’s important to choose an optimization metric (or KPI) that directly aligns with your business objectives and that you can clearly measure from your data. AI will not deliver the ROI you expect if the metric you choose is not well aligned with your overall business goals, you may not drive the results you expect. For example if your business goal is to increase the average number of products or goods purchased per customer your metric would not be marketing’s total revenue. Instead you’d want to gain a deeper understanding of the right-size incentive offered to each customer optimizing interactions as they evolve.
Q: What are you most excited about in terms of AI and the customer experience?
While AI is an integral part of our everyday lives now – think Siri, Tesla, Amazon recommendations, we’re just at the early stages of seeing it’s impact within marketing. They’ll be many interesting use cases across different industries where we will see the capacity of AI to drive high impact customer experiences. Customers’ loyalty and satisfaction is generally highest when a business remembers them, understands their preferences and treats the customers with personalized attention across all touchpoints. We’re already seeing the impact that AI tools like chatbots and virtual assistants are making. They have streamlined customer service operations and time savings. I’m also excited about the changes in telecom industry with AI-enabled customer experience technology. They are relying on media to please their customers. Social and community impact along with customer experiences will ultimately determines which brands last. nbsp;
Stay tuned for the next Conversations at the Core feature with Bruce Cleveland, Founding partner at Wildcat Ventures. To get the latest in AI delivered to right your inbox, sign up for our monthly newsletter here.
Amplero lets brands be human again.
Despite the exponential growth of customer data, major brands still fail to understand individual consumers. Moments of interaction on the phone, in the app, or at the store, don’t connect. Each channel of communication is driven by its own data and automation rules.
But where humans and existing marketing systems are overwhelmed, artificial intelligence marketing (AIM) thrives. Amplero now enables customer-obsessed brands to build lasting relationships at a scale not humanly possible. No more mindless automation. No more fragmented interactions. No more feeling misunderstood.
Launched with AI at the core and used by companies across the globe, Amplero’s award-winning AIM technology experiments, learns, and optimizes each interaction as customer relationships evolve. Intelligently orchestrating meaningful, individualized cross-channel experiences, Amplero AI Marketing Cloud drives measurable lift for the metrics that matter most.
As a recognized leader in the AIM category, Amplero was named to the “Top 5 Most-Promising AI Startups” by VentureBeat and “CB Insights AI 100” featured in Fortune Magazine. Amplero founder and Chief Scientist Olly Downs, PhD, holds 35 patents in machine learning and computational mathematics.
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