Gone are the days of spammy marketing where everyone got the same message. Marketing can be so much more efficient if the right lead or customer gets the right message, at the right time. This is personalization, which can be powered by AI-based prediction algorithms. Dynamic Yield is a company offering solutions for this.
In this blog post, I interview Mike Mallazzo, head of content at Dynamic Yield and responsible for brand, content, communications and press/analyst relations.
He is an alumnus of LinkedIn, two-failed startups, and Northwestern University where he once crawled in a dumpster in the name of journalism. His writing on AI and the future of technology and commerce has been featured in Quartz, Entrepreneur, Forbes, The Next Web, MediaPost and the Chicago Tribune.
Now, over to the interview!
What is Your Company’s Background?
After selling his second company to Sears, Dynamic Yield’s founder Liad Agmon became VP of New Services when Sears was at the cutting-edge of trying to create unified omnichannel experience for their shoppers. However, he quickly became frustrated by the inadequacy of technology on the market.
A couple of years later, he and co-founder Omri Mendellevich were sitting in the offices of Bessemer Venture Partners, frustrated by the inability of publishers to create unique experiences for different customers. This brought on a eureka moment where Liad and Omri decided to build the technology platform Liad wished he had at Sears that could also create superior experiences for users across verticals.
Over time, Dynamic Yield has evolved, finding product-market fit as a solution built for enterprise companies to personalize and optimize their digital experiences across web, mobile app, email and advertising. The company has evolved to meet the needs of enterprises in the US, South America, Europe, Africa and Southeast Asia.
All of this success comes amidst a “retail apocalypse” and challenging macro economics in the retail industry which accounts for 60% of Dynamic Yield’s customer base. While many larger retailers have contracted, Dynamic Yield is growing 100% YoY and has added industry titans such as IKEA, Stitch Fix, Land’s End, URBN Brands, Barnes & Noble, YETI and Zalora in the last year. Additionally, Dynamic Yield has enterprise customers in media (NBC), travel (Click &Go), gaming (Playtech, William Hill) and financial services (VkB).
In an era of hyper-commodotization in retail and other industries, the only way for brands to compete is on the basis of offering a superior customer experience. Dynamic Yield helps enterprises do just that.
What Problem Does Dynamic Yield Solve?
Dynamic Yield is the world’s first personalization technology stack, empowering marketers to serve individualized digital experiences to their customers at scale.
What Does Dynamic Yield do, and why is it Important to Your Customers?
In its annual “What’s Hot in Digital Commerce” report, Gartner cited personalization as the #1 strategic investment are for brands in 2017. Boston Consulting Group predicts that personalization will push an $800 billion revenue shift to the 15% of brands that get it right in the next five years. McKinsey refers to digital personalization at scale as “marketing’s holy grail.”
The message from the marketplace is clear. Personalization is no longer a nice to have- it is the single most important strategy for boosting revenue and brand affinity online. Legacy providers just don’t offer the functionality the market is asking for. Dynamic Yield does.
The first iteration of personalization came in the form of point solutions designed to address very specific and narrow use cases, such as A/B tests, product recommendations, cart abandonment and email. While scores of successful businesses were built in this model, consumer behavior and expectations have changed.
As conversions occur across a myriad of touchpoints and devices and marketers are expected to manage all these channels across the funnel, there is a massive need for an end to end platform that allows marketers to act in real-time to personalize the entire customer journey.
Dynamic Yield’s mission is to become the world’s leading personalization OS. Built from the ground up as an open-architecture platform, Dynamic Yield is the first company to bring a comprehensive personalization stack to market.
It all starts with a best in class data platform that can mesh massive datasets in real-time from every source available and an audience explorer tool that allows users to instantly build customer segments based on that data.
The ultimate payoff is that with Dynamic Yield, marketers have one interface to personalize experiences, serve product recommendations, run automatic optimization and serve behavioral messages based on a single unified dataset. This replaces the traditional need for up to five software products.
As an AI platform specifically, competitive differentiation for Dynamic Yield again starts with data. We provide a data activation platform where marketers can onboard data from across web, mobile apps, email, CRM, 3rd party data sources, point of sale platforms and customer support touchpoints to form one cohesive dataset. Basically, we provide our machine learners the best textbooks.
As a result, our machine learning and artificial intelligence applications are juiced with the the most robust dataset in the industry. Ultimately, your AI is only as strong as the information it can ingest and our AI can take in data from all elements of onsite personalization and optimization to serve the best experience to each user.
How Does Dynamic Yield use AI?
While AI is embedded into all core facets of the product, there are three primary product features that leverage artificial intelligence to deliver individualized experiences to users:
Automated Optimization – Contextual bandit algorithms powered by machine learning determine which variations perform best for each user and automatically allocate traffic to higher performing variations. Leading retailers use this ability to automatically direct their website and app traffic to the most relevant content for each user, delivering 10% uplifts in revenue per visitor at leading customers.
Recommendations – Dynamic Yield understands which recommendation rules work best for each customer. Over time, machines learn if customers are most interested in similar items, popular products, items bought together or recommendations personalized based on history. Thus, our technology can then help to build recommendations strategies to drive the most engagement.
However, a key point of differentiation is that our recommendations platform is not “closed” or “black box” AI. Marketers can turn control over to the machines or insert guardrails in the form of merchandising rules to control which recommendations unit each visitor sees.
For example, a high-end fashion brand may want our AI to recommend the products delivering the most revenue but may not want to place certain brands next to each other. In Dynamic Yield, it is possible to set this condition, allowing the machines to go to work within controls set by the marketer.
Predictive Targeting – Our technology proactively identifies opportunities in tests to provide users with more relevant experiences that boost revenue and engagement.
For example, for football fans if you run an A/B test of a banner promoting Manchester United gear vs Manchester City kits, the United variation will always win. However, if you show a United kit to an ardent City fan, you’ll almost certainly make blood boil.
Dynamic Yield’s Predictive Targeting automatically identifies audience segments that will prefer the less popular variation overall and allows marketers to serve this experience with one click.
Dynamic Yield surfaces these opportunities automatically across all tests being run on our platform.
In Your Mind, What is the Future of AI in Business and Marketing?
Even in a time of declining early-stage VC investment, nearly $5B was pumped into artificial intelligence startups in 2016. Though eCommerce is not commonly thought of as a hotbed for AI innovation, there are scores of machine learning startups that serve the sector. Many of these startups are now maturing, achieving product-fit and attracting customers, bringing AI into the mainstream.
Expect increasing adoption of AI to solve basic problems/automate simple tasks. This alone, will be enough for many AI companies to see acquisitions well into the hundreds of millions as the AI arms race heats up between America’s Big 5 (Alphabet, Facebook, Amazon, Google, Apple) and top Chinese companies such as Baidu, Tencent and Alibaba.
However, there are still major obstacles. The biggest challenge for business looking to adopt AI is understanding exactly what they would like their AI to accomplish. Much like personalization, AI has become such a hot item that every company wants to have AI but hasn’t clearly defined what they would like AI to accomplish. In that regard, it is becoming the new teen sex.
Another major challenge is mindset and actually trusting machine learning to deliver superior outcomes. By nature, humans are often incredibly overconfident in their own abilities and believe themselves to be paragons of rationality when there are many times machines can make better decisions by overriding human bias. Truly trusting AI is a challenge that won’t go away soon.
Translation: Before AI can transform eCommerce, many retailers STILL need to figure out how to stop recommending high heels to men.
Do You Think Marketers Will be Replaced by AI Robots?
No. While automation presents a very credible threat to many industries, AI is actually poorly suited to replace marketers. Despite the predictions of futurists, AI has yet to show a propensity to accomplish any creative tasks. At the end of the day, while companies may turn some logistical jobs to AI, we are decades away from AI building brands.
Ultimately, AI will make marketers jobs better in the same way that it will improve quality of professional life for most knowledge workers. AI will eliminate hours of tedium and allow marketers to spend less time on mundane tasks and more time on more fulfilling work that requires truly human intellect.
Do You Have Any Other Thoughts on AI in Business and Marketing?
One final thought – it is unfortunate that the definition of “artificial intelligence” has been broadened by companies claiming that everything under the sun that they build is AI. Hell, google “AI-powered toothbrush” and you’ll see several brands hawking this capability.
Thus, it is important to maintain a healthy dose of skepticism.
In this interview, Mike Mallazzo gave an interesting view into how eCommerce and digital marketing can be improved with personalization.
I am an author, speaker and consultant in marketing automation and artificial intelligence.
Do you need help with marketing automation or AI-based solutions? Contact me and let’s discuss how I can help you!