Customer First Thinking

Download for offline listening. Businesses are “drowning in data but starving for insight”, as the saying goes, and that problem is about to get worse. As society becomes increasingly connected, marketers are facing a rising tide of digital interactions. Somewhere inside that massive pool of data are the answers to questions marketers haven’t even thought to ask – yet are essential to creating a better customer experience. Relief is on the way in the form of artificial intelligence. The capability to skip right to the answers without even forming the questions will be the salvation of marketers, who until now have had to rely on their own made-up rules or overworked data scientists. With AI, the analytical load shifts to machine learning algorithms that will help marketers reach the promised land of hyper-personalization. The first wave of commercial AI solutions has already made landfall. AI is being used today to improve audience targeting for programmatic media buying; make dynamic content and product recommendations; and drive demand-level pricing. Most of the major marketing automation and CRM vendors have already integrated AI capabilities into their platforms (like Salesforce’s Einstein). Companies also have the option of outsourcing the analytical work to software-as-a-service platform providers who will help them benefit from the technology immediately. One of those SaaS providers is Toronto-based Daisy Intelligence founded by CEO Gary Saarenvirta. The company, which specializes in retail merchandising solutions and insurance fraud detection, was recently awarded the first-place prize at the 2018 Elevate AI Pitch competition in a tough field of 16 start-ups. The “deep learning” platform developed by Daisy Intelligence can ingest a massive amount of SKU-level transactional data and through its self-learning algorithms determine the best product price points; adjust the promotional mix to minimize cannibalization; identify optimal store locations and layout, and much more, saving merchandisers from needing to figure it out themselves. All the retailer needs to do is turn over as much sales history as possible, and let the platform perform its magic. “Deep learning” is very much like magic because no one can ever say how it arrives at the answers it comes up with. Known as a “convolutionary neural network”, the idea was first conceived by Toronto computer math wizard Geoffrey Hinton in 1986. Since that time advancements in GPU computing has allowed “deep learning” to grab the pole position in the AI race, much to the excitement – and sometimes dread – of futurists who imagine a world where machines are smarter than humans. Remember Elon Musk’s dire warning that AI would “summon the demon”? For the time being, AI evangelists like Gary Saarenvirta are proving that “deep learning” offers clear advantages over traditional approaches to data mining and analysis, both in speed and precision. And he should know: Gary ran the analytics practice at Loyalty Consulting Group for years, and once led IBM’s analytics and data warehousing practice areas. He’s also a trained rocket scientist, having entered the workforce with an aerospace engineering degree. But today Gary’s focus is much more grounded in making AI an indispensable tool for data-driven businesses. He explains why in this “deep learning” interview which started with the genesis of the company’s name.

Download for offline listening. In 1983 a Professor at Texas A&M University by the name of Leonard Berry coined the term “relationship marketing” in a paper he presented at an American Marketing Association event. His basic premise: businesses should focus more on serving existing customers. Now as obvious as that may sound, it seemed heretical in an era dominated by mass advertising. Berry never actually thought his paper was “any kind of breakthrough”; he just figured it was “foolish”, as he put it, that marketing only thought about winning new customers, rarely about retaining them. Yet at the time his argument was so far out of the mainstream that nearly a decade went by before marketers warmed to the idea. What happened to revive interest was the unraveling of the mass market by the late 1980s. Relationship marketing was deemed by academics to be the antidote to audience fragmentation. Their endorsement led to countless books, papers, conferences, along with a dedicated scholarly journal. But with the rapid adoption of CRM technology in the early 1990s the definition became muddled. Did CRM now mean “customer relationship management”, as in automated after-sale support and service, or “customer relationship marketing”, as in data-driven one-to-one marketing? The idea that relationship marketing simply meant giving customers the attention they deserve was overtaken by the fixation with technology, to the dismay of early proponents. One of the earliest proponents was Jagdish Sheth, the esteemed Professor of Marketing at Emory University. Last year in a paper he authored called “Revitalizing Relationship Marketing”, Professor Sheth ruefully observed, “The focus on ‘relationship’ in RM got relegated to ‘marketing’.” He wrote that relationship marketing was suffering from an “identity crisis” and that marketers needed to stop treating customers as “ID numbers”. For relationship marketing to mature into a higher order business strategy, he suggested, marketing must shift toward “bonding with customers on an emotional plane”, where “the brand itself acts as a moral compass”, seeking to win a greater “share of heart”. Professor Sheth favours a new “purpose-driven relationship” where customers feel connected to a brand based on shared values. He reasons that people today are drawn to brands offering a “transcendent” experience, a theory he first advanced twelve years ago in a book called ‘Firms of Endearment”. This more humanistic definition of relationship marketing shares many of the same thematic overtones as the brand purpose movement which is all about businesses making the world a better place. The convergence of relationship marketing and brand purpose might end up becoming a whole new branch of academic study. And there is no one more qualified to lead that conversation than Jagdish Sheth, given his renowned scholarly work over the years, his many accolades and awards, and his widely acknowledged contribution to the advancement of marketing theory and practice. We started our conversation by considering whether marketers needed to hit the reset button and forget everything they ever learned.

Download for offline listening. Customer analytics has come a long way since banks first started building Customer Information Files in the 1990s. “CIFs”, as they were called, were the primitive forerunner to what we now call “data lakes”. Just getting access to data was the main barrier back then – closely rivalled by the suspect quality of the contact data. Today the biggest analytical challenge for banks isn’t the limitations of technology: it’s embedding data-driven decision-making into the operational DNA of the bank. Most banks still limit their analytical muscle to product cross-selling, risk management and fraud detection. But as banks face stiffening competition from fintech interlopers, eager to capitalize on the mass migration of customers toward web and mobile banking, they can see the smoke signals: either they make it easier for customers to do business with them across multiple touchpoints, or they face the likelihood of losing them to less costly providers. Lori Bieda, who heads up the Analytical Centre of Excellence at Bank of Montreal, believes that banks can only succeed if they master the science of journey analytics. People crave both the convenience of online banking and personal attention from their local branch. They expect to be able to easily open an account, apply for a loan, pay a bill, cash a cheque, or monitor their investments using any channel or device of their choice, at any time. Identifying the breakpoints in that experience – where the journey is interrupted or disconnected due to faulty wiring - is crucial to customer loyalty and retention. “I’m a huge advocate of journey analytics”, she says. Lori’s mandate stretches far beyond journey optimization. She’s also focused on cultivating a data culture at the bank: one where analytical literacy and data fluency are found at all levels of the company, not simply concentrated in a “genius” pool of data scientists. Which explains her missionary work on the speaking circuit. She’s an eloquent ambassador of analytics, promoting it as a strategic tool for converting insight into business outcomes, whether that’s higher satisfaction scores or attrition reduction or simply increasing the average number of accounts. But realizing the full value of analytics, according to Lori, means looking at many different types of data horizontally – events, click paths, transactions, interactions – to isolate the moments that make or break an experience. With a wealth of experience in the analytical field – from her formative years at the database marketing agency Rapp Collins to heading up Client Insight at both CIBC and TD to serving as Executive Lead of Customer Intelligence at SAS – Lori has the rare ability to connect data science to business strategy. In short, she is the ultimate business translator, as she demonstrates in this insightful (excuse the pun) interview.

Download for offline listening. His book "Marketing Management: Analysis, Planning and Control", first published in 1967, ranks amongst the most influential, and enduring, business textbooks of all time. Now in its 16th edition (retitled "Principles of Marketing"), it remains the most authoritative guide to the practice of marketing ever written. Other textbooks come and go with the passage of time. But Philip Kotler’s tome has stood the test of time, in part because it continues to challenge marketing orthodoxy. Half a century ago marketing played a relatively minor role in influencing business strategy. But Kotler, a trained economist, understood that the growth rate of a company was dictated by much more than simply product and price: it depended on the medley of go-to-market strategies that drove demand (what is commonly referred to as the 4Ps of the marketing mix). Since then, each new edition of his ground-breaking work has reflected the current state of the profession, keeping it as relevant as ever, even to a generation of digital natives raised on social media. His latest (co-authored) book, Marketing 4.0, addresses even more directly the need to transform marketing practices, calling for a more humanistic model, where the goal is to move customers from awareness to advocacy by connecting more closely with their values and needs. "Marketing’s job today is to sell materialism and consumption," he writes. "Tomorrow’s marketing will be markedly different." Today Philip Kotler is the S.C. Johnson & Son Professor of International Marketing at the Kellogg School of Management at Northwestern University. His reputation as the "Father of Modern Marketing" was earned through his prolific writing, extensive speaking engagements around the world, and the many blue-chip companies he has advised over the years. The American Marketing Association calls him "the most influential marketer of all time". In this charming and insightful interview, Professor Kotler generously shares his perspective on the current state of marketing and the future role it will play in shaping a better world.