Digital Marketing + Data Science = Programmatic Marketing
Salesforce CEO Marc Benioff said at a recent conference: This is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what’s the next best offer, next best opportunity, how to make things a little bit more efficient.
The retail store that does not have a meaningful relationships with the consumer is dead or going to be dead.
But meaningful relationship are easy to engineer. Today’s marketer is faced with an almost impossible task: Create relevant, individualized journeys for a customer whose channel preferences, purchase behaviors, and tastes evolve with unmatchable speed.
The cutting edge in data-driven digital and mobile marketing is “marketing in the moment”, which is the ability to identify and optimize precise moments of marketing influence across multiple channels and devices. In digital advertising, firms like Facebook and RocketFuel are using continuous scoring algorithms that score each moment to predict whether an individual will react favorably to an ad shown (display ads, search, social media and video) at a given time.
So how do marketers (and advertisers) understand what their audience wants or will see as valuable? That’s where data science comes in. When you strip away the rhetoric, data science is just about finding meaningful insights through analyzing large datasets.
Data Science is increasingly fueling data-driven digital marketing strategies at cutting edge firms…. Marketing learns, acts, and evolves across the consumer journey. Programmatic real-time bidding platforms is growing to dominate ad spending.
“Marketing and Advertising That Learns” Strategies
Every day, 2.5 quintillion bytes of data are created—90 percent generated in the last few years due to the rise of social media and digital interactions. There is a race to turn this “big data” into a personalized, complete portrait of an consumer, customer, prospect or visitor.
New strategy areas—such as the end-to-end digital customer experience, enabling relevant digital marketing, using data for real-time marketing—are the foundation for relevant, targeted “marketing that learns” strategies.
The “marketing that learns” strategies firms employ generally tend to cluster in the following patterns:
Know Your Customer…. First, they want to know everything about their audiences. Who, what, where, why, when and how they access content, shop for products and interact with brands. Cutting-edge firms want to truly listen to customers, understand visitors, appreciate fans or loyal customers and accurately/deeply describe the preferences of all audience members. They want this insight so customer interactions can be personalized. See Customer Journey Mapping Analytics for more details.
Predictive Purchase Propensity Models…. develop cognitive-behavioral models that will give advertisers unique insights into who their customers are and what they truly want. By incorporating the transactional data most of advertisers collect, firms are also already starting to develop predictive purchasing propensity models that can be used to more effectively and more profitably segment audiences and identify those who are likely to be more receptive to a particular product, service or campaign.
Monetization via Click-thru…. When we pair advertisements with content we are concerned with whether those ads have the reach, relevance and resonance with the appropriate audience: Are we getting the right ad for the right product or service to the right person at the right time in the right medium? How likely are our audience members to repurchase from us? Do they recommend our brands to their friends? Are they only occasional readers or truly devoted fans? We likewise work with many of our advertisers to see what impact our combined efforts have on their bottom line. All that said, the ultimate criterion for all core metrics is profit.
Convert Insights into Action… Second, every firm is now in the business of leveraging the insights to continually develop new products, services and capabilities that delight prospects/clients, bring additional value to customers and collaterally provide additional data to help us know everything about different audiences, creating a virtuous data cycle. Example of this is “Buy Button” commerce.
Mobile Geo-Fencing and Targeting… The challenge that many retailers are racing to address… how to drive coupon downloads, encourage coupon use at nearby stores. Precise mobile-targeting abilities to serve coupons to consumers in a tight radius of store locations.
The typical mobile “direct response with local lift strategy” goal:
- Drive coupon downloads or views
- Build awareness and interest for specific products
- Get consumers to “mobile” download a coupon and redeem it at a nearby store
Executing Evolving Marketing Strategies
The marketing function in every large corporation is moving toward three core platforms:
- better systems of record (traditional CRM),
- new cloud-based systems of engagement (sales, marketing, commerce, community, service) platforms, and
- programmatic marketing (data driven analytics).
Programmatic marketing platforms use Big Data and AI to optimize marketing decisions in milliseconds and can be embedded into any CRM or marketing software platform, whether Oracle, IBM, Salesforce, Marketo or Adobe to enhance their performance. Vendors who dominate this area include RocketFuel.
Programmatic marketing is aiming at addressing the rapidly changing and highly-fragmented consumer environment. Consumers’ digital-media habits are evolving, with consumers accessing and consuming content across many different Internet-connected devices, resulting in highly-fragmented audiences. As a result, marketers are demanding the ability to adjust and customize their strategies in real time to reach and influence their prospective consumers.
Increasingly, marketing teams are attempting to leverage Big Data and data scientists to make strategic and tactical decisions. The most important metrics they track are predicated on and predict actual behaviors: who, what, where, when, why and how.
This trend started in digital advertising and is now spreading to marketing. Rather than focusing on data analysis by humans, the objective is to have tools that perform analysis and make decisions autonomously. The benefit of a general platform that autonomously adapts and learns while solving multiple problems instead of solving one specific problem at a time is that, with very little manual configuration, the platform simultaneously runs multiple strategies with highly diverse goals.
A large company may have tens of millions of interactions daily with its customers and prospects through its customer relationship management, or CRM, channels such as its website, mobile apps, call centers, email and point-of-sale terminals. The goal of every smart company is to optimize its customers’ experiences and its own marketing and financial metrics by making the best use of each of these opportunities.
Programmatic marketing, leveraging AI and Big Data-driven platforms, aims to “learn” from each interaction & message delivered and apply that learning to future decisions — “Marketing that Learns.” This enables marketers to deliver solutions designed to optimize 1:1 consumer direct-response campaigns focused on generating specific consumer actions geared towards lifting brand metrics, or other marketing programs designed to acquire, retain, and grow customers.
The holy grail of programmatic marketing…. help marketers optimize the consumer journey.
Additional References and Notes
- See Mobile Marketing Automation for more insights into Mobile Geo-Fencing and Targeting. Mobile ads are rapidly overtaking desktop ad spending in all categories—display ads, search, social media and video
- The key to cross-channel marketing is knowing who you’re engaging when. See Digital Marketing Architecture for the changes taking place in marketing and the CMO playbook.
- Salesforce, the biggest maker of customer-relationship management programs, bought RelateIQ in 2014 for $390 million. RelateIQ uses machine learning to automatically pull and summarize information from a team’s contact books and e-mail accounts to make it simpler to communicate with potential customers.
- Salesforce Buys Tempo, Enters Artificial Intelligence Space…. Salesforce has been aggressively pursuing opportunities to build its capabilities in the data analytics segment as can be seen from the launch of its Wave Analytics Platform
- Customer engagement takes place across channels, screens and experiences. Sales AI is designed to answer one question: “What specific actions can I take today in order to increase my odds of sales success?”
- In a digital world, consumers’ path to purchase is very complicated, with a myriad marketing touchpoints (mobile, social, web, community etc.) affecting their ultimate buying decisions. Brands are scrambling to influence the decision-making process and ultimately improve conversion. See Systems of Engagement for more on how the front-office is transforming.
- Interesting Programmatic Marketing and Advertising companies to watch – RocketFuel, DataXu, YuMe, MediaMath
- Shopping still being screen-based so display advertising created huge companies like Google. What will advertising look like when Voice Assistants like Alexa/Echo are prevalent? Will voice assistants will hurt Google’s search engine advertising cash-cow business model?