Digital Influence Mapping Project

CDP vs. DMP vs. XYZ: What is Your Brand’s Approach to Marketing Data Management?

Data management sounds so boring. The field is rife with acronyms for solutions that remain a bit vague like Customer Data Platform (CDP), Data Management Platform (DMP) and, in our case, Master Data Management (MDM). This is not exactly what I imagined I would be obsessing about as a marketer at this point in my career.

But think about this:

By 2020, 51% of consumers expect that companies will anticipate their needs and make relevant suggestions before they make contact. (Source) 

78% of U.S. Internet users said personally relevant content from brands increases their purchase intent. (Source) 

Personalization reduces acquisition costs as much as 50%, lifts revenues by 5-15%, and increases the efficiency of marketing spend by 10-30%. (Source) 

 

Delivering increasingly personalized and valuable content, tools, and offers to customers and sales agents (e.g. independent insurance agents, financial advisers, physicians) helps marketing become the measurable growth and revenue engine it is intended to be.

And it all starts with data and a data operating model that ensures the value of the data and how you can use it grows over time. That’s managing data for marketing and customer experience and that’s not so boring after all

 

Prospect to Customer Data for Marketing and Experience

From the time a lead is identified (i.e. marketing qualified lead, MQL) through a renewal on a multi-year, loyal customer, we have the opportunity to use data to manage how we attract and engage via:

• Segmentation and targeting of ideal customer types or accounts (e.g. account based marketing or ABM)
• Identifying new leads that look like your best customers (e.g. look-a-likes)
• Behavioral tracking and insight (e.g. paying attention to what people do and where their interests lie)
• Preference management (i.e. what are people’s stated interests and preferred channels
• Predictive marketing and service offerings (e.g. combine new data sources to reveal predictable behaviors; a person who buys and installs smart home technology for security and safety benefits is likely a better insurance risk as they are more inclined to maintain their home and anticipate problems before they ever become claims.)
• Individualized marketing by delivering personalized content, tools and offers to individuals that increases the value they feel from the brand and drives them to valuable actions (e.g. delivering content relevant to where and how they live)

While there is a natural break-point between pre- and post-acquisition, strategically it makes sense to think about the entire customer journey from unknown lead to loyal customer. If your business model includes a sales agent of some sort like a financial advisor in wealth management, then that agent has their own journey. They deserve the same increasingly personalized engagement now made a bit more complex as you now need to associate them with an individual prospect and customer.  

 

DMPs vs. CDPs vs CRMs

Acquiring, using, updating “cleaning” and growing customer data for marketing requires a creative mind and an intensely detailed obsessive mind. The creative part is about imagining what is possible. Very few brands are delivering on seamless and intuitive personalized marketing. Even Amazon, the archetype for all things good and data-focused still promotes gift product ideas to me based upon a one-time gift purchase for cousin Bob. Imagining what could be possible – delivering personalized recommendation based upon the house and property you own and the trending weather in your neighborhood and the search intent of your neighbors, for example – takes creative vision.

Detailing all of the data fields necessary to make that possible and then map out how to source and maintain that data takes the marketing equivalent of an obsessive compulsive (dis)order.

 

Data Management Platforms or DMPs were defined by advertisers, publishers and the tech companies who support them – like Adobe. Here’s how Kiki Burton defines DMP on the Adobe Blog:

“A DMP, or data management platform is a tool used to consolidate the disperse data sets of a company across first, second, third-party channels. It manages the segmentation and identity definition of user to then provide cohesive audience targeting across all of the various channels where you might be engaging with a consumer.

Forrester Research defined a DMP as “a unified technology platform that intakes disparate first-, second-, and third-party datasets, provides normalization and segmentation on that data, and allows a user to push the resulting segmentation into live interactive channel environments.”

In other words, it pulls data from a bunch of different, potentially unrelated sources and allows organizations to define specific audience segments to which they can provide distinct marketing experiences.”

Many consultants sum up DMPs as useful for acquisition marketing – finding and attracting your next customers.

 

Customer Data Platforms or CDPs are newer. They stress the first party data part of the first, second, third-party data layer cake. Some definitions focus on known customers while many extend from prospect to customer. Tealium (channeling the CDP Institute) describe it this way:

“The CDP Institute defines a Customer Data Platform as “a marketer-managed system that creates a persistent, unified customer database that is accessible to other systems.” Basically it’s a system that centralizes customer data from all sources, unifies this data into customer profiles and then makes this data available to other systems for marketing campaigns, customer service and all customer experience initiatives.”

Digiday tries to sum up the difference between CDPs and DMPs:

“CDPs mainly analyze first-party data based on real consumer identities, while DMPs largely examine consolidated third-party data based on cookies. A good way to think about the difference is CDPs can be used to nurture the relationship with existing consumers, while DMPs are mainly used to acquire lookalike audiences.”

Some people describe CDPs as the CRM for the B2C set. It’s not clear whether the rise of CDP is due more to what attracts venture capital (money in search of a problem and solution) or a true unmet need at the enterprise level.

 

Customer Relationship Management systems or CRMs have been around since the eighties and nineties (the acronym, CRM, is traced to 1995.) They are most often described either as the sales team’s answer for customer and prospect sales management or the company’s for delivering service and value to existing customers.

The Salesforce definition of CRM goes like this:

“Customer relationship management (CRM) is a technology for managing all your company’s relationships and interactions with customers and potential customers. The goal is simple: Improve business relationships. A CRM system helps companies stay connected to customers, streamline processes, and improve profitability.

When people talk about CRM, they are usually referring to a CRM system, a tool that helps with contact management, sales management, productivity, and more.

A CRM solution helps you focus on your organization’s relationships with individual people — including customers, service users, colleagues, or suppliers — throughout your lifecycle with them, including finding new customers, winning their business, and providing support and additional services throughout the relationship.”

 

What is Your Data Solution?

There is a lot of overlap in the definitions of these systems. Each industry and each brand is different enough that there is no one right answer. Unfortunately, it leaves the brand marketer with the chore of defining their own need and then labeling it – whatever the hell you want.  

• Companies with strong sales practices, teams and culture may want to build on a CRM-based model with some additional system for acquisition marketing
• B2C companies who rely on attracting a steady flow of customers and need to scale via look-a-like marketing via ad networks and publishers may want to lean into DMPs
• Companies who may sell via a few channels including bridging B2C and B2B marketing or who might be behind in marketing automation may want to define a CDP-like solution that serves both acquisition and customer marketing.

Here are some suggestions to help the non-data geek marketer consider the right options (you can also check out CDP-provider NGData and their comparison of CDPs vs. DMPs and Digiday's comparison of all three):

Create a vision for how you want to attract and engage new customers and how you want to serve those you already have. How “individualized” will you go? How important will Account Based Marketing (ABM) be to acquiring new customers?

Understand how you can create a continuous path for unknowns to paying customers. Can you practically hope to build a single data resource or do you need to separate acquisition efforts from customer experience?

Define your commitment to finding new ways to combine 1^st, 2^nd, and 3^rd party data to improve the efficiency of potentially both acquisition and customer marketing. If you want to aggressively experiment and “own” the lessons learned and outcomes then it may point you to a Cloud-based, owned solution.

Clarify the vision and commitment for integrating sales and marketing. If your future depends on it, then start putting the data infrastructure in place now. It may take years to get it right.

Welcome to the next age of data-drive marketing. Let’s learn from each other.

 

(The images in this post are from PETER CRNOKRAK. They are some of the most interesting "data-art" (my term) and I am a big fan. You can also acquire poster versions of many of them at The Luxury of Protest) 

 

How Deep Learning May Change Marketing

Listening to Hugo Larochelle, researcher at Google Brain, talk about the relationship between artificial intelligence, deep learning and the, apparently, much-maligned neural networks, two things become clear. First he is super smart. Like PhD+ smart. Like unattainably smart. The second is that Canada is where all the neat AI and deep learning breakthroughs happened – speech recognition that actually works, image recognition and more.

When I challenged him jokingly on the Canadian thing, I was expecting a light-hearted response like the character Chekhov in the original Star Trek series who always claimed Russia came up with everything – “It was inwented by a leetle old lady in St. Petersburg.”

Hugo is serious and backs up his claim about the Canadian heritage via smart folks like Geoffrey Hinton and others (see a great story from MIT Technology Review on Hinton and Deep learning here ) Then Hugo gave us a “deep learning” demo-for-dummies. It was almost impenetrable. What he took for obvious and self-evident gave me a headache. Still, it left me with a clear sense that deep learning and AI will radically change many aspects of marketing someday.

 

What is Deep Learning

With all apologies to Hugo, my simplified take is that deep learning is computers using human brain-like neural networks to find or apply meaning to data. Here’s how Bernard Marr (@BernardMarr ) puts it:

“…it’s probably most helpful to think of Deep Learning as the cutting-edge of the cutting-edge. ML (machine learning) takes some of the core ideas of AI and focuses them on solving real-world problems with neural networks designed to mimic our own decision-making. Deep Learning focuses even more narrowly on a subset of ML tools and techniques, and applies them to solving just about any problem which requires “thought” – human or artificial.”

 

Four Ways Deep Learning May Change Marketing

Pulling meaning from quantitative and qualitative customer research

Think about the last quantitative survey you did and the effort to derive insights and meaning from it. What a laborious process. There’s all the mechanical procedures and strategic choices that go into creating the test instrument and then administering it. At the end of the day, humans review that data and assign meaning to it.

Deep learning and AI may not only be able to sort through the data to more insightful and defensible insights, it may be able to detect better inputs than self-reported responses. Hershey did this via Affectiva with their smile-detector – technology that reads and analyzes human emotion from a single expression, in this case a smile.  AI or AI-like image recognition played a role here to better understand the nature of the smile delivered.

 

Understanding customer intent more precisely

Think about how we use search engine optimization to understand intent and then deliver something valuable. By sorting through keywords, phrases and the complete questions people now type into Google, we can draw conclusions about what the person is trying to accomplish. Pretty crude and straightforward.

With deep learning and big data sources from search engines, social media and where people go online next, it may be possible to better understand (infer?) what people are trying to accomplish. Marketers can then be more effective at serving their needs. How to accomplish this and stay on the better side of privacy is key as is the potential misuse of this hypothetical knowledge.

Deep learning would be seeking “patterns within patterns” of data over time. Rather than being able to recognize visual or auditory patterns, this might give us a decipherable view of an elongated customer journey. What happens over the course of 4 months, 8 months or two years as someone approaches buying a new house, car or, for that matter, insurance? Deep learning may finally make the complex and long buyer journey into a navigable course where marketers can reliably be more useful to shoppers at different points along that journey. 

 

Preference recommendation systems

We all experience preference recommendations today. Amazon product recommendations, Spotify’s related artists, publishers suggestions of related content are examples of recommendations fueled in part by AI.

“Like Flipboard, other publishers around the world are tapping into AI to improve their content recommendations. Some 60% of the 184 publishers in the Reuters Institute poll said they are using AI to improve content recommendations. Significant numbers were also using it to automate workflows and to target ads. (Flipboard, too, uses AI for ad targeting.)” (see more)

Current systems seem to be blunt instruments. How often do you take a Netflix movie recommendation? Still, they are terrific discovery “engines.” I have discovered plenty of music artists via iTunes recommendations.  If I am in a ‘discovery’ mood – willing to click around a bunch – this can be a much better alternative to randomly wading into the haystack of online music.

Deep learning may provide ever-more precise recommendations as multiple data sources combine together on individual users (blinded, of course). Browsing history, demographics, geolocation, past shopping history, content choices, other users’ behavior – may all be combined in some way that provides ever-better recommendations via constant learning.

Deeper knowledge will also lead to more personalized messaging about recommendations. Product choices can be presented with just the right headline that hits on the concerns of that individual. (image from Adelyn Zhou's great presentation)

 

 

Fuel effectiveness and efficiency in account based marketing

The two super-powers of account based marketing (ABM) is reaching the individuals from a particular company or organization and delivering personalized messaging that drives action.

A third super-power is finding ‘look-a-likes’ based upon the original prospect list and a ton of relevant data.

Deep learning may allow marketers to analyze and growing data set of the users from a particular account/company, understand their individual intent and deliver the right messages and content that meets that person’s need. This goes beyond wedging the company name into the banner ad as a nod to personalization. Of course, these messages and the sequencing of content would all be informed by what causes a prospect to convert.

 

Final word

How much of any of these applications are fueled by artificial intelligence, writ-large, versus machine learning versus the particular capabilities of deep learning? I don’t really know. I would have to ask Hugo Larochelle and I wasn’t quick enough to put that question together when I saw him.

AI, ML, and DL will change marketing. Time to learn.  

 

 AI resources:

AI Can Drive New Insights to Drive SEO 

Is AI in Marketing Overhyped

Deep Learning at Affectiva 

10 Ways Machine Learning is Revolutionizing Marketing 

AI, Machine Learning and Their Application for Growth 

What Marketers Can Expect from AI in 2018 

AI - Closer to Relevance at Scale 

 

Crash Course in AI for Marketing: 4 Videos

 

Continuing down the “roadmap of exploration” on how artificial intelligence and machine learning may impact marketing, I have discovered 4 videos which serve as a good foundation for thinking about the opportunity. These videos provide 4 important concepts for me as a marketer:

• Which applications of AI in marketing could I focus on first
• What are the right questions to pose when framing an AI-for-marketing challenge
• How does machine learning work and what is an easy way to talk about it
• How does a marketing/tech geek think about AI and machine learning

 

 

A.I. for Marketing & Growth - Where do I start?

This 5:02 video from Growth Tribe maps AI application to marketing and business along a maturity curve. That curve outlines which applications are more ready or more accessible for marketers from “must haves” to “nice to haves.” The simple rating system will be different for different businesses and marketers, and it’s a simple way to think about the potential behind AI in marketing. I particularly like David Arnoux’s (@darnocks ) rapid, no-nonsense approach to explain all of the topics from Growth Tribe.

Must Haves:

• Predictive Analytics
• Clustering/Customization

Should Haves:

• Recommendation Engines
• Natural Language Processing

Nice To Haves:

• Psychographic Personas
• Image Recognition

 

 

AI for Marketing & Growth #1 - Predictive Analytics in Marketing

Specifically focused on the needs of the marketer, David quickly (3:16) outlines AI’s application to use cases like focusing on leads with the highest ROI and which offer to recommend when and to which customer. Most usefully, the video outlines how to think about an AI challenge via 3 simple questions:

• Which questions am I trying to ask
• Which metrics am I trying to forecast
• Which future behavior am I trying to predict  

 

 

The 7 Steps of Machine Learning 

This 10:00 video from Google Cloud and @YufengG (Yufeng Guo) outlines the basic steps needed to get to machine-based predictions. This is foundational knowledge meant to help non-tech geeks understand the concepts and have a productive conversation with tech and data teams.

The 7 steps include:

1. Gathering Data
2. Preparing that Data
3. Choosing a Model
4. Training
5. Evaluation
6. Hyperparameter Tuning
7. Prediction

 

 

Machine Learning in Marketing from the Conductor event, C3 2017 

In 32:10, Dharmesh Shah, CTO at Hubspot (@dharmesh) offers a way to think about Machine learning in marketing. He quickly answers the question of what is the difference between data-powered and machine learning-powered marketing.  His concept of “Autonomous, Self-driving, Marketing Automation” is simple and powerful. He grounds the talk in real marketing use cases like smart lead routing (sending the right lead to the right salesperson) and persona development. He covers some useful basics like the difference between unsupervised machine learning vs. supervised machine learning and makes a great point that success will be about data not the algorithm. He also offers a glimpse of his sandbox for chatbots which he sees as the future of Web sites (growthbot.org).

 

 

How AI and Machine Learning May Apply to Marketing

Most of the talk about current use of artificial intelligence (AI) in marketing cites what the 4 Horsemen are doing or likely doing. Google, Facebook, Apple and Amazon have digital, data and automation at the heart of their businesses. They have the skills and the culture to make the application of machine learning and artificial intelligence a core competency.

Meanwhile, many marketers still struggle just to get their customer data source in order never mind speculating on how AI might improve the customer experience and/or the profitability of these customers. The hype curve of AI creates this nervous energy for marketers. To not do anything in AI seems wrong. On the other hand, to leap in expecting it to solve big, hairy 2018 problems also seems naïve.

I would recommend creating a simple ‘road map of exploration.’ Andy Betts (@andybetts1) has a useful post and diagram (above) counselling CMO’s to start planning in 2018. For once, it’s okay to start learning and exploring vs. jumping into action. I have a ‘bias-for-action’ like most marketers. We want to learn by doing. In this case, I want to pick and choose the pilots and proof-of-concepts (PoCs) while not derailing my marketing and customer service operations who already have a pretty full agenda next year. I will start by defining use cases where AI may have a positive effect on marketing.

 

Five Use Cases for AI in Marketing

Show the right, next call-to-action – Are people looking for a home-plus-valuables insurance quote? Trying to get answers about coverages in the weeks leading to an insurance need? Winterizing their home before jetting off to Florida for the season? These are pretty simple states but often hard to detect amongst the unknown visitors to your digital properties. Knowing the next right action to suggest and guide them is hard.

Propensity modeling used to be the answer. Defined as “a statistical scorecard that is used to predict the behaviour of your customer or prospect base,” propensity modeling works best when you know a bunch about the person. Think rich customer databases vs. a few Web clicks from strangers on your Web site.

Would you rather have a “scorecard” or a really smart robot (i.e. AI)? Imagine paying attention to the patterns of visitors to your site and all of the content they consume. Imagine if you could reliably predict/understand what they are trying to accomplish and you were able to deliver just the right calls-to-action on their screen. No more junking up their view with every possible choice they may consider. No more agonizing over Web site navigation models. With AI, you could understand what they are after and just give it to them.

 

Identify more accurate and actionable “look-a-likes” – Finding more prospects that look like your most valued customers and then selling them your product is generally a good thing. Most of the social networks have offered look-a-like audiences for a few years now. And given their facility with data and AI, these models will get better and better (and be worth more and more to advertisers).

As brands build their own audience pools for advertising, we need to find ways to use machine-learning and AI to identify more precise matches for our most valued customer. Just finding people in the same demographic or even the same interests as your current customer base isn’t enough. What if you could find more or less perfect matches for your top 20% most valued customers? And what if you could overcome the problem of “diminishing audiences” (when your target is so narrow you cannot cost effectively market to them) and engage them with just the right offers? AI could make that possible.

 

Automate market research and performance insights – We need a steady flow of timely insights about our customers (and prospects) and the performance of our marketing efforts. Currently, this process is so manual my bones ache thinking about it. If I think about all of the effort we are spending just automating a dashboard –a visualization of performance data, I have to laugh.

I want my performance AI to find patterns I would never recognize and draw insightful connections that help me market today and tomorrow.

When will my little machine-learning robot sift through all of the online panel data on a particular group of customer-types and reveal something I didn’t know to ask it for?

 

AI-generated content - I am less interested in teaching a robot to create a compelling article or video, than using AI to generate relevant content at a scale that is prohibitively expensive otherwise.

Let’s say you serve the small business market with a solution that lots of different business types could use. But small businesses want to buy a solution made for their business-type. If they are a dry cleaner they want a solution provider for dry cleaners. If they are a dog groomer or an art supply store, same thing. How can a solution provider create content for hundreds of small business types?  

Discovering how AI can “learn” your existing content and then assemble industry-specific content to create something of value and relevance to the dog groomers and art supply store owners is the trick.

 

Solve customer problems conversationally – AI-driven chatbots are the most popular customer experience application judging by the number of companies offering chatbot solutions. Still, they remain in their infancy.

Get a computer to consume a “corpus” or body of data, teach it some lessons and “walla!” you have a way to quickly get personalized answers for customers and cut down on the overhead to deliver said answers.  This is the type of solution that can serve both customers and prospects and will take sustained iteration and use to make very valuable.

These are just a few of the use cases on my road map of exploration in AI and machine learning. What’s on yours?

 

Good resources:

3 Ways AI Will Affect Marketing - Smart Insights

The CMO’s guide to AI’s marketing impact for 2018 - MarTech Today

How Will Artificial Intelligence Impact Digital Marketing in 2018? – NextWeb

8 Ways Intelligent Marketers Use Artificial Intelligence – Content Marketing Institute

10 Ways to Use Chatbots for Marketing and Sales – Entrepreneur

 

 

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Why Agile Marketing is New and Important

I have seen a lot of “new and improved” marketing POVs –guerrilla marketing, disruptive marketing, social media marketing, “Marketing 4.0”, “Growth Hacker Marketing,” content marketing, performance marketing and more. Some are onto something. Social media marketing was a true shift that really started around 2004 with the publishing empowerment of the individual. It gave marketers a way to make sense of changing customer behaviors and the shift in attention that undermined the effectiveness of marketing-as-usual. Some of these new marketing concepts won’t take hold in any big way but may successfully sell a book or two.

Agile marketing will be a significant shift in how marketers run their teams. It is as much about how we develop high-performing marketing as it is a response to changes in customer behavior. Agile marketing offers a way to apply the best of account planning and performance marketing to a discipline that can no longer sustain the stratified, agency teams of the eighties.  It is a way to work smarter, cheaper and better. Yes, all three.

 

Account Planners Gave us Customer Insights

Account planning introduced the notion that deep customer knowledge – from psychology to where and how people spend their time – could add intelligence and effectiveness to the unknowns of marketing. That model saw the rise of the “planners” in marketing agencies, first in London, and soon across the world. These were the guys and gals who ingested customer research and insights to design marketing programs that, hopefully, produced results. Planners came in many shapes and sizes – account planner, communications planner, brand planner, “connections planners.” Born in 1965 in London and Australia, brought to the US by Jay Chiat in 1982, the planners most often were the voice of the customer in marketing.  They had ways of mining insights from quantitative and qualitative research which often included anthropological excursions that looked as much like deep undercover assignments as anything. They were thorough.

The future of planners and the bigger agencies they sit within is in question. Not because they aren’t good or relevant. More likely it’s because they have only one piece of the puzzle – moment-in-time customer insight.

 

Performance Marketing Brings Real-Time Instrumentation

When customers shifted to digital shopping, buying and influence, performance marketing was born. Suddenly we had actual data behind customer behaviors. Direct marketers discovered they could tweak and optimize their marketing to drive up effectiveness and drive down marketing cost (e.g. cost-per-click) based upon what the buyer actually did. This real-time instrumentation was taken to extremes by the ‘bottom-of-funnel” ecommerce marketers who only cared for an efficient cost-per-purchase. 

Performance marketing seemed to overcome the age old ‘Wanamaker Problem’ of determining whether marketing was producing a result or not. It favored a ‘last click’ analysis which doesn’t reflect the complexity of the buyer journey – all those soft touches in the weeks of research leading to a purchase. More recent improvements in tracking a buyers experience across devices and even online and offline touchpoints may strengthen the richness of performance marketing. This may give a view of what content and experiences provide an “assist” to a sale. That is if we can weight these touchpoints in a way that reflects how the actual customer weights them.

Performance marketing-thinking drives us to get marketing materials into market faster to see how they will perform. We don’t want to overinvest in those assets as we fully expect to optimize – drop, add, change them – based upon actual behavior.

 

Software Development Popularized “Agile”

Meanwhile, software development had evolved from the careful, planned approach that mimicked factory work – moving the process along an assembly line – to one that was more nimble, more responsive to feedback and ultimately relied on teams to be more “agile.” The long development process of traditional software tended to veer away from user needs either because the research phase was so removed in time for the release of the software or, more likely, no research in the world did a good enough job of anticipating how customers would actually interact with software applications once relased. Best to get a ‘pretty good’ version of the software done and out quickly by a multidisciplinary team. Agile teams work in sprints with small teams achieving incremental progress towards and on top of a ‘minimum viable product.’ Get that MVP in hands of actual users and see what resonates.  (Get more on the history of agile development from Gary Police)

 

What is Agile Marketing?

Agile Marketing combines lessons from the quick, collaborative approach of agile software development with the reliance on insights from the planners and the use of response data in performance marketing to create a way to plan and execute marketing that is fast, responsive, and measured.

Here’s a definition: Agile marketing assembles small teams that represent makers and planners and equips them with customer insights to work collaboratively in short sprints to develop marketing content, tools, and plans that are tested and optimized for effectiveness and efficiency.

Here are a few more:

“At its core, Agile marketing is a tactical marketing approach in which teams identify and focus their collective efforts on high value projects, complete those projects cooperatively, measure their impact, and then continuously and incrementally improve the results over time” from the Workfront Blog and Andrea Fryrear

 

“Agile, in the marketing context, means using data and analytics to continuously source promising opportunities or solutions to problems in real time, deploying tests quickly, evaluating the results, and rapidly iterating. At scale, a high-functioning agile marketing organization can run hundreds of campaigns simultaneously and multiple new ideas every week.” From McKinsey Insights and David Edelman, Jason Heller, and Steven Spittaels

 

“A mindset and methodology that transforms culture and operations so you can execute more efficiently, champion the customer, and achieve better performance through a data-driven iterative approach.” From CMG and Russ Lange.

 

Is Agile a Strategic or Tactical Approach?

These definitions of agile marketing don’t preclude the value of big, pivotal insights about customers that can inform strategy. Still, we cannot build cathedral-like marketing programs that are carefully crafted prior to ever seeing the light of day.

Agile marketing may be a new way of being more customer-centered when behaviors and channels are changing more quickly than the last half of the prior century.

Agile may be a better way to develop marketing strategy and to execute once you have a strategy. The choice to work in a more nimble, responsive way could be its own strategic choice to remain relevant to customers. The concept of agile emerged from an operational mindset – how to make software development better – better result, more efficient.   It’s a leap to apply new operating methods for software to contemporary marketing. Still, something has to change. The over-architected marketing strategies of yesterday that required a whole ecosystem of marketing agencies to plan and execute will disappear. Those agencies are fast-evolving as they look to better ways to deliver value to the brands and companies they once worked for.

Internal marketing teams need ways to cut costs of marketing as that marketing is now being evaluated as a revenue-driver and not simply as brand-building.  As marketing gets more technical and data driven, we need collaboration methods that bring creative, data-geeks, UX wizards and storytellers together.

Agile marketing methods may just be the way forward.   

 

(image from Twin Cities Obedience training Club) 

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Hacking the Marketing Insight

True customer insights are gold for marketers. They serve as the key to unlock relevance and drive important behaviors. They are not new to marketing yet remain as elusive and misunderstood as ever. In our fast-paced, performance marketing world where we figure we can just test our way to relevance, true insight seems superfluous

Whether you are developing a product, the marketing for that product, or, hopefully both, having some valuable insight into the needs and wants of your customers or the salespeople who sell your product, can make all of the difference as to whether your product actually sells. If your product or service doesn’t capture their attention in pretty short order, it may just flow past them like their daily newsfeed. 

What is an insight?

It’s not a statistic. (like the Facebook Cards used in the illustration above)

It is not a simple observation.

An insight is a deep, penetrating truth about a customer – their beliefs, behaviors, needs and wants – that reveals an understanding that can be used in more effective marketing.

There are related definitions out there including these:

“A deep ‘truth’ about the customer based on their behaviour, experiences, beliefs, needs or desires, that is relevant to the task or issue and ‘rings bells’ with target people. ” Elena Ozeritskaya 

"An insight is a disturbance in discourse." A good insight creates a whole new way of thinking. Dove's Campaign for Real Beauty did this — it allowed expression of something that previously was inexpressible.” From Think with Google: Andy Davidson, Head of UK Practice at the global insight and brand consultancy Flamingo

“There are different types of creatives, and I've come to the opinion that you're either a creationist or an evolutionist. Creationists get the insight and then craft a perfectly formed concept. Evolutionists — especially in digital — use insights that come from data and our creative work evolves over time. In creationism, there is a sense that the insight is written in stone and can't be changed. For evolutionists on the other hand, the insight is more mercurial and flexible. Often creationists and evolutionists can come to a fairly similar place, but the insights come from very different methodology.” From Think with Google: Caitlin Ryan, Group Executive Creative Director for the advertising agency Karmarama

For small business people in the US, I know that 43% say wearing many hats is most challenging part of their job. That’s a data point.

SBOs will turn to family, friends as “informal advisors” to help understand and make complex, hard, decisions. That’s an observation.

Small business people need help being enough of an expert in many things with just enough information to supplement their own radar and make “pretty good” decisions to advance their business. They want quickly verifiable, expert guidance to build their confidence that they know what they are doing. They need help they can ingest quickly – speaking in simple business terms, not arcane, technical language – and verify via their personal/professional connections or through Google. This is an insight, or at least, the beginnings of one.

I can do something with that as a marketer. It can tell me what to engage them with and how. It points the way where I can be of-service to them in a rational and emotional way. I pretty much just spit-balled that insight. But I stand by it. I can verify it via more research if I feel it’s necessary. Call it a hypothesis, call it hacking the insight, either way it gets me to something I as a marketer can build on beyond just data.

Hacking the Insight

I love research folks. I love planners. The insights they have generated in my years in marketing have been impressive. The traditional method for getting to insights takes time, money and often specialism.  It’s hard to want to skimp on research yet in marketing today, we move pretty fast and there is more pressure for ‘lean marketing.’  

Lean marketing is a made up concept. Due to digital marketing where we can get instant feedback on marketing and then optimize, there is less demand to build a cathedral of a marketing plan ahead of time. That doesn’t mean we don’t need insights. They tell us where to start.

One thing it means is that we need researchers and marketers to collaborate to uncover those insights. It’s not fair to rely on the researchers to do it alone. That’s sort of where planners came in. Good marketers are part planners. They can now link up with researchers to find useful insights faster. Planners are fast becoming an anachronism carried into other strategic positions as the British-influenced agency model of the nineteen eighties finally sunsets. Today, we need to combine the specialism of the modern researcher and the marketing strategist to deliver the actionable insights we need.

 

Here’s a recipe for hacking the insight:

Step One: Curate existing primary and secondary research that answers the simple questions:

How do prospects research and buy your product/service? What are their pain points and needs as it relates to those products and services and the role they provide in their lives? How has this changed due to new digital behaviors and technologies? And so on.

If the researchers believe there is a critical mass of existing research then move on to Step Two. If not, commission some targeted additional research to fill the gap.

Step Two: Have researchers and marketers workshop the insights

Take the curated research into a room with 1-2 researchers and marketers and have them whiteboard insights driven by that research and their cumulative experience with both customer and product. Don’t be afraid to headline insights you’re not sure the data supports. We can call these ‘insight hypotheses.’

Step Three: Do targeted research against the insight hypotheses to fill gaps

Chances are, the existing research you had in-house wasn’t enough. Once you have a sense of where the insights might be headed, qualitative or quantitative research can be a terrific boost to corroborate or refute your hypothesis. Even interviewing 3-4 customers can be hugely revealing.

Step Four: Share your insights with everyone working on the product and/or marketing

We all want our work to be more customer-driven. Solid insights can really help but only if you get them into marketers hands to apply. When you deliver them, show examples of how those insights can influence:

• Content
• Marketing messages
• Calls-to-action
• Tone and character
• Journeys and moments

 

Hacking, agile and design-thinking

The enduring need to understand and be empathetic to our customers’ lives is getting new life from the ‘design-thinking’ community. The need to be more collaborative and iterative in our work is being popularized by the agile-anything movement. The direct-response nature of digital is changing how we get research (“hacking” vs. cathedral-building) but not the critical value of insights that help us connect at a deeper level with our customers and find authentic ways to be valuable to them as they live their lives.

More

Jonathan Dalton, CEO of Thrive, has a particularly good set of posts on Insights starting with "What is Insight? The 5 Principles of Insight Definition" 

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6+ Promising Sessions at Content Marketing World 2017

Content marketing remains a critical strategy for brands eager to earn the attention and interest of customers – ones they already have and those they seek to attract. It’s different than advertising messages but is just as high-tech and data-driven.

Since content sits at the heart of a lot of the marketing that I plan, I am heading off to Content Marketing World on September 5^th – 8^th in Cleveland  (home of Bonin Bough's Cleveland Hustles) to learn from many of the smart people attending and speaking. I will have my own session on how we use data-driven, integrated content marketing to drive business impact. I plan to share what we have learned putting content marketing to work in a large enterprise.

If you go, connect with me via Twitter – @jbell99 . I would love to compare notes on enterprise-level content marketing. Here are some of the sessions I am looking forward to (I hope I can get to most!):

 

The Messaging Science behind Customer Acquisition and Customer Renewal Messaging

Sounds like I will get a good dose of sales ‘behavioral economics.’ And Tim Riesterer (@TRiesterer ) appears to be a very smart guy in this space.

 

Content ROI – How Marketers Demonstrate Value to the Brand and the Boss (a panel)

Connecting the business value for content marketing is not as simple as ‘how much did it sell.’ While that is important, I am looking forward to hearing what this panel including Jenifer Walsh/GE (@jlansky) and Laura Cameron/Key Bank (@lcameron80) have to say in terms of communicating value within an organization.

 

Driving Content Marketing Success in Your Organization: Sales, Product and Global-Regional Collaboration

This session is marked for the “Beginner” yet, even still, I am guessing that Jillian Hillard/Electrolux (@jillianhillard) will offer some good tips on how she engages others like product people in content marketing.

 

Machine-Assisted Narrative: How to Transform and Scale Your B2B Content with Artificial Intelligence

Okay, I know it’s early days yet for applying machine learning and AI to content but we are doing it now on the customer service side and I am keen to see how Paul Roetzer (@paulroetzer) sees the immediate future.

 

Time to Leave the Lead-Gated PDF Behind: How More Engaging Content Can Transform Your Lead Gen and Accelerate Growth

This may sound a little tactical but I am hoping that Aaron Dun (@ajdun) can deliver some insights that get us beyond the “whitepaper.”

Also eager to hear:

• Matthew Glick/Marriott (@matthewglick123)  – these guys do some interesting things
• Matt Heinz (@HeinzMarketing) – I hear this company is smart in this space
• Scott Monty/Brain +Trust Partners (@scottmonty)  – always good to connect with Scott
• Lee Odden/Top Rank Marketing (@leeodden) – a very smart consultant

 

Here’s my session on September 6 at 11:20am:

Under the Hood: Deconstructing Content Marketing the Travelers Way

Travelers publishes hundreds of articles, videos and visuals to engage consumers and business leaders at different points along their buyer journey. This fuel for integrated marketing programs attracts customers before they are even ready to shop for insurance. This session will cover building belief in data-driven content, integrating an editorial team inside marketing, and how we pulled it all together inside the Travelers Engagement Center – a new approach to digital marketing.

 I hope to see you in Cleveland.

(Thanks to Cleveland Leadership Center for the image of The Sixth City) 

How Hard is Marketing Personalization?

Econsultancy's Digital Trends in Financial Services and Insurance Report from December 2016 outlines here top priorities for marketers in 2017:

• Targeting and Personalization
• Customer Journey Management
• Content Marketing

Clearly, these three are all connected. They reflect a shift in financial services away from product-centric marketing towards customer-centered marketing. To be customer-centered we must know our prospects and customers and deliver relevant services and experience to them. That means personalized content and services.

But anyone who has wrestled with creating a more personalized Web experience for anonymous visitors (prospects) and creating a personalized omni-channel experience for known customers can speak to the complexity of the task. It challenges even the most mature marketing organization. Then there is financial services, if you know what I mean.

So, is marketing personalization hard? You betcha. How hard? Darn hard.

Personalizing to anonymous users

• Someone follows an organic search result about planning for business continuity in small businesses. When they arrive at the site, they should not only get the content they are after but other small business content as they move through the site.
• A user clicks and reads a series of home and boat safety content on the site. The next time they visit, they should receive more home and boat content and be spared the chore of clicking past enterprise business content that is not relevant.

We want to pick up on signals from people we don’t yet know visiting our Web sites and draw reasonable conclusions about who they are in order to give them the most relevant experience.

The biggest challenge towards personalizing the experience for someone we don’t yet know is deciding the segments that matter to personalize against and what we do to personalize to those segments. So, pretty much the whole thing is a big challenge.

Here’s how @AlhanKeser put it in Entrepreneur:

“Other than showing return visitors product categories they already looked at instead of whatever is on sale that day, the strategy for what you do in response to any data point is highly unclear, as soon as you go beyond basics.

So what if you know that I listen to podcasts, work in Manhattan and just got married? Will any of that help you figure out how to best sell me something? Because I am a man, does it mean I like to see other men wearing clothes? Perhaps I’m shopping for my wife. How can you know? Are you going to rely on my returning to the website after showing interest in something, therefore only personalizing for a fraction of visitors?”

Personalizing to customers

We should know a lot about our customers. Many businesses did not establish central data profiles for their customers and are trying to catch up. There is a terrific example where many companies could have set these up ten years ago. The cost of catching up continues to accrue.

The three big challenges of delivering a strong and increasingly personal experience to customers include:

Integrating multiple data sources to establish an always-contemporary view of the customer

@erikober outlined the tension for CMOs in Target Marketing:

“A Forrester report revealed that while 87 percent of CMOs anticipate the need to integrate customer data into their digital strategy over the next four years, only 16 percent currently do so.

So what makes personalization difficult? Data silos pose the biggest obstacle. The fact that customer activity takes place across so many different platforms — social, web, mobile, email, e-commerce and CRM — makes it extraordinarily challenging for marketers to get a single, coherent view of the customer and understand the context of the interaction with the company.”

Just think about the data that would be the most helpful to know to get marketing personalization right:

• All the customer’s sales and service data – what do they own and how have we interacted with them
• Their demographic, geographic and household circumstances – what is the context of their daily lives
• Key life events like moving, college and more – what disruptive changes do they face
• Their stated and behavioral preferences – content, settings, delivery, channels
• Their digital and social channel behavior – what they actually do online
• Their social media profiles – what they say and do online and how influential they may be

That shopping list of data comes from very different stores, so-to-speak. Getting it all together and relating it in a view of the customer that is always growing is, well, hard. 

Connecting the experience across channels (e.g. what they receive via email relates to their Web experience)

We all want an “omni-channel” world where we can start a conversation or service transaction with a client via phone or email and have it continue via social or a customer portal. Conceptually, we get it. Let customers determine which channels we communicate with them on. It just isn’t that easy. Using marketing automation tools like Salesforce and Maximyser, we can sync up the email with the Web site. But connecting the call center and social channels are chores unto themselves. 

Creating the rules to trigger content and services via all channels to the right customer

When does one prospect receive a specific Web experience? When does a customer receive a sequence of emails in response to some behavior (e.g. they showed an interest in smart home technology and would now benefit from some follow-up information)? 

Remember, this is 'marketing automation' and we are trying to develop rules or triggers that cause different content or messages to fire based upon what we know about our visitors. Developing those triggers and monitoring how well they work such that we are not inadvertently annoying customers with irrelevant or poorly-timed content or messages is important and hard.  And trigger-based marketing is a bit of a blunt instrument. 

Here's how @MikeBrondello puts it in the Optimizely blog:

"In a trigger campaign, a user’s action (such as a click, email open, pageview, etc) triggers the marketing automation software to perform an action – such as send a specific email or show a specific piece of content. This type of marketing requires a marketing automation tool, like Marketo or Hubspot for instance, that uses this basic set of user information to personalize content....

...A reason that trigger-driven personalization can be ineffective is that the content triggered to the visitor is predefined based a persona they may fit, rather than the actual indications of their wants or needs."

 

There are plenty of other challenges but these seem to be some of the big ones. Its one thing to acknowledge the obvious - that more personalized experiences will serve customers better. It's another thing to break this down into a practical strategy and start to implement i. That part is hard. 

 

Helpful resources

Interview with Jim Conning, the head of Data Services at Royal Mail

33 Experts Reveal the Most Important Piece of Customer Data To Help Make Smarter Marketing Decisions

A Roadmap to Creating An Effective Personalization Strategy

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The Marketing Technology Stack Dilemma

How to Bring the Buyer Journey Into the Enterprise

We have replaced the marketing and sales funnel with the buyer journey. After three years of re-framing funnel references to buyer journey terms, using key slides consistently in presentation after presentation and sharing outside-in thinking on the customer/buyer journey, we have injected the idea into our lexicon.

And still, there is a long road ahead to fully develop and apply this newer, customer-centered approach to how we market and sell.

 

 

 

We have room to grow in terms of actually analyzing and using journey maps in practical ways. Journey maps can inform the work of several departments within the enterprise. That means it’s key to get buy-in across these groups to create and apply journey maps. Otherwise you risk losing the department IP wars that often spring up as groups look to establish their own way of doing things. That one detail – journey maps used across departments – can mean complex budget negotiations between groups as you decide who maintains the research and creates any derivative materials.

One of the reasons the sales funnel persisted for so long is that it was so generic that no one owned it inside the enterprise. Of course, being generic meant that it wasn’t particularly useful at understanding any customer who wasn’t ready to buy that day.

Integrating the buyer journey in the organization is its own journey. I certainly don’t have it all figured out. Here are some learnings that may make ingesting it in a big organization more successful:

Use research to create a shared understanding of the problem: Buyers are researching solutions earlier and earlier. That means the span of time when they begin research to actually buying something is likely longer than you think. Customer research can reveal this along with the various drivers that help folks make progress in their journey.

Start with simple concepts to articulate a new view: We all know that the influences on a final buying decision and the steps that buyer can take can get crazy complicated. Remember how simple the funnel was? It’s important to introduce a new view that is simple and only tries to make 3-4 initial points (the diagram above is one example of a purposely over-simplified view).

Identify practical and simple applications: No one needs a new academic exercise within the company. How will the journey be used? Organizing a content strategy in marketing? Defining a channel strategy? Informing the user experience of the Web sites? Structuring a sales approach? Pick 1-2 tactical applications and start with those.

Share the IP with no strings attached: Whatever research, slides, frameworks, spreadsheets that come out of journey development and planning, be liberal in sharing them with anyone across the company. The benefits of everyone having these materials outweighs the risks of them misusing them.

Make an efficiency argument to solidify a common approach: Once there is enough familiarity with the buyer journey and at least a couple of success stories (“wow, it really helped us organize and prioritize our thinking around creating marketing content”), try to make it a standard tool/toolbox across the company. It really will save you money and boost your relevance with customers.

 

Helpful resources

A simple view from Hubspot on 3 key differences with the modern buyer journey

The Content Marketing Institute gathers 4 models of the buyer journey that can inform content marketing efforts

 

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How to Evaluate Native Content Programs

I love Native Advertising. When it’s done well, it is user-centered and delivering value to the reader/user. Valuable content allows a brand to demonstrate rather than claim its strengths. It’s one thing to say you are an expert in supply chain risks and disruptions, it’s another to offer practical and detailed content to help manage that risk.  The latter shows your expertise and lets the user come to their own conclusion as to whether you have smarts in that space or not. It’s more believable than a claim. Native advertising is a great leap forward.

The measurement of native content and advertising remains behind the times, though. Too many publishers venturing into this space have not cracked the code on measuring effectiveness. Too many rely on the metrics of display advertising (e.g. clickthroughs) or on “engagement” metrics with no context.

Two Types of Native

Think about two different expressions of native advertising. Working with an experienced publisher – Bloomberg, Forbes, NYTimes, etc... - brands can create co-produced content that appears on the publisher’s site. Advertising across the site drives publisher traffic into the content. Links within or adjacent to the content drive clickthroughs to the brand’s web site. For simplicity’s sake, I will call this approach ‘native content.’

A variant comes from the native ad networks like Taboola, Sharethrough or Outbrain that distribute content headlines into contextually relevant places within established content sites across the Web. My brand story on supply chain risks for small business might sit at the bottom of a Businessweek article on supply chain and drives interested readers directly to my site. Let’s call this a native network.

 

Need a Step Change Improvement on Native Metrics

Both are examples of native advertising. Both need a step-change improvement in measuring effectiveness.

With native content, brands want to drive engagement metrics (i.e. time spent, pages consumed, shares) with the content itself. They also want to improve brand metrics through some combination of delivering authentically valuable content and doing so alongside a trusted media brand. Are clickthroughs important? If I have an article on Businessweek on supply chain risks and one on my own brand Web site, my goal is similar. I want to connect with prospects/customers before the buying cycle. I want them to get value out of the content and associate that value with my brand. That happens when they consume the content or share it. They don’t need to clickthrough. Still, clickthroughs from native content to my Web site help connect readers with similar content and establish my brand as the source of the expertise. Clickthroughs are important, they just aren’t the only KPI nor the way to justify the expense of a native content program.

The WSJ blog covered the problem as reflected in a recent ANA survey:

“The lack of consensus on those questions (how to measure native) was highlighted by a recent Association of National Advertisers survey, in which marketers reported using a wide range of metrics to judge their native ad campaigns. When asked which measurement is “most important,” respondents said everything from brand lift and awareness to click-throughs, social media sharing rates, time spent, purchase intent, lead generation and more.”

 

 

Engagement Metrics, Attribution and Brand Lift

The get it right, to know what these native content programs do for our marketing mix and how to price and therefore pay for them, we need to focus on engagement numbers, crack the code on attribution – both short term and longer term – and value ‘brand lift.’

Time spent on a site or with an article is valuable. Number of pages read within a site of related content also matters. How many times a piece of content is shared is also a really good thing. These KPIs are at the heart of the value of a native program. But too often we lack any type of benchmarking or context. The publishers are just now accruing experience with brand programs. We don’t have a bank of benchmarks to compare performance of brand content specifically. Publishers routinely don’t want to share editorial performance metrics as a benchmark believing the advertiser-sparked content is a different species all together.

@BrianHonigman wrote an interesting piece on the Skyword blog about focusing on engagement or “attention.”

 “Take the social sharing your sponsored content receives and measure it against the attention minutes each piece of content drove while considering clicks, conversions, and referral traffic as a part of the equation. This will help you determine how much of an impact your content made on readers or viewers. By analyzing the social shares your sponsored content generates, you’ll likely be able to see the brand lift achieved by leveraging a publisher’s brand equity. In other words, you’ll get a better idea of the trust you’re developing with customers by sponsoring content on a trusted publisher’s website.” 

How does engagement sell? That’s always the next logical question. We need to adopt a view of the buyer journey that puts value on engaging someone over a longer buying cycle. That requires some POV on how content engagement plays an ‘assist’ role in a sale. Part of that might be reflected in brand lift metrics. These are usually done by the publisher and ask survey questions about preference or demand.  Of course, brand metrics require business leaders to believe that ‘brand sells,’ but that is a whole different argument and blog post. For the sake of this one, I believe strongly that brand sells.

One way to value how content contributes to sales is to understand the hand-off between native content or native networks and brand web sites where the latter have strong calls-to-action as readers move further into the journey (e.g. when business leaders wrestling with supply chain problems are ready to consider and evaluate commercial insurance to protect them from supply chain exposures.)

Peter Chen @ZPCeee, a marketing consultant, is thinking of ways brands can get at this value.  The Content Promotion Lifecycle is one way to better understand if the yield of native advertising networks (e.g. Taboola, ShareThrough, Outbrain) is valuable or too unqualified

“We can quantify the effectiveness of native advertising in two dimensions. One dimension is the advertising metrics measuring the efficacy of the ad itself – did the audience actually engage with this ad?

The other dimension is the engagement metrics assessing how people engaged with the “product” after viewing the ad. In theory, those who clicked on the ad were qualified, but how can we find the ads that generated the most amount of qualified traffic?”

 

DIY Content Metrics for Brands

Even as publishers refine their native content opportunities for brands, they will continue to lag in terms of the most relevant metrics. Their sales force sells against cpms, clickthourghs and the like. That ocean liner will take a while to navigate to a new course.

Brands will have to establish their own values for engagement with their content and begin to amass benchmarks from working with several publishers.

 

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