Trusting Personalization without Sacrificing Privacy

In today’s digital age, consumers are increasingly seeking more personalized products and services, and a customized experience.  And although personalization is nothing new, it has evolved radically in the last 30 years with large businesses able to use AI and technology to cater to these demands.  Just look at the latest frenzy, GPT, to get a sense for how technology is accelerating its impact on marketing, customer service, and personalization.

Balancing Personalization & Privacy

Already a wide variety of personalization techniques are employed by businesses to tailor to individual preferences and improve the customer experience.  It’s been 30 years in the making, starting with early adopters using simple techniques such as addressing a direct mail letter with first name rather than “Current Resident,” to today’s savvy enterprises using massive databases of consumer behavior and advanced analytics to provide hyper-conditional content, individualized promotions, and concierge-like digital services.

But a counter dynamic is also at play.  As personalized products and services have become more prevalent, consumers have also awakened to how their data is collected and even misused.  Because of this some are less likely to share information, push for more legislation, more frequently opt out, and even ask for their data to be deleted.  Clear battle lines have been drawn between hyper personalization and privacy.

This presents a dilemma for businesses, as they attempt to balance providing personalization in a responsible and controlled manner.  Consumers are fickle.  They want great experiences, but they also expect that any data they turn over is secure and is used in compliance with their wishes.  In this context, it’s crucial for companies to strike the right balance between personalization and privacy protection. 

The Value of Personalization

When asked, consumers repeatedly respond that they want more personalization (in many cases greater than 75%), especially younger people. [i]   When they see customized ads fitting their preferences and behaviors, they’re more likely to engage and then purchase. This benefits both the consumer, who gets offered interesting products or services, and the company, which increases its revenue and profits.  Moreover, personalized recommendations can create a sense of loyalty and trust between the consumer and the company, leading to repeat business, long-term customer retention, and positive word-of-mouth marketing.

Personalization has become a principal factor in consumer decision-making. A study by Epsilon found that 80% of consumers are more likely to do business with a company if it offers a personalized experience. [ii]  Personalization has many benefits for consumers, such as saving time and increasing convenience. For instance, personalized recommendations on e-commerce websites can help consumers find and then ask for products that they may not have otherwise considered. Similarly, personalized apps can help users achieve their goals by providing services like tailored financial plans and wellness insights & activities.

Some consumers may view the collection of their personal data as a fair trade-off to get more personalization.  They accept that the exchange of their data is worth getting good recommendations, more perks, and a better overall experience.  And these consumers assume and inherently trust that businesses will use their data responsibly and will take the necessary steps to protect their privacy. 

But not everyone thinks that way about turning over their data and trusting businesses.

The Need for Privacy

There is another camp of consumers that prioritize privacy and may view personalization as a threat to their rights. They hold that their personal data is being exploited without their consent and that businesses are profiting from their information.  And they are very vocal about this, and are influencing their friends, followers, and even law makers.

These consumers are skeptical of businesses’ ability to protect their data and worry that their information could be used against them in the future.  As a result, they have pushed for more protections and use various mechanisms (ad blockers, opting out, surfing incognito) to avoid sharing personal information. 

They are very hesitant to share what they consider extremely personal information, such as their location, browsing history, or purchase behavior. And they are not a small faction. In fact, a Pew Research Center study found that 79% of Americans are concerned about the way their personal data is being used by companies beyond what they intended.[iii]  Years ago, the Cambridge Analytica scandal highlighted these fears, when the data of millions of Facebook users was harvested without their consent and weaponized for political purposes.

Consumers believe they have a fundamental right to privacy and are increasingly demanding more control over their personal data.  In recent years, there have been several high-profile data breaches and scandals involving the misuse of personal data. These incidents have increased consumer awareness about the importance of privacy, and they are becoming more vocal about their concerns. Consumers want to know how their data is being used, who has access to it, and how it is being protected.

Consumers are also becoming more aware of their digital footprint and the potential consequences of sharing personal information online. They’re concerned about identity theft, fraud, and other forms of cybercrime. In addition, they worry that their personal information could be used against them, such as by insurance companies or potential employers. As a result, consumers are becoming even more cautious about sharing personal information online, and as such only do it with businesses they trust.

The Role of Trust & Regulation

Trust is a crucial factor in the relationship between consumers and businesses. Consumers are more likely to share personal information with businesses that have not breached that trust and that prioritize their privacy. Even so, trust is fragile, and businesses must work hard to never violate it and always maintain it.

Businesses can build trust with consumers by being transparent about their data collection practices and providing clear explanations of how they use consumer data. They should obtain explicit consent from consumers before collecting any personal information and should provide consumers with the option to opt-out of data collection. And they must ensure that data is protected from data breaches and cyberattacks.

Privacy concerns can also impact consumer behavior in a more general sense. A review of the literature by Taylor and Francis Online found that online privacy concerns can lead to reduced trust in online transactions and lower engagement with online platforms.[iv]  This can have significant consequences for companies that rely on online channels for marketing, sales, and deepening relationships with existing customers.  What’s more, privacy concerns have a ripple effect across businesses and industries, as consumers become more skeptical with each new incident.

Best Practices

Here is a baker’s dozen of best practices to effectively balance personalization and privacy.

For Personalization:

  • Rollout individualized personalization – Individualized personalization uses preferences and behaviors of individuals (not segments they belong to) to custom tailor products, servicing, and messaging.  It affords numerous benefits, including increased convenience, engagement, and ultimately overall satisfaction.  And done right, it will build long-term trust.  When personalizing for pre-login purposes (such as for unknown browsers or mobile devices) pay careful attention to whether adtech data collection vendors are using tricks, like cname cloaking, to mask a 3rd party domain still receiving data when the end consumer may not approve of this.  Using a trick like this might erode trust and ultimately backfire.
  • Build cross-functional personalization teams – Personalization requires input and collaboration from multiple departments within an organization, including marketing, IT, data & analytics teams, and customer support. Building cross-functional teams can help companies break down silos and ensure that all stakeholders are aligned on personalization goals and strategies.
  • Adopt agile methodologies for feedback and testing – Agile methodologies enable companies to iterate quickly and respond to changing customer needs and preferences. Adopting agile methodologies can help companies test and refine personalization strategies and ensure that they are delivering the right content and experiences to the right customers at the right time.  Pick an agile personalization platform.  It should be capable of always-on variation testing (runs champion / challenger experiments automatically) and able to deploy necessary adjustments to programs in a day or less not weeks.

For Privacy:

  • Appoint a Chief Privacy Officer – Appointing a Chief Privacy Officer (CPO) is a critical organizational change that can help companies balance personalization and privacy. The CPO is responsible for ensuring that the company’s data privacy policies and practices align with industry standards and regulatory requirements, while also driving data-driven innovation and personalization.
  • Establish a clear data governance framework – Companies must have a clear governance framework for data management that outlines data privacy policies, data protection practices, and compliance requirements. This framework should be regularly reviewed and updated to ensure alignment with changing regulations and best practices.
  • Use privacy nudges – Nudges can be an effective way to help consumers make more informed choices about their data privacy, while still allowing for customization.  Privacy nudges are interventions designed to influence behavior without restricting freedom of choice.  For example, a company could use a privacy nudge to encourage consumers to read the privacy policy before accepting it. A study by Balebako et al. (2015) found that privacy nudges can be effective in improving privacy outcomes for consumers on social media platforms. [v]
  • Conduct regular privacy impact assessments – Privacy impact assessments (PIAs) can help companies identify privacy risks and implement appropriate controls to mitigate these risks. Conducting regular PIAs can help companies stay ahead of changing privacy regulations and address potential privacy concerns before they become major issues.
  • Foster a culture of privacy first – A culture of privacy first starts at the top of the organization, with senior leadership setting an example and emphasizing the importance of privacy in all aspects of the business. Companies can also provide privacy training and awareness programs to all employees, to ensure that everyone understands the importance of privacy and how to protect it.

For making the right technology choices:

  • Invest in a consent management platform – These platforms allow companies to manage user consent and data collection preferences, enabling users to choose what data is collected and how it is used. Consent management platforms can also help companies comply with regulations such as the GDPR and CCPA, which require informed and explicit user consent for data collection and processing. Invest in a customer data platforms (CDPs) only if first-party data and device identity management is scattered or missing.   CDPs are a centralized system combining customer data from multiple sources (such as transaction & behavioral data), device identity, and consent. Companies must be transparent about their data collection and usage practices and provide clear and concise information about how data is being used. This can be accomplished through user-friendly privacy policies, clear consent mechanisms, and open communication channels.  
  • Leverage the right artificial intelligence (AI) – AI can help companies analyze vast amounts of customer data and identify patterns and trends that can inform personalization strategies. Companies can use the right AI for the right job do better. 
    • For example, firms using Bayesian models to deliver personalized next-best-action recommendations tailored to each individual customer (that adapt in real-time as preferences shift) report up to 6x lift in response rates. [vi] 
    • They also enjoy an added advantage that these models are transparent and can be pre-checked for bias, responsibly deployed, and explained.  Compare that to this statement in a recent research paper on using GPT: “We do not intend for the model to be used for harmful purposes but realize the risks and hope that further work is aimed at combating abuse of generative models.” [vii]
    • Use other responsible techniques, such as federated learning and homomorphic encryption, which enable machine learning models to be trained on user data without accessing or exposing individual user data.
  • Use anonymization and pseudonymization techniques – These are methods of data de-identification that can be used to ensure that sensitive personal information is protected while still allowing for effective analysis to improve personalization. Anonymization removes all identifying information from a dataset, while pseudonymization replaces identifying information with a pseudonym, allowing for the data to be re-identified if necessary.  If data is shared externally with other parties, use clean rooms.
  • Consider differential privacy algorithms – Differential privacy is a technique that adds noise to a dataset to protect individual privacy, while still allowing for effective data analysis and personalization. Differential privacy algorithms can be used to provide personalized recommendations, while still ensuring that individual user data remains private.
  • Employ secure data storage and transfer protocols – Companies must ensure that user data is stored and transferred securely, to prevent unauthorized access or data breaches. Technologies such as encryption, secure sockets layer (SSL), and transport layer security (TLS) should always be used secure data storage and transfer.

Conclusion

Being customer-centric is about adopting a mindset that improving customer experience is paramount to business success. Personalization is an essential strategy for businesses to improve the customer experience. However, as personalization becomes more prevalent, consumers are also becoming more concerned about their privacy.

As the debate between personalized products and services and data privacy rages on, companies must navigate a delicate balance between meeting consumer demands for customization and respecting their privacy. Failure to do both right could have profound consequences, including loss of trust and customer loyalty, as well as legal and regulatory penalties. However, those companies that can offer personalized products and services while also prioritizing transparency and data privacy will be well-positioned to succeed in the digital age.

The future belongs to those companies that can harness the power of data-driven personalization in a responsible and transparent way, while respecting the privacy and autonomy of their customers. In short, the choice is clear: companies can either embrace data privacy as a core value and use it to build lasting relationships with their customers, or risk being left behind in an increasingly competitive market.


[i] Capco. (2021). Insights for Investments to Modernize Digital Banking. https://www.capco.com/Intelligence/Capco-Intelligence/Insights%20for%20Investments%20to%20Modernize%20Digital%20Banking

[ii] Epsilon. (2018). The power of me: The impact of personalization on marketing performance. https://www.epsilon.com/-/media/files/epsilon/whitepapers/emea/the-power-of-me.pdf

[iii] Pew Research Center. (2019). Americans and privacy: Concerned, confused, and feeling lack of control over their personal information. https://www.pewresearch.org/internet/2019/11/15/americans-and-privacy-concerned-confused-and-feeling-lack-of-control-over-their-personal-information/

[iv] International Journal of Human-Computer Interaction. (2020).  Online Privacy Breaches, Offline Consequences: Construction and Validation of the Concerns with the Protection of Informational Privacy Scale. https://www.tandfonline.com/doi/full/10.1080/10447318.2020.1794626

[v] Balebako, R., Danish, R. K., Hong, J. I., & Cranor, L. F. (2015). Privacy nudges for social media: An exploratory Facebook study. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, https://dl.acm.org/doi/abs/10.1145/2487788.2488038

[vi] Pegasystems. (2022).  Coutts: Delivering world-class banking experiences at scale.  https://www.pega.com/customers/coutts-customer-decision-hub

[vii] Semantic Scholar. (2023). Structure and Content-Guided Video Synthesis with Diffusion Models. https://www.semanticscholar.org/paper/Structure-and-Content-Guided-Video-Synthesis-with-Esser-Chiu/07be0ec1f45e21a1032616535d0290ee6bfe0f6b

The Hyper-Personalization Paradox: being relevant without crossing the CREEPY LINE

Brands are using AI to drive hyper-personalization, but can it also help them avoid being hyper creepy?

hyper-personalization

Source: https://www.adclarity.com/2015/04/digital-marketing-2015-hyper-personalization-display-ads/

Apparently, I have 8 seconds to grab your attention, so here goes.  What if I personalized every aspect of this blog for you?  That is, I knew so much about you – your reading behavior, the writing style you prefer, subjects you love – took all of it into account, and assembled these words and pictures just for you?  Would you find that creepy or cool?

At our conference in Las Vegas recently, I was a guest on Sam Charrington’s, podcast series “This Week In Machine Learning and AI.”  In that episode, we discussed a similar hyper-personalization scenario, where an automotive company used intimate knowledge about a consumer and her connected car to custom-tailor each marketing and service treatment[i].  And half-way through (at 23:07), Sam observed that although “consumers appreciate personalized experiences,” it can go too far and “sometimes come across as creepy.”

And suddenly, we both realized something.  Customer experience experts haven’t used AI to govern this.  In other words, CX pros personalize without recognizing if their personalization levels are approaching creepiness.

Which led to this question: can creepiness be quantified?  And if so, with that knowledge, could a company effectively use it?  With the right tooling, could they safely test and simulate how far personalization should go, carefully delivering each customer a tailored experience with the right level of relevance and value, without crossing into their creepy space?  Simply put, hyper-personalizing without being hyper-personal — the personalization paradox.

You’re marketing is creeping me out

Creepy land is that forbidden zone where consumers call out businesses for using personal data and revealing insights that are a bit too private.  And though consumers increasingly want personalized experiences (according to a recent Epsilon study[ii], 90 percent of consumers find it appealing), ironically, they will happily make examples of brands that invade their personal space.

No brand wants a creepy reputation as it implies:

  1. Stalking, snooping, or spying; collecting personal data and invading privacy
  2. Revealing something private, no matter how valuable the insight
  3. Not having customers’ best interests in mind
  4. Ill-intent, even when there isn’t intention to do harm

With big data galore, a culture of a data sharing, and pressure to mass personalize to remain competitive, you need ways to safely and systematically explore the creepy line’s location without ever crossing it.  Understanding what customers expect and why they love a product (or don’t) is crucial to great personalization.  Avoiding a creepy moniker means effectively steering clear of areas that are, frankly, none of your business.  And if the customer says it’s none of your business, it’s none of your business.

Today, the digital world abounds with copious quantities of demographic, psychographic, and behavioral data.  There’s a sea of it, because for decades companies have wired up clients and monitored them like lab rats.  And with more IoT tech and data coming every day, firms increasingly misuse it, giving customers more reasons to demand privacy.  The problem is the definition of what’s private and sensitive can be different for each person.  Hence the dilemma: under personalize and risk being labeled clueless, not cool, and worse miss out on revenue; over personalize and risk breaking trust and doing irreparable damage to your reputation.

Sorry we’re creepy. We apologize for any inconvenience 

Customer engagement professionals need new and scalable ways to survey buyers, collect preferences and permissions, sense their intent and moments of need, and personalize appropriately.  So, they need ways to test where that creepy boundary is.  That line is fluid and ever shifting and finding the right level of personalized insights and recommendations without crossing into risky territory is never without some uncertainty.

Where that line lurks changes with time because initially customers may be leery of something, then later adapt to it.  It also changes because privacy legislation changes, individual consumers have distinct levels of sensitivity, and varying levels of awareness. It can even differ by geography.  For instance, a 2016 study of 2000 consumers in Europe found that 75 percent were uncomfortable with facial recognition software used to target them with personalized offers (consumers in the US were much less sensitive)[iii]

Data-driven marketers have evolved their practices (Figure 1) using data to acquire more customer knowledge which in turn powers more personalization.  Over time, more marketers have evolved their practices, from the general advertising Mad Men approaches of the 1960’s to the super-personalized, AI-Powered approaches possible today.  It also highlights how that pushes them closer to the creepy space.

hyper-personalization evolution

Figure 1: Evolution of Data-Driven Marketing

Here’s the bottom line: if a given customers perception is it’s creepy, it’s creepy.  And depending on who slaps that label on, and whether their rights that have been violated, firms may face legal battles, fines, and reputation damage leading to significant commercial impact.  For instance, potential fines for GDPR privacy law violators can reach 4 percent of a firm’s revenue (up to a maximum of €20 million).

And none of that is music to a businessperson’s ear.

Creeping toward creepy

In 2014, Pinterest managed to spam a major segment of customers when they sent emails to unengaged women congratulating them on their upcoming weddings.  And Shutterfly made an even bigger spam faux pas that same year, congratulating women on the birth of babies they didn’t have.

In Figure 1, these events fall into the SPAM circle because marketers placed people into the wrong macro segments, and the resulting emails were both irrelevant and hilariously erroneous.   Clumsy customer experiences indeed, but not creepy-smart marketing.

Here are some other examples of Mad Men SPAM marketing:

  • You market wedding offers after a wedding – low sensitivity
  • You market wedding offers after a cancelled wedding – high sensitivity

On the other hand, the risk of being labeled a creepy marketer increases when knowledge of customers goes up, insights increase, yet marketers fail to understand an individual’s sensitivity to certain marketing actions.

For each marketing treatment, you need to determine if it will be creepy to everyone or only some:

creepy meter

Figure 2a:  Creepy Meter detecting creepy treatments

If it’s clearly creepy to everyone, during the pre-market approval process you should reject it.  But, if its potentially cool to some, and creepy to others, then provided you can discriminate at runtime using eligibility rules, you can approve its use for those who will find it cool.

To do this, get a readout on consumers’ sensitivity to hyper-personalization.  Build a model that learns this, and use this score to select, by individual, the levels of personalization they’re eligible to receive.

creepy index

Figure 2b:  The Creepy Sensitivity Index readout on each consumer

Here are a few examples of events, corresponding covert marketing approaches, and creepy readings:

 Event Covert Marketing (but not illegal) Creepy Meter Approve?
Hospital admittance / serious health issues detected Mortuary makes discount offers Extremely creepy Reject
Conversation recorded (without clear permission to use for marketing) Ads for products related to keywords in the conversation (e.g., pet toy video recently, which illustrates the point yet is likely a hoax) Very creepy Reject
Facial recognition or location detection Upon a patron entering a branch or store, their profile & preferences are relayed to a salesperson Borderline creepy Conditional
Consumer traveling; recent activity and calendar scanned Push notifications offering travel recommendations based on triangulating travel intent and destination Borderline creepy Conditional
Consumer browsing a web page with product offers Website background, images, language, offers, and other page fragments hyper-personalized Borderline creepy Conditional

Table 1: Examples of potentially creepy marketing

Leading-edge 1:1 marketers are constantly listening for keywords, tracking interaction device, time & location, codifying behavior, sensing mood, recording preferences, and using that knowledge to hyper-personalize with content variations in the millions.  The risk, however, is meandering into that forbidden creepy zone (even if it’s legal), so discerning this by customer by treatment is vital.

Suggestions

As you move into deeper levels of hyper-personalization, do so deliberately and methodically, fully grasping the implications before rolling out.  Consider taking this approach:

  • Collect only data that matters to your ability to personalize specific experiences – that your customer will value. For example, if you sell insurance, you don’t need to understand pet preferences unless you’re selling pet insurance.
  • Start with simple / minimal risk personalization strategies. These should easily pass the creepy test.  For instance, if you can tune you web experience to shopper color preferences, do it.  No one will find that creepy.
  • Gradually apply regional and demographic personalization strategies.
  • Use AI to crawl your products and content to extract taxonomies, attributes, cross-classifications, and descriptions. This will help better match customer intent and preferences to products that will match needs.
  • Use AI to match the right products to clients (making relevant recommendations) and doing so in a personalized way that enhances their experience
  • Use sampling to test hyper-personalization treatments, selecting a wide variety of customers.  Essentially, you get a stratified sample of creepiness raters.
  • In general, avoid even borderline covert marketing unless you have a firm handle on any backlash that might result if customers discover it. In a recent survey, most consumers (81%) think firms are obligated to disclose they’re using AI – and how they’re using it.[iv]
  • Be sensitive to consumers’ preferences for public recognition.  Some might love it if you great them by first name and show appreciation for their loyalty in public.  A few, however, may be mortified.

Hyper-personalization requires great data, great technology, and great sensitivity.  With GDPR now in effect, most businesses are proactively disclosing their data collection practices and privacy policies.  As consumers, we’re consenting to and accepting new privacy policies more than ever before, and in some cases, we’re even reading and understanding them.  Less clear, however, is exactly how that data is used, combined with other data, and when it might show up as an insight, recommendation, or hyper-personalization – and again, which of us might be freaked out by this personalization.

AI is driving personalization to new levels.  There’s no stopping that.  It automatically figures out what works and what doesn’t.  Techniques, such as Bayesian algorithms, quickly learn which offers work, when, and in which channels.  Others, like collaborative filtering, find which products pair best, that in turn drives cross-sell and bundling strategies.  Design of experiments and monitoring devices measure the impact and enable fine tuning.

What’s missing, however, are tools to sense consumers’ sensitivity to personalization, so overt practices are optimized with the right people, and so covert methods are prevented from ever reaching production, or if they are approved for use, are carefully applied.

The study shown in Figure 3 provided some proof that overt personalization pays off.   Yet the very definition of overt blurs as AI improves, content becomes hyper-conditional, and levels of personalization get more complex.  Thus, you’ll need more sophisticated ways to gauge levels of personalization relative to creepiness, and the sensitivity levels of different people.

personalization

Figure 3: Overt vs covert personalization performance[v]

Conclusion

Great marketers push beyond perceived barriers by understanding customers, knowing products, and then elegantly combining creativity and technology to provide valuable recommendations and experiences to customers.  Ironically, when done right in the eyes of the receiving consumer, they don’t appear to be selling anything; instead simply providing a service.

With website personalization, one-to-one content, natural language generation, image recognition, and countless other AI tools, businesses inexorably march toward hyper-personalization.  Make sure you manage it, so you’re always cool and never creepy.


Endnotes:

[i] https://www1.pega.com/insights/resources/pegaworld-2018-pegas-ai-innovation-lab-sneak-peek-and-your-vote-counts-video, June 2018

[ii] http://pressroom.epsilon.com/new-epsilon-research-indicates-80-of-consumers-are-more-likely-to-make-a-purchase-when-brands-offer-personalized-experiences/, January 2018

[iii] https://www.forbes.com/sites/fionabriggs/2016/07/04/fingerprint-scanning-is-cool-but-facial-recognition-creepy-new-richrelevance-survey-shows/2/#493b953f3d68, July 2016

[iv] https://www.richrelevance.com/blog/2018/06/20/creepy-cool-2018-richrelevance-study-finds-80-consumers-demand-artificial-intelligence-ai-transparency/, June 2018

[v] https://www.sciencedirect.com/science/article/pii/S0022435914000669#abs0005, March 2015

 

“Thanks Marketing” Said No One Ever…Until Now

Thanks Marketing

The Formidable Years

As a child, Marketing was always loquacious, a doodler, and squirmy – voted “Most Likely to Be Told to Shut-Up,” she sharpened her latent skills in secret, occasionally posting her art on billboards, doing voice overs on an old cassette recorder, and even making a fluke cameo in a school play with a minor sing-song role.  But no one noticed.

Generously provided a diploma and sent on her way, after graduation she meandered through part-time jobs – waiting tables and moonlighting as a street artist selling a few pieces to magazine execs who used them in ads.  Family muttered at dinners and gigs were scarce.

Single with Jingles

Her first good-paying job was as a broadcaster on a local radio show.  She loved the writing and performing – being able to broadcast her message to a larger audience.  She came up with little ear-catching jingles that were memorable to some.  Still, she felt only “half successful” – never sure which half of her act worked.

Soon, she enrolled in night school, studied statistics (of all things), and brought to the station the idea of doing surveys to see which bits the audience liked.   It worked.  The station manager even coined a new term for the audience breakdown by age groups – calling them “segments.”  They crafted shows to cater to different segments that listened at various times.  The station quickly became tops in the market.  Yet, something was still missing – that direct connection with her listeners.  She’d do local events, having drinks with fans, but habitual cocktailing was exhausting and not scalable.

One night, at one of those station events, Marketing met Technology.  Tech gushed about emerging addressable channels, world-wide webs, email at scale, and mobility, making an impression on Marketing.  Although he was socially awkward, they hit it off, further confirming the Laws of Adjacency Physics that complementary opposites attract.  A few months later they married and the rest, as they say is Database Marketing, Content Marketing, and Martech history.  The they went on to have a few offspring… well over 5,000 in fact.

Unsung Heroes

Thanksgiving season calls on us to be thankful for things we otherwise take for granted. Marketing, eternally lambasted by non-believers as that group that creates logos and pretty slides, home of the artsy-fartsy types, creators of junk mail, and hosts of the two-drink minimum parties (they’re just jealous) – deserves better this year.

Like so many things, there’s good, bad, and ugly.  Surgeons are not all good.  Neither are marketers.  But how many times have you ever heard someone thank a great marketer.  Here’s an argument for why you should this year.

Court Proceedings

The collected evidence, submitted for your consideration:

Great marketing doesn’t happen by chance.  It takes devoted and creative people – brilliant, diverse, methodical and collaborative people, many with incredible range of art-science motion, who come together from all walks of life:  artists, sociologists, journalists, improv artists, movie producers, broadcasters, computer scientists, data scientists, quants, researchers, linguistics experts (and occasionally a trained marketer) ….and together they’ve brought us…

~ Humor and entertainment:

Exhibit #1: Arguably all starting with this Fedex ad in 1981, opening a new advertising chapter using laughter to engage us.

Exhibit #2: Anheuser Busch’s Bud Light TV ad oeuvre not only makes us laugh, as they pitch a watered-down lager, they also put commercials on center stage with edgy material constantly pushing marketing’s comedic and acceptable lingo boundaries.  Case in point…check out this one, taking the liberalization of profanity to new levels (up or down – depending on your view).

It’s strange nowadays to see any successful ad that doesn’t have some wit, jocularity, or chuckle-worthy aspect.   Admit it – half the reason you tune into the Super Bowl is for the commercials – and it’s not because you’re hoping to discover a more absorbent paper towel to wipe up your coveted light beer.

~ Amazingly eye-pleasing art and creativity in ad visuals – meaning we don’t hate the ads we view.

~ Innovative products, that we want, because someone cared to listen to us or went out of their way to push their corporate culture to innovate.  Such as:

  • Better ways to make reservations, vacation, get from point A to point B, shop, find a job, keep in touch with family and friends, and watch movies

~ A better more personalized experience with products and services…

  • You like personalized music consumption – thank marketing
  • Enjoy your video-on-demand with recommended content – thank marketing
  • Dig the nudges you get to exercise more, so you don’t waste away on a couch – thank marketing
  • Fancy discounts, rebates, and points for stuff you buy and use – thank marketing

Thanks Marketing….

Presently, I’ve got no glib prognostications, no “Five Marketing Best Practices,” and no “2017’s most disruptive Martech startups” (maybe next post).

Rather, today I’m pausing to admire how far marketing’s come, how much smarter she is, how attractive she’s become (she put blood, sweat, and tears into that beach body), and how proud we should be of her when she does excellent work.

So, this thanksgiving season, go out of you way to thank someone you probably have never thanked before.  Thank a great marketer.  I will.

AI-Based Promotions – Welcome to the Creative Machine

Mad Men & AI - Promotions

Source: Exploration of Saturn’s Moon’s by Kacper H. Kiec

As a Marketer, when you craft successful promotions, you’re especially proud of their creative aspects.  And it’s understandable because creativity seems our last bastion against the perceived onslaught of machine domination, so we fiercely defend that turf.  The tenuous argument being, “robots are no match for human creativity!”  This viewpoint, besides inviting a cage match between humans and machines, also smacks of keeping math and machines out of any solution, lest boring and stiff digital influences ruin the warmth of our marketing art and experience show.   However, for all the aspiring “Michelangelos” out there, it’s time to rethink this, lest you find yourself selling one-off ad creatives at street-side craft shows.

A promotion is fundamentally your story; your pitch in a nutshell – delivered through a channel to an audience of one – assuming it gets through.  And the fact that it oozes creativity and garners the right emotional response can be critically important to a customer’s reaction.  But what is its true worth? Compared to what?  Is there a chance that for most eyes it will succumb to fading into the backdrop with all the other one-size for all advertising clutter?

With a fickle, time-pressed consumer, your promotion has – at best – a fleeting chance to capture an individual’s attention, make an emotional connection, explain a deal, plus convince that person they should care.  On average, you’ll get about five seconds to grab interest; succeed and you may earn another five to emotionally connect, and so on.   In most cases, no matter the channel, you’ll be afforded about thirty seconds, a few minutes tops – to deliver the goods.

Given this, every top-line pitch needs a “No Boring Zone” mentality with visually appealing facets – nonetheless cookie cutter theatrics alone won’t win the day.  You need to get serious about how to use math along with machines (artificial intelligence) to radically fine-tune sales messages and custom-fit them for individuals – in other words, personalize them.  To do that requires scaling up a promotion production and testing factory.

A canvasing of the available marketing automation tooling finds that very few help solve for the bona fide business problem of creating and testing a wide variety of promotions across a plethora of channels.  In fact, most simply give you a facility to manually key enter the metadata for each version, creating them from scratch – calling this Offer Management or an Offer Library.  The problem is as an artisan, you basically run out of material and time in a futile attempt to manufacture a decent collection for the library.  Thus, the conundrum –  to cut through the noise, and find the right version that resonates for each nuanced individual, you must create and test thousands of versions, but old-fashioned human means alone cannot keep up.  And if you muster the means to produce numerous alternatives, it’s equally difficult to monitor their effectiveness and pick the winners.  You need tools that automate mass testing and response tracking, and math to tell you exactly what’s working and why, yet few such tools exist.

Everyone talks about knowing customers better; using that knowledge to personalize.  It’s an admirable aspiration.  However, commendable goals don’t necessarily translate to better outcomes. In this case, it doesn’t matter how well you know customers if you can’t hyper-customize content, messages, and other creatives – and produce tailor-made promotions that really fit what customers expect in the moment of interaction.

You won’t entice my response by extrapolating from a few of my preferences and placing me into some huge segment.   All the “Hey, Vince wouldn’t you love to travel, drink exceptional wine, and eat at these fine places” in the world won’t matter if I don’t get something that is fabulously timed, speaks directly to me and visually jumps out, elicits an emotional connection, stays engaging, and commands attention due to its specific relevance – in other words the message needs to be personalized to my promotional preferences and exact product needs.  In fact, the promotion itself (in its entirety) must be an enjoyable experience.

Moreover, the same goes for financial services, transportation, telecommunications, insurance, and healthcare promotions.

Marketing’s 4th Dimension – Promotions

Marketing technologists (martech types), and the automation applications available to them, tend to focus mainly on these big three dimensions that drive response rates:

~Data:  Stockpiling and codifying key customer data

~Behavioral Analytics:  Gleaning intent and preference, scoring response propensity, and segmenting

~Channel & Time Optimization:  Delivering messages through the right medium at the right time

 

Space-time warp

All of these dimensions are important pieces to solving overall marketing optimization.  However, without the ability to generate thousands, if not millions of promotions (with varying copy options, incentive levels, calls to action, creative versions and such), about one third of what drives response and conversion is woefully underserved in assuring messages are noticed, relevant, and responded to.

Presently, this 4th dimension, promotions, has received practically no attention from marketing automation technology and AI – and instead marketers merely accept that snail-like non-scalable A/B testing is the best way.  The fact is, even with armies of humans crafting variations and A/B testing, the number of manageable versions you can juggle will be in the hundreds at best – when what you need to compete is the ability to create & test thousands of these.

Ok, not convinced yet?  Then perhaps a little math is in order (as he locks the classroom door and places nails…I mean chalk… on chalkboard):

Problem: Calculate the number of email message variations.

Email promotion components:

  • 100 products to sell
  • 10 images per product
  • 10 subject lines
  • 100 email templates (to test fonts, color, container locations, call-to-action button)

Answer:

100 x 10 x 10 x 100 = 1 million promotional variations

News Flash!  You have no chance with just brute human force to create and test this many variations.

 

Lucy & Ethel couldn’t keep up – and neither can you

chocolate factory

In this famous Chocolate scene from I Love Lucy, an illustrious TV series from the 1950’s, Lucy & Ethel prove that manual human labor, no matter how clever, can’t keep up – quickly becoming the bottleneck in an otherwise automated system.

Given this seventy year old lesson, why do we think that humans alone can drum up and test an acceptable level of promotional assortment?  They can’t.  But still, stubbornly, we hand-crank creative versions, accepting less variation.  Yet the better way is to let people fashion the promotional raw materials as re-usable creative elements, combined with letting artificial intelligence test the combinations – surfacing the winners – automating and individualizing the wrapping of your chocolates.

Marketers, as well as many businesspeople, are warming up to the current power and future potential of AI and what’s at its core – Data Science.  In fact, in a recent study by the Boston Consulting Group of more than 3000 executives, 61% of those surveyed see developing a strategy for AI as urgent[i].  And in this case, machines and math can assist.  As a marketer, you already know the power of AI and machine learning.  It’s what helps you calculate customer value, score a customer’s propensity to respond to a given incentive for an applicable product, and even predict when to present the offer.  And to get started, you don’t need a million options.  Instead, use human judgement to field a reasonable set of challenger creative components (perhaps a dozen of each), then use AI to perform champion –  challenger tests on the combinations.

Exactly how will AI and machine learning help generate and test copious quantities of creative offer variations?  Enter natural language generation and automated (multivariate) testing.

Natural Language Generation (NLG), Visuals, and Templates

In our email example, we discussed written variants (e.g., different subject lines), various visuals (fonts, graphics), and template alternatives (where to place the copy and graphics).  Let’s break these 3 elements down:

Natural Language Generation (NLG)

Computers can generate language.  In fact, they’ve been doing so for over 30 years.  Today, they can even take into consideration emotional aspects. In 2015, Gartner went on record forecasting that by 2018, twenty percent of all business content would be computer generated[ii].  Although aggressive at the time, and unlikely now, it still highlights the potential of NLG, and progress nonetheless has still been impressive.

For marketers, there’s already good examples of how NLG is used today, and can be helpful in solving for the promotional version dilemma.

For example, Persado Go uses NLG to generate variations of email subject lines, and then records performance broken down by specific elements such as emotions, formatting, descriptions, and so forth.  Candidate subject lines are generated from a huge database, and a sixteen-version test is setup.

Visuals

Visuals are combinations of text aspects (font type, styles, size), color, video, pictures, and graphics. A picture is not only worth a thousand words it’s also capable of sparking an emotional connection.  And although AI is encroaching on even this human endeavor, for now people (assisted by AI) are still superior to pure machine generated creative assets.

Templates

Templates drive how you both organize and showcase content.  For an email, it controls where recommended content will display, where a call-to-action button will be placed, what font will be used for written copy, and so on.

As with any element, a wide assortment of templates should be tested, each with innovative ideas about where containers should be located, and which font and color scheme will work best.

Now that you have all the ingredients, just mix and serve.  Except how will I know which versions work best in which circumstances?

Multivariate Testing & Adaptive Machine Learning

Enter multivariate testing – which sounds complex and geeky – but it’s not that difficult (although admittedly the term is geeky).  A multivariate test is simply a series of A/B tests, done simultaneously – which means you won’t spend months testing; instead doing one test (testing a string of modifications all at once) in as little as a few days or weeks.

And using an adaptive machine learning approach, such as this one available from Pegasystems (in full disclosure I do work for Pegasystems), the whole testing process can essentially run automatically, as the machine (the math algorithm) determines the eventual winners by ranking them higher as the digital response evidence pours in on which promotional variant get the best take-rate in which situations.

You and The Machine will go far

Too often we fall victim to pitting ourselves against machines, rather than exploring a symbiotic relationship with them – like the one we have with our smartphones.  As marketers, we need to think the same way.  AI can assist us, and we must embrace that.  Exploit technology for what it does well, and weave that into your promotional factory, leveraging its ability to scale things to new levels never imagined with manual methods.

[i] S. Ransbotham, D. Kiron, P. Gerbert, and M. Reeves, “Reshaping Business With Artificial Intelligence,” MIT Sloan Management Review and The Boston Consulting Group, September 2017.

[ii]Gartner,  http://www.gartner.com/smarterwithgartner/gartner-predicts-our-digital-future/, 2015

What it Takes to be a CX Transformer

Thirty years ago, when I unpacked my first computer, a Commodore 64, rigged it to my 13-inch tube TV, and wrote my first program, the process of creating a digital experience hooked me.  That I could design and assemble mere bits and bytes, package them up into an asset, refine it, and eventually share it for the benefit of others – for entertainment or problem solving – just enthralled me.

CX Transformer

With time and market efficiency sorting who gets paid to do what, I altered my path away from programming and toward design and consulting, leaving the coding and compiling jobs to those more talented than me in that trade. That wonderful feeling of accomplishment, however, never left me and still drives me today.  Whether it’s creating visual concepts, designing software, or producing media, creating a re-usable asset with experiential worth (striving to be a CX transformer), for me, is a universal and time-tested motivator.

Experiential assets, originally made from scratch, must evolve to the liking of their benefactors.  They invariably play a role in nearly every commercial experience.  For example, a vehicle manufacturer produces a physical product, but the agency who markets it as well as the dealer who sells and services it – all add crucial elements into the customer’s journey of shopping for, buying, and owning that vehicle – all contributing to (or subtracting from) accumulated impressions of overall worth and value.

Organizations are either born with this mentality, where it’s baked into the fabric at every level and function of the organization, or they must transform.  Startups who don’t adopt this mentality burn through money and soon dissolve.  Legacy firms are faced with odds not unlike that of a recovering addict.  Most hit bottom, before they realize the extent of their problem, and by then it’s often too late.  Few are afforded the chance to recover and most who try will regress.  In fact, a recent Forrester study [i]indicated as many as 77% of those who embark on CX transformation will fall short.

With all this buzz, don’t we already get great CX?

The short answer is, not really.  According to a global survey [ii]of 7000 consumers, 89% “think brands need to work harder to create a seamless experience for customers.”   There’s lots of talking about seamless and personalized experiences, and less walking the walk.  And consumers continue to report a deficit of it, as evidenced in an Infosys survey [iii]indicating that 73% have never experienced online personalization.  Here’s the reason:  Many of us, and the firms we work for, aren’t practicing what we preach.

Regardless of what you do, you’re in the business of creating customer experiences.  Whether in sales, marketing, service, or operations; whether you set vision, do design work, code, implement, consult, or the like, your ultimate mission is creating something that someone else appreciates and finds value in – because it makes their life better.  It improves their experience.  If you can’t tie what you do and why you do it back to that, your mission is misdirected.

The only reason customers buy, use, or recommend products or services is because they experience value.  So, if you simply blabber about CX but don’t improve it, you’re subtracting value, like in figure 1:

CX Talk vs Walk

Figure 1: All Talk Equals Value-Subtracted from CX

Everyone plays a role in experience management.  For example:

  • If you’re a banker, during any interaction, clients are judging each aspect of your services. When they point out friction, dissatisfaction, annoyances, frustrations – they aren’t being pests – they’re handing you gold.
  • If you manage a telco’s call center, though one step removed from direct feedback, front-line agents will hand you that gold. Will you ignore it, or will you investigate, catalog it, document it, and act on it?
  • If you design software used by that banker or agent, you’re instrumental to how the total experience comes off when moments of customer truth occur. Software augments customer facing CX delivery, either enhancing it or contributing to its malfunctions.

Software and AI technologies have already changed our lives, and continue to transform how we experience life.  From when we wake to the minute we doze off, the way we interact with the world, for business and pleasure, is vastly different now from the day I cracked open that Commodore box.

Data is abundant and the right intelligence in software is available. Yet how both are captured and deployed is what spells the difference between memorable moments versus forgettable incidents.  Dated advice, cloaked as sage recommendations, abounds on what data to tap and which AI technologies to trust.

Beware of the CRM “Catchers in the Rye” who have a vested interest in selling old software disguised as AI and one-to-one personalization, spruced up with fancy new names like Customer Data Platforms, but stuck in a forgone era. Peel these back and see if they rest on an old batch and blast architectures with no real proven use cases for predictive analytics, built essentially for pushing emails to segments.  You’re sure to hit a wall with these, since they were never built to handle real-time, analytics based one-to-one contextual engagements. If you’re interested, I cover this topic in more depth in this article.

Or worse still, beware the do-it-yourself CRM & AI pushers, selling piles of new programming gadgets with exotic names such as Python, Storm, Spark, and Kafka, but missing the warning label that says, “Much assembly required.”

CX Transformation Process

The transformation process, contrary to overhyped tales of sudden disruption, is mostly evolutionary.  It involves creative minds with an unwavering and relentless obsession to improve experiences – as measured by customers.  But today you must do everything you can to go through this process fast.

Iteration (figuring out how to improve) means executing various steps in succession – speedily and repeatedly to learn fast.  It also takes a flexible methodology and tools supporting rapid revisions.  Each time Thomas Edison’s filament didn’t work, he wasn’t failing, he was learning.  When asked about racking up so many failures, Edison replied, “I have not failed 10,000 times. I have not failed once. I have succeeded in proving that those 10,000 ways will not work. When I have eliminated the ways that will not work, I will find the way that will work.”

Be unyielding in finding gaps, filling needs, overcoming shortcoming, and plugging them with an improved asset.  Find the simple stuff, that exacerbates customers, but is easily addressed.  Do ten thousand little things right – and fast.

To succeed, you’ll need to be well-equipped with the right CX transformation methodology and technology. Speed to market and economies of scale matter now more than ever.  It takes steadfast customer centric vision, modern tooling, and an agile methodology.  Let’s explore the four key steps shown in figure 2.

CX Transformer

Figure 2: Depicting the CX transformational process steps

 

 

CX Transformer Step #1: Conceive Innovation

As you come up with a concept, consider the objectives…. making things better, faster, cheaper.  Ideally, you’ll eventually address all these, but practically you’ll need focus. Will the proposed innovation fix something that is terribly broken?  Better yet, will it preemptively address a shortcoming.  Often, fixing inadequacies is simple, yet the consequences of not fixing them are huge.

To find opportunities for CX innovations, use analytic heatmaps fed by behavior data on websites and mobile devices to zero in on where customers struggle or bail out.  Mine reviews, comments, call logs to find repeating themes.

Here’s an example I heard from a person I sat next to on a flight.  He had booked a trip to Dubai, but the travel service never proactively alerted him that travel to UAE requires a passport that doesn’t expire in less than six months.  On his departure day, he couldn’t check in, and subsequently was on the phone for hours, working the problem and seeking amends for this horrible experience.  The root cause was recorded in logs. The fix (innovation if you will) was rudimentary and excruciatingly easy.

“If customer books trip to country X, and passport expiration date is Y, alert customer about passport rule.”

In this case, the customer placed a gold nugget into the lap of the brand, begging them to fix it for future customers.  Will they?  Only if they’ve institutionalized collecting hiccups like this, and weaving them into the innovation and improvement process.

Think of innovations in sets.  Will the CX innovation set be press-worthy; will the total experience be unique and better?  Take the innovation set and break it down into manageable chunks. To improve service usability, for example, consider whether the specific design is elegant, visually appealing, modern, stylistic, easily navigated, intuitive, and so forth.  Remember, even when just creating a form, such as an insurance policy application, all the above matters in CX.

Spend three times as much effort on design versus construction.  If service improvement is your aim, pick (as your innovation set) a critical customer journey that cuts across various functions and channels, and obsess with its design. While iterating on the design, always apply a range of customer sniff tests tied to customer personas.  How would customer X use this?  How would customer Y perceive this?

Just as incentive drives employee behavior, it drives customer behavior.  Customers are motivated by the value they both perceive and achieve from using your products and services, regardless of the organizational excuses they encounter along the way.

CX Transformer Step #2: Judge Harshly

Critique innovations, not just with self-criticism, but with the varied feedback of others. Compare to market alternatives and what big competitors are doing and what customers complain about.  Once again, view the current state of the experience through customer eyes.  Clients not only measure success, they also give clues about required innovations.  If an asset works they use it, open it, share it, like it, and buy it.

Watch exactly how customers use the innovation.  Designers call this usability testing, and too often, it’s shortcut out of the development process in the name of speed.  Watch how customers interact, how they shop, how they decide, whom they consult with, and why they buy.  Look for where they struggle, the questions they ask, why they need help, and ask what went wrong. Then go back to the drawing board to create a new experience, craft a new email, create a form, redesign a web page, or work on ideas to improve how agents engage with customers.

Use a basic four quadrant Risk / Reward matrix, as shown in figure 3, to prioritize a backlog of CX improvement opportunities.

Value Matrix

Figure 3: Risk (Effort) / Reward (Value) matrix used to prioritize innovation ideas

Don’t make your goal mimicking competitors, but instead to gauge your inferiorities to them, study their winning ways, and chart your course –  but dare to be different – then test and learn.  Compare your asset to others available in market.  This guides, both in terms of whether you’re behind, but also what hasn’t been done – thus presenting opportunities to do something new, something unique.

Pattern yourself on proven winners, not just in your industry, but also in very different ones.  Why?  Because that’s where unique ideas come from – not from copying your competitors, but from proxies that when applied to a different problem become a new idea.

For instance, to transform the branch experience for its customers, Capital One recently introduced café style locations, drawing on a combination of Starbucks and Apple store concepts.

CX Transformer Step #3: Apply a Value Test

Determine whether your innovations improve experience. To do this, perform behavior tests and not just surveys.  People don’t always do what they say they’ll do.  Test your innovation by getting real customers to use it in production pilots, and then measure whether, for instance, the task was accomplished faster.

Getting there may not be easy, cheap, or fast, but if your product isn’t passing these tests, you haven’t improved your customer’s experience.  Each innovation should pass at least one of these tests, and collectively overtime, it must pass all three.

At this stage, the test is if your customers are buying or using your asset.  If they see value, they’ll do these things, so measure for it, and use this as your ultimate yardstick.

CX Transformer Step #4: Analyze Objectively

Once you release your concept into the memorialized world of production, objectively (and recurrently) evaluate its worth.  What works today may not work tomorrow. In addition to pure customer feedback, consider getting an objective third party to scrutinize it, since creators as well as customers have blind spots and biased views.

For all its advances, and there are many, CX today – when analyzed objectively – is still mostly choppy, dysfunctional, too slow, and places too much burden on the customer.  Admittedly, some industries (such as banking and telecommunications) have made more progress than others, yet largely, especially for massive enterprises, CX is frankly still very siloed.

Firms spend millions of dollars on data collection, design thinking, journey mapping, voice of customer, CRM systems, employee training, and so on.  Yet when these efforts are not coordinated around a systematic process, data, technology, and culture – hyper coordinated and committed to improving CX –most of that investment will be for naught.

It’s human nature to either ignore feedback or want to defend your baby’s looks, and if you’re busy defending versus fixing simple things, CX won’t improve much.  It’s also human nature to pass the buck – meaning no one will take responsibility, because even though at our core we’re pack animals, it’s ironically not in our nature to communicate issues across organizational pillars.

CX transformation doesn’t come easy and it doesn’t come cheap, and rarely comes fast.  But for those who listen to and watch customers, fix ten thousand small things fast, live by the adage innovate or die, and cross-functionally collaborate on behalf of better customer experience, the rewards will be plenty.

[i] Forrester, http://www.datastax.com/wp-content/uploads/resources/whitepaper/Forrester-CX-TLP_DataStax.pdf, April 2017

[ii] Zendesk,  http://d16cvnquvjw7pr.cloudfront.net/resources/whitepapers/Omnichannel-Customer-Service-Gap.pdf, November 2013

[iii] Infosys, https://www.infosys.com/newsroom/press-releases/Documents/genome-research-report.pdf, 2013

Marketing Timing & Content: Machine Learning – Episode #3

Great timing & content lead to great marketing tactics and performance.

In this episode, I explore how you can use machine learning (ML) and artificial intelligence (AI)  to improve your message timing and the content you employ – further improving experiences for your customers.

I explore “2 Cool Areas” of Machine Learning & Artificial Intelligence applications – to make your marketing smarter:

  • Timing Optimizing for your Marketing Execution & Tactics
  • Automated Content Generation and Predictive Content Recommendations

My tips are aimed at improving your marketing efficiency & effectiveness.

5 traits of Super Marketing & Content – All year round

superbowl

As a lifelong marketer, I love this time of year.  Super Bowl season is when everyone is talking about one of its main attractions – The Super Bowl Commercials and Content.   Which are the most creative, funny, and memorable?  Can you actually remember the brand that ran them?   Do you remember that ad from 20 years ago?

What dawned on me was this.  As marketers, if we could only harness this level of excitement, interest, engagement and positive reactions toward marketing and selling, year round, then we would have the right recipe for massive success.   But like the concept of Growth Hacking, we also need to find a way to harness key elements, but then execute without shelling out inordinate amounts of money.

If you have noticed, there has also been an expanding halo effect around game day commercials.   Supporting digital programs in the weeks leading up to the big day, social media contests, Top 10 greatest ad videos, and chatter for days afterwards.

So what makes Super Bowl advertising so compelling (besides the inordinate amounts of money shelled out)?   I think it boils down to these key things.

  • Great ads are extraordinarily funny, surprising – sometimes even a bit shocking – and certainly unique and innovative, in a way that relates to a wide audience.
  • They tease out emotions we can relate to (universal appeal), and are amplified in a way that we cheer.
  • Everyone gets to be a critic, in the moment, and our emersion in current events is often tested. So since we feel our opinion matters, we pay attention and weigh in.  In other words, we are engaged.
  • There is a sense of anticipation and a high bar. We have seen the stage and set before, yet the actors and props are unknown.   Nonetheless, we know those that take the stage have to be pretty good.
  • Products aren’t being sold by the company. Instead, it’s either an experience or feeling, or if it’s a product it’s being sold by a celebrity, an ordinary child, or even an animal.  All things we love and relate to more than companies and brands.

Easier said than done?  Not necessarily.  Although your budget, creative powers, and your casting may be more limited than Apple or Coke, you can still follow these rules of thumb, asking yourself this question each time you create content, “Is this going to capture the imagination of my audience, and build upon a great story and reputation.”

Take an example.   Suppose you are selling business software – conceivably not the most glamorous product in the world to try to sell.   Is your content creation approach really just like everyone else in your market?  Describe your features; show your return on investment, blah blah blah.   Is that catching and unique?   Will you pull some emotion out of me?   Will I champ at the bit to share my opinion with others?  Will I wait with bated breath for your next piece of content?

Rhetorical questions with obvious answers.   You need to take risks to win.  Yet you can do this in a way where the risks won’t really be that great, and the rewards are likely.   Stand out.  Use others to tell your stories, be funny and visually stimulating, and articulate the extraordinary experiences I will have when I sign up to take my journey with you.

Note:  These views are my own, and not that of my employer