2020: A Big Year for Media Spend Will Underscore Data’s Role in Marketing Strategy

With the longest U.S. economic growth span on record, one might think the wheels may be about to come off of the economy — and marketing spend along with it. Not so, says Bruce Biegel, senior marketing partner at The Winterberry Group, during his annual forecast about marketing strategy.

It is the best of times.

With the longest U.S. economic growth span on record, one might think the wheels may be about to come off of the economy — and marketing spend along with it. Not so, says Bruce Biegel, senior marketing partner at The Winterberry Group, during his Direct Marketing Club of New York annual presentation, “The Outlook for Data Driven Advertising & Marketing 2020.”

marketing strategy
Source: Winterberry Group (2020), with Permission | Credit: Winterberry Group

Sure, there is caution. The Great Recession displaced many — and served to accelerate digital disruption from retail to finance to certainly marketing, forever. Perhaps businesses have never felt safe, sound, and secure ever since. One might call it “wise agitation.” And it really has been consumer spending that has served as the primary driver of growth, particularly in 2019.

Not the R Word …

Outside of business caution and flat earnings, where are the signs of another recession? They are hard to find.

Inflation and wage growth are hardly sputtering — even as the nation’s unemployment rates are at record lows. Trade rows and impeachment proceedings only appear to buoy the stock market. Even inside the world of marketing, privacy restrictions have not diminished the luster of data deployed for marketing and insight. And with the Olympics and a General Election this year, it should be times aplenty for many media channels, agencies, data providers, and tech companies — as these events are traditional hallmarks of spending.

So who are some of the winners in the current marketing and media environment?

… But plenty of D, Even Still

D, as in Direct: Biegel noted that “Buy Direct” is creating continuous rise and sale in DTC [direct to consumer] brands. The subscription economy is booming and traditional distribution channels — read, retail — continue with a “D” of their own, “disintermediation.”

“The five-year growth (through 2019) of DTC retail is four times that of the retail market revenues — 7.64% growth vs. 1.78%,” he reports.

That doesn’t translate to digital-first success, however, as such approaches are not scaling as rising costs in paid social, for example, are inhibiting customer acquisition.

marketing strategy
Source: Winterberry Group Spend Estimates (2020)

D, as in Digital: Online media spending overall grew by 19.1% in 2019 — compared with a 5.9% decline in offline media spending for the same year. Among all digital media categories in 2019, paid search grabbed the largest share — followed by display and paid social. Yet search spending “only” grew by 13.2%, compared to 21% growth for display, and 23% growth for paid social. For 2020, online media spending will continue to climb — reaching $166.4 billion in spending, while offline media will reverse its decline and post a 2.3% climb this year (remember, Olympics and Elections) to $223.1 billion.

D, as in Data: Data spending also posted healthful growth in 2019 — up by 5% — with another 6.2% growth expected in 2020. Is data working harder for marketers — as in, increasing marketing efficiency? Possibly. Spending on offline data dropped 5.5% in 2019 — while spending on email data and analytics posted 22.4% growth, and spending on digital media data and analytics (other than email) grew by 14.4%. Yet businesses are wholly satisfied with their own level of “data-centricity.” Biegel says, “Organizations are slightly more ‘data-centric’ this year than when asked in 2017 — on the whole, industry data-centricity is not progressing as envisioned.”

marketing strategy
Source: IAB-Winterberry Group Data-Centric Org (2020)

What’s Driving Data Strategy at Businesses?

Beigel reports three primary facilitators:

  • A desire to deliver better customer experiences;
  • Heightened regulatory compliance requirements and need to honor consumer preferences; and,
  • Increased demand to better leverage both first- and third-party data assets.

With a data-for-marketing marketplace in the United States now valued — both offline and online —- at $23 billion, those are three very important drivers that marketing professionals needs to get right. Or else our C-suite credibility may be diminished.

Artificial intelligence also has benefited from this reverence for data. Beigel reports that $11 billion has been invested globally in AI in the past five years — with 80% of marketers seeing AI “revolutionize” marketing in the next five years. Much of this investment is set on drawing insights from both structured and unstructured data sources.

And Where Are There Lingering Concerns?

Besides enterprise command of data assets, which could go either superbly or not, there are other concerns — both macro and micro, Biegel reports.

U.S. economic growth will likely slow to 1.9%, with global growth at pronounced risk. Corporate earnings may disappoint — leading to tightened purse strings. Tariffs may be reduced – nation by nation, region by region — but to what immediate impact? In short, Biegel says, “Limited tailwinds indicate that growth must be earned or bought.”

Among offline media there will be pockets of growth — outdoor, shopper marketing, linear and addressable TV — though direct mail will only squeak growth, with radio, newspaper, and magazines continuing their declines (even as their digital counterparts grow).

Search, display, and social will continue to dominate online media spend — but less mature channels, such as influencer marketing, digital video, and OTT [over-the-top] streaming, and digital audio will post rapid growth from much smaller bases. That portends good times for online data — but is it all rosy?

marketing strategy
Source: Winterberry Group Spend Estimates (2020)

For example, are customer acquisition and retention costs, though, declining in these channels? It may be that media inflation will eat into marketing efficiency, particularly if “targeting” data gets less precise and, as a result, relevance gets more elusive. Privacy restrictions, while well-meaning, are not always implemented in such a way that serve best consumers. Still, only 16% of businesses have reduced their spending and reliance on certain kinds of data as a result of new and potential data privacy regulations, Biegel reports.

So, come December 2020, will all of these predictions and concerns bear out? That’s one of the reasons I attend Bruce Biegel’s Annual Outlook at DMCNY each year. As great a prognosticator as he is and as on-target as his business, data, and economic models are — he’s always close enough to the market to say where struggles remain, where the work of data-driven marketing is hard, where hiccups happen, and the like. These are all of the many micro and macro reasons that any best of times can go awry.

His January 2020 predictions are now in the books — and we will all be back again in January 2021 — barring any hiccups.

Data Will Lead Marketers Into a New World in 2020

What will be so different in this ever-changing world, and how can marketers better prepare ourselves for the new world? Haven’t we been using data for multichannel marketing for a few decades already?

The year 2020 sounds like some futuristic time period in a science fiction novel. At the dawn of this funny sounding year, maybe it’s good time to think about where all these data and technologies will lead us. If not for the entire human collective in this short article, but at the minimum, for us marketers.

What will be so different in this ever-changing world, and how can marketers better prepare ourselves for the new world? Haven’t we been using data for multichannel marketing for a few decades already?

Every Channel Is, or Will Be Interactive 

Multichannel marketing is not a new concept, and many have been saying that every channel will become interactive medium. Then I wonder why many marketers are still acting like every channel is just another broadcasting medium for “them.” Do you really believe that marketers are still in control? That marketers can just push their agenda, the same old ways, through every channel? Uniformly? “Yeah! We are putting out this new product, so come and see!” That is so last century.

For instance, an app is not more real estate where you just hang your banners and wait for someone to click. By definition, a mobile app is an interactive medium, where information goes back and forth. And that changes the nature of the communication from “We talk, they listen” to “We listen first, and then we talk based on what we just heard.”

Traditional media will go through similar changes. Even the billboards on streets, in the future, will be customized based on who’s seeing it. Young people don’t watch TV in the old-fashioned way, mindlessly flipping through channels like their parents. They will actively seek out content that suites “them,” not the other way around. And in such an interactive world, the consumers of the content have all the power. They will mercilessly stop, cut out, opt out, and reject anything that is even remotely boring to “them.”

Marketers are not in charge of communication anymore. They say an average human being looks at six to seven different screens every day. And with wearable devices and advancement in mobile technologies, even the dashboard on a car will stop being just a dumb dashboard. What should marketers do then? Just create another marketing department called “wearable division,” like they created the “email marketing” division?

The sooner marketers realize that they are not in charge, but the consumers are, the better off they would be. Because with that realization, they will cease to conduct channel marketing the way they used to do, with extremely channel-centric mindsets.

When the consumers are in charge, we must think differently. Everything must be customer-centric, not channel- or division-centric. Know that we can be cut off from any customer anytime through any channel, if we are more about us than about them.

Every Interaction Will Be Data-based, and in Real-time

Interactive media leave ample amounts of data behind every interaction. How do you think this word “Big Data” came about? Every breath we take and every move we make turn into piles of data somewhere. That much is not new.

What is new is that our ability to process and dissect such ample amounts of data is getting better and faster, at an alarming rate. So fast that we don’t even say words like Big Data anymore.

In this interactive world, marketers must listen first, and then react. That listening part is what we casually call data-mining, done by humans and machines, alike. Without ploughing through data, how will we even know what the conversation is about?

Then the second keyword in the subheading is “real-time.” Not only do we have to read our customers’ behavior through breadcrumbs they leave behind (i.e., their behavioral data), we must do it incredibly fast, so that our responses seem spontaneous. As in “Oh, you’re looking for a set of new noise-canceling earbuds! Here are the ones that you should consider,” all in real-time.

Remember the rule No. 1 that customers can cut us out anytime. We may have less than a second before they move on.

Marketers Must Stay Relevant to Cut Through the Noise

Consumers are bored to tears with almost all marketing messages. There are too many of them, and most aren’t about the readers, but the pushers. Again, it should be all about the consumers, not the sellers.

It stops being entirely boring when the message is about them though. Everybody is all about themselves, really. If you receive a group photo that includes you, whose face would you check out first? Of course, your own, as in “Hmm, let me see how I look here.”

That is the fundamental reason why personalization works. But only if it’s done right.

Consumers can smell fake intimacy from miles away. Young people are particularly good at that. They think that the grownups don’t understand social media at all for that reason. They just hate it when someone crashes a party to hard-sell something. Personalization is about knowing your targets’ affinities and suggesting — not pushing — something that may suite “them.” A gentle nudge, but not a hard sell.

With ample amounts of data all around, it may be very tempting to show how much we know about the customers. But never cross that line of creepiness. Marketers must be relevant to stay connected, but not overly so. It is a fine balance that we must maintain to not be ignored or rejected.

Machine Learning and AI Will Lead to Automation on All Fronts

To stay relevant at all times, using all of the data that we have is a lot of work. Tasks that used to take months — from data collection and refinement to model-based targeting and messaging — should be done in minutes, if not seconds. Such a feat isn’t possible without automation. On that front, things that were not imaginable only a few years ago are possible through advancement in machine learning or AI, in general.

One important note for marketers who may not necessarily be machine learning specialists is that what the machines are supposed to do is still up to the marketers, not the machines. Always set the goals first, have a few practice rounds in more conventional ways, and then get on a full automation mode. Otherwise, you may end up automating wrong practices. You definitely don’t want that. And, more importantly, target consumers would hate that. Remember, they hate fake intimacy, and more so if they smell cold algorithms in play along the way.

Huge Difference Between Advanced Users and Those Who Are Falling Behind

In the past, many marketers considered data and analytics as optional items, as in “Sure, they sound interesting, and we’ll get around to it when we have more time to think about it.” Such attitudes may put you out of business, when giants like Amazon are eating up the world with every bit of computing power they have (not that they do personalization in an exemplary way all of the time).

If you have lines of products that consumers line up to buy, well, all the more power to you. And, by all means, don’t worry about pampering them proactively with data. But if you don’t see lines around the block, you are in a business that needs to attract new customers and retain existing customers more effectively. And such work is not something that you can just catch up on in a few months. So get your data and targeting strategy set up right away. I don’t believe in new year’s resolutions, but this month being January and all, you might as well call it that.

Are You Ready for the New World?

In the end, it is all about your target customers, not you. Through data, you have all the ammunition that you need to understand them and pamper them accordingly. In this age, marketers must stay relevant with their targets through proper personalization at all stages of the customer journey. It may sound daunting, but all of the technologies and techniques are ripe for such advanced personalization. It really is about your commitment — not anything else.

3 Ways to Derive Actionable Sales Insights From Content Marketing Data

Nearly all businesses these days are aiming to build content marketing strategies that enable them to “rise above the crowd” or “be heard above the noise.” Whether they’re succeeding or not is anyone’s guess. The trick with content marketing data is to know how each dataset feeds into the bottom line.

As we ring in 2020, talking about the importance of content marketing and why every brand should be doing it is a record that has been broken for quite some time.

Nearly all businesses these days are aiming to build content marketing strategies that enable them to “rise above the crowd” or “be heard above the noise.” Whether they’re succeeding or not is anyone’s guess. What’s for sure is that branded content campaigns are yielding copious amounts of big data about customers and their behaviors. Whether it’s web traffic, conversion rates, or engagement levels, the trick with content marketing data is to know how each dataset feeds into the bottom line.

With so much data being created and collected every day, it can be very difficult and overwhelming to translate this information into sales insights. In fact, one of the biggest challenges marketers face is associating content with revenue:

marketers' top challenges
Credit: MarketingCharts.com

So how can you show ROI from content marketing without letting your head spin from data overload? Let’s find out.

1. Unify Data Streams

Data collection is only getting more complex as sources and systems continue to grow. Depending on how far-reaching your content strategy is, the data streams that relate to your sales regime won’t always yield black and white answers. Therefore, market research data, customer data, and pretty much all company data should be unified in a single ecosystem. This will let decision-makers spot key trends that tie directly into the bottom line.

For example, you need to know things like the content channels that are bringing in the strongest leads, the common threads among your most profitable customer profiles, the types of content that get the most engagement, where your referrals are coming from, and so on.

Marketers these days are growing increasingly dependent on the constantly-growing number of data sources. The major tasks at hand involve monitoring, analyzing, and finding benchmark performances for each campaign.

Until recently, it was a huge (and expensive) effort to develop tool integrations that aligned content marketing data sources in ways that boosted the sales process. Thankfully, AI-enabled business intelligence and CRM platforms allow businesses to efficiently analyze their data streams. One such tool is Salesforce’s Einstein, which can unify company data to identify new audiences, deliver sales projections, create in-depth customer profiles, and even automate storytelling.

Salesforce Einstein
Credit: Salesforce.com

AI-based content platforms are designed to score touchpoint information to discover patterns that help determine which leads are likely to convert. They can create associations between varied data sets, such as website engagement and publicly available demographic information, for example, and turn these into stories.

The way you set up these stories determines which datasets you will unify, and how your content or CRM platform will evaluate the information for predictive purposes. For instance, you might want to use a story to maximize potential earnings from a particular product. This could involve data sets related to engagement rates, lead nurturing, landing page conversion, and so on.

The more data you feed into such a system, the more precise the predictions you’ll be able to make. AI and machine learning are enabling data scientists to apply a combination of predictive analytics and meta data management to business. This lets marketers anticipate changes in consumer behavior and the impact of macroeconomic trends on business.

2. Identify Snags in the Buyer’s Journey

Making a sale in B2B requires way more than flashy advertisements and bold promotions. The modern buyer’s journey is typically made up of three key stages: Awareness, Consideration, Decision.

buyer's journey
Credit: HubSpot.com

Ideally, each stage should work as a vector to ultimately produce sales.

While it’s easy for marketers to design content marketing strategies to play to each stage, the parts that tend to get overlooked are the transitions. In other words, how well does your content bridge the gap between one stage of the buyer’s journey and the next? This is perhaps where data provides the most valuable insights related to sales.

Funnel visualizations can reveal patterns in regard to where people drop out or delay the progression through the buyer’s journey. Using this data, businesses can refine their transitions and work to eliminate the major roadblocks. Some simple metrics to start out with are bounce rates, session duration, and conversion rates of your landing pages — all of which can be tracked via Google Analytics.

google analytics behavior flow
Credit: Google Analytics

For example, let’s say you run a SaaS company and your Awareness stage content (blog posts, e-books, podcasts, etc.) is doing a fantastic job in getting traffic to your Consideration stage content on your website, which includes landing pages to sign up for a webinar or download a white paper.

However, you notice that the bounce rate for these pages is very high (around 95%) and the time on page is only a few seconds. This is a good indicator that there is interest, but the transitions from your Awareness content aren’t giving people enough information or motivation to convert. Therefore, it might be time to re-examine content at the transition point (email invitations to the webinar that you send to people who’ve read your blog posts or subscribed to your newsletters) or add more information to your landing pages.

Keep in mind, snags in the buyer’s journey can have much deeper-rooted issues than the example above — all of which can impact your sales numbers. Understanding how your content impacts the success or failure of your customer journey will likely require a great deal of critical thinking (and digging into funnel data).

3. Use Intent Data to Constantly Refine Your Sales Model

The term “intent data” is a buzzword that has been floating around the marketing world for all of a hot second. Intent data refers to behavioral information that gauges a person’s online activity and how likely they are to take a desired action. In terms of how this relates to your content marketing and sales efforts, these insights combine both topic and contextual data.

intent data
Credit: Infer.com

Topic data refers to the level of interest someone expresses about a subject when they search for something on the web. For example, if someone Googles “how to simplify customer service,” and lands on your blog about how to program a chatbot, they are showing some degree of intent. There are generally four categories of topic data:

  1. Anonymous First-Party Behavioral — These are visitors to your website who haven’t taken any action that identifies themselves. It is possible to identify their company by their IP addresses.
  2. Known First-Party Behavioral — These are visitors to your website who have shared personal information by filling out a form.
  3. Anonymous Third-Party Behavioral — These are unknown visitors to other websites with similar content to yours. You can identify them via the topics they browse and track them via their IP addresses.
  4. Known Third-Party Behavioral — These are known visitors to other websites who’ve shared information and whose content preferences are recorded. You can then use tools to measure and capitalize on the purchase intent of a pre-segmented audience.

Now, topic data is more or less useless without the right context. Contextual data revolves around diving into the who of the person taking the action. For instance, if the visitor reading your article on chatbots is a business owner, there is a good chance the person is considering a solution for customer service needs. On the other hand, if the reader is a programmer, it’s very possible the professional is looking for information about how to build or improve a chatbot. In this way, intent data plays a key role in how you define your sales process.

Different types of web visitors will have slightly different views of the buyer’s journey in relation to your business. You need a system that gauges the intent of a visitor from how they interact with your content on various platforms; the insights you glean from this form the basis of how you craft your landing pages.

Intent data lets marketers put the right content in front of the right eyes. Start by personalizing your website to “anonymous” users. Solutions like Evergage can be synced with CRM data and use machine learning to better understand the intent of visitors. It can then draw on a wide range of behavioral insights to help you serve ultra-targeted content.

Evergage
Credit: Evergage.com

For example, the system can sort visitors by industry and automatically build segments based on key attributes. From here, you can deliver customized messaging that fits into the narrow views of each of these segments.

Next, you should base the processing of inbound leads on engagement. Ideally, this should work to quantify the visitor’s intent based on the manner in which they interact with your content. If someone is looking at your blog section, they would likely fall lower on your lead scoring model. If they are looking at pricing, they would obviously rank higher.

scoring model
Credit: Business2Community.com

Intent data should always play a key role in how you nurture leads and go about making sales.

Over to You

In many ways, the data you get from your content marketing strategy is the lifeblood of your sales efforts. As big data continues to grow at exponential rates, both in size and application, the challenge will always be using these insights to boost your bottom line.

Refining your content strategy is a task that never truly ends. As long as you keep up with what your analytics are telling you, and identify and iron out the weak spots, spikes in sales are always around the corner. Good luck!

Marketers’ New Year’s Resolution: ‘I Will Give Customers More T-R-A’

The turning of the calendar may mean a new fiscal year for many marketing organizations, but there is one constant that remains paramount for customer-centric enterprises:  TLC (tender loving care) and how we demonstrate such sentiments to our prospects, customers, and donors — whomever applies.

The turning of the calendar may mean a new fiscal year for many marketing organizations, but there is one constant that remains paramount for customer-centric enterprises: TLC (tender loving care) and how we demonstrate such sentiments to our prospects, customers, and donors — whomever applies.

According to its most recent survey of more than 13,400 C-suite leaders, IBM is recommending data users to pursue another approach in their efforts to build consumer trust: T-R-A, as in transparency, reciprocity, and accountability. See the IBM report, “Build Your Trust Advantage: Leadership in the Age of Data and AI Everywhere” (Opens as a PDF)

The report states:

“To satisfy the modern requirement for trust, leading organizations are adopting three basic principles as their guide: transparency, reciprocity, and accountability. Each provides assurance to customers, but is more than good marketing. These principles are the scaffolding that supports the modern enterprise, remade to propagate trust.”

In a time when trust is increasingly harder to earn — and where consumers question the data-for-value exchange — one may think to shun the data quest. But that is not the correct course of action, nor a viable option, at all. Instead, the answer is to triple up efforts — to seek out and ensure higher quality data sources, to ensure chain-of-trust on permissions and consumer controls, and to hold ourselves and data partners accountable for results.

According to IBM, enterprise leaders — “torchbearers” — have fused their data and business strategies as one. “The torchbearers defy data fears, enhancing the trust of customers.”  Eighty-two percent say they use data to strengthen customer trust, compared with 43% of “aspirational” enterprise data users.

So what does T-R-A entail?

Transparency

“Customers demand transparency of data associated with the products and services, and, in the case of personal data, assurances that it’s used in a fair manner and kept safe,” the report states.

Three Keys to Consumer Love: Transparency, Reciprocity and Accountability. | Credit: Pexels.com

And it’s not just about data used in marketing — it’s also about data regarding how products are developed and manufactured, for example, and user reviews and recommendations. Any data that informs the customer journey, and enables the brand promise, really.

Reciprocity

“C-suite executives understand that to get access to data, they have to give something meaningful in return,” the report states. “The challenge? Organizations often don’t know what their customers would consider a fair exchange.”

That’s a fair assessment — as most consumers say they are skeptical about data-sharing benefits; particularly where privacy is concerned. So it is incumbent upon us to discover — probably using data — what truly motivates consumers’ sense of trust and value. I don’t think we do as good a job as we could as brands, and perhaps as an industry, in explaining data’s value to the consumer. Thus, we must do better.

Accountability

“Accountability is synonymous with brand integrity,” the report opined. “To succeed in retaining trust while growing business or expanding into new marketers, marketers need to establish governance and policies to combat cyber risk and protect consumer trust and brand.”

To me, accountability extends beyond data security — and the lawsuits and brand erosion that may follow data breaches. Data governance is closer to the accountability mark: making sure our data supply chains are “clean,” and that they adhere to industry ethics and best practices.

Here’s Wishing You T-R-A in 2020

So I’m hoping my New Year and yours has a lot more T-R-A in the offing. If the consumers equates sharing of data with a loss of privacy, then no one wins — especially the consumer.

 

 

 

Marketers Caroling to CCPA: ‘Winter Wonderland’

Marketers caroling may not be what immediately comes to mind to get you in the holiday spirit, but here’s a little ditty about how useful data is to marketers. Sing it along to the melody of “Winter Wonderland.”

To all my many friends who are marketers in the field — the California Privacy Protection Act, new data privacy laws in Maine and Nevada, and who’s next? — this, too, we will endure. All the same, we shall all find new paths to prosper in the New Year, and the consumer will be better for it.

And yes, we should all be looking — shouting from the rooftops — for a single standard law from Congress sooner than later. Americans deserve better!

Is this working for you? I accept, I accept, I accept, I accept, I accept, I accept. Opt-out. Opt-Out. Opt-Out. Opt-Out, infinitum. In your face on every site you visit, and on every app you use?  I want to control data flows about me — not with a browser, not with a default that fails the financing of relevant content — but this is too much. Better for all to have acceptable uses discerned from unacceptable ones — defined by benefits and harms, respectively — legislate THAT, and let innovations flow.

So please join me in my sing-along:

“There’s a tale, are you listening?
Data flails, for the christening.
A new law in sight.
About to take flight,
Drownin’ in a regulated land.

Gone away is the long tail …
Within the walls, a new prevail.
Competition, insights,
Strategies in plight,
Drownin’ in a regulated land.

On the home page we can place an opt-out
Make it clear that data’s not for sale

Another referendum will get plopped out
‘I accept’ and the Internet will fail.

Innovation, on a vacay…
As a patchwork, takes a mainstay
Know better than us
Who can we trust?
Drownin’ in a regulated land

In the filings we can set it all right
Consumer trust is all that we care
They’ll say, ‘are you kidding, you get no rights
Except for private actions in the air.’

And so we toil, we perspire
As the relevance gets retired
They say privacy!
We know it’s not free,
Drownin’ in a regulated land.

[And the big ending…]

Did you say $55 billion?

[Oh, yes] Drownin’ in a regulated land.

Happy Holidays, everyone!

Why Pulling Out of Amazon Is the Smartest Decision for Your Brand

Nike announced that as part of the company’s focus on elevating consumer experiences through more direct, personal relationships, it will stop selling its merchandise directly to Amazon.com. Here’s why Nike made the right decision.

Nike announced that as part of the company’s focus on elevating consumer experiences through more direct, personal relationships, it will stop selling its merchandise directly to Amazon.com. Here’s why Nike made the right decision.

Partnering with Amazon undoubtedly has benefits — namely, a built-in audience and speedy delivery options. However, it’s crucial to consider what you’re jeopardizing in exchange. You’re losing control of how your brand is presented. Even if you’re lucky enough to benefit from Amazon’s search algorithm — another thing brands have no control over — you essentially have no say in how your brand experience is delivered.

Last year, Nike partnered with Jet.com, and given what Jet’s chief customer officer said, I’m not surprised. David Echegoyen told Footwear News, “the way in which people find, discover and use your product is as much part of the experience as the fact that you buy them and use them.” Echegoyen explained that Jet’s focus would be on delivering an experience that would allow both brands to utilize customer insights to enhance their experience. And because Nike products would only be sold direct from the brand on the Jet site, the confusion and brand dilution that shoppers often experience on marketplace platforms would effectively be eliminated.

What every brand should seek in its retail partners — and, really, all partners, to the extent that it’s possible — is recognition of the importance of delivering a cohesive brand experience at every touchpoint, and the desire and capabilities to do so. The advantages of owning your brand experience are abundant.

Control Your Customer Journey

By limiting the channels where your products are available, you’re better able to deliver the best experience to your customers. This includes everything from product recommendations to delivery preferences, the physical unboxing experience, and more. This controlled approach also serves as a preventive measure against counterfeiting issues that could otherwise tarnish your brand’s reputation.

Own Your Data

Amazon traces every shopper’s step, utilizing that data to make product suggestions based upon its own algorithm. These are insights that would be incredibly valuable to brands, arming them with information that can help to deliver a better experience across all platforms, ultimately earning loyal customers. The problem is Amazon owns that data and doesn’t share it with brands. Now, selling direct to consumer on your own channels provides you with 100 percent of your data, the benefits of which warrant its own article. With a compatible, focused retail partner, there may be more room for a discussion about data sharing.

Secure Better Profit Margins

It’s difficult to predict revenues when the sales process is out of your hands. Going direct to consumer gives brands the most control over profit margins. However, as a new or emerging brand, third-party channels are commonly part of the mix. Profit margins are dependent upon the type of partner and the value they bring to the table — or in this case, the cart. Amazon controls the market, so the terms of merchant agreements are almost certainly dictated. However, when you have a like-minded partner dedicated to delivering an experience, the terms may be subject to negotiation.

Shatter the Delivery Myth

Thanks to the “Amazon Effect,” brands and retailers have had to figure out how to meet delivery expectations. My company conducted a 2019 study that revealed online shoppers weigh shipping costs and delivery speed more heavily in their purchasing decisions than ever. Consider that 58 percent of respondents said shipping costs greatly impact their decision to make an online purchase, and 62 percent said free shipping was the most influential factor in their decision to make future purchases. By utilizing sales and transportation data from your fulfillment team, you can map your customers’ journeys and customize shipping pricing and delivery speed to meet their unique expectations.

Selling through partners can be a huge asset, but it means there will always be an intermediary between you and your customer. If your sales channels include third-party retailers, make sure you’re all on the same page. Amazon can bolster brands in the short term, but to build a sustainable business, you must control how customers experience your brand, wherever they are.

Maria Haggerty is CEO and one of the original founders of Dotcom Distribution, a premier provider of B2C and B2B fulfillment and distribution services. 

Marketers Find the Least-Wrong Answers Via Modeling

Why do marketers still build models when we have ample amounts of data everywhere? Because we will never have every piece of data about everything. We just don’t know what we don’t know.

Why do marketers still build models when we have ample amounts of data everywhere? Because we will never have every piece of data about everything. We just don’t know what we don’t know.

Okay, then — we don’t get to know about everything, but what are the data that we possess telling us?

We build models to answer that question. Even scientists who wonder about the mysteries of the universe and multiverses use models for their research.

I have been emphasizing the importance of modeling in marketing through this column for a long time. If I may briefly summarize a few benefits here:

  • Models Fill in the Gaps, covering those annoying “unknowns.” We may not know for sure if someone has an affinity for luxury gift items, but we can say that “Yes, with data that we have, she is very likely to have such an affinity.” With a little help from the models, the “unknowns” turn into “potentials.”
  • Models Summarize Complex Data into simple-to-use “scores.” No one has time to dissect hundreds of data variables every time we make a decision. Model scores provide simple answers, such as “Someone likely to be a bargain-seeker.” Such a model may include 10 to 20 variables, but the users don’t need to worry about those details at the time of decision-making. Just find suitable offers for the targets, based on affinities and personas (which are just forms of models).
  • Models are Far More Accurate Than Human Intuition. Even smart people can’t imagine interactions among just two or three variables in their heads. Complex multivariate interaction detection is a job for a computer.
  • Models Provide Consistent Results. Human decision-makers may get lucky once in a while, but it will be hard to keep it up with machines. Mathematics do not fluctuate too much in terms of performance, provided with consistent and accurate data feeds.
  • Models Reveal Hidden Patterns in data. When faced with hundreds of data variables, humans often resort to what they are accustomed to (often fewer than four to five factors). Machines indiscriminately find new patterns, relentlessly looking for the best suitable answers.
  • Models Help Expand the Targeting Universe. If you want a broader target, just go after slightly lower score targets. You can even measure the risk factors while in such an expansion mode. That is not possible with some man-made rules.
  • When Done Right, Models Save Time and Effort. Marketing automation gets simpler, too, as even machines can tell high and low scores apart easily. But the keywords here are “when done right.”

There are many benefits of modeling, even in the age of abundant data. The goal of any data application is to help in the decision-making process, not aid in hoarding the data and bragging about it. Do you want to get to the accurate, consistent, and simple answers — fast? Don’t fight against modeling, embrace it. Try it. And if it doesn’t work, try it in another way, as the worst model often beats man-made rules, easily.

But this time, I’m not writing this article just to promote the benefits of modeling again. Assuming that you embrace the idea already, let’s now talk about the limitations of it. With any technique, users must be fully aware of the downsides of it.

It Mimics Existing Patterns

By definition, models identify and mimic the patterns in the existing data. That means, if the environment changes drastically, all models built in the old world will be rendered useless.

For example, if there are significant changes in the supply chain in a retail business, product affinity models built for old lines of products won’t work anymore (even if products may look similar). More globally, if there were major disruptions, such as a market crash or proliferation of new technologies, none of the old assumptions would continue to be applicable.

The famous economics phrase Ceteris paribus — all other things being equal — governs conventional modeling. If you want your models to be far more adaptive, then consider total automation of modeling through machine learning. But I still suggest trying a few test models in an old-fashioned way, before getting into a full automation mode.

If the Target Is Off, Everything Is Off

If the target mark is hung on a wrong spot, no sharpshooter will be able to hit the real target. A missile without a proper guidance system is worse than not having one at all. Setting the right target for a model is the most critical and difficult part in the whole process, requiring not only technical knowledge, but also deep understanding of the business at stake, the nature of available data, and the deployment mechanism at the application stage.

This is why modeling is often called “half science, half art.” A model is only as accurate as the target definition of the model. (For further details on this complex subject, refer to “Art of Targeting”).

The Model Is Only as Good as the Input Data

No model can be saved if there are serious errors or inconsistencies in the data. It is not just about bluntly wrong data. If the nature of the data is not consistent between the model development sample and the practical pool of data (where the model will be applied and used), the model in question will be useless.

This is why the “Analytics Sandbox” is important. Such a sandbox environment is essential — not just for simplification of model development, but also for consistent application of models. Most mishaps happen before or after the model development stage, mostly due to data inconsistencies in terms of shapes and forms, and less due to sheer data errors (not that erroneous data is acceptable).

The consistency factor matters a lot: If some data variables are “consistently” off, they may still possess some predictive power. I would even go as far as stating that consistency matters more than sheer accuracy.

Accuracy Is a Relative Term

Users often forget this important fact, but model scores aren’t pinpoint accurate all of the time. Some models are sharper than others, too.

A model score is just the best estimate with the existing data. In other words, we should take model scores as the least-wrong answers in a given situation.

So, when I say it is accurate, I mean to say a model is more accurate than human intuition based on a few basic data points.

Therefore, the user must always consider the risk of being wrong. Now, being wrong about “Who is more likely to respond to this 15% discount offer?” is a lot less grave than being wrong about “Who is more likely to be diabetic?”

In fact, if I personally face such a situation, I won’t even recommend building the latter model, as the cost of being wrong is simply too high. (People are very sensitive about their medical information.) Some things should not just be estimated.

Even with innocuous models, such as product affinities and user propensities, users should never treat them as facts. Don’t act like you “know” the target, simply because some model scores are available to you. Always approach your target with a gentle nudge; as in, “I don’t know for sure if you would be interested in our new line of skin care products, but would you want to hear more about it?” Such gentle approaches always sound friendlier than acting like you “know” something about them for sure. That seems just rude on the receiving end, and recipients of blunt messages may even think that you are indeed creepy.

Users sometimes make bold moves with an illusion that data and analytics always provide the right answers. Maybe the worst fallacy in the modern age is the belief that anything a computer spits out is always correct.

Users Abuse Models

Last month, I shared seven ways users abuse models and ruin the results (refer to “Don’t Ruin Good Models by Abusing Them”). As an evangelist of modeling techniques, I always try to prevent abuse cases, but they still happen in the application stages. All good intentions of models go out the window if they are used for the wrong reasons or in the wrong settings.

I am not at all saying that anyone should back out of using models in their marketing practices for the shortfalls that I listed here. Nonetheless, to be consistently successful, users must be aware of limitations of models, as well. Especially if you are about to go on full marketing automation. With improper application of models, you may end up automating bad or wrong practices really fast. For the sake of customers on the receiving end — not just for the safety of your position in the marketing industry — please be more careful with this sharp-edged tool called modeling.

Earn Consumer Trust Through ‘Surprise and Delight’ in a Post-Privacy Age

Recent consumer research from Pew Research Center shows we have some work to do persuading consumers to let us use data about them for marketing. Right now, the risks seem to outweigh the benefits, in consumers’ view. At least for now.

Recent consumer research from Pew Research Center shows we have some work to do persuading consumers to let us use data about them for marketing. Right now, the risks seem to outweigh the benefits, in consumers’ view. At least for now.

Marketing may be an annoyance to some — but too often, it’s conflated by consumers (and privacy advocates, and some policymakers) to our detriment into real privacy abuses, like identity theft, or hypothetical or imagined outcomes, such as higher insurance or interest rates — to which clearly marketing data has no connection.

There needs to be a bright line affixed between productive economic use of data (such as for marketing) — and unacceptable uses (such as discrimination, fraud, and other ills).

As consumers feel they have lost all data control — perhaps one might describe the current state as “post-privacy” — it is doubtful the answer to consumer trust lies in more legal notices pushed to them online. Consumers also have told Pew the emerging cascade of notices are not well understood or helpful.

Consumer Trust
Image Source: Pew Research Center, 2019

When Pew explores more deeply the root of what consumers find acceptable and unacceptable, opportunities for marketers may indeed arise. For example, the study summary states:

“One aim of the data collection done by companies is for the purpose of profiling customers and potentially targeting the sale of goods and services to them, based on their traits and habits. This survey finds that 77% of Americans say they have heard or read at least a bit about how companies and other organizations use personal data to offer targeted advertisements or special deals, or to assess how risky people might be as customers. About 64% of all adults say they have seen ads or solicitations based on their personal data. And 61% of those who have seen ads based on their personal data say the ads accurately reflect their interests and characteristics at least somewhat well. (That amounts to 39% of all adults.)”

This is why regulating privacy — from self-regulation to public policy — is so challenging. A broad brush is not the right tool. We want to preserve the innovation, we want to improve consumer experiences, while giving consumers meaningful protection from data use practices that are harmful and antithetical to their interests.

An Industry Luminary Lends Her Perspective

Image: Martha Rogers, Ph.D. (LinkedIn)

Martha Rogers, Ph.D., who co-authored the seminal book “The One to One Future”with Don Peppers in 1993, helped to usher in the customer relationship management (CRM) movement. Today, CRM  often manifests itself in brands seeking to map customer journeys and to devise better customer experiences, and a lot of business investment in data and technology.

Reflecting on privacy last month in New York, Rogers said, “The truth of the matter is, we always judge ourselves by our intentions. Yet we judge others by their actual actions. The problem is that everyone is doing the same thing with us [as marketers].”

How much of that business spending resonates with consumers? “When 400 chief executive officers were asked if their companies provided superior customer experiences, 80 — that’s eight-zero — percent said ‘yes.’ Yet only 8% of customers said that companies were providing superior customer experience. Customers also judge us by our actions, not by our intentions.”

Rogers told two “surprise and delight” stories that illustrate how powerful smart data collection, analysis, and application can be.

“We need customer data to get the job done. A regular Ritz-Carlton customer I know once asked hotel staff for a hyper-allergenic pillow for his room. Now when he goes to a Ritz-Carlton, he always has a hyper-allergenic pillow in his room. He told me he just loved how the Ritz-Carlton had changed over all its pillows to hyper-allergenic ones.”  Rogers said she didn’t have the heart to tell him it was just his room — and the hotel simply had recorded, honored, and anticipated his preference.

Another story came from insurer USAA. Upon returning from tours of duty in Iraq and Afghanistan, USAA sent a refund on auto insurance premiums in the form of a live check and a letter. The letter thanked the soldiers for their service, and reasoned that a car must not have been used much or at all, while a soldier was overseas — hence, the refund. “Do you know 2500 of these checks were returned by customers, uncashed?” Rogers reported, noting that many of these military families have limited means. “Wow, stay strong … keep your money — some of the policy holders said to the company. How do you compete in that category if you’re another insurance company?”

These two cases both show smart data collectoin — applied — builds customer trust and loyalty, no matter what their feelings may be about privacy, in general.

“There are three reasons why we care about privacy,” Rogers said. “One is because there are criminals out there. We don’t want to give data to the robbers or the hackers. Second is because some of us do have secrets — and I’m not naming any names. And we don’t want people knowing every blessed thing about us. And the third reason that we just want our privacy is because [our lives] can be embarrassing.”

Consumer Trust Is Like a Pencil Eraser

“Privacy in an interconnected world is a pipe dream, an oxymoron,” she continued. “Still, we have to access and use customer data to give those great customer experiences. So what happens now? We have to do things [with data] that are good for customers, and not for ourselves [as marketing organizations]. Regulations and laws are really just a floor.”

“If you want to be truly trust-able, it’s about doing things right. One lie can ruin a thousand truths,” she said. “Trust is sort of like the eraser on a pencil. It gets smaller and smaller with each mistake we make. So we have to be careful. Do things right. Do the right thing. Be proactive.”

“No matter how fantastic technology is, it can’t top that trust,” she said.

How many Ritz-Carltons and USAAs — surprise and delight — does it take to undo a Cambridge Analytica or an Equifax? I’m actually optimistic on this. Because better customer experiences, brand relevance, and resonance through data insights will continue to win. We just have to prove it, to the customer, millions of times, one by one, every day — in the very important data-driven marketing work we do.

 

Developing Technology Standards to Support Privacy Regulations of the Future

Advertising has played a vital role in the Internet’s mass adoption. But, as the industry evolved, consumer privacy took a back seat. Today’s technologies provide an opportunity to rebuild the digital advertising infrastructure to benefit publishers, brands, and consumers — and build in privacy, from the ground up.

Advertising has played a vital role in the internet’s mass adoption, but as the industry evolved, consumer privacy took a back seat.

Consumer privacy became a national conversation after Cambridge Analytica, a political consulting firm used by the Trump campaign, was able to obtain raw data harvested from up to 87 million Facebook profiles and use it to segment and target users in ways that critics argue amounts to voter manipulation.

Since then, congressional committees and governmental agencies have expanded investigations into Facebook, Google, and other ad tech industry players. GDPR came to the US in the form of CCPA, the California Consumer Privacy Act, a law designed to give consumers similar power over the data they generate online.

Our industry is now struggling to prove to both consumers and regulators that we can be trusted with their data, but there’s hope. Cutting-edge technologies provide an opportunity to rebuild the digital advertising infrastructure to benefit publishers, brands, and consumers — and build in privacy, from the ground up.

The First Step: Joining Forces

Cryptography and blockchain have already emerged as solutions for adding verification and validation layers that ensure accountability and efficiency in the media supply chain. But the only way to drive adoption of these forward-thinking solutions and solve for consumer privacy is by bringing together key stakeholders in the industry, educating them on the benefits and developing the technical standards that will create the change the industry needs.

“I knew blockchain paired with cryptography could deliver significant change to the advertising industry,” says Adam Helfgott, CEO of MadHive and founding member of AdLedger. “I also knew it would take a concerted effort to drive adoption across such a broad landscape of stakeholders.”

Uniting brands, agencies, publishers, and technology vendors provides an open forum for collaboration, allowing the industry to express their concerns and tackle the issues head on. Advertising industry leaders like Meredith, Hershey, IPG, Publicis, and GroupM are forming working groups that release findings for broader industry education, while companies like Omnicom, MadHive, and Beachfront are already engaging in proof-of-concept projects to tackle issues like fraud, brand safety, and transparency.

So, it begs the question: Why not leverage these technologies for privacy as well?

The Privacy Solution = Privacy-by-Design

Cryptography is already being used to keep consumer data safe, at-scale, in an industry adjacent to advertising: e-commerce. Every time you buy something on your favorite website and the little green lock pops up in your browser as you type in your credit card information, cryptography is being used to protect that sensitive information.

But cryptography’s potential runs much deeper than this single application. It can provide mathematical proof for things like data provenance, while simultaneously ensuring regulatory compliance. This gives publishers the ability to secure their first-party data and thereby control access to their most precious resource – their audience. For advertisers, this immutable chain of custody and identity validation of supply-chain participants creates a brand-safe environment in which customers are reached with the right message at the right time.

The best part? Cryptography and blockchain can be baked into the underlying digital advertising infrastructure, which will automate this entire process and create a system with privacy-by-design. But the only way to integrate these technologies and drive mainstream adoption is through the unification, education, and collaboration of key industry stakeholders.

Long-term fixes take time, but the value prop for publishers and advertisers is evident. And maybe the GDPR and CCPA regulations are the push the industry needs to join forces and work toward a long-term solution.

Marketing Pros Provide Advice for Peers

When marketing pros provide advice, marketing practitioners listen. One of the high points of the New York marketing community calendar each year is the Silver Apple Gala hosted by the Direct Marketing Club of New York. The fete toasts the business and industry leadership success of honored individuals.

When marketing pros provide advice, marketing practitioners listen. One of the high points of the New York marketing community calendar each year is the Silver Apple Gala hosted by the Direct Marketing Club of New York. The fete, held this year on Nov. 7 near Times Square, toasts the business and industry leadership success of honored individuals, and at least one corporation or organization.

Each “Silver Apple” recipient has contributed for 25 or more years to our field, and since 1985, there have been 248 such honorees, including these four individuals in 2019:

Marketing, Career Wisdom They Share

So when more than 200 of your friends, family, and peers come together, what pearls of wisdom do you have to share?

Carl Horton, IBM

“The ability to execute against the dream in real time,” is what excites Carl Horton, Jr., in his current position in B2B marketing at IBM. Horton credits colleagues who have placed “personal investments in me” and dared to let him take crazy ideas (artificial intelligence applications don’t seem so crazy today) and make them reality, as well as the unconditional love of family.

One key takeaway from Horton:

“The importance of diversity in leadership and innovation: The NextGen of innovation may come from someone of experience, income, race, gender, gender identity, very different from our own.”

Here, here, we need to foster it.

Britt Vatne, ALC

Britt Vatne, who leads the data management practice at ALC, talked about a career pivot 15 years ago, when she worked with a nonprofit client for the first time, March of Dimes, and it showed to her how critical acquiring, retaining, and growing donors are. She also credited industry luminaries, such as the late Bob Castle and the energetic Donn Rappaport (in the room) – as well as her father, who came to America from Norway, never finished primary school, and taught her “there is no substitute for hard work.” She was the first of her family to go to college.

“Being human, being respectful, and having integrity are non-negotiable,” she said. “Be a positive role model, and you’ll have the love and loyalty of family.”

And probably, quite a few colleagues and clients, too.

Joe Pych, NextMark & Bionic Advertising

Joe Pych, who is the startup founder of two companies — NextMark and Bionic Advertising, says his “go-to metric is sales growth.” CRM [customer relationship management] is so much more of an opportunity than simply managing costs, he says. Set a goal, uncover an idea, execute, and measure results.

”I feel selfish standing alone with so much support I’ve received over the years,” he said, referring first to his mother, who put four children through college on an electrician’s salary – and then went and got a masters herself.

He also thanked many of his client data businesses that helped make his first company take off — companies, such as MeritDirect, ALC, Worlddata, and Specialists Marketing Services (SMS), among others – who took a chance on a Hanover, NH-based enterprise. To his wife, Robin.

“Those missed vacations, I’m sorry … again.”

Gretchen Littlefield, Moore DM Group

Gretchen Littlefield, CEO of Moore DM Group for the past two years, also served at Infogroup for 14 years, where she helped develop its nonprofit, political, and federal government marketing practice – which propelled her into her current role atop Moore.

In 2018, she co-founded the Nonprofit Alliance, where she serves as vice chair, to advance in Washington the interests of nonprofit and charitable organizations.

“I fell into this business like everyone else,” she said, starting from data entry and advancing to “getting data [insights] out of the industry.”

She thanked many industry leaders among her mentors and influencers, among them Jim Moore, Larry May, and Vin Gupta.

“It seems as if on every innovation, we are working together and competing all the time. Coopetition,” she said. “The flow of data – from list rentals, to coops, to marketing clouds. We share data for growth.”

Littlefield also emphasized investment in education, citing Marketing EDGE and Direct Marketing Club of New York, for their respective roles in attracting bright students to the marketing field.

“Time goes by faster than we expect — Joe [Pych] and I were Marketing EDGE Rising Stars back in the day. I’m just as excited today as my first day in direct marketing, but mostly grateful for the friendships.”

In addition, there were three special honors bestowed, among them a first-time “Corporate Golden Apple” to Marketing EDGE for its more than half-century of creating and connecting market-ready college students for careers in marketing. And two Excellence Apples:

  • 2019 Apple of Excellence, Advocacy:
    Tony Hadley, SVP, Regulation and Public Policy, Experian (Washington, DC)
  • 2019 Apple of Excellence Disruptor:
    Mayur Gupta, CMO, Freshly (New York, NY)

There’s more to share – but that likely will be another post! Stay tuned …