Understanding What a Customer Data Platform Needs to Be

Marketers try to achieve holistic personalization through all conceivable channels in order to stand out among countless messages hitting targeted individuals every day, if not every hour. If the message is not clearly about the target recipient, it will be quickly dismissed. So, how can marketers achieve such an advanced level of personalization?

Modern-day marketers try to achieve holistic personalization through all conceivable channels in order to stand out among countless marketing messages hitting targeted individuals every day, if not every hour. If the message is not clearly about the target recipient, it will be quickly dismissed.

So, how can marketers achieve such an advanced level of personalization? First, we have to figure out who each target individual is, which requires data collection: What they clicked, rejected, browsed, purchased, returned, repeated, recommended, look like, complained about, etc.  Pretty much every breath they take, every move they make (without being creepy). Let’s say that you achieved that level of data collection. Will it be enough?

Enter “Customer-360,” or “360-degree View of a Customer,” or “Customer-Centric Portrait,” or “Single View of a Customer.” You get the idea. Collected data must be consolidated around each individual to get a glimpse — never the whole picture — of who the targeted individual is.

You may say, “That’s cool, we just procured technology (or a vendor) that does all that.” Considering there is no CRM database or CDP (Customer Data Platform) company that does not say one of the terms I listed above, buyers of technology often buy into the marketing pitch.

Unfortunately,the 360-degree view of a customer is just a good start in this game, and a prerequisite. Not the end goal of any marketing effort. The goal of any data project should never be just putting all available data in one place. It must support great many complex and laborious functions during the course of planning, analysis, modeling, targeting, messaging, campaigning, and attribution.

So, for the interest of marketers, allow me to share the essentials of what a CDP needs to be and do, and what the common elements of useful marketing databases are.

A CDP Must Cover Omnichannel Sources

By definition, a CDP must support all touchpoints in an omnichannel marketing environment. No modern consumer lingers around just in one channel. The holistic view cannot be achieved by just looking at their past transaction history, either (even though the past purchase behavior still remains the most powerful predictor of future behavior).

Nor do marketers have time to wait until someone buys something through a particular channel for them to take actions. All movements and indicators — as much as possible — through every conceivable channel should be included in a CDP.

Yes, some data evaporates faster than others — such as browsing history — but we are talking about a game of inches here.  Besides, data atrophy can be delayed with proper use of modeling techniques.

Beware of vendors who want to stay in their comfort zone in terms of channels. No buyer is just an online or an offline person.

Data Must Be Connected on an Individual Level

Since buyers go through all kinds of online and offline channels during the course of their journey, collected data must be stitched together to reveal their true nature. Unfortunately, in this channel-centric world, characteristics of collected data are vastly different depending on sources.

Privacy concerns and regulations regarding Personally Identifiable Information (PII) greatly vary among channels. Even if PII is allowed to be collected, there may not be any common match key, such as address, email, phone number, cookie ID, device ID, etc.

There are third-party vendors who specialize in such data weaving work. But remember that no vendor is good with all types of data. You may have to procure different techniques depending on available channel data. I’ve seen cases where great technology companies that specialized in online data were clueless about “soft-match” techniques used by direct marketers for ages.

Remember, without accurate and consistent individual ID system, one cannot even start building a true Customer-360 view.

Data Must Be Clean and Reliable

You may think that I am stating the obvious, but you must assume that most data sources are dirty. There is no pristine dataset without a serious amount of data refinement work. And when I say dirty, I mean that databases are filled with inaccurate, inconsistent, uncategorized, and unstructured data. To be useful, data must be properly corrected, purged, standardized, and categorized.

Even simple time-stamps could be immensely inconsistent. What are date-time formats, and what time zones are they in?  Dollars aren’t just dollars either. What are net price, tax, shipping, discount, coupon, and paid amounts? No, the breakdown doesn’t have to be as precise as for an accounting system, but how would you identify habitual discount seekers without dissecting the data up front?

When it comes to free-form data, things get even more complicated. Let’s just say that most non-numeric data are not that useful without proper categorization, through strict rules along with text mining. And such work should all be done up front. If you don’t, you are simply deferring more tedious work to poor analysts, or worse, to the end-users.

Beware of vendors who think that loading the raw data onto some table is good enough. It never is, unless the goal is to hoard data.

Data Must Be Up-to-Date

“Real-time update” is one of the most abused word in this business. And I don’t casually recommend it, unless decisions must be made in real-time. Why? Because, generally speaking, more frequent updates mean higher maintenance cost.

Nevertheless, real-time update is a must, if we are getting into fully automated real-time personalization. It is entirely possible to rely on trigger data for reactive personalization outside the realm of CDP environment,  but such patch work will lead to regrets most of the time. For one, how would you figure out what elements really worked?

Even if a database is not updated in real-time, most source data must remain as fresh as they can be. For instance, it is generally not recommended to append third-party demographic data real-time (except for “hot-line” data, of course). But that doesn’t mean that you can just use old data indefinitely.

When it comes to behavioral data, time really is of an essence. Click data must be updated at least daily, if not real-time.  Transaction data may be updated weekly, but don’t go over a month without updating the base, as even simple measurements like “Days since last purchase” can be way off. You all know the importance of good old recency factor in any metrics.

Data Must Be Analytics-Ready

Just because the data in question are clean and error-free, that doesn’t mean that they are ready for advanced analytics. Data must be carefully summarized onto an individual level, in order to convert “event level information” into “descriptors of individuals.”  Presence of summary variables is a good indicator of true Customer-360.

You may have all the click, view, and conversion data, but those are all descriptors of events, not people. For personalization, you need know individual level affinities (you may call them “personas”). For planning and messaging, you may need to group target individuals into segments or cohorts. All those analytics run much faster and more effectively with analytics-ready data.

If not, even simple modeling or clustering work may take a very long time, even with a decent data platform in place. It is routinely quoted that over 80% of analysts’ time go into data preparation work — how about cutting that down to zero?

Most modern toolsets come with some analytics functions, such as KPI dashboards, basic queries, and even segmentation and modeling. However, for advanced level targeting and messaging, built-in tools may not be enough. You must ask how the system would support professional statisticians with data extraction, sampling, and scoring (on the backend). Don’t forget that most analytics work fails before or after the modeling steps. And when any meltdown happens, do not habitually blame the analysts, but dig deeper into the CDP ecosystem.

Also, remember that even automated modeling tools work much better with refined data on a proper level (i.e., Individual level data for individual level modeling).

CDP Must Be Campaign-Ready

For campaign execution, selected data may have to leave the CDP environment. Sometimes data may end up in a totally different system. A CDP must never be the bottleneck in data extraction and exchange. But in many cases, it is.

Beware of technology providers that only allow built-in campaign toolsets for campaign execution. You never know what new channels or technologies will spring up in the future. While at it, check how many different data exchange protocols are supported. Data going out is as important as data coming in.

CDP Must Support Omnichannel Attribution

Speaking of data coming in and out, CDPs must be able to collect campaign result data seamlessly, from all employed channels.  The very definition of “closed-loop” marketing is that we must continuously learn from past endeavors and improve effectiveness of targeting, messaging, and channel usage.

Omnichannel attribution is simply not possible without data coming from all marketing channels. And if you do not finish the backend analyses and attribution, how would you know what really worked?

The sad reality is that a great majority of marketers fly blind, even with a so-called CDP of their own. If I may be harsh here, you are not a database marketer if you are not measuring the results properly. A CDP must make complex backend reporting and attribution easier, not harder.

Final Thoughts

For a database system to be called a CDP, it must satisfy most — if not all — of these requirements. It may be daunting for some to read through this, but doing your homework in advance will make it easier for you in the long run.

And one last thing: Do not work with any technology providers that are stingy about custom modifications. Your business is unique, and you will have to tweak some features to satisfy your unique needs. I call that the “last-mile” service. Most data projects that are labeled as failures ended up there due to a lack of custom fitting.

Conversely, what we call “good” service providers are the ones who are really good at that last-mile service. Unless you are comfortable with one-size-fits-all pre-made — but cheaper — toolset, always insist on customizable solutions.

You didn’t think that this whole omnichannel marketing was that simple, did you?

 

Brands Need to Keep Engaging – Don’t Just Stop Because of Crisis

We are in extraordinary times – and it’s only prudent to recognize this. While the Fed may be doing everything possible to keep our economy afloat, we likely will remain in limbo until a public health victory is apparent. It’s time to take stock of what we do on behalf of our brands and clients, to immediate effect.

Among thousands of businesses these past two-plus weeks, many of us have effectively handed our marketing decisions over to finance and accounting. Which means, if you’re not producing an immediate revenue gain, you’re probably being cost-reduced to the bone, if not entirely out of work. Such is the illiquidous, flash-frozen effect of COVID-19 on our economy. We’ve lost more U.S. jobs in three weeks than we did during the expanse of the Great Recession.

Cash is in crunch, and though The Fed may be doing everything possible to keep our economy afloat (will it work?) we likely will remain in limbo until a public health victory is apparent. That could be months. It may yet take longer to resume growth – and who knows how business and consumer behavior may have changed by then? We are in extraordinary times – and it’s only prudent to recognize this.

It’s time to take stock of what we do on behalf of our brands and clients, to immediate effect. There is much work to do.

Marketing Must Continue … With Prudence

  • Every pharmacy, drug store, food store, and big-box retailer – and the agencies that support them – should proactively communicate store safety measures, and elevate “conveniences” such as shop-online-and-pick-up-in-store to the preferred method of distribution. This is an opportunity to build consumer and brand trust.
  • For financial marketers, the need to connect with consumers right now regarding savings, budgeting tools, and capital preservation should be a high priority. Make it happen.
  • On television, I’ve seen the messages of optimism from the likes of Walmart, Toyota, and Ford. (Post your inspired ad in the comments section below to share, please.) We need these messages right now. Beyond our own mortality, we will emerge on the other side of this. Brands need to be megaphones for hope and empathy. And certainly not insensitivity.
  • Perhaps TV spending is too steep for many brands’ budgets. In my email inbox, my favorite restaurants offer meals-to-go, my coffee house enables virtual tips for unemployed baristas and healthcare workers, and nonprofit organizations are postponing their live fundraising events with an online ask for the here and now. Needs don’t stop, in fact, the chronic has become acute. For those of us who can afford to help, there’s a collective mood to give. There are reasons to keep relevant communication appropriately flowing to audiences.
  • My previous post addressed data quality. Let me repeat: all those mobile and data visitors to your sites right now must not go unrecognized. Ensure you have a data and tech plan to identify (perhaps in the form of free registration, analyze, and engage accordingly.
  • Respond to the Census. Yes, do it for democracy. But we in the marketing business also know how invaluable Census data is to the economy, and the strategies we map for our brands.

So, yes, we’re all facing a flash freeze. And marketing-as-normal needs to be re-calibrated. So let’s re-calibrate … show our CFOs the likely payback, and let’s get going.

 

 

The Grand Reopening of the U.S. Economy Will Happen, Plan for It

We are in uncharted territory, much as we were in previous economic downturns and recessions. Yet, do know, another expansion will follow … eventually. There will be a grand reopening of our economy, and as marketers, we need to plan for it.

I love defaulting to optimism – even in the darkest of times. It’s been part of my survival mechanism through all sorts of crises. That being said, we are in uncharted territory in this new normal, much as we were in previous economic downturns and recessions. “The Great Recession” of 2008-2009 was largely Wall Street born and Main Street slammed. But remember, the Great Expansion followed. A possible recession stemming from COVID-19, however, would be largely reversed, with millions of livelihoods suddenly denied, and both Main Street and Wall Street being slammed in tandem. Yet, do know, another expansion will follow … eventually. There will be a grand reopening of our economy, and as marketers, we need to plan for it.

Listening to the U.S. President talk about getting parts of our country back to some semblance of normal by Easter may seem wild-eyed and some might say irresponsible. In reality, China is reportedly already back on line – after six-to-eight weeks of paralysis. Does this mean a possible “V-shaped” recession (very short), a “U-shaped” one (mild), or an “L-shaped” one (long term)? We don’t know.

It’s always dangerous to make prognostications, but we can learn from patterns elsewhere in the virology. With the United States now the most afflicted nation in sickness, we yet have a massive fight ahead to control viral spread. And doubt and fear have taken hold as two debacles have come about, one public health and one economic.

Unfortunately, there is no “on/off” switch for the viral crisis. Even when its spread is curtailed, which will happen, we’ve been shaken and edginess is going to remain. That’s only human.

Patterns of consumption will not resume as if nothing happened. Unemployment shocks will not reverse as easily as they came. So there will be a “new” normal.

However, a reopening is coming. You might say that’s my optimism, but folks – we are going to be okay in a time. It may not be of our choosing, as Dr. Fauci faithfully reports, but one that will be here nonetheless. As marketers, let’s get ready for it.

Look to Your Data to Prepare for What’s Next

Recessions are actually good times to look to the enterprise and get customer data “cleaned up.” The early 90s recession gave us CRM, and database marketing flourished. The end of the Internet 1.0 boom in 2000 brought data discipline to digital data. And the Great Recession brought data to the C-suite.

So let’s use this time to do a data checkup. Here are four opportunities:

  1. Data audits are often cumbersome tasks to do – but data governance is a “must” if we want to get to gain a full customer view, and derive intelligent strategies for further brand engagement. Quality needs to be the pursuit. Replacing cookie identification also is a priority. Understand all data sources to “upgrade” for confidence, accuracy, privacy, and permissions.
  2. March 15 might be a good date to do an A/B split with your customer data inputs – pre-virus and during-virus. What new patterns emerged in media, app usage, mobile use and website visits? Are you able to identify your customers among this traffic? If not, that’s a data and tech gap that needs to be closed.
  3. Customer-centricity or data silos? It’s always a good time to tear down that silo and integrate the data, yet sometimes healthy economic growth can mask this problem. Use the recessions to free up some time to actually get the work done.
  4. Test new data and identity solution vendors to increase match rates across your omnichannel spectrum – to better create a unified view of audiences, both prospects and customers. I’ve already seen one of my clients come up with a novel offer to analyze a subset of unidentified data to drive a substantive lift in matches.

As we work remotely, it’s important to understand that this current state of crisis is not a permanent state. Only once the virus is conquered, on its weaknesses not ours, can we really have any timetable to resume the economy. That being the health science, it just makes great business sense now to “stage” your data for that eventual Grand Reopening.

Is Identity Resolution the New, Must-Have Martech Solution?

There’s a bit of growing confusion and buzz in the martech space around the topic of identity resolution. It’s the new elixir being pitched as the critical additive to make your marketing technology stack work better, faster, and deliver better results. But is it?

There’s a bit of growing confusion and buzz in the martech space around the topic of identity resolution. It’s the new elixir being pitched as the critical additive to make your marketing technology stack work better, faster, and deliver better results. But is it?

For those of you familiar with the marketing technology space, every new solution comes with a blend of real value, hyperbole and needless complexity. Identity resolution is no different. Here I will try to unpack this relatively “new” capability and put it into perspective for marketing leaders. (Why did I put new in quotes? Keep reading to find out.)

What is Identity Resolution?

Identity resolution uses artificial intelligence (AI) to connect customer interactions and achieve a single customer view. The concept of capturing all customer interactions (marketing, engagements, sales, post sales), at the individual level, has been around for many years. However, achieving this goal has been very hard.

The reason is that customers interact with your brand across multiple channels (online and offline) while using multiple devices. Additionally, some interactions are anonymous or only provide limited identifiers. This interaction variability results in very complicated, disjointed customer data.

Until recently, most efforts at achieving a single customer view involved creating rules engines by which each interaction could be matched with other interactions and assigned to a single customer. Due to differences in the technology stack, channels employed, and the customer experience, rules engines had to be custom-built for each organization. This was expensive; enter AI.

Identity resolution uses AI in generating matching logic vs. using a team of analysts. The basic idea is to train the AI algorithm using known matches and then validate future correct matches the algorithm makes. This is why I refer to it as a “new” capability. In reality, it is only new because rules engines have been replaced by AI. For most marketers this change is only relevant if the match rates are better and the solution is cheaper than existing efforts are at achieving a Single Customer View.

What’s the Hype and Confusion About Identity Resolution?

While the addition of AI is innovative, it does not always translate into better match rates. Other major challenges with single customer view, such as the accurate collection of relevant data, still remain. AI, like any other analytic solution, also suffers from bad data and can put out spurious results. Therefore, verifying and validating AI matches is a task in and of itself.

The next issue to keep in mind is that identity resolution is probably not going to be sold as a separate solution in the near future. Within a short period of time, it will be integrated into larger martech solutions such as CRM or marketing clouds. Waiting to implement identity resolution could mean leaving the difficult task of systems integration to the cloud solution providers. However, the trade-off will be losing first mover advantage.

What Is the Value?

Single customer view has been the holy grail in marketing for good reason. With it, marketers can better understand the impact of interactions across the full customer experience life cycle. As an added benefit, marketers could also generate data-driven justifications for modifying or redesigning large segments of the customer experience. This will result in significant growth opportunities for your brand.

Despite the hype and confusion, identity resolution presents a great opportunity to finally achieve a single customer view. In theory, the introduction of AI should make identity resolution a desirable solution with better match rates and lowered costs. This means the evaluation of identity resolution tech is somewhat straight forward (though not necessarily easy).

The core evaluation question becomes, “Is the identity resolution solution cheaper and better at creating a single customer view vs. current efforts?”

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!

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.

 

Navigating Martech Amid the Land of Shiny Solutions

The marketing technology landscape has seen explosive growth the last couple of decades, but even when the field was a bit smaller, it was a challenge for marketers to clearly understand what all the solutions did.

The martech landscape has seen explosive growth the last couple of decades, but even when the field was a bit smaller, it was a challenge for marketers to clearly understand what all the solutions did.

Firms like CabinetM and others, as well as Scott Brinker’s Chief Marketing Technologist Blog, have tracked the growth of marketing technology solutions, with CabinetM cataloging more than 8,000 products across over 300 categories. And the growth doesn’t show signs of slowing or stopping.

This proposes a major problem, as marketers must decide where to expend their limited time and energy. Even after categorizing martech solutions by function, the job can feel impossible — because there are several hundred solutions per category.

The pressure to keep up with competitors and fear of missing out are strong impediments to developing a successful martech strategy. But rest assured, there is a method to getting through the madness. Let’s first review two steps any marketer needs to take when considering their marketing technology needs, and then dive into some key categories that marketers should be considering first when it comes to martech investments.

Step 1: Square Away Customer Strategy

The first step is to develop a technology-agnostic, but technology-aware customer strategy.

Knowing what technology to invest in really begins by thinking about what your customer strategy is and what it aspires to be. With thousands of solutions in the market, martech is the land of shiny objects. There are really cool innovations, such as augmented reality, geo beacons, IOT, AI, etc.

It’s natural to be attracted to these innovative solutions. However, investing in solutions based primarily on their cool factor generally results in a confusing customer strategy and poor ROI.

The world of retailer apps is a good example: There are countless innovative and helpful branded mobile apps available for download. According to Statista, however, only a handful of apps are used with any real frequency, and most are deleted within 30 days. This is not to say that brands can’t have success with apps. However, solutions also need to be compelling and well-thought-out components of a larger winning customer strategy.

Target’s app, for example, helps drive a better physical in-store experience by helping you find what you need and informing you of relevant sales. Target could have added VR games or other gimmicks, but it chose to stay focused on improving the shopping experience.

By thinking about the brand, customer strategy, and customer pain points first, the martech universe becomes significantly easier to navigate.

Step 2: Decide on Investment vs. Outsource

The next step is to decide what tech solutions you want to invest in and which ones you will outsource. There are three questions to ask:

  • Is the solution essential to my customer strategy? In other words, would your brand be fundamentally
    impacted by the solution? Customer experience solutions would be prime examples, because customer experience has a straight-line relationship to how your brand is perceived today.
  • Does the solution require intense domain expertise? Some capabilities are constantly in flux. SEO, for example, is always a moving target. Staying ahead of search engine algorithms and how digital assistants — such as Alexa and Google Assistant — find information for their users takes some focused dedication.
  • Do I have or can I hire the appropriate talent? This can sometimes be the ultimate arbiter when deciding to invest time and energy on a solution. For example, while analytics and measurement solutions would qualify as essential to customer strategy, the ability to hire, retain, and manage an analytics capability can be very difficult. As a result, brands frequently outsource at least some of their analytical solutions.

Martech Categories Marketers Must Consider

While working through those steps can help to guide martech investments, there are four (plus one) solution categories that merit near-universal attention from marketers.

These solutions not only dominate tech-driven marketing, but also are constantly integrating more specialized solutions under their umbrella to provide end-to-end capabilities. (That said, even these dominant categories do not play in distinct sandboxes, and often overlap.)

Investing time and energy on these larger solutions is a great way to begin forming the foundation of a good marketing technology stack.

Customer Relationship Management (CRM)
This should be the central repository of important customer information and behavioral data. Most CRM
solutions also integrate modules that help make customer decisions based on the data. Some CRM solutions, such as Salesforce, have so many modules that it’s nearly impossible for one person to understand the full ecosystem. Nevertheless, understanding how to manage and utilize CRM systems will continue to be the foundation of managing brands well.

Customer Experience (CX)
These solutions help connect, measure, and improve the customer journey. Today, most brands are defined by their customer experience and less by what they advertise. Most CX solutions enable highly personalized interactions with customers and increase loyalty, making CX tech a critical investment for marketers. What’s more, each interaction increases knowledge of customer preferences and behaviors to be applied in future experiences.

Sales Automation
These solutions are focused on helping marketers complete time-consuming and repetitive tasks, such as sending communications or selecting the next offer based on customer behavior. Today, sales automation solutions make intelligent decisions on millions of marketing interactions at the individual customer level. This is also the technology segment most likely to make certain marketing jobs obsolete. For marketers worried about job security, developing skills in managing and executing automation software will be valuable insurance.

Analytics and Reporting
Data-driven marketing decisions are now the norm, along with measurement and ROI. Most martech solutions have a strong data foundation and generate appropriate reports automatically. That said, there is still a need to understand the larger analytical story and solutions, such as web and social analytics, data visualization, and BI tools, provide a critical view into marketing success. All marketers do not need a degree in data science. However, all marketers should understand the role of analytical solutions in driving marketing decisions from content to budget allocations.

Adtech (the Plus-One)
This category is purposefully separated from the other four. It contains ad buying solutions for programmatic display, search, social, mobile, and digital video advertising. Some large internal marketing departments may choose to invest in building this capability and there are real cost benefits involved. However, the digital ad industry is complex, in constant flux and highly algorithmic. While in-house marketers should be familiar with adtech trends, they should consider adtech investments carefully. In many cases, adtech is probably best left to digital ad agencies.

Navigating the Martech Landscape

By focusing on the dominant martech categories, there are many valuable solutions left on the table: such as content and asset management, SEO, geo and proximity-based marketing, social management, and chatbots. They all have an important role to play but are more likely to be integrated into larger solutions, over time. Unless these solutions are mission-critical to your customer strategy, it is better to outsource solution expertise.

Billions of venture capital dollars have been invested in martech this decade, and most industry insiders agree that there are too many solutions. The expectation is that the landscape will eventually shrink as winners separate from losers, but there is no sign of this happening soon.

Nevertheless, the overwhelming landscape can’t be a deterrent to jumping in and getting comfortable with marketing technology. It is being used by most marketers today and will only grow in influence.
What is important is to keep focused and not let the land of shiny objects distract you from executing your customer strategy.

The Danger of a Single Story for Marketers in the Age of Storytelling

We marketers today are really the new age of storytellers. Instead of coming up with those clever ads we once used to live to create, or live POS promotions when people actually went to stores, we now live, breathe, and exist pretty much to write and share stories.

We marketers today are really the new age of storytellers. Instead of coming up with those clever ads we once used to live to create, or live POS promotions when people actually went to stores, we now live, breathe, and exist pretty much to write and share stories.

Facebook stories drive SEO and build our network, so we can troll for new business. Instagram tells our stories visually and helps our brands come alive. Linkedin allows us to tell our business stories to peers and prospects in a “news” orientation.

Our websites, white papers, and content marketing are written just like classic novelettes. A teaser to create intrigue, a climax that builds with all of the reasons a customer needs us and needs us now, and a conclusion for how customers can get what they need from us. For a price.

Brands that win the most likes, posts, shares, retweets and resulting web traffic, live traffic and ultimately sales  are those same brands that know how to tell the best stories. Stories about our founders, our values, our products, our mission, and how customers can be part of our tribe. Patagonia is a master storyteller. Its catalogues read like diaries in the life of a customer who is living the life we’d like to live: canoes over white water to school, rock climbs at 80 years old, treks in Asia with sherpas, and more. In fact, its stories have been so well-received Patagonia’s published several books with content from its catalogues which you can buy on Amazon. True story!

Most reading this post likely have mastered the art and science of crafting solid brand stories and sharing them across all of the diverse communications channels we use today. So let’s shift perspective for a moment and look at storytelling another way.

What Are the Stories of Your Customers?

We invest enormous energy into CRM programs and systems that tell us about customer transactions, anniversary dates, revenue spend, demographics, and so on. This information helps us form “mass personalization,” as we lump them into categories of like customers and try to make them feel singularly special. They’ve caught on. Personalization at this level does little for sales and loyalty these days. Largely because we are telling a Single Story and trying to make it fit many diverse people.

My amazing daughters introduced me to Chimamanda Ngozi Adichie, a Nigerian author famous for her TED Talk on “The Dangers of a Single Story.”

She points out just how much people within all societies look at others, issues, the world from the lense of a single story, instead of multiple stories that, when combined, present a more accurate story of a person, a population, an issue, culture, a brand, and a customer. She discusses what it was like to go from Nigeria to school in the U.S. and how she was put into a story that others believed, as it was the only one they knew

You are from Africa. You must be poor, hungry, uneducated, and so much more.

Marketers are so often guilty of listening to and acting upon a single story when it comes to our customers. Women in a given demographic all shop alike, want the same products, have the same values. Men from coastal cities like purses, men from middle America do not. We craft our customer profiles around these stories and build messaging, content workflows, and experiences, accordingly. And it works, to a limited degree.

But what if we went a little deeper in researching our customers, so we could really tell amazing stories about them or to them that really struck at their heart and soul?

What if we asked them for their stories? Not testimonials about how wonderful we are; but instead, stories about them? How they feel about the world in which we live? Their communities? What inspires and moves them in life? How they like to spend their free time? Their favorite jobs, hobbies, and so on?

If we could create customer profiles that go deeper than transactions captured in our CRM systems, we would see our customers from many different perspectives. We would know what moves them to do what they do, choose what they do, and how we might be able to be part of a more meaningful story than just what they value enough to buy. In other words, a story about their life.

Takeaways

Slow down for a moment and listen to your customers speak about anything BUT your product. Discover those fascinating stories that make customers more than statistics. Move away from the “Danger of a Single Story” about customer groups you manage and sell to. As you do, you could just compete with Patagonia someday for the top-selling book on loyal customers!

How the Right Data Technology Can Fuel Your Organic Sales Growth

We’re all on a quest for organic sales growth. We all want to find ways to increase our conversion rate, improve our customer lifetime value, expand into adjacent markets, and launch new products successfully.

We’re all on a quest for organic sales growth. We all want to find ways to increase our conversion rate, improve our customer lifetime value, expand into adjacent markets, and launch new products successfully.

The problem is there are more ideas out there than we have time or money to implement. Do we try to target a fresh audience on LinkedIn, or do we invest in developing a new events business? Do we revamp our content marketing strategy to improve our conversion rates, or do we get into user experience redesign to help retention? With so many good growth ideas — and simultaneously so much pressure to grow our businesses — it can get stressful.

The sort-of good news is that data can help us optimize our decision making, so we can get the most bang for our buck with our limited resources. But here’s the rub: For most publishers, data is all over the place. It’s housed in every system under the sun, from the cloud to Excel spreadsheets to the old CFO’s hard drive to who knows where else.

Data is hot right now, so you might be panicking about the scattered state of data within your own organization. You might even feel tempted to go out and license the latest technology ASAP — maybe a CDP, DMP, or CRM.

Not so fast! If you take nothing else away from this article, remember this: Don’t spend a dollar on technology until you have a plan.

Now, I’m not saying don’t buy technology. These customer data tools are essential for leveraging one of our greatest assets, namely, a lot of information about our readers and customers. I’m saying approach this investment strategically. After all, a large technology investment that flops can be a fireable offense.

Where Do I Start?

If you’re going to spend money on technology, it has to be coupled with a strategy for either getting new customers or keeping existing customers.

Data technology can empower organizations in their quest for new customers in a number of ways. You can use data tools to evaluate the ROI of various marketing and sales channels to get more customers per dollar of overall marketing/sales spend.

Data can help you understand which content and actions reduce the sales lead time. Knowing what worked with your old leads enables you to move new ones down the funnel more quickly, and it makes life easier for your sales and ecommerce teams. Not only that, data can help you identify the predictors of a quality lead versus a waste-of-time lead (what we like to call a whale versus barnacle) so that you’re only sending marketing-qualified leads to your sales team in the first place.

Understanding various segments of your audience — otherwise known as personas or target audiences — can help you identify the groups that really jibe with your value proposition, so you can find the easiest markets to target. Plus, tracking how these segments react to various pricing, discounting, and bundling offers throughout their journey allows you to offer the right product to the right prospect at the right time.

When it comes to keeping customers, data technology can help you understand behaviors that lead to renewal or upsell versus behaviors that lead to churn. Understanding these actions and behaviors can also clue you into the things that customers love (and what they don’t like so much), so you can provide a better customer experience that leads to greater upselling and more repeat sales. Plus, you can create new products and services that suit your best customers’ needs to drive new revenue streams and encourage even greater retention rates.

How Do I Choose the Right Tech?

Selecting the right technology for your business starts with setting specific and measurable goals – and it’s a good idea to put them in writing. Once you do that, you can start looking at technology solutions that will help you achieve those goals.

If you implement a CDP, for example, what are you expecting to see? Maybe it’s a 20% increase in traffic, 30% increase in new business, and 15% increase in retention rates. Do the math to calculate the economic benefit of these results, and compare that to the cost of investing in the data technology. If you’re not happy with the ROI, either keep brainstorming to find new ways to drive revenue, or wait until you see a clearer path to ROI before making the investment.

How Do I Screen Vendors?

Just as important as the technology itself is the company and team behind it. When you’re considering your options, you don’t want a free dinner. You don’t want a fancy PowerPoint. You don’t want a flashy demo.

You want to be able to hand your strategy off to the vendor and have them show you exactly how their solution will deliver your desired results. How have they achieved similar results with other customers? What exactly do you need (or not need) in order to hit these goals?

If the vendor’s sales team doesn’t have the acumen to answer these questions, buyer beware. It may be a sign that the technology is a shiny new object, not something that will deliver ROI for your organization.

What Else Do I Need to Consider?

Don’t expect data technology implementation to be an overnight success. Think of it as a project to take on over a 12-month time horizon. The first step is a small one, and that’s listening to the right data. From there, you can analyze the data you’ve been listening to, and then, finally, take action on those insights.

It’s also important to remember that technology does not use itself. You need to properly staff and educate your team to act on the insights generated by the data tool you choose.

Implementing your new system will require a lot from the people in your organization. They need to learn how to use the new platform, spend time inputting data, assess and analyze the results of the information they’re receiving, make recommendations to leadership on how to change the business’s approach based on analytics received from the technology, and then make those changes happen.

Data technology can do incredible things to fuel your organization’s organic growth. But an investment in new technology is just that: an investment. You wouldn’t buy a house or put money in the stock market without doing some research and laying a solid groundwork first. The same must be true for your preparations to incorporate a new technology tool into your organization. When you properly strategize for, select, and resource your investment, you’ll be well on your way to predictable organic growth.

DTC Brands — How Data Fluency Enabled a Digital Disruption

My small apartment building’s lobby is a testament to these changing behaviors — there’s barely any room for the incoming DTC brands and related subscription economy shipments, daily. UPS, Amazon, FedEx and USPS — and their contractor networks — are delivering the goods that pile up. No drones just yet.

One of the entrepreneurial wonders of the 21st Century economy is actually not a very new concept at all. Direct-to-the-consumer (DTC) brands have been around since the first mail-order catalogues. Names such as LLBean, Orvis and Lands’ End revolutionized remote selling, as they understood the power of data and measurement in building these enterprises, by earning customer loyalty through superior products and customer service, and generating lifetime value.

So perhaps it’s only natural that in an increasingly digital, social and mobile world where data enables such direct connections more fluidly and products can be personalized at-scale DTC startups would come to be powerful brands in their own right. Bonobos, Casper and hundreds of others are rising to disrupt consumption and create new patterns of consumer behavior for even the most everyday product. Just this week, Rent the Runway officially became the newest unicorn in the venture capital investment world.

My small apartment building’s lobby is a testament to these changing behaviors there’s barely any room for the incoming DTC and related subscription economy shipments, daily. UPS, Amazon, FedEx and USPS  and their contractor networks  are delivering the goods that pile up. No drones just yet.

If You Can’t Beat Them …

Most retailers today report that their biggest threat comes from DTC brands (see Figure 1). Yes, Amazon and private labels also are leading concerns … but the truth is that building a business with seamless data flows enables the customer, and not the product, to be front-and-center. Brands that embrace customer-centricity, and have the customer data directly, cull the benefits.

Figure 1.

DTC brands
Credit: eMarketer, 2019. Used with Permission.

When database marketing and customer relationship management came of age, we knew that pesky problems such as data silos, legacy systems, senior executive buy-in and lack of data bench strength were crippling. Where entrepreneurs love data and have great products and service, those hurdles don’t exist.

No wonder traditional brands are quickly starting up or buying their own DTC brands and relationships. There’s power in data, and having first-party data relationships with consumers even as third-party data, and perhaps a few social influencers, enable discovery and facilitate connection – has brought about the mail-order bonanza of the digital age.

Physical retailers are not powerless in this mix after all, point-of-sale transactions still rule, and hybrids are flourishing (online to offline, buy online pick up in store). It’s how quickly these stores can integrate POS and transaction data with other forms of advertising data, and even serve as data-sharing coops with the brands they carry, to serve customers better. It’s about more relevance and more personalization. We haven’t heard the last roar from Main Street, Big Box and shopping malls. They’ll need to tap data’s power in similar fashion to go back on offense.