The Biggest Obstacle to Personalization Is the Creative Element

In a world where everyone is exposed to constant marketing through every conceivable media channel every day, messages that are not relevant to the target will be utterly ignored. And don’t blame the consumers for it, either. You, as a consumer, are trained to ignore irrelevant messages, as well.

In this consumer-centric environment, personalization is something all marketers must practice constantly, not only to increase the level of customer engagement, but also to not be ignored completely. And if your messages keep getting ignored, decreasing click-through rate isn’t just some annoying KPI that doesn’t look good in front of your boss, it may be an existential threat to your organization.

Unfortunately, personalization isn’t easy, simple, or cheap. There are many elements that must work harmoniously, so that each target sees something that is uniquely relevant to “her.”

4 Elements of Personalization

First, you need data about the target. What is she about, and what does she look like? That may require data from all kinds of sources — be they online or offline transactions, browsing history, store visits, reactions to previous campaigns (requiring both campaign and response history data), call-center logs, third-party demographic data, etc. Putting them all in one place, and rearranging them to create coveted Customer-360 View is often the first hurdle. But that is just the beginning. Without customer-centric data, there is no personalization — unless you count on your guesswork.

Then you need to make sense out of collected data. We often call such work analytics, which includes segmentation (or clustering), modeling, personas development (a series of affinity models), etc. Many marketers consider this to be the highest hurdle, as it requires different types of talents. Data scientists tend to think that the modeling work is the pinnacle of personalization, and they may not be wrong. But is it enough? So, what if they have 40 personas meticulously built by top-notch statisticians? How would you use them to differentiate messages for “each” target?

That leads to the third and forth elements in personalization, which are “Display Capability” and “Content and Creative.” Basically, you need to be able to show different creatives to different targets. If you are uniformly displaying the same content to everyone, what is the point in all this, no matter how many personas or affinity models you built?

Display capability is a technical hurdle. And you can procure technologies to overcome it, whether the challenge is dynamic web content, or personalized email delivery. You have to align pieces of technologies to make it happen. If Person A shows up on your website, and her affinity score is higher for “Luxury Travel” category in comparison to “Family Oriented Activities,” you should be able to show a picture of luxury cruise ship sailing in the Caribbean sunset, not necessarily a picture of happy children surrounded by cartoon characters.

As you can see, I am actually mixing three elements in this one example. I am assuming you built a series of personas (or affinity models). Your website should be dynamic so that such models can trigger different experiences for different visitors. Then of course, I am assuming you have ample amount of marketing creatives to differentiate messages. Display technology is a prerequisite in all this. If you don’t have it, go get it.

Your Persona Menu

Building a Customer-360 View is a customer-centric activity, but creating a persona menu is a selfish activity. What do you want to sell? And what kind of person would be interested in such products or services?

If you are selling fashion items, personas such as “Fashionista” or “Trend Setter” would be helpful. If you are pushing cutting-edge products, an “Early Adopter” persona would be necessary. If you are selling various types of insurance or security-related products, you will benefit from personas such as “Security Conscious.”

The important point here is that you should create persona menu based on your product and marketing roadmap. Be imaginative and creative. What kind of persona would be interested in your services? Once the goal is set, we need some samples of people who actually displayed such tendencies or behaviors. If you are building a persona called “Luxury Travel,” gather samples of people who actually have been on a luxury cruise ship or checked into luxury hotels (of course you have to define what constitutes “luxury”). Modelers do the rest.

Now, here is the reason why setting up a proper persona menu is so important. Not only will we define the target audience with it, but also categorize your marketing contents and digital assets with personas.

The most basic usage of any model is to go after high score individuals in a given category. You want to send messages to fashion-oriented people? Just select high score individuals using the Fashionista model.

But personalization is a little more complex that that. Let’s just say this one individual showed up at your website (or your store for that matter). You may have less than one second to show something that “she” would be interested in. Pull up all persona scores for that person, and see in which categories she scores high (let’s say over 7 out of a maximum score of 9). Going back to the previous example, if the target has score of 8 in Luxury Travel, and 4 in Family-oriented Activity, pull out the content for the former.

The Creative Element

Now, why is this article titled “The Biggest Obstacle to Personalization Is the Creative Element”? Because, I often see either lack of enough creative materials or lack of proper content library is the roadblock. And it really breaks my heart. With all the dynamic display capabilities and a series of models and personas, it would be a real shame if everyone gets to see the same damn picture.

I’ve seen sad and weird cases where marketers balk at the idea of personalization, as their creative agency is not flexible enough to create multiple versions of marketing materials. In this day and age, that is just a horrible excuse. What are they dealing with, some Mad Men agency people from the 1950s with cigarettes in their mouths and glasses of Scotch in their hands?

I’ve also seen other strange cases where proper personalization doesn’t happen – even with all good elements ready to be deployed – because departments don’t know how to communicate with one another. That is why someone should be in charge of all four elements of personalization.

How will the persona menu be created with grand marketing goals in mind? Who would procure actual data and build models? How will the resultant model/persona scores be shared throughout the organization and various systems, especially with the dynamic display technologies? How will the content library be tagged with all the relevant “persona” names (e.g., Tag “Luxury Travel” persona name to all digital assets related to “Luxury Cruise Ships”)?

Model scores (or personas) may function as a communication tool that binds different departments and constituents. Personalization is a team sport, and it is only as good as the weakest link. If you invested in building CDP solutions and analytics, go a little further and finish the work with the creative elements.

If you have a bunch of pictures stored in someone’s PC (or worse, some agency guy’s drawer), go build a digital content library. And while you’re at it, tag those digital assets with relevant persona names in your persona menu. Even automated personalization engines would appreciate your effort, and it will definitely pay off.

A Map or a Matrix? Identity Management Is More Complex By the Day

A newly published white paper on how advertisers and brands can recognize unique customers across marketing platforms underscores just how tough this important job is for data-driven marketers.

As technologists and policymakers weigh in themselves on the data universe – often without understanding the full ramifications of what they do (or worse, knowing so but proceeding anyway) – data flows on the Internet and on mobile platforms are being dammed, diverted, denuded, and divided.

In my opinion, these developments are not decidedly good for advertising – which relies on such data to deliver relevance in messaging, as well as attribution and measurement. There is a troubling anti-competition mood in the air. It needs to be reckoned with.

Consider these recent developments:

  • Last week, the European Court of Justice rendered a decision that overturned “Privacy Shield” – the safe harbor program that upward of 5,000 companies rely upon to move data securely between the European Union and the United States. Perhaps we can blame U.S. government surveillance practices made known by Edward Snowden, but the impact will undermine hugely practical, beneficial, and benign uses of data – including for such laudable aims as identity management, and associated advertising and marketing uses.
  • Apple announced it will mandate an “opt-in” for mobile identification data used for advertising and marketing beginning with iOS 14. Apple may report this is about privacy, but it is also a business decision to keep Apple user data from other large digital companies. How can effective cross-app advertising survive (and be measured) when opt-in rates are tiny? What about the long-tail and diversity of content that such advertising finances?
  • Google’s announcement that it plans to cease third-party cookies – as Safari and Mozilla have already done – in two years’ time (six months and ticking) is another erosion on data monetization used for advertising. At least Google is making a full-on attempt to work with industry stakeholders (Privacy Sandbox) to replace cookies with something else yet to be formulated. All the same, ad tech is getting nervous.
  • California’s Attorney General – in promulgating regulation in conjunction with the enforcement of the California Consumer Privacy Act (in itself an upset of a uniform national market for data flows, and an undermining of interstate commerce) – came forth with a new obligation that is absent from the law, but asked for by privacy advocates: Companies will be required to honor a browser’s global default signals for data collection used for advertising, potentially interfering with a consumer’s own choice in the matter. It’s the Do Not Track debate all over again, with a decision by fiat.

These external realities for identity are only part of the complexity. Mind you, I haven’t even explored here the volume, variety, and velocity of data that make data collection, integration, analysis, and application by advertisers both vital and difficult to do. As consumers engage with brands on a seemingly ever-widening number of media channels and data platforms, there’s nothing simple about it. No wonder Scott Brinker’s Mar Tech artwork is becoming more and more an exercise in pointillism.

Searching for a Post-Cookie Blueprint

So it is in this flurry (or fury) of policy developments that the Winterberry Group issued its most recent paper, “Identity Outlook 2020: The Evolution of Identity in a Privacy-First, Post-Cookie World.”

Its authors take a more positive view of recent trends – reflecting perhaps a resolve that the private sector will seize the moment:

“We believe that regulation and cookie deprecation are a positive for the future health and next stage of growth for the advertising and marketing industry as they are appropriate catalysts for change in an increasingly privacy-aware consumer environment,” write authors Bruce Biegel, Charles Ping, and Michael Harrison, all of whom are with the Winterberry Group.

The researchers report five emerging identity management processes, each with its own regulatory risk. Brands may pursue any one or combination of these methodologies:

  • “A proprietary ID based on authenticated first-party data where the brand or media owner has established a unique ID for use on their owned properties and for matching with partners either directly or through privacy safe environments (e.g.: Facebook, Google, Amazon).
  • “A common ID based on a first-party data match to a PII- [personally identifiable information] based reference data set in order to enable scale across media providers while maintaining high levels of accuracy.
  • “A common ID based on a first-party data match to a third-party, PII-based reference data set in order to enable scale across media providers while maintaining high levels of accuracy; leverages a deterministic approach, with probabilistic matching to increase reach.
  • “A second-party data environment based on clean environments with anonymous ID linking to allow privacy safe data partnerships to be created.
  • “A household ID based on IP address and geographic match.”

The authors offer a chart that highlights some of the regulatory risks with each approach.

“As a result of the diversity of requirements across the three ecosystems (personalization, programmatic and ATV [advanced television]) the conclusion that Winterberry Group draws from the market is that multiple identity solutions will be required and continue to evolve in parallel. To achieve the goals of consumer engagement and customer acquisition marketers will seek to apply a blend of approaches based on the availability of privacy-compliant identifiers and the suitability of the approach for specific channels and touchpoints.”

A blend of approaches? Looks like I’ll need a navigator as well as the map. As one of the six key takeaways, the report authors write:

“Talent gaps, not tech gaps: One of the issues holding the market back is the lack of focus in the brand/agency model that is dedicated to understanding the variety of privacy-compliant identity options. We expect that the increased market complexity in identity will require Chief Data Officers to expand their roles and place themselves at the center of efforts to reduce the media silos that separate paid, earned and owned use cases. The development of talent that overlaps marketing/advertising strategy, data/data science and data privacy will be more critical in the post-cookie, privacy-regulated market than ever before.”

There’s much more in the research to explore than one blog post – so do your data prowess a favor and download the full report here.

And let’s keep the competition concerns open and continuing. There’s more at stake here than simply a broken customer identity or the receipt of an irrelevant ad.

Beware of One-Size-Fits-All Customer Data Solutions

In the data business, the ability to fine-tune database structure and toolsets to meet unique business requirements is key to success, not just flashy features and functionalities. Beware of technology providers who insist on a “one-size-fits-all” customer data solution.

In the data business, the ability to fine-tune database structure and toolsets to meet unique business requirements is key to success, not just flashy features and functionalities. Beware of technology providers who insist on a “one-size-fits-all” customer data solution, unless the price of entry is extremely low. Always check the tech provider’s exception management skills and their determination to finish the last mile. Too often, many just freeze at the thought of any customization.

The goal of any data project is to create monetary value out of available data. Whether it is about increasing revenue or reducing cost, data activities through various types of basic and advanced analytics must yield tangible results. Marketers are not doing all this data-related work to entertain geeks and nerds (no offense); no one is paying for data infrastructure, analytics toolsets, and most importantly, human cost to support some intellectual curiosity of a bunch of specialists.

Therefore, when it comes to evaluating any data play, the criteria that CEOs and CFOs bring to the table matter the most. Yes, I shared a long list of CDP evaluation criteria from the users’ and technical points of views last month, but let me emphasize that, like any business activity, data work is ultimately about the bottom line.

That means we have to maintain balance between the cost of doing business and usability of data assets. Unfortunately, these two important factors are inversely related. In other words, to make customer data more useful, one must put more time and money into it. Most datasets are unstructured, unrefined, uncategorized, and plain dirty. And the messiness level is not uniform.

Start With the Basics

Now, there are many commoditized toolsets out in the market to clean the data and weave them together to create a coveted Customer-360 view. In fact, if a service provider or a toolset isn’t even equipped to do the basic part, I suggest working with someone who can.

For example, a service provider must know the definition of dirty data. They may have to ask the client to gauge the tolerance level (for messy data), but basic parameters must be in place already.

What is a good email address, for instance? It should have all the proper components like @ signs and .com, .net, .org, etc. at the end. Permission flags must be attached properly. Primary and secondary email must be set by predetermined rules. They must be tagged properly if delivery fails, even once. The list goes on. I can think of similar sets of rules when it comes to name, address, company name, phone number, and other basic data fields.

Why are these important? Because it is not possible to create that Customer-360 view without properly cleaned and standardized Personally Identifiable Information (PII). And anyone who is in this game must be masters of that. The ability to clean basic information and matching seemingly unmatchable entities are just prerequisites in this game.

Even Basic Data Hygiene and Matching Routines Must Be Tweaked

Even with basic match routines, users must be able to dictate tightness and looseness of matching logics. If the goal of customer communication involves legal notifications (as for banking and investment industries), one should not merge any two entities just because they look similar. If the goal is mainly to maximize campaign effectiveness, one may merge similar looking entities using various “fuzzy” matching techniques, employing Soundex, nickname tables, and abbreviated or hashed match keys. If the database is filled with business entities for B2B marketing, then so-called commoditized merge rules become more complicated.

The first sign of trouble often becomes visible at this basic stage. Be aware of providers that insist on “one-size-fits-all” rules, in the name of some universal matching routine. There was no such thing even in the age of direct marketing (i.e., really old days). How are we going to go through complex omnichannel marketing environment with just a few hard-set rules that can’t be modified?

Simple matching logic only with name, address, and email becomes much more complex when you add new online and offline channels, as they all come with different types of match keys. Just in the offline world, the quality of customer names collected in physical stores vastly differs from that of self-entered information from a website along with shipping addresses. For example, I have seen countless invalid names like “Mickey Mouse,” “Asian Tourist,” or “No Name Provided.” Conversely, no one who wants to receive the merchandise at their address would create an entry “First Name: Asian” and “Last Name: Tourist.”

Sure, I’m providing simple examples to illustrate the fallacy of “one-size-fits-all” rules. But by definition, a CDP is an amalgamation of vastly different data sources, online and offline. Exceptions are the rules.

Dissecting Transaction Elements

Up to this point, we are still in the realm of “basic” stuff, which is mostly commoditized in the technology market. Now, let’s get into more challenging parts.

Once data weaving is done through PII fields and various proxies of individuals across networks and platforms, then behavioral, demographic, geo-location, and movement data must be consolidated around each individual. Now, demographic data from commercial data compilers are already standardized (one would hope), regardless of their data sources. Every other customer data type varies depending on your business.

The simplest form of transaction records would be from retail businesses, where you would sell widgets for set prices through certain channels. And what is a transaction record in that sense? “Who” bought “what,” “when,” for “how much,” through “what channel.” Even from such a simplified view point, things are not so uniform.

Let’s start with an easy one, such as common date/time stamp. Is it in form of UTC time code? That would be simple. Do we need to know the day-part of the transaction? Eventually, but by what standard? Do we need to convert them into local time of the transaction? Yes, because we need to tell evening buyers and daytime buyers apart, and we can’t use Coordinated Universal Time for that (unless you only operate in the U.K.).

“How much” isn’t so bad. It is made of net price, tax, shipping, discount, coupon redemption, and finally, total paid amount (for completed transactions). Sounds easy? Let’s just say that out of thousands of transaction files that I’ve encountered in my lifetime, I couldn’t find any “one rule” that governs how merchants would handle returns, refunds, or coupon redemptions.

Some create multiple entries for each action, with or without common transaction ID (crazy, right?). Many customer data sources contain mathematical errors all over. Inevitable file cutoff dates would create orphan records where only return transactions are found without any linkage to the original transaction record. Yes, we are not building an accounting system out of a marketing database, but no one should count canceled and returned transactions as a valid transaction for any analytics. “One-size-fits-all?” I laugh at that notion.

“Channel” may not be so bad. But at what level? What if the client has over 1,000 retail store locations all over the world? Should there be a subcategory under “Retail” as a channel? What about multiple websites with different brand names? How would we organize all that? If this type of basic – but essential – data isn’t organized properly, you won’t even be able to share store level reports with the marketing and sales teams, who wouldn’t care for a minute about “why” such basic reports are so hard to obtain.

The “what” part can be really complicated. Or, very simple if product SKUs are well-organized with proper product descriptions, and more importantly, predetermined product categories. A good sign would be the presence of a multi-level product category table, where you see entries like an apparel category broken down into Men, Women, Children, etc., and Women’s Apparel is broken down further into Formalwear, Sportswear, Casualwear, Underwear, Lingerie, Beachwear, Fashion, Accessories, etc.

For merchants with vast arrays of products, three to five levels of subcategories may be necessary even for simple BI reports, or further, advanced modeling and segmentation. But I’ve seen too many cases of incongruous and inconsistent categories (totally useless), recycled category names (really?), and weird categories such as “Summer Sales” or “Gift” (which are clearly for promotional events, not products).

All these items must be fixed and categorized properly, if they are not adequate for analytics. Otherwise, the gatekeepers of information are just dumping the hard work on poor end-users and analysts. Good luck creating any usable reports or models out of uncategorized product information. You might as well leave it as an unknown field, as product reports will have as many rows as the number of SKUs in the system. It will be a challenge finding any insights out of that kind of messy report.

Behavioral Data Are Complex and Unique to Your Business

Now, all this was about relatively simple “transaction” part. Shall we get into the online behavior data? Oh, it gets much dirtier, as any “tag” data are only as good as the person or department that tagged the web pages in question. Let’s just say I’ve seen all kinds of variations of one channel (or “Source”) called “Facebook.” Not from one place either, as they show up in “Medium” or “Device” fields. Who is going to clean up the mess?

I don’t mean to scare you, but these are just common examples in the retail industry. If you are in any subscription, continuity, travel, hospitality, or credit business, things get much more complicated.

For example, there isn’t any one “transaction date” in the travel industry. There would be Reservation Date, Booking Confirmation Date, Payment Date, Travel Date, Travel Duration, Cancellation Date, Modification Date, etc., and all these dates matter if you want to figure out what the traveler is about. If you get all these down properly and calculate distances from one another, you may be able to tell if the individual is traveling for business or for leisure. But only if all these data are in usable forms.

Always Consider Exception Management Skills

Some of you may be in businesses where turn-key solutions may be sufficient. And there are plenty of companies that provide automated, but simpler and cheaper options. The proper way to evaluate your situation would be to start with specific objectives and prioritize them. What are the functionalities you can’t live without, and what is the main goal of the data project? (Hopefully not hoarding the customer data.)

Once you set the organizational goals, try not to deviate from them so casually in the name of cost savings and automation. Your bosses and colleagues (i.e., mostly the “bottom line” folks) may not care much about the limitations of toolsets and technologies (i.e., geeky concerns).

Omnichannel marketing that requires a CDP is already complicated. So, beware of sales pitches like “All your dreams will come true with our CDP solution!” Ask some hard questions, and see if they balk at the word “customization.” Your success may depend on their ability to handle exceptions than executing some commoditized functions that they had acquired a long time ago. Unless you really believe that you will safely get to your destination on a “autopilot” mode.

 

Website Features You Don’t Really Need

That barrage of options and possibilities can be hard to resist, which is why so many websites begin to look more like Frankenstein’s monster than Prince Charming. All of those website features — the widgets and toolkits and plugins — begin to add up.

You may think you’re a marketer by day and a consumer by night, but considering the number of marketing tools, services, experts, and ideas we’re bombarded with every day, we’re consumers even while wearing our marketing hats.

That barrage of options and possibilities can be hard to resist, which is why so many websites begin to look more like Frankenstein’s monster than Prince Charming. All of those website features — the widgets and toolkits and plugins — begin to add up.

It’s true that some live up to their promise, but all that noise these features create can blunt the effectiveness of your site.

Put more bluntly, you think you want website features. What you really want is effectiveness. Here’s how to keep your website on track.

Evaluate Web Marketing Tools Individually

Begin by evaluating any new feature you are tempted to include against your goals. Which goal(s) will it help you reach and what effort and resources will reaching those goals require? In other words, establish an expected ROI for the tool that you can measure its contribution against.

Evaluate Web Marketing Tools as a Whole

Examine the effort and resources mentioned above should also lead you to reviewing the new tool in relation to existing tools already in place. Is the new tool a 1:1 replacement of an existing tool? If so, can you A/B test them against one another?

Will the new tool work in tandem with an existing tool? Will it have an impact on that tool’s effectiveness? Is there still a net gain overall?

Evaluate Web Marketing Tools from Your Audience’s Perspective

Part of the ROI calculations above have to include audience attitudes and expectations. It would be great to know each prospect’s budget right from the start, but a new tool that asks for that information is going to drive your traffic down. Way, way down.

Real-world examples aren’t going to be that cut-and-dried, which circles us back to the idea of testing new tools whenever possible before implementing them across your entire marketing plan.

The One Feature Your Website Really Needs

More than anything else, you want a nimble website. One that helps you present a relevant message to each audience segment. One that speaks to prospects at each step in their buying cycle. One that encourages engagement and provides you with the opportunity to connect with prospects as they near their decision point.

Add all the bells and whistles you think will be effective, but track their impact on your web marketing metrics and make sure they support your ultimate goal — conversions.

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?

 

Are You Prepared to Handle the Oncoming Martech Consolidation?

For those marketers who rely on marketing technologies while navigating an industry landscape that changes almost daily, here are four considerations to make when adapting to the oncoming martech consolidation.

In previous posts, I have often referred to the vast martech landscape as the land of shiny objects. This was a term of derision and admiration. The landscape is filled with amazing innovations. It also can overwhelm even the most tech-savvy marketers and cloud strategic thinking.

We marketers were often so enthralled by what we could do, we often lose sight of what we should do. Today, as the economic impact of COVID-19 grows, the effect on marketing technology spend will be significant. The martech landscape has been built on billions of speculative investments from private equity. However, most of these products were barely profitable, if at all, before COVID-19. Most of them are now burning significant cash, and they were never capitalized with a pandemic in mind.

Soon, investors will be making hard choices. Many martech solutions will be sold at huge discounts, some will close. I believe the much-anticipated industry consolidation is around the corner. This is not the way we wanted martech consolidation to happen, but this is the painful reality. For those marketers who rely on these technologies while navigating an industry landscape that changes almost daily, here are four considerations to make when adapting to the oncoming martech consolidation:

  1. Hire “The” technology expert. Many martech companies have implantation consultants; the best ones are often held closely and deployed on the most complex projects. This could be your opportunity to hire them. If new hires are not in the budget, perhaps a contracting agreement might work. In either case, if you have invested in the technology, why not invest a bit more for the right talent who will help you get the most out of your investment?
  2. If you are using a niche technology, reach out to your account rep. Find out how they are doing and what their plans are. If you have a good relationship with your rep, they will hopefully share any changes afoot, availability of on-going product support, the possibility of a sale or even closure.
  3. If you need to invest in new technology, look for solution providers with a broad base of active clients. (Notice the word “Active”). In some cases, one or two large clients can support a solution provider just fine. However, if typical license fees are $60,000 per year and the solution provider has a staff of 20 people, a broad base of clients will be critical for survival. (It’s just math.)
  4. The exceptions to No. 3 are cases where the solution provider has recently been acquired by a larger concern, especially post COVID-19. In such cases, someone with deep pockets thought enough of the technology to buy and invest in its survival. Although deep pockets do not always translate into smart money, it is enough of a reason to consider the technology seriously.

Those of us who have been keeping track of the martech universe know that the growth was unsustainable (There were over seven thousand solutions in the market as of 2019). The hope was that the best products would survive and eventually lead to industry consolidation. It seems that COVID-19 will abruptly end the natural evolution of the industry, for the time being. Innovations and investments will return, but exactly when is anyone’s guess.

In the meantime, we need to be kind and helpful to those who will be affected. In doing so, we may benefit from their wisdom, which was often drowned out in the previously noisy clamor of martech.

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?”

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.

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.

Why Many Marketing Automation Projects Go South

There are so many ways to mess up data or analytics projects, may they be CDP, Data Lake, Digital Transformation, Marketing Automation, or whatever sounds cool these days. First off, none of these items are simple to develop, or something that you just buy off the shelf.

As a data and analytics consultant, I often get called in when things do not work out as planned or expected. I guess my professional existence is justified by someone else’s problems. If everyone follows the right path from the beginning and everything goes smoothly all of the time, I would not have much to clean up after.

In that sense, maybe my role model should be Mr. Wolf in the movie “Pulp Fiction.” Yeah, that guy who thinks fast and talks fast to help his clients get out of trouble pronto.

So, I get to see all kinds of data, digital, and analytical messes. The keyword in the title of this series “Big Data, Small Data, Clean Data, Messy Data” is definitely not “Big” (as you might have guessed already), but “Messy.” When I enter the scene, I often see lots of bullet holes created by blame games and traces of departed participants of the projects. Then I wonder how things could have gone so badly.

There are so many ways to mess up data or analytics projects, may they be CDP, Data Lake, Digital Transformation, Marketing Automation, or whatever sounds cool these days. First off, none of these items are simple to develop, or something that you just buy off the shelf. Even if you did, someone would have to tweak more than a few buttons to customize the toolset to meet your unique requirements.

What did I say about those merchants of buzzwords? I don’t remember the exact phrase, but I know I wouldn’t have used those words.

Like a veteran cop, I’ve developed some senses to help me figure out what went wrong. So, allow me to share some common traps that many marketing organizations fall into.

No Clear Goal or Blueprint

Surprisingly, a great ,many organizations get into complex data or analytics projects only with vague ideas or wish lists. Imagine building a building without any clear purpose or a blueprint. What is the building for? For whom, and for what purpose? Is it a residential building, an office building, or a commercial property?

Just like a building is not just a simple sum of raw materials, databases aren’t sums of random piles of data, either. But do you know how many times I get to sit in on a meeting where “putting every data source together in one place” is the goal in itself? I admit that would be better than data scattered all over the place, but the goal should be defined much more precisely. How they are going to be used, by whom, for what, through what channel, using what types of toolsets, etc. Otherwise, it just becomes a monster that no one wants to get near.

I’ve even seen so-called data-oriented companies going out of business thanks to monstrous data projects. Like any major development project, what you don’t put in is as important as what you put in. In other words, the summary of absolutely everyone’s wish list is no blueprint at all, but the first step toward inevitable demise of the project. The technical person in charge must be business–oriented, and be able to say “no” to some requests, looking 10 steps down the line. Let’s just say that I’ve seen too many projects that hopelessly got stuck, thanks to features that would barely matter in practice (as in “You want what in real-time?!”). Might as well design a car that flies, as well.

No Predetermined Success Metrics

Sometimes, the project goes well, but executives and colleagues still define it as a failure. For instance, a predictive model, no matter how well it is constructed mathematically, cannot single-handedly overcome bad marketing. Even with effective marketing messages, it cannot just keep doubling the performance level indefinitely. Huge jumps in KPI (e.g., doubling the response rate) may be possible for the very first model ever (as it would be, compared to the previous campaigns without any precision targeting), but no one can expect such improvement year after year.

Before a single bite of data is manipulated, project champions must determine the success criteria for the project. In terms of coverage, accuracy, speed of execution, engagement level, revenue improvement (by channel), etc. Yes, it would be hard to sell the idea with lots of disclaimers attached to the proposal, but maybe not starting the project at all would be better than being called a failure after spending lots of precious time and money.

Some goals may be in conflict with each other, too. For instance, response rate is often inversely related to the value of the transaction. So, if the blame game starts, how are you going to defend the predictive model that is designed primarily to drive the response rate, not necessarily the revenue per transaction? Set the clear goals in numeric format, and more importantly, share the disclaimer upfront. Otherwise, “something” would look wrong to someone.

But what if your scary boss wants to boost rate of acquisition, customer value, and loyalty all at the same time, no matter what? Maybe you should look for an exit.

Top-Down Culture

By nature, analytics-oriented companies are flatter and less hierarchical in structure. In such places, data and empirical evidences win the argument, not organizational rank of the speaker. It gets worse when the highest-ranking officer has very little knowledge in data or analytics, in general. In a top-down culture, no one would question that C-level executive in a nice suit. Foremost, the executive wouldn’t question his own gut feelings, as those gut-feelings put him in that position in the first place. How can he possibly be wrong?

Trouble is that the world is rapidly changing around any organization. And monitoring the right data from the right place is the best way to keep informed and take actions preemptively. I haven’t encountered any gut-feeling — including my own — that stood the test of time better than data-based decision-making.

Now sometimes, the top-down culture is a good thing, though. If the organizational goals are clearly set, and if the top executive does not launch blame games and support a big data project (no pun intended here). Then, an indefinite amount of inter-departmental conflicts will be mitigated upfront (as in, “Hey, everyone, we are doing this, alright?).

Conflicts Among Teams — No Buy-in, No Use

But no amount of executive force can eliminate all infighting that easily. Some may say “Yeah, yeah, yeah” in front of the CEO or CMO, but sabotage the whole project behind the scene. In fact, I’ve seen many IT departments get in the way of the noble idea of “Customer-360.”

Why? It could be the data ownership issue, security concerns, or lack of understanding of 1:1 marketing or advanced analytics. Maybe they just want the status quo, or see any external influence on data-related matters as a threat. In any case, imagine the situation where the very people who hold the key to the of source data are NOT cooperating with data or analytics projects for the benefit of other departments. Or worse, maybe you have “seen” such cases, as they are so common.

Another troublesome example would be on the user side. Imagine a situation where sales or marketing personnel do not buy into any new way of doing things, such as using model scores to understand the target better. Maybe they got burned by bad models in the past. Or maybe they just don’t want to change things around, like those old school talent scouts in the movie “Moneyball.” Regardless, no buy-in, no use. So much for that shiny marketing automation project that sucked up seven-figure numbers to develop and deploy.

Every employee puts their prolonged employment status over any dumb or smart project. Do not underestimate the people’s desire to keep their jobs with minimal changes.

Players Haven’t Seen Really Messy Situations Before

As you can see, data or analytics projects are not just about technologies or mathematics. Further, data themselves can be a hindrance. I’ve written many articles about “good” data, but they are indeed quite rare in real life. Data must be accurate, consistent, up-to-date, and applicable in most cases, without an excessive amount of missing values. And keeping them that way is a team sport, not something a lone tech genius can handle.

Unfortunately, most graduates with degrees in computer science or statistics don’t get to see a real bloody mess before they get thrown into a battlefield. In school, problems are nicely defined by the professors, and the test data are always in pristine conditions. But I don’t think I have seen such clean and error-free data since school days, which was indeed a lifetime ago.

Dealing with organizational conflicts, vague instructions, and messy data is the part of the job of any data professional. It requires quite a balancing act to provide “the least wrong answers” consistently to all constituents who have vastly different interests. If the balance is even slightly off, you may end up with a technically sound solution that no one adopts into their practices. Forget about full automation of anything in that situation.

Already Spent Money on Wrong Things

This one is a heart-breaker for me, personally. I get onto the scene, examine the case, and provide step-by-step solutions to get to the goal, only to find out that the client company spent money on the wrong things already and has no budget left to remedy the situation. We play with data to make money, but playing with data and technology costs money, too.

There are so many snake oil salespeople out there, over-promising left and right with lots of sweet-to-the-ears buzzwords. Yeah, if you buy this marketing automation toolset armed with state-of-the-art machine-learning features, you will get actionable insights out of any kind of data in any form through any channel. Sounds too good to be true?

Marketing automation is really about the “combination” of data, analytics, digital content, and display technologies (for targeted messaging). It is not just one thing, and there is no silver bullet. Even if some other companies may have found one, will it be applicable to your unique situation, as is? I highly doubt it.

The Last Word on How to Do Marketing Automation Right

There are so many reasons why marketing automation projects go south (though I don’t understand why going “south” is a bad thing). But one thing is for sure. Marketing automation — or any data-related project — is not something that one or two zealots in an organization can achieve single-handedly with some magic toolset. It requires organizational commitment to get it done, get it utilized, and get improved over time. Without understanding what it should be about, you will end up automating the wrong things. And you definitely don’t want to get to the wrong answer any faster.