3 Resource Allocation Questions to Ask for Better Returns

Here are three questions data-driven marketers and those in customer-focused functions need to ask in order to evaluate their resource allocation during uncertain times.

These are obviously times of great uncertainty and change. Smart business people know that with change comes new opportunities. Somewhere, entrepreneurial spirits are already making bets and shifting strategies. There is another powerful axiom, however, which rarely gets enough airtime during times of change: In times of uncertainty, focus on what is certain. One certainty in business is that resources can always be better relocated to achieve better returns.

Unless you are one of the lucky businesses booming in these times, there will be budget cuts. This is the perfect time to reevaluate resource allocations using an agile, data-driven picture of your business. Considering that there are few industries untouched by COVID-19, agile decisions will need to be made based on sparse but recent data.

Here are three questions data-driven marketers and those in customer-focused functions need to ask in order to evaluate their resource allocation.

1. Do I know who my best customers are and are they okay? Your best customers should be based on current sales and lifetime value. Yes, your best customers today are important. However, most businesses survive on the 20% to 30% of customers who are consistently loyal and profitable over many years. Once you have identified the most important customers, you should evaluate if their buying behaviors are changing and why? How can you reallocate resources to better serve this segment?

2. Do I know the channels where most of my business comes from and is it under threat? The first step to answer this question should involve a data-driven accounting of your marketing and sales channels. However, some of your most influential channels may be the most difficult to track. Therefore, it is important that you establish or refresh your multi-touch attribution models so that you can better allocate sales to channels. Right now, it might be very tempting to simply rely on direct attribution or easily measurable channels. After all, this approach feels more certain, but it is rarely the right answer.

3. Do I have the data I need to make quick decisions? If your data was messy and hard to work with before COVID-19, then it will be even less helpful now. This might be the right time to think about the minimal data needed to make agile decisions. The word minimal is critical here as the more data you collect, the more complex the solutions become, and agility diminishes. Do you know what measures are most important? Do you need to spend resources on agile data-driven capabilities?

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.

 

4 Tips for Targeted Customer Acquisition Marketing

Customer acquisition is the most expensive part of marketing, but no company can afford to abandon marketing for new customers. Acquisition marketing is essential, but brands must find a way to do it more effectively, and that starts with tighter, more data-driven targeting. 

Most marketing is blind. Brands put out messages and hope they are found by enough people who want to be customers that it justifies the spend. Even with targeted marketing, most campaigns are sent to broad audiences defined by a few key attributes, but not enough to eliminate the massive waste inherent in customer acquisition marketing.

Customer acquisition is the most expensive part of marketing. It can cost five times more than retention, and the costs keep rising. Still, no company can afford to abandon marketing for new customers. Even the best retention strategies bleed customers at an alarming rate; prospecting is the only way to offset that loss and grow.

Acquisition is essential, but brands must find a way to do it more effectively, and that starts with tighter, more data-driven targeting.

Data-Driven Acquisition Marketing

Customer modeling is the key to better targeting your prospecting. If you dig into your existing customers, you can identify commonalities and buying signals that allow you to direct marketing spend more effectively and reduce the overall cost to acquire new customers.

The hard part is knowing which attributes correlate most closely to the likelihood of a prospect becoming a customer.

Demographics Aren’t Enough

Demographics are a mainstay of target marketing, but in 2020 they’re not enough.

While demographics do have power in targeting your marketing, they don’t reflect buying signals in their own right. They can still be useful for targeting messaging and creative around more impactful modeling methods, but it’s important to look deeper.

Ideally, you want to build a target list around buying signals, then segment that by demographic information and target your creative to those segments. This means optimizing the creative and/or offer by doing things like matching people in the imagery to the demographics of that segment.

Demographics are also useful in building look-a-like audiences to target new customers based on the customers you already have. Even though demographic data does not directly indicate buying behavior, it can reveal insights when analyzed as part of the wider customer picture with data modeling tools.

4 Data Points for Better Customer Acquisition Marketing

With the above qualifiers in mind, which information actually does line up with more successful acquisition marketing? There are four key data points we like to use for omnichannel targeting.

1. Buying Behavior

When the goal is to understand what type of offer motivates what type of people to buy, purchasing behavior is one of the most important data points to consider.

When you identify that certain list segments respond to deep discounts, you can hold them out from general mailings and bring them back in when you have deep discounts to talk about.

When you can identify audiences with a propensity to buy around certain price points, build offers around those price points. If it’s above your product price, bundle a strong package deal that will lift response and increase your average order value. If your price is above the target, present it as an installment option with payments in the target zone.

This is exactly the kind of actionable information you can get from deep-dive data that is missing from demographic information. You’re not just targeting an age group, area, etc. You’re making a surgical strike at the behavior you want to influence.

2. Personal Life Triggers

Timing is everything. Once you’ve narrowed your target market by interest and buying signals, life triggers become a powerful way to spur new action.

Life triggers can be tied to events ranging from birthdays and graduations to buying a home, getting a new job, retirement, and other once-in-a-lifetime moments. By targeting marketing to a specific time in a prospect’s life when they are most likely to be interested in your offer, you stand a much better chance of making the conversion.

3. Shared Interests

One of the most important indicators of customer potential is evidence of interest in the product category or the industry it serves. While you may not be able to read prospect’s minds directly, there are many data points brands can use to pinpoint interest.

One way is to target audiences and lists built around interests that are relevant to your target customer, such as subscriber files for related media.

Perhaps a more exciting option: Social media provides new opportunities to leverage interest data points. Facebook, for example, allows you to build custom audiences including specific interests.

4. Searcher Intent

“Search data captured across e-commerce, pricing comparison, and product review sites are one of the strongest signals of intent and best sources for new customer acquisition,” says James Green, CEO of Magnetic, and he’s right. Harnessing this data in your customer models is one of the best ways to more tightly target your acquisition efforts and cut down on wasted prospecting spend.

This is why Google now uses searcher intent as the main factor in targeting its search algorithm. The intent is the most reliable indicator of what searchers actually want, and that makes it a powerful marketing tool.

In practice, this means identifying visitor paths, either on your website or across the web, and matching them with desired outcomes. What product pages are they looking at? Did they come from a related external website? Did you catch them on a specific search ad that is relevant to what they may want? All of this data can be used to build a better, more efficient plan for your acquisition marketing.

Don’t Be Afraid to Ask for Help

All these data points are important for optimizing your acquisition marketing, but they’re not necessarily easily accessible. When you’re trying to do advanced customer lift modeling that includes things like buyer intent seen through visits to other websites, it really helps to have data scientists on your side. These experts can isolate those variables and build them into a view of the audience you’re trying to target.

These are essential tactics that businesses are using now, and more businesses will use them in the future. Make sure you get ahead of the curve by digging into the data points today.

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?

 

Don’t Blame Personalization After Messing It Up

In late 2019, Gartner predicted “80% of marketers who have invested in personalization efforts will abandon them by 2025 because of lack of ROI, the peril of customer data, or both.” But before giving up because the first few rounds didn’t pay off, shouldn’t marketers stop and think about what could have gone wrong?

In late 2019, Gartner predicted “80% of marketers who have invested in personalization efforts will abandon them by 2025 because of lack of ROI, the peril of customer data, or both.” Interesting that I started my last article quoting only about 20% of analytics works are properly applied to businesses. What is this, some 80/20 hell for marketers?

Nonetheless, the stat that I shared here begs for further questioning, especially the ROI part. Why do so many marketers think that ROI isn’t there? Simply, ROI doesn’t look good when:

  1. You invested too much money (the denominator of the ROI equation), and
  2. The investment didn’t pay off (the numerator of the same).

Many companies must have spent large sums of money on teams of specialists and service providers, data platforms featuring customer 360, personalization software (on the delivery side), analytics work for developing segments and personas, third-party data, plus the maintenance cost of it all. To justify the cost, some marginal improvements here and there wouldn’t cut it.

Then, there are attribution challenges even when there are returns. Allocating credit among all the things that marketers do isn’t very simple, especially in multichannel environments. To knock CEOs and CFOs off their chairs – basically the bottom-line people, not math or data geeks – the “credited” results should look pretty darn good. Nothing succeeds like success.

After all, isn’t that why marketers jumped onto this personalization bandwagon in the first place? For some big payoff? Wasn’t it routinely quoted that, when done right, 1:1 personalization efforts could pay off 20 times over the investment?

Alas, the key phrase here was “when done right,” while most were fixated on the dollar signs. Furthermore, personalization is a team sport, and it’s a long-term game.  You will never see that 20x return just because you bought some personalization engine and turned the default setting on.

If history taught us anything, any game that could pay off so well can’t be that simple. There are lots of in-between steps that could go wrong. Too bad that yet another buzzword is about to go down as a failure, when marketers didn’t play the game right and the word was heavily abused.

But before giving it all up just because the first few rounds didn’t pay off so well, shouldn’t marketers stop and think about what could have gone so wrong with their personalization efforts?

Most Personalization Efforts Are Reactive

If you look at so-called “personalized” messages from the customer’s point of view, most of them are just annoying. You’d say, “Are they trying to annoy me personally?”

Unfortunately, successful personalization efforts of the present day is more about pushing products to customers, as in “If you bought this, you must want that too!” When you treat your customers as mere extensions of their last purchase, it doesn’t look very personal, does it?

Ok, I know that I coveted some expensive electric guitars last time I visited a site, but must I get reminded of that visit every little turn I make on the web, even “outside” the site in question?

I am the sum of many other behaviors and interests – and you have all the clues in your database – not a hollow representation of the last click or the last purchase.  In my opinion, such one-dimensional personalization efforts ruined the term.

Personalization must be about the person, not product, brands, or channels.

Personalization Tactics Are Often Done Sporadically, Not Consistently

Reactive personalization can only be done when there is a trigger, such as someone visiting a site, browsing an item for a while, putting it in a basket without checking out, clicking some link, etc. Other than the annoyance factor I’ve already mentioned, such reactive personalization is quite limited in scale. Basically, you can’t do a damn thing if there is no trigger data coming in.

The result? You end up annoying the heck out of the poor souls who left any trail – not the vast majority for sure – and leave the rest outside the personalization universe.

Now, a 1:1 marketing effort is a number’s game. If you don’t have a large base to reach, you cannot make significant differences even with a great response rate.

So, how would you get out of that “known-data-only” trap? Venture into the worlds of “unknowns,” and convert them into “high potential opportunities” using modeling techniques. We may not know for sure if a particular target is interested in purchasing high-end home electronics, but we can certainly calculate the probability of it using all the data that we have on him.

This practice alone will increase the target base from a few percentage points to 100% coverage, as model scores can be put on every record. Now you can consistently personalize messages at a much larger scale. That will certainly help with your bottom-line, as more will see your personalized messages in the first place.

But It’s Too Creepy

Privacy concerns are for real. Many consumers are scared of know-it-all marketers, on top of being annoyed by incessant bombardments of impersonal messages; yet another undesirable side effect of heavy reliance on “known” data. Because to know for sure, you have to monitor every breath they take and every move they make.

Now, there is another added bonus of sharing data in the form of model scores. Even the most aggressive users (i.e., marketers) wouldn’t act like they actually “know” the target when all they have is a probability. When the information is given to them, like “This target is 70% likely to be interested in children’s education products,” no one would come out and say “I know you are interested in children’s education products. So, buy this!”

The key in modern day marketing is a gentle nudge, not a hard sell. Build many personas – because consumers are interested in many different things – and kindly usher them to categories that they are “highly likely” to be interested in.

Too Many Initiatives Are Set on Auto-Pilot

People can smell machines from miles away. I think humans will be able to smell the coldness of a machine even when most AIs will have passed the famous Turing Test (Definition: a test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human).

In the present day, detecting a machine pushing particular products is even easier than detecting a call-center operator sitting in a foreign country (not that there is anything wrong about that).

On top of that, machines are only as versatile as we set them up to be. So, don’t fall for some sales pitch that a machine can automatically personalize every message utilizing all available data. You may end up with some rudimentary personalization efforts barely superior to basic collaborative filtering, mindlessly listing all related products to what the target just clicked, viewed, or purchased.

Such efforts, of course, would be better than nothing.  For some time.  But remember that the goal is to “wow” your target customers and your bosses. Do not settle for some default settings of campaign or analytics toolsets.

Important Factors Are Ignored

When most investments are sunk in platforms, engines, and toolsets, only a little are left for tweaking, maintenance, and expansion. As all businesses are unique (even in similar industries), the last mile effort for custom fitting often makes or breaks the project. At times, unfortunately, even big items such as analytics and content libraries for digital asset management get to be ignored.

Even through a state-of-the-art AI engine, refined data works better than raw data. Your personalization efforts will fail if there aren’t enough digital assets to rotate through, even with a long list of personas and segments for everyone in the database. Basically, can you show different contents for different personas at different occasions through different media?

Data, analytics, contents, and display technologies must work harmoniously for high level personalization to work.

So What Now?

It would be a real shame if marketers hastily move away from personalization efforts when sophistication level is still elementary for the most.

Maybe we need a new word to describe the effort to pamper customers with suitable products, services and offers. Regardless of what we would call it, staying relevant to your customer is not just an option anymore. Because if you don’t, your message will categorically be dismissed as yet another annoying marketing message.

 

How Do We Leverage Data to Drive a Faster Economic Recovery?

As growth leaders, we will be waking to a world with fewer resources and businesses desperate to grow again once we get past the coronavirus pandemic. However, in our struggle to regain our financial footing we will have a very valuable resource that previous generations did not: data and data science.

As growth leaders, we will be waking to a world with fewer resources and businesses desperate to grow again once we get past the coronavirus pandemic. And despite the global hardships that will be felt by many, in our struggle to regain our financial footing we will have a very valuable resource that previous generations did not: data and data science.

When used well, data science will help direct scarce resources to the right opportunities and efficiently drive growth. I am convinced this will be a big differentiator versus previous recoveries of this magnitude.

Over my career, I have consistently encountered inefficient and counter-productive practices in data-driven decision management and have written about them often. They are paralleled in the crisis today. Below are three issues I would like us all to think about when we leverage data science to rebuild the national and world economy.

1. Customer Data Hoarding

Companies collect so much data that many are “drowning in data.” If you have no idea of the value of what you are collecting, then it is digital garbage.

We were led to believe that AI and data mining would help make sense of the data. It does to some extent, but more often it leads to head-scratching conclusions. We can’t leverage what we can’t understand.

As a data-driven consultant, I am amazed at how much time is spent sifting through data just trying to make sense of it all before any valuable insights can be generated. Going forward we cannot afford this luxury. If there are 10 gallons of fuel in the tank, we can’t spend five gallons trying to figure out if the engine works. However, when it comes to mining company data, we often do.

2. It’s About Qualitative, Not Just Quantitative

We can’t be slaves to the data we have. Collecting the right data is often cheap and easily done, if time is taken to plan. This means that measurement strategy can’t be a retrospective exercise. Too often, I have engaged clients in the post-mortem analysis of very important projects. In many cases, my team is often limited to the data that is available and not the data that was needed. Critical answers are sometimes left unanswered. This is a waste of time, resources and most importantly, valuable information.

3. Data Is Not the Solution, It’s the Tool

We must regularly remind ourselves that data does not solve problems or create opportunities. Rather, brave decision making solves problems and creates opportunities. Data is a valuable tool that can only inform the decisions we need to make. It can help lower the risk and provide valuable insights. Sometimes, collecting more data before acting can be wise. Other times it can also be the delay in action that leads to disaster.

What is happening today has no parallel in recent memory. While the 1918 flu pandemic had similar infection rates, the world was a different place then. Today, we have advanced tools and technology to aid our recovery.

Data science will be one of those important tools, especially if we collectively decide to use it to its true potential. As a result, I am hopeful that we can come out of this faster than we realize.

The Intersection of Personalization & Privacy: How to Communicate with Consumers

Consumers expect to get whatever they want, whenever they want it, delivered how they want it. You can credit (or blame) Amazon for setting expectations so high, but those same expectations extend to online publishing and marketing.

[Editor’s note: While this is geared toward the publishing audience in language, there are numerous valuable takeaways for marketers.]

Consumers expect to get whatever they want, whenever they want it, delivered how they want it. You can credit (or blame) Amazon for setting expectations so high, but those same expectations extend to online publishing.

Increasingly, publishers must personalize to thrive — a mission that can be at odds with new privacy mandates.

What Exactly Do Consumers Expect?

Virtually every publisher now promises customized content, but that promise can mean a few different things. On the one hand, it’s a promise to deliver a certain type of content that’s tailored to your reader’s individual interests. But it also means a promise to deliver content according to that person’s consumption preferences for device/channel (desktop, mobile, or tablet/website, social, or email).

Publishers deliver on these promises through a variety of features. Notifications that push to a consumer’s preferred device are one popular way to meet audiences on the most personal level. Likewise, social integration (both as commenting platforms and logins) is now seen as essential because it not only customizes the experience, but also makes it friction-less.

But, as publishers are well aware, building these features and executing personalization strategies takes significant resources that aren’t necessarily part of the core business.

Brands Are Doing the Same Thing with More Resources

While brands and publishers typically sit on opposite sides of the media ecosystem, their challenge is the same when it comes to personalization. Publishers and advertisers must both deliver the right message to the right person, at the right time. Tellingly, brands and publishers have tackled this challenge in different ways.

By and large, brands and bigger media companies have taken this kind of work in-house. But most small and medium-sized publishers have gone in the opposite direction, turning to agencies and vendors to navigate the complexities of data analytics, personalization, and monetization.

These are technical and costly undertakings. Small publishers may struggle because of limited expertise, but even big publishers may prefer to invest in content rather than building in-house technology. And just finding, vetting, and holding vendors accountable is a challenge for many publishers.

But regardless of how publishers solve for personalization, the brand context is important because well-resourced brands are setting the bar for consumer expectations here. As privacy compliance adds layers of complexity to personalization, brands and publishers will have to adapt to perform the same mission, albeit with varying levels of resources.

Personalization Is the Crucible of Privacy Chaos

To understand how personalization and privacy intersect, start with a fundamental question: How do I personalize something for you if I don’t know anything about you?

The question illustrates the tension between personalization and privacy. The more consumers share, the greater the level of personalization. Of course, the opposite is also true. If you don’t want to share anything personal, be prepared to accept the generic experience.

While that may sound like common sense, the reality is that publishers are stuck in a bind. You must reconcile the chaos that comes from a patchwork of state-mandated privacy laws — including California’s CCPA, plus laws in 10 other states — with consumer expectations that value privacy on the one hand and expect seamless, personalized experiences on the other. To be clear, there’s no “right answer,” in part because just as personalization preferences vary by individual, so, too, do our feelings about privacy.

Publishers, perhaps better than any other stakeholder, are uniquely positioned to lead this conversation. After all, consumers seek out publishers because they are trusted sources. But when it comes to explaining the tradeoffs between personalization and privacy, publishers usually fall back on their lawyers. That can be a mistake. Instead of relying fully on lawyers, publishers should communicate with their consumers in a clear, authentic voice. Here are some suggestions:

  • Speak in your brand’s voice. Typically, conversations that touch on the tradeoff between personalization and privacy get off to a bad start because privacy policies are written in a foreign language called legalese. Using your brand voice is more effective because it’s authentic. If your brand is edgy or sarcastic, talk about privacy with an edgy or sarcastic tone. Two examples: 1) Fitbit’s privacy policy is written in easy to navigate bullet points for users who may not have the time to take a deep-dive into the brand’s Terms of Service; 2) Apple’s privacy, which is quite in-depth, is written in the same easy-to-understand language Apple uses for its product copy.
  • Tell people what information you want and why you need it. A concept like “personally identifiable information” means a lot to lawyers, but it’s not something consumers think about in their daily lives. Instead, make specific asks for email, social media, or cookies and then explain why you need that information. Be clear that your product might not work as advertised unless the user shares some private information. The key is context. If you want movie screening times “near you,” for example, we need to know your location. Instead of just asking for a user’s location, say something like, “Tell us where you are so we can find a movie near you.”
  • Explain how the consumer benefits in concrete terms. If you’re using language like “so we can best serve you…” you’re being too vague. Instead, state the value proposition directly. Explain how you want to serve the consumer by telling them what they can expect — content tailored to their interests, timely notifications, etc. When you do that, you empower the consumer to make their own informed choices about the tradeoffs between privacy and personalization.
  • If you plan to share someone’s information with a third-party, be upfront about it. Reserving the right to share consumer data with third-party partners sounds like legalese, but it also sounds like you’re hiding something. There are valid reasons to share data with others. Tell consumers why you’re sharing their data, who you’re sharing it with, and how the opt-out works.

Navigating these delicate waters will be challenging, but putting the time and energy into incorporating your brand identity into privacy compliance will pay for itself in the long run. Your users will appreciate the effort and better personalization, and you will (hopefully) have stronger user connections and fewer people opting out.

Were Publishers the First DTC Brands? How 2 Areas of Marketing Align

DTC brands are hot entities. Practically any consumer product can be translated to a paid subscription business model. As a direct result, circulation and subscription marketing professionals have become very attractive new hires to the growing bevy of direct-to-consumer brands.

DTC brands are hot entities. Practically any consumer product can be translated to a paid subscription business model.

As a direct result, circulation and subscription marketing professionals — a mainstay of the direct marketing discipline for decades — have become very attractive new hires to the growing bevy of direct-to-consumer brands. In reverse, too — publishers are enriching their content offerings for their customers in service to them, acting as DTC brands, themselves.

That was a main thrust at a recent joint meeting of the Direct Marketing Club of New York and The Media and Content Marketing Association. The joint meeting, titled “What DTC Brands and Publishers Can Learn from Each Other in Today’s Subscription Economy,” allowed publishers to exchange ideas with DTC brand reps and others.

DTC brands meeting
Source: DMCNY, Twitter @dmcny | Direct-to-Consumer Brands, Publishers and their Admirers exchange perspectives around customer value and experiences.

“Magazines are the original DTC,” said Mike Schanbacher, director of growth marketing at Quip, a subscription business for toothbrushes and dental care,. He noted that traditional circulation metrics, such as lifetime value and churn rates, very much factor in the business and marketing plans of a subscription commerce company.

Alec Casey, CMO of Trusted Media Brands Inc. (TMBI, which manages 13 brands, among them Reader’s Digest), described how his business continually explores expansion of product and content — to books, book series, music and video — and potentially podcasts and subscriber boxes.

“We are always DTC,” he said, meaning that customers’ interests drive every brand extension in the company.

Data can reveal interesting patterns, he noted. Visitors to Family Handyman digital content is 50% men, 50% women, for example, while print content is dominated by men.

DTC Is High-Speed

One hallmark of the newest DTC brands is velocity.

“When bananas and avocados are sitting in the warehouse beneath you, there’s urgency,” said Tammy Barentson, CMO of Fresh Direct, who previously had had a lengthy career in publishing with Time, Meredith, Hearst, and Conde Nast. Innovations are sought for and tested constantly … and rapidly: “There’s a mindset here … ‘That bombed. What did we learn?’’ ” she said, which is a marked change from her previous publishing posts, where testing was more considered.

Barentson also noted that the Fresh Direct executive team meets every morning to listen in collectively on each department’s dashboard of metrics — and that can inspire action.

“There’s a lot I can learn from operations and customer service data,” she said. “For example, how many deliveries are made per hour might tell me geographies where I might focus more customer acquisition.” Her own team pores through subscription data — who orders groceries one, two or three times a week, or just for special events — “how do we bring them up the food chain?” she quipped.

One of the first publishers to capitalize on digital was Forbes and Forbes.com, said Nina LaFrance, who is Forbes’ lead for consumer marketing and business development. Today, the corporation’s digital sites generate 80 million unique visits per month — but it’s the drill-down on the data that is perhaps the most exciting, enabling Forbes to help advertisers connect with customers across print, digital, programmatic display, brand voice, social channels, live events, apps, webinars, and more. Forbes has its own in-house studio to help brands develop content for marketing across the portfolio.

“We adapt and embrace,” LaFrance said, responding to the all the challenges and opportunities presented to publishers and DTC brands alike — issues, such as coping with “walled gardens,” tech giants, privacy laws, data restrictions and regulations, and the Cookie Apocalypse.

Communities Are Sticky

A common theme expressed by the panel was the desire to create a sense of “membership” and “community” — going beyond the transaction to create “stickiness.” That’s where content development matters. “

At Quib, we try and give a membership feel,” Schanbacher said. “Data is the goal,” noting the better consumer understanding and insights that come from content engagement, data collection, and analysis.

However, not every piece of content translates equally to profit, LaFrance reports.

“Visitors to our home page, or who respond to direct mail, may be more profitable to us than those who link to an article from a social post,” she says — and the ability to measure that customer value across channels is a success, in its own right.

Which is probably the most valuable insight of all. These professionals — DTC brands and publishers — revere how data serves, bolsters, and builds the customer relationship, and they have all pursued a shared culture for measurement, insight, and application to build the brands, build the business, and connect to consumer experience. As subscription commerce grows — it has doubled in the past five years — we know how invaluable such data reverence can be.

What Are Customers Really Worth? Turning the ‘Customer Data’ Concept Into Something Meaningful

What’s the value of customer data? What is its value to our political aspirants, a value measured by many different and often conflicting metrics, not least of which is the power of the elected to change society for better or worse? And often, sadly, as we increasingly see around us, for personal economic gain?

The headline, “Legislation Would Force Google, Facebook to Report Value of Customer Data to SEC,” in the Media Daily News got this maverick marketer wondering just what kind of a gargantuan task it would be to try and determine the value of customer data.

Imagine what you would do if some legislation or only your boss asked you to put a rational price tag on the data in your company’s possession? The easy way, if you are a direct-to-consumer marketer, might be to add to your total year’s profit, a factor for the likely future profit contribution driven by your knowledge or assumption of the lifetime value of your customer base. Or you could offload the task to your bean counters and let them have a field day playing with the numbers, instead of doing something more useful.”

Searching for what the British call a “bargain,” or the price at which a willing buyer buys and a willing seller sells, can be said to establish real value. The traditional way of determining a bargain for the acquisition of a data-driven marketing business is to pay a negotiated multiple of the number of customers, times the best guess of discounted future revenue from these customers. From there on, it’s horse trading. The fact is, we all may have ideas (usually over-optimistic) about data value, but few if any of us know for sure what it is. And today’s “bargain” may not seem so attractive a couple of years down the road.

That’s why you have to wonder if the financing of our political election system has gone completely off the rails. According to the Wall Street Journal, “Political Ad Spending Will Approach $10 Billion in 2020.” That’s an increase of almost 14% over the last time, which is far greater than the population increase during the same period.

Political ad spending will total $9.9 billion in 2020, according to the latest U.S. advertising forecast from WPP PLC’s ad-buying unit GroupM. That would be up from $8.7 billion in 2018, when midterm congressional elections were held, and from $6.3 billion in 2016, when President Trump was elected.

The growth between presidential campaign years is accelerating. Political ad spending rose by $2 billion between 2012 and 2016, according to GroupM, and by $1.1 billion between 2008 and 2012.

If we look at this against the number of likely voters, we can estimate the spend for each one. The Census Bureau estimated that there were 245.5 million Americans ages 18 and older in November 2016, about 157.6 million of whom reported being registered to vote. Historically, about 60% of those eligible to vote actually show up to do their democratic duty in a presidential election. This means that the actual number expected to be voting is 94.5 million.

If the political marketers were able to target only those 60%, the cost per voter would be $38.07. Because that kind of tight targeting of marketing spend is almost certainly impossible, and we spread the total spend against all the 157.6 million registered voters, the cost per voter is only $27.86.

A maverick marketer’s fantasy view is that it might be more cost-effective to use the $27.86 just to buy those voters not already committed to one party or candidate or the other, just as long as you could determine who they were.

Only $27.86 or $38.07 per prospect? That’s more than consumer goods and services advertisers spend in a year, a lot more. Proctor & Gamble, one of the largest FMCG companies spent $4.39 billion last year ($13.43 for each member of the population) or less than half the estimated cost per voter, and AT&T spent $3.52 billion.

What does this tell us about the value of customer data? (Or, in this case, potential voter data?) What is its value to our political aspirants, a value measured by many different and often conflicting metrics, not least of which is the power of the elected to change society for better or worse? And often, sadly, as we increasingly see aound us, for personal economic gain?

One thing it certainly does tell us is that in our society, where more than three-quarters of the total wealth is owned by the top 10% of earners and the lowest 50% own only 1.2%, valuing each cohort is extremely difficult. Ironically, at least in theory, every vote — whether from the 10% or the other 90% of the voting population — has equal value.

That’s a big difference from the relative value of segmented cohorts of buyers and prospects who make up the Google and Facebook universe, buyers who can be valued based on past performance and prospects, whose value can be guesstimated — based on other characteristics.

Ask yourself, “How much am I worth? And please comment below on how you determined the amount. It should be fun to share the different answers.

New Privacy Regulations Coming Your Way: California Consumer Privacy Act (CCPA)

Have you recovered from last spring’s GDPR adrenaline rush yet? As much anxiety as GDPR regulations provoked, that may soon look like the good old days. Now California passed a privacy initiative you will be expected to follow starting Jan. 1, 2020.

Editor’s Note: While this piece is directed at publishers, CCPA also will be something marketers will have to be compliant with, just like GDPR.

Have you recovered from last spring’s GDPR adrenaline rush yet? Everybody in publishing was nervous about finding the right way to comply with new European privacy regulations. It did not seem like there was one clear path to compliance.

As much anxiety as GDPR regulations provoked, that may soon look like the good old days. At least in the EU, 27 countries came together with one edict. They also spent the time necessary to be smart and coherent, whether or not you agree with all the details.

Now California passed a privacy initiative you will be expected to follow starting Jan. 1, 2020. In many industries as goes California law, so go U.S. standards. This will be, in practice, a new national standard. California is too dominant a market, larger than most countries on the globe. Add to that a quirk in the drafting of the law, which says you must treat anyone who has left California and intends to return as a Californian. What?

Newly minted California Governor Gavin Newsom hailed the “first-in-the-nation digital privacy law” in his first State of the State address, according to reporting by Wendy Davis in MediaPost. “Companies that make … billions of dollars collecting, curating, monetizing our personal data also have a duty to protect in. Consumers have the right to know and control how their data is being used.”

CCPA Is Not Like GDPR

“The California law was written in five days, and really shows,” says Christopher Mohr, VP of intellectual property and general counsel at SIIA. “It is an extraordinarily complicated and poorly written statute.” Adding insult to injury, it is grammatically inconsistent and difficult to understand. I can’t imagine what compelled them to rush such important legislation through. It sounds irresponsible when you consider the EU worked on GDPR for more than three years.

“This is not the same as GDPR — it’s much broader.” Not a statement the already GDPR-fearing publishing industry wants to hear. Mohr continues, “In GDPR the information is tied to a data subject, for example, an individual. The CCPA covers ‘households’ as well as individuals. In addition, the CCPA’s potential ban on the use of information extends not only to the information but to the ‘inferences’ you might draw from it.” Inferences? Yikes! The law goes on to explain what is meant, but the idea of inferring conclusions sounds ripe for misinterpretation to me.

The main goal of the law is to regulate the collection and sale of personally-identifiable (PI) consumer data to third parties and service providers. You do not need to get paid for the data. If you disclose it to another party, it is considered a transaction. Using outside vendors to help manage your data is not a problem, because you are the controlling party.

Everyone will now have the “right to delete.” I asked Mohr to confirm that means deleting people from your database, not from your articles. “That’s the intent, I think. Whether the words match the intent is a completely different issue, and it’s not as clear as it could be. Personal information covers any information that could be associated with an individual.”

Anyone can tell you to cease disclosing their data to others; and you must comply. You cannot deny goods or services to anyone because of their data opt-out. That becomes the new Catch-22: In order to know you are not supposed to have data on an individual, you must have that individual in your database. And since it is likely you must have data on an individual in order to do business with him or her, how do you conduct business with data exceptions? For those rare European GDPR complainants, admittedly some American publishers will simply delete; good-bye. In the Hotel California, “you can check out any time you like, but you can never leave.”

Preventing a Privacy Tower of Babel

Fortunately, enforcement is by state attorney general, not by individuals. In other words, thank God this is not an invitation to everyone in California to sue. Of course this law will be challenged in court. It may be too vague, according to some. It may be discriminatory, since non-profits (and government agencies) can ignore it and do what they want, the way it is written.

Living in this hyper-intrusive world, it’s hard to disagree with the intent of CCPA since we are all being personally data mined. But play this out. Imagine what mischief the other 49 states can do. Davis reports, Washington state “lawmakers are considering a bill that would not only give consumers the right to learn what data is collected about them, but would also allow them to prevent their personal data to be used for ad targeting.”

Federal legislation is coming on this after the recent grillings on Capitol Hill of some of the leading big-tech luminaries. Typically federal legislation trumps local law, which is what makes interstate commerce work. Hopefully there will be one law of the land, so any company handling data can maintain sanity versus bowing to every state, city, or county passing a law. But in these Alice in Wonderland times we are in, I will leave that speculation to you.

You have complied with GDPR so that means you now have DPO (data protection officer). The CCPA gives your DPO a little more to do.

I’m no lawyer, so I’ll provide the usual disclaimer on all the above. On the other hand, I am a member of and advocate for the Specialized Information Publishers Association, part of SIIA, whose general counsel Chris Mohr was invaluable in enabling me to share an understanding of this law. I believe it makes great sense to occasionally be involved with your peers and work on common problems like privacy laws. As a member of SIPA or Connectiv, you won’t need to call your lawyer every time there is a question about the new privacy landscape. You can take advantage of knowledgeable experts in your corner.

Do I have you pining for the muddy clarity of GDPR yet?