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.

 

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?

 

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.

Session Spotlight: Learn How Brands Can Take Advantage of CDPs at FUSE Summit

In a few short years, customer data platforms (CPDs) have made a big splash on the marketing technology scene. Gartner named them one of six marketing technologies to watch in 2018 and the category is expected to reach $1 billion in revenue by 2019. Adoption is expected to grow based on marketers’ growing interest in permission-based marketing, first-party data, and providing unified cross-platform customer experiences.

CDPs are expected to be a hot topic at the FUSE Digital Marketing Summit this November. During a session led by Customer Data Platform Institute founder David Raab will explore how CDPs can enable marketers to better gather and act on customer data, how CDPs fit into the martech stack, tips for sizing up potential vendors, and examples of how marketers are growing sales with CDPs.

Check out the video below to learn how CDPs differ from other customer data solutions and whether a CDP might be a fit for your martech toolbox.

Learn more about the agenda for FUSE Digital Marketing here.

http://players.brightcove.net/2045965075001/SJJ40dyKg_default/index.html?videoId=5787739316001

 

How a CDP Can Be Used to Build Consumer Trust & Comply With GDPR

How a CDP can be used to ensure accurate first-party data and consistent brand messaging – which help build consumer trust – while also maintaining compliance with consumer data protections such as GDPR.

For anyone who has ventured into the “Quotes” section of Pinterest, you’ve seen thousands of quippy memes dealing with loss of trust. The gist is once trust is lost, it’s hard to regain. Although mostly focused on romantic relationships, the same can be said for relationships with brands and business.

Consumer trust in businesses is low and dropping. According to the industry standard measure of consumer trust, the Edelman Trust Barometer, overall consumer trust dropped 10 full percentage points during 2017 from 58% to 48%. Coincidentally 2017 was a record high point for US data breaches (1,579 data breaches in all), as well as ushering in the birth of the Cambridge Analytica/Facebook debacle.

In this series on specific customer data platform (CDP) use cases, you’ll see the core competencies of CDP’s go a long way toward maintaining consumer trust. In this post we’ll look at how a CDP can be used to ensure accurate first-party data and consistent brand messaging – which help build consumer trust – while also maintaining compliance with consumer data protections such as GDPR.

Managing First-Party Data

All communication from a brand/business to its customers and prospects is an expression of its brand. Many brands and businesses have relied heavily on third-party sources to provide targeting options for reaching prospects and customers.

Understanding the flaws in this method is as simple as creating an account at https://aboutthedata.com. Sponsored by Axciom, the leading aggregator of third-party targeting data, this portal will allow you to access your digital profile. Each of the characteristics in this profile identifies how you are being targeted. Now think about brands and marketers crafting messages directed to YOU based on this data. A mismatch between messaging and targeting will chip away at authenticity and brand trust.

First-party data collection and activation are the reasons the CDP exists. By ingesting, organizing, reconciling, segmenting, and activating first-party data across all customer data siloes, the CDP creates the opportunity to communicate around specific data gained from the direct, first-party relationship between brand and consumer. Imagine the following:

  • Adjusting the content of your website based on the user’s past content tastes and interests. Right message.
  • Determining the appropriate channel for your message based on the behavior of an individual target. Right channel.
  • Choosing the appropriate timing of your message based on the intensity of your customers behavior. Right time.

GDPR and Data Management

Aside from creating more consistent and authentic conversations between customers and brands, a CDP also creates a potentially smoother path to compliance with recent privacy policy legislation including GDPR and the California Privacy Act. Key to compliance are two factors, both of which should be core capabilities of any CDP system.

  1. Choice: A core capability of CDP technology is the identification and reconciliation of known and unknown users. As unknown users are accessing your site, the ability to offer them the appropriate experience (cookies for tracking or not) can be offered or directed and the preference maintained. More and more tools in the marketing technology stack are offering this capability, but maintaining these preferences in one environment that is used for all customer data collection and interaction makes the most sense.
  2. Transparency: The portability aspects of the GDPR and California Privacy Act specifically relate to delivering a comprehensive profile of all data points and their use for an individual. Whenever asked, an organization must be able to produce a succinct and complete picture of the user’s data and how it might be used within the organization. There is really no better place to create and extract that comprehensive picture than the CDP.

Being a steward of your customer data is not just a nice thing to do but an absolute requirement in an age where consumer trust is rapidly eroding and regulations on data protection are mounting. Adopting a philosophy and discipline in growing and activating first-party data from customers and prospects pays off by creating more authentic relationships grounded in trust. Statistically speaking, a highly-personalized relationship steeped in authenticity converts and performs optimally every day of the week. To cite one of those Pinterest quotes, “To be trusted is a greater compliment than being loved.” For marketers, trust is the pathway to business success.