The European Union’s General Data Protection Regulation (GDPR) and now the California Consumer Privacy Act (CCPA) are being joined by other data protection regulations that make privacy a fact of life for marketers. Nevertheless, the US is following what happened in the EU—many companies aren’t prepared. The CCPA ruling went into effect on January 1, 2020. Some stats peg the percentage of firms that aren’t ready for this significant new regulation from 56% to as many as 88%. Whether companies are non-compliant because of a wait-and-see approach, lack of funding or confusion about the law, there are compelling reasons to take action. For marketers, who are most impacted by privacy regulations, it’s wise to internalize some simple truths about CCPA.

1. CCPA likely impacts your company if you have California databases.

Not being physically located in California by no means gets you off the hook for CCPA compliance. If you hold data on even one California resident, you must comply with the regulation. Keep in mind the qualifications for CCPA oversight: annual gross revenues of $25 million or more; buying or selling more than 50,000 individuals’ data; and making more than half of annual revenues from selling customer data. This throws a long shadow across many companies.

2. It’s unwise to wait until CCPA enforcement goes into effect on July 1, 2020, to begin compliance.

CCPA sets in stone a new way of handling data, and such a large change takes time to implement. Now you’ll need to disclose what information you’re collecting and reveal how personal data is being used for your social media campaigns, email surveys and any other marketing programs. Also, you’ll need to give consumers the right to opt out of having their data sold to third parties and you’ll need to let them see what information has been gathered and allow them to delete it if desired. Implementing such new processes is time intensive.

Other articles you might enjoy:

• On Privacy Regulation: Insights for Marketers on CCPA Compliance
• CCPA: Consider It a Blessing, Not a Burden
• GDPR One Year In: How Are Marketers Doing?

3. Do the math on what kind of penalties might be waiting for you.

The CCPA states that companies can be penalized $2,500 for each record of unintentional violation and $7,500 for each record of intentional violation. This is for each record but a company could have hundreds, thousands or even millions of data records. For this reason, waiting to see what enforcement looks like could be regrettable. It’s true that enforcing the CCPA, like the GDPR, will take a bit of time to hit its stride but it will inevitably grow. So, playing it safe through compliance makes business sense.

4. GDPR compliance doesn’t ensure compliance with the CCPA.

Yes, there are some similarities between the two privacy regulations beyond the focus on EU versus California customers. But there are also some notable differences with greater impact on marketers in the CCPA. It goes beyond the scope of GDPR in that it: includes household information as part of what’s covered; gives consumers absolute opt-out rights; requires stricter privacy notices; and is much more focused on direct marketing companies or digital advertising companies. While the GDPR covered government entities as well as non-profit organizations, the CCPA puts its focus on for-profit businesses, which makes it a bigger deal for many marketers.

5. Take a savvy approach toward implementing CCPA compliance.

Privacy regulations have now been around long enough that best implementation practices have emerged. Begin with a thorough data inventory and know where all data resides, building compliance into development cycles rather than being bolted on at the end. In fact, data protection should be part of every new product or service from the beginning of development, with sensitive personal data tracked across an entire product lifecycle. Work with the teams that have the best insight on data infrastructure. When it comes to data records, shift from renting to owning because this not only ensures less-expensive, less-outdated data but is safer in the long run and prepares your company for the important step of rebuilding customer trust by collecting and applying data in the most transparent way.

Shane Nolan is senior vice president of consumer and business services for IDA Ireland.

The post Consumer Privacy: Five Things Marketers Need to Know About CCPA appeared first on Chief Marketer.

So, what does this mean for the digital marketing community? The downside, according to an AdExchanger column by Ari Paparo, CEO at Beeswax, is that different types of online tracking, such as view-through attribution, will no longer be possible, content management systems may need upgrades and reporting systems will change. However, Google has created an initiative, called the Privacy Sandbox, to develop new tracking standards that will replace third-party cookies for online advertisers—thus not leaving them without techniques for measurement.

There is potentially a light at the end of the tunnel: It’s possible that the new, cookie-less techniques Google is developing for ad tracking could become standards that are instituted across multiple browsers—and even apps. Only time (about two years’ worth) will tell.

Read on for a deep dive into the implications of Google Chrome’s decision and what changes online marketers can expect.

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• Formulating Your 2020 Data-Driven Marketing Strategy
• Insights for Marketers on CCPA Compliance

The post Google Chrome’s Phase Out of Third-Party Cookies: Implications for Marketers appeared first on Chief Marketer.

Data management is increasingly falling within the purview of the modern CMO. But with the advent of CCPA and the consequences felt as a result of GDPR (to say nothing of future potential privacy regulations), the onus is on marketers to protect consumers data while still depending on it. Looking ahead to 2020, a sound data management strategy is critical as the landscape becomes more complex.

According to AdExchanger, companies seeking to develop a complete data strategy should consider a few things. First, businesses should work on formulating data across department silos and orient the strategy around the customer. They should also focus on delivering value in order to build customer trust, embracing new analytics platforms that combine their first-party data with platform-level customer data and building flexible teams that embrace collaboration. Read on for how your business can use these insights to accelerate digital growth in 2020.

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The post Formulating Your 2020 Data-Driven Marketing Strategy appeared first on Chief Marketer.

A recent piece from sister publication Event Marketer looks at four additional insights to consider gleaned from a conversation with a privacy manager from Microsoft. With the legislation’s reach extending far beyond California, it’s critical for marketers to determine what compliance looks like for organizations affected. The act’s purpose is to secure privacy rights for California residents involving data collection and sharing personal information. But that can include those businesses that host California residents at an event (even out-of-state) or on its website.

Read more in Event Marketer about which companies are subject to the CCPA, in terms of revenue, size and function; which other states might follow suit; what could constitute a “sale” of personal data; and how third parties might come into play.

Other articles you might enjoy:

• CCPA Update: It’s the Final Countdown
• GDPR: Three Tips for Compliance
• CCPA: Consider It a Blessing, Not a Burden

The post Insights for Marketers on CCPA Compliance appeared first on Chief Marketer.

More and more marketers are adding artificial intelligence (AI) to their toolbox of late, and with good reason. AI promises significant automation of workflows and intellectual processes, and to a great extent, it delivers on that promise. However, it’s crucial that marketers not lose sight of the fact that AI is not magical or omnipotent.

The algorithms behind AI are beholden to the integrity—or lack thereof—in the measurements and datasets used to train them. If a model is trained to predict future states of the market, the data it is built with must be representative of that market. Unfortunately, this is often not the case. As a result, algorithms risk becoming as biased as their constituent data sets. Left unaddressed, such errors and inconsistencies can cascade through an entire marketing strategy and severely hobble its performance. Among the numerous potential pitfalls to monitor, AI bias often remains the most undetectable, and therefore, dangerous. The best way to mitigate this bias is by maintaining the right level of human interaction throughout the marketing chain while applying healthy scrutiny to data sources.

The Danger of Relying on Internal Data

Bias issues increase exponentially when marketers rely primarily on their own data to train their algorithms. This is increasingly common across many industries (due at least in part to growing privacy restrictions) and is particularly evident in the movie industry.

Organizations that assume their own data is somehow representative of the market as a whole are making a big mistake. Their AI algorithms are heavily biased toward what they have done in the past. Ironically, young companies are at the greatest disadvantage, as they have the smallest data pools, despite the best of data-driven intentions.

The hard truth is, algorithms are unable to imagine a future that is different from the past. Nowhere is that more clearly evident than in box-office prediction models. While there is technically no limit to their variety, there are three approaches that are often taken when developing such forecasting algorithms. For each, accuracy depends on the quality of the data made available to them.

Historical Box Office Models

Marketers don’t have access to paid engagement data or tracking data (surveys measuring awareness and intent) prior to eight weeks out from the film’s release. As a result, early-phase box office models rely largely on historical databases such as IMDB and Box Office Mojo. The algorithm’s understanding of the film’s prospective viewers is based entirely on how similar releases have fared in the past. It matches metadata points like cast, director, rating and synopsis to historical analogs, crunches the numbers, and projects how well the new release will do. Not surprisingly, sequels and franchise installments tend to get glowing projections.

The issue is that, historically, Hollywood has been biased toward white, male films and not very representative of minorities. That built-in bias prevents early-phase algorithms from recognizing the tremendous value in unprecedented films such as “Get Out,” Jordan Peele’s directorial debut, which , which turned out to be the 10^th-most-profitable film of 2017.

More broadly, if studios are relying on these algorithms to greenlight films—before even getting into the marketing and distribution—AI becomes a blocker to new and different kinds of content being produced. Producers can say their decisions to greenlight sequels and franchises are data-based, and they are, but the data is biased by historical box office performance.

Paid Media Engagement Models

As a film’s release date becomes visible on the horizon, marketers begin serving media on various paid digital platforms, typically beginning with a trailer drop and a few unique spots. User engagement with said media then begins to generate measurable data, which accrues to indicate absolute brand health as well as relative interest levels between different audiences. A media engagement-driven algorithm uses an understanding of how paid engagement patterns correlated with box office performance for past films, and it identifies similar patterns in the upcoming release. Bias is an issue here because different audiences engage with platforms in different ways.

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If your algorithm uses a metric biased toward one audience to predict the box office, you will be blind to signals from other audiences that might engage in different ways. It will provide false signals. Similarly, certain ages, genders, or ethnicities that don’t use (or overuse) social media won’t be taken into account, despite the fact that they may be hand-raising elsewhere. The model will often over-predict for movies that skew toward younger demographics and underpredict ones that skew older.

Survey Tracking Models

As a film’s release draws near, analysts can gain access to industry-standard survey panel reports that track audience awareness and intent. This tracking information offers data scientists a rich data set with which to train algorithms for predicting box office success.

Once again, bias can be a stumbling block. If the composition of the panel or the survey being used values certain demographics over others, it may reach faulty conclusions. The sample for surveys is not always representative of the general market audience, even in the best circumstances. For instance, the very fact that surveys are often delivered online means that the sample is skewed toward people with spare time to fill out surveys, or those in search of compensation for their time and opinions.

Similarly, algorithms must make judgments about the relevant value of different measured attributes, i.e., aided awareness, interest, etc., which may not hold true equally across all audiences. For example, if word-of-mouth promotion disproportionately drives audience turnout for certain audiences, their lifetime value (LTV) may not be taken into account by the model.

Human Involvement is Always Needed

Marketers can reduce their vulnerability to AI bias somewhat by relying on as many different, complementary models as possible. Combining multiple inputs results in much richer insights. In addition, it’s important for companies to be conscious of where they are sourcing their data from, selecting against misrepresentative or inapplicable data sets.

The single most important thing marketing organizations can do to mitigate the negative impact of AI bias is to constantly maintain human involvement. That may sound self-evident, but it’s lacking in a surprisingly large number of cases. The output of the model is not the end-all and be-all, and it should not serve as the foundation on which deterministic decision-making processes are built. AI is first and foremost artificial, and its value begins and ends with the input of both human creativity and critique.

Alex Nunnelly is senior director of analytics at Panoramic.

The post Don’t Let AI Bias Derail Your Marketing Efforts appeared first on Chief Marketer.

The question marketers are failing to ask, however, is “Are consumers satisfied with the buying experience?”

The paradox of today’s data-driven, AI-driven marketing is that despite a large number of channels that provide information and customer service (Hello, chatbots!), consumers are craving more human experiences.

In a report titled Are You Listening? The Truth About What Customers Want in a Digital World, Calabrio found that three out of four consumers in the US and UK are more loyal to businesses that give them the option to interact with a human as opposed to only chatbots or digital channels. That’s not all—a full 37% question the legitimacy of the company itself, if not given the option!

So how does a brand reap the benefits of marketing automation, and become more “human” at the same time? The answer lies in organizational empathy.

Does Empathy Matter in Marketing?

In marketing, we talk so much about customer journey and experience. Antonio Damasio, eminent neuroscientist and Professor of Psychology, Philosophy, and Neurology, at the University of Southern California, said: “We are not thinking machines that feel, we are feeling machines that think.”

This is underscores the importance of empathy in marketing and customer experience, especially where AI is involved.

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In an article on Clickz, Kevin Lindsay, director of product marketing at Adobe, explained how AI has the potential to beat human marketers at analyzing visitor and customer attributes, processing the most relevant ones, personalizing touchpoints in real-time, and delivering the right solution, all at scale. However, using AI to know how their customers are feeling at the moment, and humanizing their experience, is still a “brand fantasy.”

Because as Mary Beech, CMO and VP, Kate Spade, so eloquently puts it, “The best marketing isn’t about the brand; it’s about the customer.”

The Limits of AI, Automation, and Martech

Even though AI has pervaded every aspect of modern life and artificial empathy is now a thing, technology still has some way to go beyond “Alexa, what should I cook for dinner?”

While we’ve been debating the ethical complexities and utopian avatars of AI for long, the deepest of deep minds still remains a few marbles short of the average human marketer. Here are some inadequacies that might limit the level of automation you can achieve:

• Complexity of implementation: “Algorithms” and “models” are terms full of limitless possibilities, but they are guzzlers of resources, time, effort and money. They need time and expertise to research, set up, test, and implement. After that, there is a steep learning curve for the end users if they are to make the most of it. Finally, there is an intensive (and expensive) maintenance process that involves constant verification of data and results.
• Robotic customer service: Chatbots can seem human-like to most customers most of the time. Until of course an issue that the machine hasn’t learned how to deal with crops up. The lack of emotional intelligence or knowledge of nuances in language can lead to bots being anything from unhelpful to offensive to spooky at times.
• Uncertainties: The absence of human discretion leads to the system making some not-so-tactful or ineffective decisions. Do you really need to send an email for every action a user takes within your app? Why do you need them to log in with their Facebook account? Should you wish them on their anniversary when you can’t be sure they’re divorced?

When businesses use technology such as AI and automation to boost efficiencies, the outcomes will scale quickly. Managing the consequences calls for not just empathy, but alignment of “purpose” between the brand and its consumers. But while humans survive on meaning and a sense of fulfillment, machines thrive on clear instructions.

Kate O’Neill, who calls herself a “Tech Humanist” and is the author of a book of the same name, explains that this is why businesses that transform themselves digitally need to do so in a human-centric way and communicate their purpose to their customers.

By clarifying their strategic purpose, organizations can not only provide better customer experiences, but also increase brand loyalty, build a community, as well as foster a meaningful and productive work culture.

Empathy is the Missing Link between AI and Humans

“Empathetic marketing” connects companies, brands, employees and customers in a harmonious, productive and win-win way. You might be forgiven for thinking that ROI and the bottom line is all that matters to companies. While authoring my first book The Content Formula, I stumbled on the counter-intuitive secret to selling: Don’t talk about the stuff you sell.

“Then what should we talk about?” I hear you asking. Show, don’t talk. Show empathy towards your customers. Help, don’t sell. Help them solve a problem.

When marketers and ad execs walk into their office every day, a strange thing happens. They relish the idea of using data to “target” their customers with branded messaging. They begin to see “people” as users, leads, personas, prospects, audience, cohorts or whatever label is the flavor of the day. They forget that people don’t want to “consume” an AI-chosen ad 15 times during the course of an hour-long show or watch a 20-second pre-roll before a one-minute video.

Noah Fenn, global lead, strategic partnerships at Google, calls this phenomenon collective amnesia of marketers.

And the only antidote on offer is empathy. Put yourself in the shoes—or more accurately, behind the screens—of consumers. Listen to them and deliver the experiences that they want, not the ones you’d like them to have.

Consider the Cleveland Clinic. They produced a video titled “Empathy: The Human Connection to Patient Care” to encourage their 40,000+ employees to understand and imbibe the brand’s core value of empathy and premise of treating every patient (their customer) as they should be treated.

The fragments show the pain, struggle, and victories that unfold in a hospital setting. Amanda Todorovich, Cleveland Clinic’s director of content, decided to go social with the video. The tear-jerker touched the hearts of over four million viewers, earned Amanda the Content Marketer of the Year award from CMI, and played a central role in taking Cleveland Clinic’s blog from 0 to 67 million visitors in a span of six years. It generates enough revenue to cover the costs of their content expenses, reaffirming the proposition that empathy is the counterintuitive secret to success.

Amber Osborne, CMO at Doghead, reinforces the notion that it’s people who make a product. “No matter what you are trying to build, if you know in your heart it’s something valuable, keep pushing, keep building, keep networking. Our community members, our customers and the amazing success stories of our product keep us going every day,” she affirmed.

Be Human, Do Human

“We don’t focus on our customers,” said no one ever. And yet, marketers fail miserably at empathizing with customers. In order to fix the brand-customer empathy gap, you need to ask (and honestly answer) yourself:

• Do you understand the core emotional motivators of your customers? Does your messaging resonate with these motivators?
• Do you build a connection before you attempt a conversion?
• Do you test your assumptions and biases for every marketing campaign?
• Does your AI-driven revenue model incorporate the nuances of empathetic marketing?

Once you base your marketing, sales, and business growth on empathy, you’ll start gleaning insights that scale up your revenues while building lasting relationships with your customers.

Michael Brenner is the author of “Mean People Suck.”

The post The AI Paradox: Why More Automation Means We Need More Humanity appeared first on Chief Marketer.

Consider what CX leaders have in common. First, their culture revolves—implicitly and explicitly—around customer experience. Jeff Bezos is quoted as saying that Amazon’s success is due to an “obsessive compulsive focus on the customer.” Second, they are all data-driven businesses. Analytical capabilities are helping brands accurately parse the multiple factors driving what customers say satisfies them and the design of actual interactions that creates economic value.

Done right, predictive analytics can enhance customers’ lives, increase brand engagement and deepen loyalty by delivering interactions that are tuned to and even anticipate customers’ desires. Of course, this requires reliable data. Ensuring data quality via automated input controls helps to verify and correct data quality issues before it leads to rework or worse, inaccurate analysis and predictive results.

But, reliable data doesn’t mean all data. Feeding superfluous data into a predictive analytics toolset risks clogging essential processes. The key is understanding and automating the appropriate data sets that are potentially useful for predictive analytics, while disregarding the rest.

Usability and transparency are also key when it comes to data and analytics. Brands suffer from one of two extremes: Either insights are so impenetrable that only data scientists can decipher them, or interpretations are so superficial that they provide no value to stakeholders. User interfaces that are purpose-built for audiences.

Here’s 5 tips for using data to create superlative customer experiences.

1- Start with the end goal in mind.

Have a clear, measurable business goal: improve customer acquisition, test marketing mix, manage customer defections or improve average order value of SKUs. Don’t get fixated on the “what,” but rather the “how” and “why.”

Ensure you’ve developed and secured agreement on KPIs and interpretations that are aligned between various business functions (marketing, service, operations, etc.) to illustrate the value of predictive analytics to business results.

2- Map the right data sources

Most importantly, map your analytics to specific stages of the customer journey. It’s about getting to know your customers and developing specific initiatives to put that knowledge into action.

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For example, during the buying phase, understanding how and when customers will purchase can help with targeting. Predictive techniques such as propensity models help marketers predict the likelihood that a customer will respond to a specific offer or message and convert. Expand wallet share with cross-sell and affinity models or understand future buying behavior through propensity models.

Or, during the post-purchase phase, use predictive analytics to uncover patterns of usage behavior and further drive customer engagement. For example, a retail site may tell you the status of your recent order the moment you land on the home page. Churn models such as uplift modeling and survival analysis can provide early warning signs of defection. Preempt customer churn with corrective actions, such as special offers or free upgrades.

3- Don’t skimp on data preparation and acquisition

Gather as much as possible, whether it’s historical data from CRM systems, real-time data from digital interactions, or streaming data from sensors. A robust data pipeline will help evaluate and enhance the performance of predictive models over time.

4- Don’t make data privacy an afterthought

There’s something spooky about a brand “knowing” something about a consumer’s innermost motivations and preferences. Yet consumers expect relevant, well-timed personalization. Surround your marketing with thoughtful data stewardship efforts (such as governance, security, protection) and transparent communications with consumers on how their data is being used.

5- Embrace the power of AI

New AI-powered technologies such as machine learning, computer vision, natural language processing and deep learning are uncovering new patterns, subtle correlations, and empower more real-time decision making in organizations.

Embrace AI as the next step on the journey to providing the best possible customer experience.

Wilson Raj is director of customer intelligence at SAS.

The post Transforming Customer Experience with Data: 5 Tips appeared first on Chief Marketer.

It’s a trend that truly kicked off in earnest in April 2016, with the passing of the General Data Production Regulation (GDPR) by the European Union. Under GDPR, any business which collects data from an EU citizen must, under penalty of a hefty fine, communicate in clear terms how that data is being collected and the explicit purpose for which it will be used, alongside the option for consumers to opt out entirely.

Since going into full effect just one year ago, GDPR has become a catalyst for a global movement around data protection and privacy. With similar legislation passed in California, Brazil and Japan, and further legislation being considered in India, giving customers a voice in how their data is used is a worldwide priority.

Here are a few important takeaways as businesses continue to navigate these new standards.

Trust Above All Else  

One of the most significant factors driving us towards data regulation is the lack of trust and visibility between brands and customers. Every interaction, every touchpoint gives businesses greater insight into the individual, and that’s a responsibility that shouldn’t be taken lightly. Customers understand that business success in the digital era hinges on data, and therefore want to be treated as an equal partner in the relationship. Furthermore, customers are keenly aware of the potential personal risks in sharing their data. Without the proper infrastructure in place, personal data could be at risk to malicious actors.

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One of the biggest year-one lessons of GDPR is that the data itself comes second to trust – trust that businesses are collecting data responsibly, treating it respectfully and leveraging it appropriately. Customers are not willing to settle for a one-sided relationship; they want an equal say in how and when organizations are engaging with their most precious resource. It’s a critical metric in ensuring customer loyalty in today’s environment.

Enforcement is a Marathon, Not a Sprint

There’s an important reason that each piece of data protection legislation comes with built-in lead time for compliance—it’s a tremendous endeavor. The sheer magnitude of transitioning an entire organization to compliance cannot be understated. This is further compounded by the lack of structure around how to become compliant. GDPR itself is among the first of its kind, but the “why” and “what” were established without the “how,” certainly a nerve-wracking prospect for executives considering the scope of fines.

The result has been a relatively measured approach to non-compliance fines as organizations determine what path is right for them. In fact, there has been only one major instance of enforcement, and even that was issued seven months after the initial implementation in May 2018. Do not mistake that, however, for lenience. The farther away we tread, the more likely regulatory bodies are to hold organizations accountable. And, enforcement will only continue to ramp up.

Putting the Customer in the Driver’s Seat

The price of inadequate data protection is not one today’s businesses can ignore. Just as interpersonal relationships require trust, so do those between organizations and their customers. Then there’s the financial component – research has found a direct correlation between customer data breaches and total financial loss, including fines and resulting loss of business.

As the very nature brand-customers interact change, organizations need to hold themselves responsible to ensuring they’re delivering value back to customers in exchange for their data. In the past year, we’ve seen customers declare loud and clear how they expect brands to engage with their personal information, and that they want a seat at the table. As these regulations further establish themselves across the world, each with their own set of standards, the common denominator is the need to deliver a positive customer experience based on trust – that should inform any strategy moving forward.

Ben Jackson is general manager of SAP Customer Data Cloud.

The post GDPR: 3 Tips for Compliance appeared first on Chief Marketer.

The company’s business—and the professional services field in general—has transformed over the last few years, says Marten Van Pelt, executive director, advisory marketing leader, KPMG. The industry is highly competitive, and deals are complex, with many decision makers involved.

In the past, KPMG—like the other “Big Four” accounting firms Deloitte, Ernst & Young and PricewaterhouseCoopers—was able to crest on a strong wave of brand awareness. A lot of relationship building happened at in-person events, and marketing was a more reactive support function, rather than a sales enablement function. Today, marketing is more proactive, with every campaign looked at as a digital-first journey. There’s a larger focus on martech and optimizing a complex ABM strategy.

“We need to stay top of mind as our clients business needs change,” says Van Pelt, who spoke at the B2B Sales & Marketing Exchange in Boston this week. “Buyers are more digitally focused, and we’re creating a full digital journey.”

The backbone of this is a greater focus on data and lead gen, where teams works to help define and design buyers’ journeys, and build scoring models. This is backed up by a lead operations team, that looks through the data on a daily basis to qualify and distribute leads.

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KPMG is also designing personalized web experiences, using Demandbase to leverage AI to create a more customized visitor experience using data such as company name and industry vertical.

The strategy helps marketing provide sales with data about target accounts, including what topics prospects are interested in before they make a buying decision, to help identify intent; what types of content and activities engage prospects on the website; and what types of content—such as blog posts or press releases—might be good fodder for talking points with individuals at key accounts.

AI is also helping the company identify keywords that can provide buyer insight, and help expand KPMG’s SEM strategy, to increase engagement and help create new content more aligned to buyers’ interests. “The keywords might be different from what we’d [assume] they are,” notes Van Pelt.

Key accounts are filtered by site activity, and their propensity to be in-market is modeled on keywords, buyer roles and their likeness to ideal customers. Awareness of the brand—gauged by recent site visits to KPMG.com—is also considered, as is research on other sites.

The post AI Helps KPMG Realign Digital Marketing Focus appeared first on Chief Marketer.

For the past decade, legislation has failed to keep pace with data’s growing role in marketing and commerce, allowing brands to plot optimal strategies with little regard for the consumer’s rights, privacy and security. In many cases, this has led to unsavory and even unsafe practices, with exploitative methods and data breaches threatening to permanently damage public trust in advertisers and companies.

Passed last year and going into effect Jan. 1, 2020, CCPA ensures three basic rights for consumers: the right to request the data which has been collected about them by companies; the right to opt out of the resale of their data; and the right to request the deletion of their data.

While the regulation is limited to the data of Californians, its implications and applications are not confined to the state border and, moreover, is undoubtedly a harbinger of far more to come.

So far, six other states have also introduced new privacy legislation, and there are numerous other bills being debated at the federal level. Indeed, it seems the new regulations surrounding data collection over the next decade promise to be as unpredictable as the lack of regulation in the preceding decade.

What’s on the Line with CCPA

The new rights guaranteed to consumers by CCPA will require brands to make several critical and concrete changes to their data practices:

Brands must ensure that their data storage is mapped out and organized. Consumers are empowered to request the data which has been collected by companies. Proper organization will allow companies to respond promptly to requests for information and help to avoid penalties. Each consumer is entitled to request their data twice per year at no cost, and the company is obligated to inform the consumer what data has been collected, how it was collected, and who else has received the data.

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Compliance requires brands to inform consumers upfront about their rights. Brands must also state the categories of data which they are collecting, which categories of data are being sold on to third parties, and how the brand will use the data. Each company must ensure that these rights are stated clearly at the outset of the brand-client relationship.

Brands must create a mechanism by which customers can request the deletion of their data. Additionally, when a customer demands that their data be deleted, all third parties must also delete the relevant data in addition to the original collector. This detail requires brands to remain in close communication with any third party which has purchased their customer data.

Failure to adequately prepare for and adapt to these regulations could be costly. Though each individual unintentional violation incurs a mere $2,500 fine (triple if deemed an intentional violation), the major risk to a company is the possibility of a class-action lawsuit. In the event of a major data breach, thousands of California residents could exercise their rights at once, bringing about catastrophic financial consequences to a company who is not in compliance.

Building Trust Instead of Fear

These new requirements imposed by CCPA may strike marketers as draconian. However, brands should instead view compliance as a necessary and positive first step toward building a longer term, more trusting relationship with consumers. The upcoming CCPA inflection point provides a perfect opportunity to prepare in this regard for the rest of the regulatory storm to come, rather than dwelling on its impending restrictions.

Today, consumer trust is at a dangerously low point, and consumers desperately wish companies like Facebook could be more trustworthy with their personal data. CCPA should propel marketers to clean up their data collection practices and reestablish trust with their customers. Building direct relationships with consumers and relying more first-party data willingly provided by the customer in a trusted relationship, rather than third-party data, creates a longer term, more productive and positive relationship. By using CCPA as jumping off point and a fresh start for the customer-brand data exchange, companies can make an immediate positive impact on their public perception—and on their marketing ROI.

After years of data collection free from oversight, CCPA will inevitably lead to growing pains and frustration. However, marketers must take this opportunity seriously: thoughtful, methodical preparation for the arrival of CCPA will not only pay off in 2020, but will also produce benefits for the next decade of new policies and legislation.

Jonathan Lacoste is the co-founder and president of Jebbit.

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