DM Magazine March 2020

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Disrupting the marketing industry

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Unlocking the door to app marketing PM 4 0 0 5 0 8 0 3

vol. 33 • No. 3 • March 2020

The Authority on Data-Driven Engagement & Operations

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Three reasons why you should never have a data strategy

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The changing role of the data scientist

❱ 14 Brand advocacy: Interview with Jay Baer

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Vol. 33 | No. 3 | March 2020 EDITOR Brendan Read - brendan@dmn.ca

Data Marketing

PRESIDENT Steve Lloyd - steve@dmn.ca DESIGN / PRODUCTION Jennifer O’Neill - jennifer@dmn.ca Advertising Sales Steve Lloyd - steve@dmn.ca CONTRIBUTING WRITERS Jeff Adee David Phillips Dave Bell Jay Rockliffe Richard Boire Stephen Shaw Greg Brown Glenn Waine Frank Palmer LLOYDMEDIA INC. HEAD OFFICE / SUBSCRIPTIONS / PRODUCTION:

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The ads of the future have arrived

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How to monetize marketing data

Data Marketing ❯❯6

Three reasons why you should never have a data strategy David Phillips, President & COO of NLogic, explains why having a data strategy can lead us astray.

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Features

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Disrupting the marketing industry

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Brand advocacy: Interview with Jay Baer

Keeping your data safe and manageable

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The changing role of the data scientist

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Meeting Customer Expectations: Marketing Success Hinges on Personalization at Scale March 2020

List Management

Events

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Calendar of Events Excellent Execution

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Unlocking the door to app marketing DMN.ca ❰

Customer Centricity

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The ads of the future have arrived D

By Jay Rockliffe

Jay Rockliffe is vice president of Marketing

and Growth at Adastra Corporation.

igital transformation has taken industries across the globe by storm. While we tend to focus on the technology that is being introduced into society, we would be remiss to ignore that data is the driving force behind all of this progress.

Success in the digital economy, and digital transformation, revolves around data. The ability to harness and effectively manage vast quantities of data, with artificial intelligence (AI) and machine learning, allows us to make better decisions at a previously unimagined scale and reach.

A marketer’s ability to capture data, leverage it and refine it will deliver actionable and impactful insights.

Modern marketing uses data to segment and target customers, thereby predicting unique consumer likes, dislikes and future behaviours.

Enter UX design The emergence of highly personalized content is set to disrupt the marketing industry and create a reinvigorated notion of user experience design (UX design). UX design is where data meets technology meets the external users (customers) meets the internal users (data analysts, Marketing, Advertising, management).

The concept of UX design is not new. Consumers have grown to not only enjoy intuitive, user-friendly digital experiences, but to expect it. Coupled with the shift in customer demand, digital transformation has expanded across all industries, encouraging increasingly nimble, data-driven campaigns.

What IS new is the infusion of advanced technologies with cutting-edge methods. To capture and utilize them, Adastra, a global data company with shared Canadian and European headquarters, acquired Prague, Czech Republic-based Proboston in 2018 to tap into its work in aesthetic, UX design capabilities.

It then launched Proboston North America to help Canadian marketers and brands unlock and drive insights from data.

UX design is the technique of creating web sites, apps or products that provide intuitive and meaningful experiences to users. UX is often conflated with UI (user interface) design, but is much broader than just the interface and involves characteristics not just of design and brand, but of usability and purpose.

UX design creates an experience for the end user that works at the primal levels of emotions and attitudes.

At its core, good UX design requires an understanding of human behaviour, values and cognitive biases, as well as the goals of consumers and employees using the applications.

Why it is needed An underlying reality of many data driven marketing projects is that they do not always reach their full potential. Project success is not solely about delivering the right data at the right moment. In fact, a common reason they fall short is from lack of adoption and stickiness. Despite a project’s sophistication, if

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both internal and external end users are not fully committed and in agreement with the project’s overall mandate, it is almost guaranteed to fail.

Studies have shown that a good UX increases adoption by users, whether they are customers, employees, managers or executives. What is often missing is not functionality, but something that is tied to the sensory and reward experiences of the end user.

If the end user of any system is not fully engaged in terms of usability, the project is unlikely to survive past its initial stages. Companies that succeed embrace this concept of UX design and are constantly honing and improving the experience based on user feedback and insights, creating a profound and positive employee and customer experience.

Nevertheless, user experience is no longer a competitive advantage. Rather, it is a baseline for success.

With emerging technologies being adopted as part of organizations’ digital transformation journeys, new types and larger quantities of data are providing in-depth insights into customer behaviour and satisfaction. This allows organizations to adapt and improve their marketing strategies, enabling them to create hyper-segmented and personalized experiences for customers.

These custom experiences can include varying content, such as products or services. Or they can dictate which marketing channels are used to execute campaigns.

The arrival of targeted video ads Science fiction has been teasing the idea of personalized video ads and content for decades.

Perhaps, most famously in the Steven Spielberg classic, Minority Report, Tom Cruise’s character is followed along by a series of personally targeted ads as he walks (John Anderton! You could use a Guinness right about now).

Fast forward nearly two decades and personalized marketing is becoming the new reality. Data drives omnichannel marketing, allowing for custom experiences based on user habits and preferences. The use of customer relationship management (CRM) data and AI help elevate personalized video, enabling it to learn and hone its results over time to become more powerful.

For example, T-Mobile improved the effectiveness of its email communication to its customers using a personalized video platform called AmetIO. The platform realizes the power of AI and the cloud to personalize content at scale and tell a visual story to engage consumers at an individual level. The use of personalized videos resulted in the fourfold increase in customer acquisitions.

When AI meets Marketing a truly watershed moment transpires. The combination of visual, aesthetically pleasing and UX design combined with an AI and data-driven approach are set to explode across the landscape of brands and savvy consumers.

MArch 2020

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Data Marketing

Three reasons why you should never have a data strategy

David Phillips, president & COO of NLogic, explains why having a data strategy can lead us astray.

By David Phillips

I

n this age of big data, it’s easy to believe that we inevitably understand more and more of the world around us.

In fact, the opposite can be true.

Data has the potential to blind us to the realities not covered numerically. So, how do you uncover your blind spots and use data smartly?

And why should you never, ever have a data strategy?

The fallibility of numbers We tend to think numbers are always right and therefore don’t challenge something if it’s a number. In a 2019 article, author and blogger Bob Hoffman illustrated how a single claim by a reputable magazine, Forbes, talked about Millennials reaching $200 billion in buying power by 2018.

The Forbes article goes on to say that Millennials will have the strongest buying power over any other generation by 2018.

Without evidence, sources or statistics, social media and other news sites repeated this statement so much that it seemed like fact.

Why was everyone so quick to assume this assertion was accurate?

Essentially, people chose to believe it because it was a number.

We also tend to think that numbers are more right than words. Using data seems to automatically make us believe we are acting reasonably and making rational decisions. We believe that data helps us make good decisions. ❱ DMN.ca

I wonder whether data can, in fact, actually lead us to make bad decisions.

Data is a funny beast. It is not straightforward, not neutral, not infallible and it’s usually misunderstood. Here are two things you need to understand about data: First, data is a means to an end, not the end itself. Data is information, not knowledge.

Many people work under the false assumption that just by having data, or by having lots more data, then they will have more knowledge. This is asking too much of data.

Second, data has limitations.

Qualitative is different from quantitative, and within quantitative there is a broad range of methodologies. While some data you deal with may be more accurate than others (i.e. “garbage in, garbage out”), almost all data are estimates and must be treated as such.

Now that we all have a better understanding around the limitations of data, here are three reasons why you shouldn’t have a data strategy: 1. A data strategy is not a thing.

It doesn’t make any sense as a concept. It’s like saying you have a “sockets and wrenches strategy” when faced with a broken-down car. Data is a means to an end, not the other way around.

2. Blind spots are the downsides of a data-first mindset.

We’ve moved to an era of “data or it didn’t happen”. We need to recognize that all data is limited, in that there are things it cannot

tell you. Or, more frequently, it creates the impression of being a holistic picture whereas in actual fact it isn’t.

Take the 2016 U.S. elections where Donald Trump surprised the entire world by being elected the 45th president of the United States. All the data pointed to Hillary Clinton, but the data wasn’t showing us the complete picture. The problem is that once you prioritize what you see, you de-prioritize what you don’t see, causing blind spots.

3. A data-created brand crisis.

Not only are we potentially missing the complete picture and prioritizing the wrong things, we are creating our own brand crisis by focusing on the wrong data.

With the growth of online activation, the rise of white-label products, and the reduction of purchasing friction through online shopping and voice assistants, branding has become more important than ever.

When almost any product can be delivered to your door within a day or so just by talking into a speaker, getting someone to say your brand versus someone else’s is vital.

And yet, blinded by data, too many brands are doubling down on short-term thinking and harming their brands and their businesses as a consequence.

of that strategy can be informed by your data assets and then see where you have blind spots.

Determine whether the blind spots are in strategically important areas that would be helped if you had more data. This will help you overcome number bias, where we think something must be true because it’s a number.

David Phillips is president and COO, NLogic.

So, what should you do? Here are the steps: Choose a business strategy over a data strategy. Identify which parts

NLogic is a member of the Canadian Marketing Association and a presenting sponsor at CMAmedia, taking place on October 1, 2020 This article is provided by the Canadian Marketing Association (www.thecma.ca).

Realize what it is, emotionally or politically, that data is actually providing. We need to be very conscious of the role that data can play when making decisions.

The role of data is to help us make decisions, it does not make the decisions for us.

We have a tendency to automatically consider data to be facts, making it difficult for us to contradict an argument that uses it. And yet we know — at least at some level — that not all numbers are factual. Not getting too caught up in the data will help you balance its relative importance around any decision and instead provide you with a better perspective.

Fix the blind spots. As mentioned before, the key is not to pretend blind spots aren’t there, but instead to identify them and find a solution. Once identified, you can get the data, tools or support you need to address them.

MArch 2020

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Data Marketing

Keeping your data safe and manageable

By Glenn Waine

W

hile carrying your smartphone in a case that also contains your wallet with debit card, credit card, driver’s licence and a host of other personal information may offer many benefits — most notably convenience — it may also raise concerns about consumer safety and security.

With the trends towards virtualization of all personal information — including digital driver’s licences that are in the works — that case may soon be your smartphone.

For losing that case or smartphone, or having them stolen, creates a real risk of damage not only from the immediate inconvenience, but also from potential identity theft and fraud in the days and weeks to come.

Yet the instinct is often to prioritize convenience and ease over the constant vigilance we should apply against theft of our personal details. There may be a lack of thought — and action — on how to prevent this type of damage, and it’s a pattern we see repeated online. For those of us who work daily to protect users, data and infrastructure, the vulnerability created by consumers or users will not be news.

Increased awareness of fraud in recent years means that there is

March 2020

a much greater understanding of the steps needed to stay safe and minimize the risks both online and offline. However, there is still a gap between what people know they should do and what they actually do.

Strengthened measures A consequence of this gap in best practices and actual behaviour is that it can fall to the businesses providing digital services to protect users as they access and use it. This is a complex role where a one-size-fits-all approach doesn’t work. Different age groups, demographics and even individuals have different attitudes and approaches to their data security and their overall use of digital tools and platforms.

Bridging this gap in ideal and actual user behaviour is important for any business that is working to help protect themselves and their users from fraud and other cybersecurity risks. The data naturally created by a person as they interact with digital tools and processes everyday can play a role in keeping consumers safe online, even without them necessarily realizing that.

Device data, biometric information, as well as location and digital identity material, can all help protect an organization’s own networks and digital assets, along with their users. Utilized in association with standard network

monitoring and security analytics programs, this information can be used to fuel relatively simple data monitoring systems that flag potential risks and highlight where intervention may be needed.

Previously, tasks like ID checks, staff training to spot suspicious activity and sanctions checks were highly manual: constrained by speed, volume and complexity considerations. But new data techniques can make these protective tasks more efficient.

Analytics challenges In Canada, companies are amassing volumes of data with the intent of optimizing performance, identifying trends and meeting rising consumer expectations.

It’s no surprise that TransUnion’s recent global analytics study with Aite Group found that nearly 65% of Canadian financial services and insurance executives admit they are challenged by the immense amount of data — mobile, clickstream, voice and contact centre data — they have access to.

Businesses understand that rich analytics capabilities are critical to remaining competitive in today’s digital marketplace.

While Canadian executives feel challenged by the vast amount of data available to them, they plan to continue investing in new and expanded data sources to enable them to better mitigate risk and

meet constantly shifting consumer expectations. Specifically, 77% of marketing executives and 59% of risk executives expect their overall budget for data analytics to increase year-over-year, which could enable their organizations to better understand data to better understand the customer experience.

Where executives are really expressing concern is their ability to integrate the data. Although institutions are now obtaining vast amounts of data at lightning speed, this data doesn’t necessarily integrate into older systems, which could impact companies’ abilities to respond quickly. This is evident in Canada, with only 20% of Canadian firms reporting the ability to integrate new data sources across all their analytic solutions.

These days, everyone expects mobility, agility and availability when it comes to accessing their own information. Though financial service executives are not only challenged by high volumes of data but also their ability to integrate them, they continue to ramp up their data collection to help accommodate public demand: despite the difficulties in putting that data to their most effective use. In fact, 74% of Canadian executives are considering integrating new analytic Continued on page 16

DMN.ca ❰

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Data Marketing

The changing role of the data scientist

By Richard Boire

I

n my last article, I discussed the evolution of advanced analytics and its use within an increasingly digital environment. One conclusion from the discussion was the need for a new or emerging role for the data scientist. With the digital revolution data is at our fingertips. But data is like oil: which is useless unless it can be transformed into meaningful information.

Transformation advancements in technology have provided better tools that are enablers for data scientists in deriving the necessary meaningful information that can be applied in the next business initiative. This could be the development of a predictive model which uses deep learning technology (the real math behind artificial intelligence (AI)), or the simple development of an analytical file which allows a variety of business users to conduct exploratory data analysis.

In both cases the need for advanced analytics (the deep learning predictive model) versus the need for non-advanced analytics (exploratory data analysis) is determined by the business problem. Yet the development of these tools alongside the more powerful data processing tools provide the capacity to solve more business problems that are data driven. The bottleneck is no longer technology or data but instead the actual human or data scientist. What do I mean by this?

Technology/software advancements In the past, the tools used to create analytical file as well as applying advanced analytics techniques required much time in terms of processing to complete the task.

This limitation mitigated the use of data that is now readily available from social media and smart devices such as phones, sensors, Fitbits, etc. Advancements such as ❱ DMN.ca

in-memory processing along with parallel type data processing or the core Hadoop (Big Data) technology have essentially eliminated processing time as a barrier.

Advancements in software development have also enhanced the toolkit of the data scientist.

Software providers have created tools that have now automated the development of model algorithms, in effect creating a “model factory” type environment.

These tools can create hundreds of models “on the fly” where the best model is presented depending on the evaluation criteria of the user. Even if the final model from a given technique has been determined, ensemble modelling is another tool which in effect combines different algorithms from different modelling techniques into one overall ”best” model.

There is no question that these developments in data processing, coupled with developments in facilitating the use of different modelling techniques, have greatly reduced the time to develop a data science solution.

The evolution of programming But there is still the need for “coding” or programming as 85%90% of a data scientist’s work is the creation of an analytical file, which still consumes much of the data scientist’s time.

In articles and posts as well as my book, Data Mining for Managers-How to use data (Big and Small) to solve business problems, I discuss the arduous process and approach in working with raw data and transforming them into meaningful analytical files or environments for a variety of analytics exercises.

Historically, the early practitioners of data science were primarily SAS programmers with SPSS programmers being a distant second. R programming gained prominence in the late 1990s, which was the evolution of S+ software that had been available since the 1980s. All of these

packages were developed out of the need for doing statistical analysis.

However, the advent of the Internet introduced languages that were more focussed in the discipline of computer science or computer engineering. Languages such as C+, Java and Python gained in popularity due to the more object-oriented nature of the data on the Internet. Another advantage of these programming languages is their open source nature where much of this technical knowledge and learning is more easily shared. Technical platforms such as GitHub are a testament to this.

The need for data science to make sense of all this data further accelerated the use of these languages with Python emerging as the key programming tool for many data scientists today.

Python’s ability to make sense out of all this data is further amplified by its ability to empower coders with the ability to develop deep learning (AI) models.

relational database and data mart systems that house much of a company’s non-online data. But the popularity of Python amongst data scientists has created integration products that are now features of most of the leading commercial software analytics providers. For example, SAS programmers can now integrate python code directly into their SAS script.

Demand for less-technical tools Yet, so far, all the languages that I have discussed require a level of technical expertise in being able to program, which is using the right logic and syntax to generate a given outcome.

But the need and demand for data scientists continues to grow.

Accordingly, companies have emerged to try and fill this void by empowering the discipline of data science towards individuals who are less technical from a programming standpoint.

Instead of writing actual syntax or script, users now generate a flow

The need and demand for data scientists continues to grow The syntax of the Python language allows the user tremendous flexibility in trying many different options when trying to develop these AI models.

These option features are often referred to as hyperparameter tuning, which is the real underpinning in building optimal AI models or algorithms. Of course, this presumes that the 85% work in creating the optimum analytical file has been done and we are now just feeding this analytical file into the deep learning routine with all its various user options and parameters.

Python’s popularity has now extended to the more traditional structured areas, such as the

chart of sequential tasks that are necessary to build the solution.

Each task is now represented as a module within the GUI interface where the user simply drops this module down onto the flowchart, instead of being coded or programmed by the data scientist.

Listed below is one schematic flowchart example from Alteryx, a leading provider of this type of software: (See chart) There are other providers as well that offer this kind of GUI interface with SAS Enterprise Miner representing another option. Vastly improved visualization tools, such as Tableau, can then take the output file from such a platform and allow MArch 2020

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Data Marketing

the user to present the solution in a variety of different options.

The key objective in the visual presentation of this solution rests on the narrative that represents the desired communication between the data scientist and the business stakeholder.

The data scientist of tomorrow The emergence of the above types of tools have expanded the data science field to individuals with minimal programming

March 2020

experience. But the discipline of data science relies on a foundation of knowledge that is programming and software independent.

For example, the rigour and discipline which is utilized in transforming raw data into meaningful data inputs or an analytical file represents a deep base of knowledge that comprises a significant component of any data science program. As mentioned earlier in the article, 85%-90% of data work is in this area according

to leading practitioners.

Another critical piece of knowledge is deep knowledge in statistics.

Knowledge of calculus and linear algebra is also a growing requirement due to the increased practical use of AI and deep learning.

From a practical perspective, though, it is about the ability to understand the mathematical and/or statistical output and more importantly, what it means to the business.

This requires extensive knowledge in how to properly measure the performance of solutions and in understanding such technical concepts as overfitting of solutions.

The underlying theme that resonates from this preceding discussion is the ability to gain data science knowledge that will

not be replaced by either existing and/or future software. This implies that training should be geared less on the technical, such as programming, and more on knowledge that is required to be a successful practitioner. Tools and technology will continue to improve, thereby resulting in reduced demand for specialists and their technical prowess.

Instead the need will be for that data science generalist who has the required depth of knowledge when it comes to data and mathematics/ statistics but who also has the breadth of knowledge to apply it to an infinite array of business problems. The demand for these generalists will focus on their ability to think through a business problem. These generalists will be the “chess masters� as they align the right tools, the right data and the right mathematics in solving the right business problem.

Richard Boire is president of Boire Analytics.

He can be reached at boire@boireanalytics.com.

DMN.ca â?°

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Data Marketing

Meeting Customer Expectations: Marketing Success Hinges on Personalization at Scale Treat customers as individuals by tapping into unprecedented data visibility

By Greg Brown

T

oday’s connected customers are bonding with brands in ways unimaginable just a few years ago. In response, marketers are tasked with delivering consistently engaging content across a growing spectrum of channels and devices. In this nonstop customer journey, for example browsing on smartphones or engaging on social media, customers have come to expect brands to treat them as human beings rather than numbers in a spreadsheet. This means marketers must personalize their content if they want to successfully engage with their audience — a genuine struggle without the right tools for single customer identification.

Single customer identities empower businesses to track individual customers across all channels and all devices, seamlessly aggregating this data into customer profiles that include customer interests, preferences, and history. Businesses without the ability to construct a single customer identity are roadblocked by costly issues such as incomplete, outdated, duplicative, and inaccurate data. Where do you land on this spectrum of marketing success?

Researching Data-Driven Personalization In July 2019, NAPCO Research conducted an online survey of marketers using the subscriber database of Target Marketing (NAPCO Media is the parent company of Target Marketing).

The total number of respondents included 101 marketers from ❱ DMN.ca

companies that personalize content and have at least $5 million in annual revenue. (See Figures 1, 2 and 3.)

Respondents were asked to elaborate on the biggest challenges they have encountered in their efforts to increase personalization to their audience (see Figure 4), pointing to these key issues: ❯❯ “Finding good reliable skilled staff” ❯❯ “Keeping personal information secure” ❯❯ “Data governance and inconsistencies among systems” ❯❯ “Cutting through the deluge of information clients receive to initiate client interaction” ❯❯ “Management’s opinion is ‘if it ain’t broke, don’t fix it’ --our company needs to get beyond this mindset” A common theme among these responses revolves around data issues such as integration, management, security, and investment. Many of the respondents recognize that personalizing effectively starts with using data effectively, even if management is slow to come around. Indeed, many brands are sitting on vast amounts of customer data but are unable to leverage it for marketing purposes.

The connection between single customer identification and personalization becomes clear when comparing the confidence companies indicated in both areas.

87% of companies reporting that they are “very confident” in their single customer identification also report that they are “very effective” at personalizing the customer experience. Of the

Figure 1

Figure 2

Figure 1 shows a breakdown of respondents by job function, Figure 2 provides a breakdown of respondents by industry, and Figure 3 provides a breakdown of respondent company by sales type.

Figure 3

MArch 2020

// 11

Data Marketing Figure 4

Lack of a robust single customer identification can mean missed opportunities.

Q: What are the top three biggest challenges you have encountered in your efforts to increase personalization to your audience? (Select UP TO three) n = 101

Figure 5

Q: What kind of data problems have you encountered in creating a single customer identity?

(Choose all that apply) n = 72

companies that are less than “very confident” in their single customer identification, only 35% report that they are “very effective” at personalization. Of those surveyed, nearly half of companies have an enormous opportunity to make better use of their attribution data and drive conversions. So, what is holding them back?

Again, the answer returns to data. The top challenges include data that is incomplete, outdated, duplicative, and inaccurate (see Figure 5). When a customer moves to a new residence, the address a company has on file becomes outdated. When a customer misspells or omits personal information on a brand’s platform, that brand is left with inaccurate or incomplete data. And when a company’s data management efforts are insufficient, data from an individual customer can appear to be fragments of data from different customers.

March 2020

Overcoming Data Challenges This all leads to the truth — impossible without accurate data.

Considering all the customer data held in various silos and business units, the most critical advantage comes from verifying data at the point of entry. Current business operations can then be fulfilled effectively, and a pattern of data governance is established for smart long-term use. Businesses are empowered to handle the “simple challenge” of matching a person accurately across various channels, fueling clear insight into all of their interactions with the brand — from call center, to website and social media. This means key areas for data capture are account creation, web forms, and call centers — all areas in which inaccuracies (i.e., data entry errors) can creep in as data is collected, making life much more difficult down the line.

Standardizing, linking and de-duping data is a big part of this operation. Problems result from

even simple errors, such as different spellings of names from people interacting on your online site versus how they call in to support.

Being able to marry those into one record that is a true representation of that specific customer at various touch points with your organization is critical. Updating data continuously helps, considering 2-4 % of customer data goes stale each month. That’s around 20% to 25% yearly. Consider the data impact of over 40 million people moving every year in the U.S., along with 2.5 million deaths, 2.3 million marriages, and 1.2 million divorces each year — particularly important for B2C communications.

Eight percent of all mail sent is undeliverable, costing mailers over $20 billion a year.

Data as a Business Driver These data issues and the resulting lack of a robust single customer identification can mean missed opportunities for targeting and personalization of marketing content, a lack of insights into customer behavior, misdirected marketing strategies, and the inability to accurately assess the impact and ROI of marketing efforts. Survey results indicate that the most successful marketers are looking both internally for technology and content solutions, as well as partnering with external vendors to handle data management needs. Thirdparty vendors play a key role in improving data quality, providing the software and solutions necessary for omni-channel and cross-device attribution and targeting, and ensuring customer data is handled responsibly and securely.

These are no small values, as marketers recognize that data security is not only a matter of

maintaining compliance to privacy regulations and avoiding fines, it is also an ethical imperative.

Because marketers and the brands they represent hold vast amounts of personal information on their customers — customers who have placed their trust in these companies — betraying that customer trust is not only irresponsible, it can be very bad for business.

Winning with Better Data Single customer identification enables marketers to target and personalize content to the right audience, track the customer journey, convert customers more effectively, and determine the ROI of marketing efforts with more precision. To achieve single customer identification, companies must not only invest in their own internal staff, technology, and data solutions, they must also partner with the right data vendor. Emphasizing data security and regulatory compliance is crucial, as is hiring skilled staff in data management and hygiene and developing proper internal data handling procedures as part of standard business operations. Taking these steps can help businesses achieve effective single customer identification, giving their brands a distinct edge through a single source of truth about the customer based on accurate internal and external data, unified across channels, locations, and business silos.

Greg Brown is vice president of Melissa

(www.melissa.com), a provider of global contact data quality and identity verification solutions that span the entire data quality lifecycle and integrate into CRM, e-commerce, master data management and Big Data platforms. Connect with Greg at greg.brown@melissa.com or LinkedIn. DMN.ca ❰

// 12

List Management

How to monetize marketing data

By Jeff Adee

C

anada is a strong, growing and stable market.

From “old school” cataloguers to the latest tech firms, Canada represents potential in new customers and increased market share figures.

What are the steps that companies can take to maximize their potential in the Canadian market?

Valuable data types The first step is to check what data you have available and how it may be valuable, not just to your company, but also to other firms marketing their products or services. Publishing companies have subscribers, seminar companies have show attendees and most all companies have customer files available, for example.

With the proper hygiene and data collection methodology, all of

these segments will be of value to acquisition efforts.

Business data segments should include contact name, title, company name, physical address and possibly phone numbers and email addresses (more on email addresses in a bit).

Additional demographic fields like company size, business type, job function and others will greatly increase demand. The baseline for consumer data is name and address although age, income and other variables come into play as well.

Going to market Once a data asset has been properly vetted, the next step is how to go to market. List management firms exist with the sole purpose of helping these efforts. These firms manage and monetize data segments by building relationships with data buyers directly and at agencies and other marketing related locations. They also can

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add value to data sets by keeping data updated, adding selectable demographic fields and filling in gaps where certain data elements may be missing.

When looking for a list management firm, managed client rosters (especially customers in your specific business segment), owned Canadian data assets, salesforce experience and company stability are all factors to be considered.

The stable of clients at a list management firm can provide a lot of insight. Strength in vertical markets — particularly markets that fit your business model — can be a good indication of what type of end-users may be available for a specific data set. Also, the length of the client relationships.

If a customer has their data with a specific firm for three-plus years, it’s a safe bet they are doing a good job.

List management partners that own their own Canadian data is another item to check. Wholly owned data assets can be utilized in enhancing and confirming accuracy, which leads to better results and additional revenue.

Business data, for instance, becomes undeliverable at a rate around 2%-3% per month. Along with internal hygiene efforts, a partner with up-to-date Canadian data can match or update regularly to ensure accuracy within needed data elements.

Sales and company tenure are also important criteria.

Salespeople that have been in the industry longer will have more and better relationships with buyers.

They often have connections across several different industries and can immediately pair data files with end users.

Companies that have been in the space for several years have procedures and systems in place for all the important facets of list management: customer service, data processing, accounting/ billing and data security, among others.

So, in the end, it all comes down to one thing: generating additional revenue. But how much? This figure depends on many factors:

file size, depth of data, accuracy of data, market focus and uniqueness being a few.

Companies that are focusing on the American marketing and monetizing U.S. data typically see a 5%-10% lift in overall revenue when like Canadian data is added, for instance. Lists that include specific decision-making contacts will demand a higher price than location-only information. Data sets in hot markets (emerging tech comes to mind) will also fetch a premium when used.

Regulations compliance That brings us to the third step: the reaching for the holy grail of Canadian data i.e. email lists. With Canada’s Anti-Spam Law (CASL), the email channel becomes trickier to navigate compared with email marketing in the U.S. CASL regulations apply to everyone: individuals, businesses and not-for-profit organizations.

The regulations involve, among other items, the sending of commercial electronic messages.

Under CASL, individual consent must be gained before an email message can be sent. The safest way to accomplish this task is to have contacts opt-in to receiving third-party marketing messages.

Data providers with lists that have the proper permission regarding email usage can charge a premium for this data and stand out among others in the field.

From local businesses increasing market share, to companies in other countries looking to increase global reach, accurate marketingrelated Canadian data assets can be of high value and provide their owners with a strong revenue stream. Collecting the right data elements, keeping data clean and finding the right partner are key steps in this initiative.

When done successfully, organizations are able to double down on data—utilizing it for the initial reasons that data was collected--but also monetizing it within the open marketplace.

Jeff Adee is senior vice president and general

manager, B2B Data Solutions, Infogroup.

MArch 2020

// 13

Features

Disrupting the marketing industry

By Frank Palmer

I

magine ordering a pizza for delivery. But not with one set of toppings on the whole pizza, or even half of it. Instead, this pizza order allows you to individualize your toppings by the slice, so everyone gets exactly what they want, all while you pay for just one pizza.

Now, how do you scale this to fulfill millions of pizza orders?

Massive scaling of hypercustomization at warp speed is the key conundrum facing the advertising and marketing industry today. While the technology to meet such pizza orders exists today, our human systems are not yet equipped to handle such lightning-fast processes.

This is why our industry is in chaos. The good news is that we are not alone. Business is no longer “as usual” today. Every industry is in the throes of chaos.

In fact, for most current businesses, culture will be the hardest part of the digital transformation.

Just as automobiles replaced the horse and buggy, and Uber and Lyft are adding stress to the taxi industry, emerging technologies like 5G and artificial intelligence (AI) will only speed up our world even faster.

The rise of collaboration and RPA One technology to watch for in this landscape is robotic process

automation (RPA), which is forecasted by Grand View Research to grow its market size to US$10.7 billion globally by 2027, up from US$1.1 billion just last year (representing a compound annual growth rate of 33.6%).

This powerful technology utilizes software robots that emulate digital data activities performed by humans. With RPA, organizations have the ability to automate workflow and processes that are time consuming and repetitive. Such technologies are enabling us to re-calibrate who we are as a workforce and discard old systems that have remained unchanged over the past century, all so we can invent new ones.

In advertising and marketing, emerging technologies are transmuting our work: evolving it into more of a thing, rather than a place. Consider companies like Communo that are utilizing such technologies to build connections between talent, agencies and clients that maximize human talent potential.

Communo predicted that, in the near future, companies will need to add outside senior talent to their agencies to stay ahead of the game. They built their business around collaboration tools that, when used effectively, support a decentralized workforce without adding expensive labour costs to company overheads.

While collaboration tools are not new, Communo acted on a shift in the ad industry that

many others overlooked. While communications disciplines have become increasingly fragmented where specialized talent is needed, clients conversely want lean, budget-friendly teams. Traditional agencies and clients were no longer speaking the same language.

Communo set out to solve that.

Emerging technologies are also turning our foundational business processes on its ear. Consider what Tesla has done, romancing the public with its innovation capital, thereby forcing the rest of the industry to scramble in pursuit.

The massive dealer car lots of today are destined to become a concept of yesteryear, with the real estate becoming more valuable than the actual dealership itself.

That’s because Millennials simply don’t shop at car dealerships anymore. Some don’t even want to own a car. They do want to own the technology, however.

That is where Tesla wooed its customers: on its unequivocal technological superiority. In the future, dealerships will, like Tesla, have to offer one to two models for the in-person experience. Car buyers will then customize their vehicle online, to be delivered to their front door.

Car shopping will be all about new tech experiences that come with bragging rights.

Taking unclaimed marketing money Co-op advertising and marketing has set itself up for a similar

disruption. Global automotive brands fund a shared marketing budget for their local retailers. This incentivizes retailers to advertise a brand’s products to their local customers. The unfortunate fact is that nearly half of the $70 billion that Fortune 500 companies offer to their retail resellers for local marketing goes unclaimed.

Why is this the case? Put simply: dealers know how to sell cars.

Manufacturers, on the other hand, know how to make the cars that dealers sell. Their expertise lies in different areas. When both of these parties are thrown into the marketing mix, it takes time to create compelling creative that then has to be approved. When ads finally run, it takes many months for dealers to get their promised incentives.

For the local retailer, if the process to get marketing dollars back is painfully slow, they’re likely to pass. These outdated processes often have too many rules and regulations to make the effort worthwhile.

Covault has devised a technology platform that eliminates all of this legwork and associated costs, while enabling real-time operations. It allows dealerships to regionalize marketing communications on templates that are pre-approved for funding and compliant with car manufacturers’ brand standards.

Accordingly, dealers receive their incentives quickly.

Continued on page 16

Do you make decisions about your marketing operations?

Are you responsible for customer acquisition, retention or loyalty?

Is your department in charge of fulfilling orders or customer service?

Visit our website at www.dmn.ca and learn more about the magazine March 2020

DMN.ca ❰

// 14

FEatures

Brand advocacy: Interview with Jay Baer

By Stephen Shaw

Jay Baer is the president of the marketing consultancy Convince and Convert and a popular speaker and author on the subject of customer experience and digital transformation.

M Stephen Shaw is the chief strategy officer of

Kenna. Stephen can be reached via e-mail at sshaw@kenna.

Jay Baer, author of Talk Triggers.

❱ DMN.ca

arketing as a discipline is going through an identity crisis.

Every marketer has been programmed to shout at passing traffic. But people’s buying behaviour has changed so radically — due to price transparency, instant access to information, brand distrust, countless distractions, shrunken attention spans, ad avoidance and more — that even CEOs realize their marketing methods have expired. This past year some notable global brands have actually jettisoned the CMO (chief marketing officer) title in favour of Chief Growth Officer or Chief Experience Officer, leading to speculation that marketing is on a fast path to extinction.

There is no doubt the marketing battleground has shifted from winning “share of mind” to “share of heart”, not by pandering to people, but by creating a distinctive, friction-free, “sharable” customer experience that makes them go “Wow!” But often businesses struggle to satisfy even the minimal expectations of customers. To fix what’s wrong takes a willingness to invest in the future, with payback measured in years. And as long as the CMO position remains a revolving door, there is never any continuity.

Taking time to climb the loyalty ladder — by making life easier, convenient and more enjoyable for customers — will never soothe impatient investors. That’s why maximizing growth is still the top corporate priority, making customer experience (CX) a secondary concern.

Convince and Convert president Jay Baer has a way out of this predicament, which he calls “talk triggers”.

In fact, he’s written a book about it, full of case studies of how companies achieved fame by taking simple steps to generate “word of mouth” through the design of novel experiences. Another book he wrote called Youtility argues that companies can only win attention these days by asking: “How can we help?” And his latest book, Hug Your Haters, shows how to turn customer complaints into opportunity.

In fact, the main theme running through all of

Jay’s books is the need to create signature customer experiences that lift brands above the “sea of sameness”.

Q: A:

Is brand advocacy the surest path to growth?

We’re at a unique inflection point where we now trust each other much more than we trust businesses and organizations. So, if you can convince your customers to go out of their way to tell other people about you, that truly is the best form of marketing.

Q: A:

What’s an example of a company that’s won fame through word of mouth?

The example I like best is DoubleTree hotels, which has been giving out a warm chocolate chip cookie upon check-in to every guest in every hotel worldwide now, and they’ve been doing it for 30 years.

That’s 75,000 cookies a day, every day. We found that 34% of them have told a story to somebody else about that cookie. So, if you do the math on that, 22,500 stories a day are told about this chocolate chip cookie.

Now, the companion question is, when is the last time you saw a DoubleTree ad? Not very much, right? They really don’t advertise much at all because the cookie is the ad.

Q: A:

Don’t they also have to get the guest experience right?

Somewhere along the line, businesses convinced themselves that competency creates conversation. It’s this misguided idea that if you’re operationally sound, it will automatically lead to word of mouth advertising. But that’s not really the way people behave. We prefer to tell stories about things we never expected.

Q: A:

Is word of mouth about awarenessbuilding? Or differentiating the CX? Awareness for sure. You think about the hundreds of billions of dollars that get spent globally on creating awareness. So, if I say to you, “What if you could create awareness for free?”, that’s certainly worth considering, right?

Talk triggers

Q: A:

What exactly is a “talk trigger”?

Is it something unique about the value proposition?

Usually something other than that, because a unique value proposition is “We are faster, we are cheaper, we are nicer, we are kinder, whatever”.

It’s about how we are incrementally better than the competition at something.

But a talk trigger, by contrast, is a story. A talk MArch 2020

// 15

Features trigger is an experience. In fact, the data shows that 81% of word of mouth is triggered by some sort of an experience. And so, when you get a cookie that you didn’t expect, it becomes a story that you feel compelled to tell somebody else about. A talk trigger is an operational choice that’s designed to create conversation. It’s not a price, a promotion, a contest, a coupon or a campaign. It’s something you do differently that your customers will talk about.

Q:

Does that mean inventing something new or spotlighting a current sharable experience?

Both. We do a ton of research. We interview lots of customers and then we look at external chatter about the company. What do people notice today, if anything? In some cases, the answer is zero, but our responsibility is to take what’s in their DNA and craft an experience that is repeatable. And being repeatable is one of the key principles of talk triggers.

A:

Q: A:

Do you occasionally come up empty?

In terms of what they have today, sure. But we never come up empty in terms of what’s possible. The way it works is we have a detailed customer journey map, so we know all the different interaction points between the company and its customers.

Once we interview a bunch of customers, we get a sense for what they expect at each touchpoint.

And when you take a customer journey map and overlay it with customer expectations, you end up with an “expectation map”. We use that expectation map to come up with potential experiences we know will be unexpected. Once you know what people expect, by definition you know what they don’t expect. The talk trigger always lies in the unexpected.

Breaking through the clutter

Q:

You make the point in your book that an exclusive focus on CX makes it more difficult to be different. Is the end game now to create a novel experience?

March 2020

A:

I would say an experience that is identifiable with your organization. What we want is an experience that becomes part of how you do business. If it becomes something competitors can mimic, it loses staying power.

Enterprise Rent-A-Car had a talk trigger for many years which was, “We’ll pick you up”. Avis won’t pick you up, Hertz won’t pick you up, Budget won’t pick you up, nobody will pick you up. That was a great talk trigger: an experience they owned. Until Uber came along.

Now I can get anybody to pick me up at any time. So their claim is no longer noteworthy. Customer expectations change.

Q:

oday we live in this world of infinite choice. It seems the tiebreaker is who do I really trust.

It’s a heck of a lot easier to give your customers a story to tell than to make your company trusted. Customers trust each other a lot more. Everybody knows that word of mouth is important, but nobody has an actual strategy for it. Fewer than 1% of all companies have actually defined a word of mouth strategy, which is crazy, right? It’s really hard to fathom. It’s too important to leave to chance.

A:

Q:

In your book Youtility you say there’s only two ways companies can break through the clutter: be amazing or be useful. Should the goal really to become “amazingly useful”?

Yes, but I would say “talkably useful”: useful enough that people actually take notice. As competition gets even more intense, the idea of usefulness is more relevant than ever. Utility as a marketing model requires a measure of patience for those initiatives to pay off. Most marketers are forced to operate with a short time horizon. They want results now, as opposed to hoping some percentage of their customers will reward them down the road.

A:

Where marketing is heading

Q:

Digital marketing has moved from the perimeter of planning to the very epicentre. Now we live in

A:

Q: A:

Q: A:

Q:

a “post-digital” world, as the saying goes. But has marketing really changed very much?

The end game is obviously the same: get people to buy more stuff. There’s no question about that. But how you get there is definitely different. Just look at how much money has shifted from offline to online. And there’s a significant percentage of CMOs now in charge of CX inside the organization. CX is marketing now.

More and more businesses, of all sizes, are starting to think about customer relationships holistically instead of just campaigns. But I think it is still early days as people are trying to get their arms around that idea.

But if growth is still the name of the game, how does a brand think holistically?

So, go back to the DoubleTree cookie example.

This Hilton brand, according to its CMO, is the warm welcome. That’s it’s thing. DoubleTree wants to own that seven or eight minutes from the moment you enter the hotel until you set foot in your room. The hotel wants to be really good at that, and consequently, it puts more time, money and effort into lobby design and front desk training than most hotels at that price point. The cookie ceremony is a big part of that. The brand positioning is the warm welcome — ergo, you get a warm chocolate chip cookie — it’s all perfectly congruent.

Q:

Let’s talk about where marketing is headed.

Should we draw lessons from China, a mobile-first society, reliant on a few massive eCommerce platforms?

I think it’s absolutely the future, and Facebook, for its part, is headed in that direction.

The one company that Facebook wishes it was is WeChat because it controls everything in China; it does it all. All the things that Facebook does, plus Amazon, plus Twitter, all in one app, one mobile-based system for really everything in your life, which from a consumer standpoint is pretty damn convenient. But I just don’t know if people here are ready to do everything on their phone.

A:

Another disruptive trend is the meteoric rise of the direct-to-consumer industry.

Oh, my God, I can’t tell you how much s*** I bought on Instagram I didn’t even know existed. Instagram is costing me $1,000 a month in unnecessary purchases. It’s the greatest. The targeting on that thing is so good.

But let me tell you about my son who started his own fashion label.

He does three collections a year, makes a series of shirts, hoodies, pants, has a web site and has Instagram ads. So, anybody can do it in theory. You can be a 17-yearold kid in Bloomington, Indiana and be pretty good at Photoshop, and now all of a sudden you have a brand and you’re selling it directly to consumers.

Another game-changer is the growing ubiquity of voice technology. What impact will that have on marketing?

Adoption of smart speakers and voice-activated technology is really accelerating, even more than I thought it would.

My experience after doing this for 30 years is that convenience always wins. When you ask Alexa for a recommendation, you get one answer. You don’t get a list of 10 answers, you get one answer. So, brands should start voice-enabling their content now!

A:

Q:

The web turned 30 last year and Tim BernersLee has warned “A backlash is coming.” Here we have the inventor of the web saying the child he produced has turned out to be a juvenile delinquent.

And recently former Unilever CMO, Keith Weed, said we’re in a trust or bust moment today.

What’s your perspective on the state of the web?

I think we’re definitely seeing some signs of fatigue. Facebook usage is actually going down in the U.S., especially amongst young people. But, again, convenience always wins. So, sure, you may have a backlash. But I’m not certain what the alternative is. In the research we’ve done with Millennials and Gen Z, they don’t feel everything should be free.

They’ll pay for quality. And so, that’s probably good news.

A:

DMN.ca ❰

// 16

Continuations

Keeping your data safe and manageable Continued from page 7

technology into their platforms over the next two years.

We see clear evidence that some businesses are working to protect consumers and themselves via data intelligence-based tools. However, we need to see much more.

Organization and customer buy-in Data-based systems such as these rely on two key elements. The most important is the quality of data inputs available and utilized. Without reliable flows of information into the systems, they may miss patterns, may fail to make connections and may fail to achieve their protective potential.

The second is ensuring organizational buy-in for the use of data intelligence and security solutions. This is an area that needs executivelevel sponsorship, endorsement and promotion to drive a culture of care and protection for businesses and customers alike.

It could be a difficult change, but unless consumers increase their level of interest and capability in self-protection in the digital world, it’s a change that businesses may be forced to make. The tools are available and so are the data services; anyone with a digital business who is not considering them may be putting themselves, and their customers, at risk.

Glenn Waine is senior director, data science, data strategy and consulting at TransUnion Canada Inc.

Disrupting the marketing industry Continued from page 13

The takeaway for all of us? Marketers today must fully align with technology in order to understand where it is going, because we must get ahead of the game.

Right now, many marketers are swimming against that current. And that’s because it’s getting harder to predict the state of where technology will be in the next five to 10 years. Many businesses that are here today might not be in business within this timeframe.

The biggest problem for mature companies is teaching old elephants to dance. But dance they must, to be relevant in tomorrow’s game.

Frank Palmer is a Canadian entrepreneur/investor, marketing and advertising legend,

philanthropist, artist and author of Let’s Get Frank.

Direct Marketing Magazine With a qualified circulation of 6,400 primary readers and another approximate 11,000 secondary readers, Direct Marketing reaches a unique audience of marketing executives and their agencies who are responsible for creating, managing, supporting and fulfilling more than $51 billion in annual sales generated through a range of direct response channels.

To advertise contact: Steve Lloyd, steve@dmn.ca ❱ DMN.ca

Events Calendar March March 10-11 DX3 Canada Toronto, Ont. www.dx3canada.com

April April 19-21 FITC Toronto 2020 Toronto, Ont. https://fitc.ca/event/to20/ April 29 Canada’s Marketing Hall of Legends Dinner Toronto, Ont. https://www.amamarketinghalloflegends.ca/

May May 7 BCAMA Vision Conference Vancouver, B.C https://bcama.com/vision-conference/ May 11-14 Gartner IT Symposium Xpo Toronto, Ont. www.gartner.com/en/conferences/na/symposium-canada May 12 The Canadian Email Summit 2020 Toronto, Ont. https://emailsummit.ca May 13-14 DigiMarCon Canada Toronto, Ont. https://digimarconcanada.ca Product Marketing Summit Toronto, Ont. https://productmarketingworld.com/location/toronto May 26-27 Store 2020 Toronto, Ont. https://www.storeconference.ca

Visit us online for complete list www.dmn.ca MArch 2020

// 17

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Unlocking the door to app marketing

By Dave Bell

W Dave Bell is co-founder and CEO,

Gummicube.

hile there are many ways to market an app, from social media to news articles to influencers, none has as much of an impact as App Store Optimization (ASO).

ASO is a group of mobile marketing strategies and best practices that can help apps increase their overall search discoverability on the app stores and boost conversions across all channels. ASO includes search optimization, conversion rate optimization and optimization of engagements inside the apps.

So why is ASO so effective? This is because 70% of all app downloads originate from initial discovery inside the App Store and Google Play: even when all of the marketing dollars for app installs are taken into account.

In order to start maximizing growth on the app stores, it’s important to understand what ASO is, why it’s key to app marketing and how it can be leveraged for growth.

How does ASO differ?

ASO software allows marketers to track all of the keywords that an app ranks for, analyze conversion trends within their category and gain an understanding of how all marketing efforts are impacting performance.

As a result, ASO as a channel has one of the strongest influences on the presence of an app in the stores. This is due to the way store algorithms work: the Apple App Store and Google Play Store look at app metadata and conversion rate of an app to determine its keyword rankings. Other factors, such as volume and velocity of traffic from external sources, are also considered by the algorithm.

One key differentiator that makes ASO a powerful channel is how it places apps in front of users actively searching for them. This is more effective than advertisements or incentivized installs via offers. Users searching for features or competitor brand names already have the intent to download an app like yours: which makes them better targets than users engaged in another activity.

Why ASO is key ASO is the key to app marketing as it sets the foundations for search discovery. Incorporating it into a marketing program can help an app generate visibility in the area ❱ DMN.ca

where the majority of users go for their apps.

In order to be discovered in searches and improve conversions, marketers and developers should ensure their metadata and creatives are optimized. This will help their apps cater to the algorithms as well as users searching in the stores.

Without an optimized approach, it can be difficult for apps to rank in searches for high volume keywords or gain installs. Even if a marketer runs paid marketing and influencer campaigns, it is still necessary to leverage organic traffic to sustain the app and drive down the overall cost per install.

The criticality of data The code to the ASO key lies in the data. But the data you use needs to come directly from app stores. To most people this would seem like common sense, but what largely goes unspoken in the mobile industry is that Apple and Google do not share key data including search volume and download volume from keywords in their stores.

Many tools that are commonly used for ASO have attempted to solve this problem by patching in data from the web or paid search to “stand in” for what they can’t get directly from the app stores.

Unfortunately, this approach leads to significant missed opportunities and wasted time. There is only a 20% overlap between web data and mobile data. This is because mobile user behaviour more closely resembles eCommerce user behaviour than web search and discovery. What people search for, their search intentions and conversion drivers are completely different.

Some tools have turned to paid search data to form their search volume estimates for ASO, but this approach is also fatally flawed. This is because most paid search data and tools in the market don’t take into consideration how decision-making for a search engine marketing campaign differs from decision-making in an organic search program.

If a developer attempts to optimize their app using inaccurate source data the wrong keywords will be selected, screenshots will not resonate with the target audience, and there will be a general failure to achieve scale. All forms of user acquisition become more expensive when the marketing funnel is broken in this way.

At Gummicube, we use our DATACUBE technology, which accurately forecasts search volume and trends inside the App Store and Google Play using a combination of big data aggregation and machine learning. There are certainly other tools in the marketplace, however it is always important to ask where the data is coming from and validate that any answer is truthful before signing up to use any ASO technology or service.

Getting going App Store Optimization is one of the most important channels for app marketing. The influence ASO can have on organic impressions and installs far outweighs that of any other channel, so it’s essential that marketers know what it is and how to use it.

With both an understanding of the stores’ algorithms, the tools to properly optimize for them and the right data app, marketers can start to reap the benefits of ASO. MArch 2020