Posts Tagged ‘Data Quality’

The Benefits of Email Marketing

Few marketing channels share the power of email. It is immediate, urgent, personalized and inexpensive. And according to the Direct Marketing Association, it has the highest ROI of any marketing channel: an amazing 4300%.

Here are some of the key benefits of good email campaigns:

  • The cost per contact of email is extremely low compared with other channels.
  • Email is easily personalized by customer, market segment, or demographic.
  • Email marketing is much kinder to the environment, versus using natural resources such as direct mail.
  • Your email assets can help you make more informed decisions, develop more effective marketing strategies and strengthen customer/prospect relationships.

That said, your email marketing strategy is only as good as the quality of your email list.

The Importance of Data Quality and Email

The allure of email has always been its scalability: with one press of the “Return” key, your message can go out to dozens, hundreds, or even millions of people. Once you absorb the cost of acquiring email contact data, the costs of its re-use are minimal. So once upon a time, not that many years ago, marketers simply accepted a certain percentage of bad or misdirected email addresses as part of the process.

Today this is no longer the case. As people’s in-boxes have become flooded with spam, and marketers compete more than ever for busy peoples’ eyeballs, the quality of your email contact list has become extremely important. Here are just a few of the reasons why:

TIME AND MONEY

As email lists continue to grow and expand, the human costs of processing bad data and updating contact lists continues to grow as well.

BRAND IMAGE AND CUSTOMER REPUTATION

Mis-directed email is almost universally unwelcome and perceived as spam, which in turn affects the public reputation of your brand and organization.

WASTED EFFORT

When someone provides a bogus email address such as “HowdyDoody@nobody.com,” particularly in conjunction with other contact information, adding them to your list of leads potentially wastes marketing resources in all of your channels.

REGULATORY COMPLIANCE

Laws and regulations such as the US CAN-SPAM act or the European Union’s General Data protection Regulation (GDPR) restrict unsolicited email marketing nowadays, with potentially severe penalties.

LOST MARKETING OPPORTUNITIES

Send unwanted email to the wrong address, and you could be blacklisted from an entire corporate domain— losing access to all of their prospects and customers. These last two reasons are especially important, because bad data now has the potential to do real harm to your business. And all of these factors add up to a future where more accurate and careful email marketing has become an increasing necessity. Email lists are a valuable business asset, but data quality— particularly authenticity and accuracy—always wins out over quantity.

That’s where real-time email validation comes in to separate the good emails from the bad ones.

An advanced email address validation and verification service, such as Service Objects’ DOTS Email Validation, uses sophisticated algorithms and dozens of rules and tests to instantly weed out invalid email addresses. It will also cross reference proprietary data for known bogus emails or spamtraps. Every email validation system should also check for the following:

  • Email address syntax
  • Individual domain specific mailbox rules
  • Improbable names (vulgar, famous, bogus, or suspicious keystroke sequences)
  • Mail exchange record of domain is valid and accepting mail
  • SMTP server for domain
  • Mailbox is accepting mail (when possible)

Want to learn more? Download our free whitepaper, The ROI of Real-Time Email Validation, to explore how to get the most profitability and customer engagement from your email marketing. The strategies presented will not only improve your response rates and effectiveness, they will help protect your organization from a host of issues including fraud, blacklisting and regulatory concerns.

Garbage In, Garbage Out – How Bad Data Hurts Your Business

The old saying “garbage in, garbage out” has been around since the early days of modern computing. Code for operator error or bad data, the adage implies that the output of a program is only as good as the input supplied by the user. With more data being collected, stored, and used than ever before, data quality at the point of entry should be a top priority for all organizations.

Now that data is informing more aspects of our businesses, it’s not difficult to imagine a future where data accuracy is vital. Think of delivery drones, which have been tested all over the US and UK in recent years. If a contact’s bad address information goes unchecked, it could feed a drone the wrong coordinates resulting in misdeliveries and lost products.

Data quality affects every aspect of your business, from sales and development to marketing and customer care. Yet a 2017 survey by Harvard Business Review found that “47% of newly-created data records have at least one critical (e.g., work-impacting) error.” So, what constitutes garbage input? It depends on the data and how it enters your system.

What Is Bad Data?

Of course, there are many kinds of data an organization may choose to collect, but here we will focus on one of the most critical – contact data. This includes a contact’s name, address, phone number and email, all of which are crucial to marketing, sales, fulfillment, and service.

Some common issues that make a contact record bad:

  • Inaccuracies like bad abbreviations or missing zip code
  • Typos caused by speed or carelessness
  • Fraudulent information
  • Moving data from one platform to another without appropriate mapping
  • Data decay as contacts move, get new phone numbers, and change positions

So, how does a bad contact record make it into your database?

Contacts entering their data online, whether downloading a whitepaper or ordering a product, are usually the first to commit a data quality error. Filling out an inquiry form on a mobile device, rushing through a purchase, or providing inexact information (such as missing “West” before a street name) are all examples of bad data leading to inaccurate contact records.

Your sales and customer service team can compound poor data quality by manually updating information that looks incorrect and making mistakes in the process. Good business practices can help mitigate operator error, but if a record was poor to begin with that likely won’t matter – you already know what happens to garbage when it ages.

What Is the Cost of Bad Data?

Much like garbage, bad data only gets worse over time.

Poor data quality can cause an organization’s sales team to waste time and effort chasing bad leads. According to a 2018 study by SiriusDecisions, B2B databases contain between 10% to 25% of critical contact data errors. That means up to 1 in 4 leads could have bad phone or email information attached, so follow-up communications may never reach the intended contact.

The customer service department loses time and money first in dealing with unhappy customers – even if they provided poor contact information, they’ll still blame your business for a package that never arrives. Additional time is spent troubleshooting problems and clarifying bad information, leading to major inefficiencies and frustration.

Overall costs to businesses include reputation related losses that occur when upset customers take to the internet to air their grievances. Time is lost due to the hidden data factories that arise within an organization when individuals working with bad data take it upon themselves to make “corrections” without understanding why or how the data is incorrect. Lastly, poor data robs a business of the ability to take full advantage of business tools like marketing, sales automation systems, and CRM.

How Can My Business Fix Bad Data?

Tightening up business policies around collecting and managing data is a start, but implementing data validation services will help ensure your data is as genuine, accurate, and up-to-date as possible and keep contacts current through frequent updates. Contact data validation can be integrated in a number of ways to best meet the needs of individual organizations, including:

  • Real-time RESTful API – cleanse, validate and enhance contact data by integrating our data quality API’s into your custom application.
  • Cloud Connectors – connect with major marketing, sales, and ecommerce platforms like Marketo, Salesforce, and Magento to help you gather and maintain accurate records.
  • List Processing – securely cleanse lists for use in marketing and sales to help mitigate data decay.
  • Quick Lookups – spot-check a verbal order or cleanse a small batch.

Service Objects’ validation services correct contact data including name, address, phone number, and email and cross-checks it against hundreds of databases to avoid garbage input. The result: cleaner transactions and more efficient processes across all aspects of your business.

Contact our team to determine which of our services can help you collect and maintain the highest quality data and kick the garbage to the curb.

The Role Data Quality Plays in Master Data Management

Enterprises are dealing with more data than ever before, which means that proper information architecture and storage is crucial. What’s more, the quality of the data you store affects your business more than ever nowadays. This goes double for your contact data records, because you need the most accurate and up-to-date lead and customer data to ensure that marketing, sales, and fulfillment all run smoothly.

Master Data Management, or MDM, involves creating one single master reference source for critical business information such as contact data. The goal of MDM is to reduce redundancies and errors by centrally storing records, as well as increasing access. No matter how well organized your database might be, the quality of its data will ultimately determine the effectiveness of your MDM efforts.

Why is Master Data Management Important?

Organizations look to MDM to improve the quality of their key data, and ensure that it is uniform and current. By bringing all data together in a hub, a company can create consistency in record formatting and ensure that updates are available company-wide.

MDM sounds simple in theory, but think of how (and how much) information is created and collected within an entire organization. Let’s take a single customer contact as an example. Over the course of a month, John Smith provides information to your company in three different instances:

  1. First, he is an existing customer of your services division, and his data exists in their customer records.
  2. Second, he visits your website and downloads a whitepaper, getting added to your marketing department’s database of leads.
  3. Third, he calls the sales team for one of your specialty products, and gets added to their database of contacts.

In a typical organization with departmental “silos,” John Smith is now part of as many as three separate databases in your company, none of which have a longitudinal view of his relationship with you as both a customer and a lead. Worse, slight differences in contact records could even turn John into three separate people in a master database. If each of these touch points get assigned to different automation drips and your sales reps are calling and emailing, you have a real disconnect in your efforts and a high likelihood of spamming your contact.

Having good data collection and storage practices is the first step to a good MDM program, but that alone may not solve John Smith’s problem. Ensuring the data he provides is correct, current and most importantly consistent at the point of entry is what guarantees that the best quality contact information is stored as master data. Implementing a lead validation service could help solve this issue at the point of entry by cross-validating each contact record with multiple databases in real time, facilitating accurate merge/purge operations later.

Lead validation not only corrects and appends contact data, it can also feed into your CRM and automation tools to help you further qualify your leads, so your sales team is only dealing with the highest quality information and pursuing genuine prospects. Once your prospects become customers, their contact data records will be passed on to other departments to complete any transactions – and through your MDM they will also have the most up to date and accurate information to conduct their business.

The Costs of Poor Data Quality

In a 2018 study conducted by Vanson Bourne, 74% of respondents agreed that while their organizations have more data than ever, they are struggling to generate useful insights. Some say this is because they are not effectively sharing data between departments, but only 29% of respondents say they have complete trust in the data their organization keeps.

Every aspect of your organization can be impacted by poor data quality, especially if your contact data is lacking. If 71% of your sales team feel that their lead information is bad, how effectively can they do their jobs?

Here are just a few of the ways that bad data can hurt your business:

  • Marketing – sending marketing materials to existing customers for discounts for new subscribers, spending extra money creating mailers for addresses that don’t exist
  • Sales – Multiple sales reps calling the same lead, the same sales rep calling the same lead multiple times, wasting time on bogus leads
  • Order Fulfillment – bad or incomplete address data causing failed deliveries and chargebacks
  • Accounts Receivable – sending invoices to incorrect addresses or old business contacts, billing addresses not matching credit card records
  • Business Development – making bad decisions for growth in a particular market based on inaccurate data

Each of these issues could be solved by creating a master data management system and making sure it is only fed validated contact data from the point of entry. Further, frequently updating the MDM system by validating name, address, phone, email, IP, and other key pieces of information ensures that each data record is as current as possible.

Finally, one major cost of poor data quality – and a key reason for the growth of MDM – is the compliance risk as new data privacy and security regulations have emerged in recent years. For example, the European Union’s recent General Data Protection Regulation (GDPR) and the US Telephone Consumer Protection Act (TCPA) both have severe penalties for unwanted marketing contacts, even when such contact is inadvertently due to data quality issues like bad contact data or changed phone numbers. Accurate, centralized data is now an important part of compliance strategies for any organization.

Trends in Master Data Management and Data Quality

The competitive advantages of consistent centralized data – together with the advent of cloud-based tools for data quality – have made MDM a growing trend for organizations of all sizes. Moreover, David Jones of the Forbes Technology Council predicts that increasing regulation and the rise of cybersecurity risks are converging to change the way data is managed at the business level. We now live in a world where things like validating contact data, rating lead quality, verifying accurate and current phone or email data, and other data quality tools have now become a new standard for customer-facing businesses.

Today, adding a contact data validation service to your processes before storing master data will improve overall data quality and increase efficiencies among all of your teams. Service Objects offers 24 contact data validation APIs that can stop bad data from undermining your master data management system. Want to learn more? Contact our team to see which API is best for your business type.

Data Quality and the 2020 Census

We talk a lot on these pages about how data quality affects your business. But once in a while, we also feel it is important to look at how data quality affects society as a whole. And one of the best examples of this in recent memory is the upcoming 2020 United States Census.

Every 10 years, the United States goes through a demographic headcount of its inhabitants. The results of this survey are pretty far-reaching, involving everything from how the Federal government allocates more than $600 billion in funding to who represents you in Congress. But this year, for the first time ever, technology and data quality loom among the biggest issues facing the next Census.

2020 Census Data Quality Doubts

These concerns are serious enough that the American Academy of Family Physicians, a healthcare advocacy organization, recently introduced a resolution entitled “Maintaining Validity and Comprehensiveness of U.S. Census Data” that has now been accepted by the American Medical Association together with other healthcare groups. It breaks down a number of data quality concerns currently facing the Census, including the following:

  • This will be the first year that a majority of responses are planned to be collected online, introducing possible sources of data error.
  • Sampling and data quality errors may disproportionately affect vulnerable populations subject to health care disparities, such as minorities and women.
  • In addition to human and data errors, there are concerns that mistrust of technology and privacy may prevent some people from completing the Census survey.
  • Above all, there are concerns over the impact of scaled-back funding for the 2020 Census, together with the departure of its director, in terms of how this will affect preparations for new technologies and survey methods.

Where Data and Politics Converge

It isn’t just stakeholders like healthcare providers who are raising a red flag about the next Census: the government itself shares many of the same concerns. In its 2020 Census Operational Plan, the U.S. Department of Commerce points to data quality as one of its key program-level risks, stating that “If the innovations implemented to meet the 2020 Census cost goals result in unanticipated negative impacts to data quality, then additional unplanned efforts may be necessary in order to increase the quality of the census data.”

This is a case where political issues also intersect with data concerns: in addition to the ongoing battle over funding levels for the 2020 Census, others have raised concerns over a proposed new citizenship question that is potentially a hot button for areas with large Hispanic and immigrant populations. According to the Brookings Institute, both of these issues may have far-reaching impacts on the quality of this next decennial Census, and recently the Attorney Generals of several states drafted a joint letter raising these as potential quality issues.

The Impact of Contact Data Quality

Finally, in an area near and dear to our hearts, the 2020 Census serves as an example of where contact data quality will have a huge impact on both costs and quality – because many addresses change over the course of a decade, and the current practice of canvassing non-responders on foot (up to six times) can be costly, time-consuming and error-prone. In 2015 the government responded to this issue by conducting address validation tests across a limited population sample, and to be fair, they must also contend with many non-standard locations (such as people living in basements, illegally subdivided units, or homelessness). But clearly, accurate address validation and geolocation will loom larger than ever for the census of the future.

These concerns are examples of some of the potential social impact of data quality issues, as society bases more of its decisions and funding choices on collected data. At a deeper level, they point to a world where data scientists may even ultimately have as much impact on these social issues as politicians and voters do. Either way, technology is playing more of a role than ever in social change.

The takeaway for all of us – in business, and increasingly in life itself – is that our world is increasingly becoming data-driven, and paying strategic attention to the use of this data is going to become progressively more important over time. And in the near future, this will include making sure that every American is accurately and properly counted in the next Census.

Why Data Quality Isn’t a Do-It-Yourself Project

Here is a question that potential customers often ask: is it easier or more cost-effective to use data validation services such as ours, versus building and maintaining in-house capabilities?

We are a little biased, of course – but with good reason. Let’s look at why our services are a lot easier to use versus in-house coding, and save you money in the process.

Collecting and using information is a crucial part of most business workflows. This could be as simple as taking a name for an order of a burger and fries, or as complex as gathering every available data point from a client. Either way, companies often find themselves with databases comprised of client info. This data is a valuable business asset, and at the same time often the subject of controversy, misuse, or even data privacy laws.

The collection and use of client information carries an important responsibility when it comes to security, utility, and accuracy. If you find yourself tasked with keeping your data genuine, accurate, and up-to-date, let’s compare the resources you would need for creating your own data validation solution versus using a quality third-party service.

Using your resources

First, let’s look at some of the resources you would need for your own solution:

Human. The development, testing, deployment, and maintenance of an in-house solution requires multiple teams to do properly, including software engineers, quality assurance engineers, database engineers, and system administrators. As the number of data points you collect increases, so does the complexity of these human resources.

Financial. In addition to the inherent cost of employing a team of data validation experts, there is the cost of acquiring access to verified and continually updated lists of data (address, phone, name, etc.) that you can cross-validate your data against.

Proficiency. Consider how long it would take to develop necessary expertise in the data validation field. (At Service Objects, for example, our engineers have spent 15+ years increasing their proficiency in this space, and there is still more to learn!) There is a constant need to gain more knowledge and translate that into new and improved data validation services.

Security. Keeping your sensitive data secure is important, and often requires the services of a team. An even worse cost can be failing to take the necessary steps to ensure privacy, which can even lead to legal troubles. Some environments need as much as bank grade security to protect their information.

Using our resources

The points above are meant to show that any data validation solution, even an in-house one, carries costs with it. Now, let’s look at what you get in return for the relatively small cost of a third-party solution such as ours for data validation:

Done-for-you capabilities. When you use services such as our flagship Address Validation, Lead Validation, geocoding, or others, you leverage all the capabilities we have in place: CASS-certifiedTM validation against continually updated USPS data, lead quality scores computed from over 100 data points, cross-correlation against geographic or demographic data, global reach, and much, much more.

Easy interfacing. We have multiple levels of interfacing, ranging from easy to really easy. For small or well-defined jobs, our batch list processing capabilities can clean or process your data with no programming required. Or integrate our capabilities directly into your platform using enterprise-grade RESTful API calls, or cloud connectors interfacing to popular marketing, sales and e-commerce platforms. You also spot-check specific data online – and sample it right now if you wish, with no registration required!

Support and documentation. Our experts are at your service anytime, including 24/7/365 access in an emergency. And we combine this with extensive developer guides and documentation. We are proud of our support, and equally proud of all the customers who never even need to contact us.

Quality. Our services come with a 99.999% uptime guarantee – if you’re counting, that means less than a minute and a half per day on average. We employ multiple failover servers to make sure we are here when you need us.

We aren’t trying to say that creating your own in-house data validation solution can’t be done. But for most people, it is a huge job that comes at the expense of multiple company resources. This is where we come in at Service Objects, for over 2500 companies – including some of the nation’s biggest brands, like Amazon, American Express, and Microsoft.

The combination of smart data collection/storage choices on your end and the expert knowledge we’ve gained over 15 years in the data validation space can help to ensure your data is accurate, genuine and up-to-date. Be sure to look at the real costs of ensuring your data quality, talk with us, and then leave the fuss of maintaining software and security updates and researching and developing new data validation techniques to us.

Saving More of Your Labor this Labor Day

Labor Day is much more than the traditional end of summer in America: it pays tribute to the efforts of working people. It dates back well over a century, with one labor leader in the 1800s describing it as a day to honor those “who from rude nature have delved and carved all the grandeur we behold.” And we aren’t forgetting our friends in Europe and elsewhere, who celebrate workers as well with holidays such as May Day.

As we celebrate work and the labor movement – and enjoy a long holiday weekend – we wanted to take a look at some of the ways that we help you save labor, as you try to carve grandeur from your organization’s data. Here are some of the more important ones:

Validation and more. Let’s start with the big one. For nearly two decades, the main purpose of our existence has been to take the human effort out of cleaning, validating, appending, and rating the quality of your contact and lead data. Whether your needs involve marketing, customer service, compliance or fraud prevention, these tools save labor in two ways: first, by saving you and your organization from re-inventing the wheel or doing manual verification, and second, by saving you from the substantial human costs of bad data.

Ease of integration. What is the single worst data quality solution? The one that gets implemented badly, or not at all. One of the biggest things our customers praise us for is how easy it is to implement our tools, to work almost invisibly in their environment. We offer everything from API integration and web hooks with common platforms, all the way to programming-free batch interfaces for smaller or simpler environments – backed by clear documentation, free trial licenses and expert support.

Speed and reliability. As one customer put it, “milliseconds matter” – particularly in real-time applications where, for example, you are validating customer contact data as they are in the process of entering it. Our APIs are built for speed and reliability, with a longstanding 99.999% uptime and multiple failover servers, as well as sub-second response times for many services – so you don’t waste time tearing your hair out or troubleshooting responsiveness issues.

Better analytics. Your contact data is a business asset – put it to work as a tool to gain business insight for faster, more informed decision-making and market targeting. You can target leads by demographics or geocoding, enhance your leads with missing phone or contact information, or leverage your customer base for better decision support, among many other applications.

Customer support. We recently interviewed a major longtime customer about using our products, and when we asked them about support they gave us the highest compliment of all: “We never need to call you!” But those who do call know that our best-in-class support, staffed by caring, knowledgeable experts who are available 24/7/365, represents a large savings of time and effort for our clients.

We hope you enjoy this Labor Day holiday. And when you get back, contact one of our product experts for a friendly, pressure-free discussion about how we can create less labor for you and your organization!

 

Data Quality, AI, and the Future

What do you think of when you hear the term “artificial intelligence” (or AI for short)? For many people, it conjures up images of robots, science fiction, and movies like “2001 – A Space Odyssey,” where an evil computer wouldn’t let the hero back on his spaceship to preserve itself.

Real AI is a little less dramatic than that, but still pretty exciting. At its root, it involves using machine learning – often based on large samples of big data – to automate decision-making processes. Some of the more public examples of AI are when computers square off against human chess masters or diagnose complex problems with machinery. And you already use AI every time you ask your phone for directions or a spam filter keeps junk mail from reaching your in-box.

In the area of contact marketing and customer relationship management, some experts are now talking about using AI for applications such as predictive marketing, automated targeting, and personalized content creation. Many of these applications are still in the future, but product introductions aimed at early adopters are already making their way to the market.

Data Quality is Key in AI

One thing nearly everyone agrees on, however, is that data quality is a potential roadblock for AI. Even a small amount of bad data can easily steer a machine learning algorithm wrong. Imagine, for example, you are trying to do demographic targeting – but given the percentage of contact data that normally goes bad in the course of a year, your AI engine may soon be pitching winter coats to prospects in Miami.

Here are what some leadership voices in the industry are saying about the data quality problem in AI:

  • Speaking at a recent Salesforce conference, Leadspace CEO Doug Bewsher described data quality as “AI’s Achilles heel,” going on to note that its effectiveness is crippled if you try using it with static CRM contact data or purchased datasets.
  • Information Week columnist Jessica Davis states in an opinion piece that “Data quality is really the foundation of your data and analytics program, whether it’s being used for reports and business intelligence or for more advanced AI and related technologies.”
  • A recent Compliance Week article calls data quality “the fuel that makes AI run,” noting that centralized data management will increasingly become a key issue in preventing “silos” of incompatible information.

The ROI of Accurate and Up-to-Date Contact Data is Larger than Ever

Naturally, this issue lies right in our wheelhouse. For years, we have been preaching the importance of data quality and data governance for contact data – particularly given the costs of bad data in time, human effort, marketing effectiveness, and customer reputation. But in an era where automation continues to march on, the ROI of good contact data is now growing larger than ever.

We aren’t predicting a world where your marketing efforts will be taken over by a robot – not anytime soon, at least. But AI is a very real trend, one which deserves your attention from here. Some exciting developments are on the horizon in marketing automation, and we are looking forward to what evolves over the next few years.

Find out more about how data quality and contact validation can help your business by visiting the Solutions section of our website.

Marketing Strategies for the New Digital Privacy Era

In a world of big data, information for sale, and people oversharing on social media, this past decade has lulled many marketers into believing in a post-privacy era of virtually unfettered access to consumer and prospect data.

Even consumers themselves share this perception: according to an Accenture survey, 80% of consumers between the ages of 20 and 40 feel that total digital privacy is a thing of the past. But today this Wild West scenario is becoming increasingly regulated, with growing constraints on the acquisition and use of people’s personal data. Directives such as the European Union’s GDPR and ePrivacy regulations, along with other initiatives around the globe, are ushering in a new landscape of privacy protections.

Much has been written about how to comply with these new regulations and avoid penalties, on this blog and elsewhere. But this new environment is also a marketing opportunity for savvy organizations. Here, we examine some specific ways you can position yourself to grow in a changing world of privacy.

Leverage Data Quality With These Five Key Marketing Strategies

Be transparent. In their 2018 State of the Connected Customer survey, Salesforce.com found that 86% of customers would be more likely to trust companies with their information if they explain how it will provide them with a better experience.

Offer value. The Accenture survey mentioned above notes that over 60% of customers feel that getting relevant offers is more important than keeping their online activity private, with nearly half saying that they would not mind companies tracking their buying behavior if this led to more relevant offers.

Give customers what they want. According to European CRM firm SuperOffice, the post-GDPR world represents an opportunity to create segmented customer lists, through techniques such as separate website pop-ups for different areas of interest and content marketing via social media.

Look at the entire customer life cycle. Many firms offer a one-time free incentive, such as a report or webinar, in exchange for contact data and marketing permission. However, this can lead to fraudulent information being offered to get the goodie (we can help with that), or even a real but never-checked “wastebasket” email address. Instead, consider offering a regular stream of high-value information that keeps customers connected with your brand.

Change your perspective. This is perhaps the most important strategy of all: start looking at your customers as partners instead of prospects. Recent regulations are, at their root, a response to interruptive marketing strategies that revolve around bugging the many to sell to the few. Instead, focus on cultivating high-value client relationships with people who want products and services you offer.

More Consumer Privacy Can be a Good Thing

Whether businesses are ready or not, they are increasingly facing a world of marketing to smaller prospect lists of people who choose to hear from them for specific purposes, starting with Europe and spreading elsewhere. But this can be a good thing, and indeed a market opportunity. By changing your selling focus from a numbers game to one of deeper and mutually beneficial customer relationships, you can potentially gain more loyal customers and lower marketing expenses. In the process, this new era of consumer privacy could possibly end up being one of the best things that happen to your business.

Protecting your customers’ privacy and creating a mutually beneficial relationship starts with having the most genuine, accurate and up-to-date data for your contacts.  Download our white paper, Marketing with Bad Contact Data, to learn more about how quickly customer data ages and the impact on your business.

Customer Expectations are Getting…Younger

Being based in the college town of Santa Barbara, California, we notice something interesting: the students seem to get younger every year. Of course, it is actually our own ages that continue to change. But this illusion contains a valuable marketing lesson for all of us.

The Rise of the Generational Customer

According to the latest State of the Connected Customer survey from Salesforce.com, consumers really are getting younger, as markets shift over time from older customers such as Baby Boomers to Generations X/Y and the Millennials. In fact, this year marks the first time that adult consumers exist who have never lived in the 20th century.

This trend means a lot more than having customers who don’t remember the 9/11 attacks, or realize that Paul McCartney was in a band before Wings. Some of the key points from this survey include:

  • Millennials and Generation Z live in an omnichannel world, using an average of 11 digital channels versus nine for traditional/Baby Boomer customers.
  • Nearly twice as many Millennials prefer to use mobile channels versus traditional/Baby Boomer customers (61% versus 31%), with 90% of Millennials using this channel versus 72% of older customers.
  • Traditional and Baby Boomer customers use less technology than their younger counterparts, but they aren’t dead yet: over 70% of them use channels such as mobile, text/SMS, and online portals and knowledge bases. However, usage falls off sharply with age for newer channels such as social media and voice-activated personal assistants like Siri and Alexa.
  • Between 77% and 86% of survey respondents believe that technologies such as chatbots, voice-activated assistants, and the Internet of Things (IoT) will transform their expectations of companies. The most important ones? AI and cybersecurity, at 87% each.
  • Over two-thirds of all consumers surveyed (67%) prefer to purchase through digital channels.

Overall, one of the key takeaways from this survey was the growing importance of customer experience. Eighty percent of respondents stated that the experience provided by a company was every bit as important as its products and services. This in turn involves greater connectivity between companies and their customers, with 70% of customers noting that connected processes are very important to winning their business.

It All Comes Down to Data

What does this mean for the future of marketing? For one thing, it is clearly becoming more data-driven. While your oldest consumers still remember ordering from catalogs, your youngest ones expect to engage you on your tablets and smartphones, with little tolerance for error. This also means that both your marketing and your customer service are increasingly becoming electronic.

We welcome this trend at Service Objects: our company was originally founded in 2001 around reducing the waste stream from direct mail. But this trend also creates a mandate for us – and for you – to keep looking beyond simple contact data validation, into a world of data analyses that range from demographic screening to compliance with growing privacy laws. It is a major challenge, but also an opportunity for all of us – and frankly a big part of what keeps us young.