4 Tips for Targeted Customer Acquisition Marketing

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

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

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

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

Data-Driven Acquisition Marketing

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

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

Demographics Aren’t Enough

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

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

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

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

4 Data Points for Better Customer Acquisition Marketing

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

1. Buying Behavior

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

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

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

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

2. Personal Life Triggers

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

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

3. Shared Interests

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

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

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

4. Searcher Intent

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

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

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

Don’t Be Afraid to Ask for Help

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

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

The Grand Reopening of the U.S. Economy Will Happen, Plan for It

We are in uncharted territory, much as we were in previous economic downturns and recessions. Yet, do know, another expansion will follow … eventually. There will be a grand reopening of our economy, and as marketers, we need to plan for it.

I love defaulting to optimism – even in the darkest of times. It’s been part of my survival mechanism through all sorts of crises. That being said, we are in uncharted territory in this new normal, much as we were in previous economic downturns and recessions. “The Great Recession” of 2008-2009 was largely Wall Street born and Main Street slammed. But remember, the Great Expansion followed. A possible recession stemming from COVID-19, however, would be largely reversed, with millions of livelihoods suddenly denied, and both Main Street and Wall Street being slammed in tandem. Yet, do know, another expansion will follow … eventually. There will be a grand reopening of our economy, and as marketers, we need to plan for it.

Listening to the U.S. President talk about getting parts of our country back to some semblance of normal by Easter may seem wild-eyed and some might say irresponsible. In reality, China is reportedly already back on line – after six-to-eight weeks of paralysis. Does this mean a possible “V-shaped” recession (very short), a “U-shaped” one (mild), or an “L-shaped” one (long term)? We don’t know.

It’s always dangerous to make prognostications, but we can learn from patterns elsewhere in the virology. With the United States now the most afflicted nation in sickness, we yet have a massive fight ahead to control viral spread. And doubt and fear have taken hold as two debacles have come about, one public health and one economic.

Unfortunately, there is no “on/off” switch for the viral crisis. Even when its spread is curtailed, which will happen, we’ve been shaken and edginess is going to remain. That’s only human.

Patterns of consumption will not resume as if nothing happened. Unemployment shocks will not reverse as easily as they came. So there will be a “new” normal.

However, a reopening is coming. You might say that’s my optimism, but folks – we are going to be okay in a time. It may not be of our choosing, as Dr. Fauci faithfully reports, but one that will be here nonetheless. As marketers, let’s get ready for it.

Look to Your Data to Prepare for What’s Next

Recessions are actually good times to look to the enterprise and get customer data “cleaned up.” The early 90s recession gave us CRM, and database marketing flourished. The end of the Internet 1.0 boom in 2000 brought data discipline to digital data. And the Great Recession brought data to the C-suite.

So let’s use this time to do a data checkup. Here are four opportunities:

  1. Data audits are often cumbersome tasks to do – but data governance is a “must” if we want to get to gain a full customer view, and derive intelligent strategies for further brand engagement. Quality needs to be the pursuit. Replacing cookie identification also is a priority. Understand all data sources to “upgrade” for confidence, accuracy, privacy, and permissions.
  2. March 15 might be a good date to do an A/B split with your customer data inputs – pre-virus and during-virus. What new patterns emerged in media, app usage, mobile use and website visits? Are you able to identify your customers among this traffic? If not, that’s a data and tech gap that needs to be closed.
  3. Customer-centricity or data silos? It’s always a good time to tear down that silo and integrate the data, yet sometimes healthy economic growth can mask this problem. Use the recessions to free up some time to actually get the work done.
  4. Test new data and identity solution vendors to increase match rates across your omnichannel spectrum – to better create a unified view of audiences, both prospects and customers. I’ve already seen one of my clients come up with a novel offer to analyze a subset of unidentified data to drive a substantive lift in matches.

As we work remotely, it’s important to understand that this current state of crisis is not a permanent state. Only once the virus is conquered, on its weaknesses not ours, can we really have any timetable to resume the economy. That being the health science, it just makes great business sense now to “stage” your data for that eventual Grand Reopening.

Optichannel Marketing Campaigns Get an Additional Boost With Direct Mail

Not every brand has a big brand’s marketing resources. Here’s are two case studies in how optichannel marketing is being used at a more reasonable level of investment by real, medium-sized businesses to increase campaign effectiveness and bottom-line results.

Not too long ago, we looked at how some of the biggest companies in the world — including Disney and Neiman Marcus — use optichannel customer experience strategies to deliver great marketing ROI. Even among big brands, though, the customer experience magic of Disney may be out of reach. So let’s take a look at how optichannel marketing is being used at a more reasonable level of investment by real, medium-sized businesses to significantly increase campaign effectiveness and bottom-line results.

Response-Lift Modeling Finds New Campers and New Revenue for Summer Learning Initiative

The hard part of operating any business focused on school-age children is the built-in rate of attrition. Students grow up, graduate, and otherwise age out of your programs every year. It’s likely that at least 25% of your customers won’t be back the following year due to matriculation alone.

To refill those seats without breaking the bank, these institutions must focus marketing on lead generation and new customer acquisition — two of the most expensive goals in marketing. It’s challenging to do that and still find a way to market profitably.

One such program is Galileo Learning, which operates 75 children’s summer camps across parts of California and Chicago, Ill. Age limits on the program mean that large portions of the customer base graduate out every year.

Finding a way to replace those students quickly becomes prohibitive. Summer Erickson, head of marketing for Galileo Learning, saw that many direct mail strategies were becoming too expensive for the ROI. The answer she found was to combine a very effective mail piece with tight customer models built on the data of current customers.

“The customer modeling tool was a game-changer for us,” says Erickson. By using response-lift modeling to identify prospects on external lists who were highly likely to respond, Galileo was able to market much more efficiently. They used the savings to create better mail pieces that would also drive better-than-normal response, and the mailers were localized to each of their nine markets where Galileo operated camps.

The results, Erickson says, surpassed her most optimistic expectations. The campaign brought in 155 new campers and $66,000 in new revenue. And she expects even better success from a wider program launched later in the year.

Holiday Direct Mail Adds Optichannel Targeting, Gets 6X More Impressions, $200k-Plus in Donations

Sometimes you need to break out beyond a single channel to get the best results. Meals on Wheels (MOW) in the Diablo Region of California spurred $230,000 in new donations by doing exactly that with its holiday donor appeal campaign.

The campaign broke with MOW’s traditional strategy in two main ways:

  • They built three audience segments defined by demographics and customer look-a-like modeling.
  • MOW added targeted digital advertising to amplify its direct mail, which made sure the target audience saw 6X more campaign impressions that they would have in a mail-only strategy.

First, much like Galileo, MOW and its agency starting working from the donor database, using existing data from real donors to identify three list segments who would be most responsive to this campaign: current donors, lapsed donors and prospective donors. Although the names sound straightforward, the segments were developed by examining the demographic and engagement data of known donors across dozens of factors.

Each person on the list received a personalized donor appeal letter with infographics highlighting the benefits of donating to MOW and a coupon CTA to make a donation.

Overall, the campaign blanketed the audience with 75,000 pieces of direct mail alone. But that was just the beginning of the campaign.

In addition to those 75,000 mailpieces, MOW built email, social media, and online display advertising to amplify the direct mail message. Together, this added 467,542 additional marketing impressions for the campaign — more than a 600% increase in overall brand exposure, compared to a mail-only control group.

The results were impressive for MOW, even for a holiday appeal: $230,000 in donations, 43% new donors, and donors from the optichannel campaign averaged 169% more than donors in the control group who only received direct mail.

3 Ways to Derive Actionable Sales Insights From Content Marketing Data

Nearly all businesses these days are aiming to build content marketing strategies that enable them to “rise above the crowd” or “be heard above the noise.” Whether they’re succeeding or not is anyone’s guess. The trick with content marketing data is to know how each dataset feeds into the bottom line.

As we ring in 2020, talking about the importance of content marketing and why every brand should be doing it is a record that has been broken for quite some time.

Nearly all businesses these days are aiming to build content marketing strategies that enable them to “rise above the crowd” or “be heard above the noise.” Whether they’re succeeding or not is anyone’s guess. What’s for sure is that branded content campaigns are yielding copious amounts of big data about customers and their behaviors. Whether it’s web traffic, conversion rates, or engagement levels, the trick with content marketing data is to know how each dataset feeds into the bottom line.

With so much data being created and collected every day, it can be very difficult and overwhelming to translate this information into sales insights. In fact, one of the biggest challenges marketers face is associating content with revenue:

marketers' top challenges
Credit: MarketingCharts.com

So how can you show ROI from content marketing without letting your head spin from data overload? Let’s find out.

1. Unify Data Streams

Data collection is only getting more complex as sources and systems continue to grow. Depending on how far-reaching your content strategy is, the data streams that relate to your sales regime won’t always yield black and white answers. Therefore, market research data, customer data, and pretty much all company data should be unified in a single ecosystem. This will let decision-makers spot key trends that tie directly into the bottom line.

For example, you need to know things like the content channels that are bringing in the strongest leads, the common threads among your most profitable customer profiles, the types of content that get the most engagement, where your referrals are coming from, and so on.

Marketers these days are growing increasingly dependent on the constantly-growing number of data sources. The major tasks at hand involve monitoring, analyzing, and finding benchmark performances for each campaign.

Until recently, it was a huge (and expensive) effort to develop tool integrations that aligned content marketing data sources in ways that boosted the sales process. Thankfully, AI-enabled business intelligence and CRM platforms allow businesses to efficiently analyze their data streams. One such tool is Salesforce’s Einstein, which can unify company data to identify new audiences, deliver sales projections, create in-depth customer profiles, and even automate storytelling.

Salesforce Einstein
Credit: Salesforce.com

AI-based content platforms are designed to score touchpoint information to discover patterns that help determine which leads are likely to convert. They can create associations between varied data sets, such as website engagement and publicly available demographic information, for example, and turn these into stories.

The way you set up these stories determines which datasets you will unify, and how your content or CRM platform will evaluate the information for predictive purposes. For instance, you might want to use a story to maximize potential earnings from a particular product. This could involve data sets related to engagement rates, lead nurturing, landing page conversion, and so on.

The more data you feed into such a system, the more precise the predictions you’ll be able to make. AI and machine learning are enabling data scientists to apply a combination of predictive analytics and meta data management to business. This lets marketers anticipate changes in consumer behavior and the impact of macroeconomic trends on business.

2. Identify Snags in the Buyer’s Journey

Making a sale in B2B requires way more than flashy advertisements and bold promotions. The modern buyer’s journey is typically made up of three key stages: Awareness, Consideration, Decision.

buyer's journey
Credit: HubSpot.com

Ideally, each stage should work as a vector to ultimately produce sales.

While it’s easy for marketers to design content marketing strategies to play to each stage, the parts that tend to get overlooked are the transitions. In other words, how well does your content bridge the gap between one stage of the buyer’s journey and the next? This is perhaps where data provides the most valuable insights related to sales.

Funnel visualizations can reveal patterns in regard to where people drop out or delay the progression through the buyer’s journey. Using this data, businesses can refine their transitions and work to eliminate the major roadblocks. Some simple metrics to start out with are bounce rates, session duration, and conversion rates of your landing pages — all of which can be tracked via Google Analytics.

google analytics behavior flow
Credit: Google Analytics

For example, let’s say you run a SaaS company and your Awareness stage content (blog posts, e-books, podcasts, etc.) is doing a fantastic job in getting traffic to your Consideration stage content on your website, which includes landing pages to sign up for a webinar or download a white paper.

However, you notice that the bounce rate for these pages is very high (around 95%) and the time on page is only a few seconds. This is a good indicator that there is interest, but the transitions from your Awareness content aren’t giving people enough information or motivation to convert. Therefore, it might be time to re-examine content at the transition point (email invitations to the webinar that you send to people who’ve read your blog posts or subscribed to your newsletters) or add more information to your landing pages.

Keep in mind, snags in the buyer’s journey can have much deeper-rooted issues than the example above — all of which can impact your sales numbers. Understanding how your content impacts the success or failure of your customer journey will likely require a great deal of critical thinking (and digging into funnel data).

3. Use Intent Data to Constantly Refine Your Sales Model

The term “intent data” is a buzzword that has been floating around the marketing world for all of a hot second. Intent data refers to behavioral information that gauges a person’s online activity and how likely they are to take a desired action. In terms of how this relates to your content marketing and sales efforts, these insights combine both topic and contextual data.

intent data
Credit: Infer.com

Topic data refers to the level of interest someone expresses about a subject when they search for something on the web. For example, if someone Googles “how to simplify customer service,” and lands on your blog about how to program a chatbot, they are showing some degree of intent. There are generally four categories of topic data:

  1. Anonymous First-Party Behavioral — These are visitors to your website who haven’t taken any action that identifies themselves. It is possible to identify their company by their IP addresses.
  2. Known First-Party Behavioral — These are visitors to your website who have shared personal information by filling out a form.
  3. Anonymous Third-Party Behavioral — These are unknown visitors to other websites with similar content to yours. You can identify them via the topics they browse and track them via their IP addresses.
  4. Known Third-Party Behavioral — These are known visitors to other websites who’ve shared information and whose content preferences are recorded. You can then use tools to measure and capitalize on the purchase intent of a pre-segmented audience.

Now, topic data is more or less useless without the right context. Contextual data revolves around diving into the who of the person taking the action. For instance, if the visitor reading your article on chatbots is a business owner, there is a good chance the person is considering a solution for customer service needs. On the other hand, if the reader is a programmer, it’s very possible the professional is looking for information about how to build or improve a chatbot. In this way, intent data plays a key role in how you define your sales process.

Different types of web visitors will have slightly different views of the buyer’s journey in relation to your business. You need a system that gauges the intent of a visitor from how they interact with your content on various platforms; the insights you glean from this form the basis of how you craft your landing pages.

Intent data lets marketers put the right content in front of the right eyes. Start by personalizing your website to “anonymous” users. Solutions like Evergage can be synced with CRM data and use machine learning to better understand the intent of visitors. It can then draw on a wide range of behavioral insights to help you serve ultra-targeted content.

Evergage
Credit: Evergage.com

For example, the system can sort visitors by industry and automatically build segments based on key attributes. From here, you can deliver customized messaging that fits into the narrow views of each of these segments.

Next, you should base the processing of inbound leads on engagement. Ideally, this should work to quantify the visitor’s intent based on the manner in which they interact with your content. If someone is looking at your blog section, they would likely fall lower on your lead scoring model. If they are looking at pricing, they would obviously rank higher.

scoring model
Credit: Business2Community.com

Intent data should always play a key role in how you nurture leads and go about making sales.

Over to You

In many ways, the data you get from your content marketing strategy is the lifeblood of your sales efforts. As big data continues to grow at exponential rates, both in size and application, the challenge will always be using these insights to boost your bottom line.

Refining your content strategy is a task that never truly ends. As long as you keep up with what your analytics are telling you, and identify and iron out the weak spots, spikes in sales are always around the corner. Good luck!

5 Big Changes in B2B Buying Behavior

If you’re a B2B marketer — especially a services provider — your environment is about to be upended. Customers are changing, and so are the ways they buy. I’ve been struck recently by five glaring developments in business buying behavior that you need to know about.

If you’re a B2B marketer — especially a services provider — your environment is about to be upended. Customers are changing, and so are the ways they buy. I’ve been struck recently by five glaring developments in business buying behavior that you need to know about.

And once you know, you must consider how to adapt and, better yet, turn the changes to your advantage. Consider these.

The Arrival of Millennials in Business Buying Positions

These 30-somethings are rapidly migrating from researcher and specifier into decision-making roles. I’ve written about this before, offering ideas for how marketers can cope. But I also see this development as part of a larger trend that has deep implications for how we need to be selling and marketing today.

Use of Ratings and Reviews Sites in B2B

Comparison sites in the mold of TripAdvisor and Yelp have entered the B2B buying process; especially in crowded categories, like software and services. You’ll find ratings sites like TrustRadius, Capterra (now owned by Gartner), Clutch.co and G2Crowd, where users leave product reviews — and sellers quake in their boots. Here are some tips for how marketers can take advantage of this new channel.

Expanded Customer Requirements for Compliance

Long prevalent in government buying, companies of all sizes are increasing their requirements of vendors in areas such as sustainability, diversity, and — for manufacturers in such categories as apparel — wages, working conditions, and safety. Christine Crandell brought this to my attention recently, with examples like Marriott embracing the UN 17 Sustainable Development Goals 2030 as a source of competitive differentiation, and how event planners are routinely making venue carbon footprints and greenhouse gas emissions an evaluating criterion in property selection.

Buyers Are Bringing Their Consumer Expectations With Them to Work

They want fast, personalized service, pricing transparency, ease of use, a human face, seamless integration across contact channels, and mobile access. We know this, but are we stepping up?

Enterprise Buying Platforms Mature

B2B buying has long been enabled by EDI, supplier exchanges, and e-procurement. But the pace is accelerating — fast. A new entrant is Globality’s platform that helps large enterprises buy services. According to Kathy Chin Makranyi, head of corporate marketing, Globality’s founders recognized that services procurement is inefficient, and ripe for change. So, they set up an AI-enabled platform that manages the entire buying process, enabling buyers to write the RFP, identify a short list of candidates — even inviting incumbents to participate, conduct the bidding process, hire for and manage the project, and handle the billing. Globality has vetted and recruited over 17,000 services providers to the platform, giving enterprises access to entirely new potential vendors. And the platform saves both time and money in managing the competitive bidding process.

“It’s a marketplace between the global 500 and a network of worldwide providers. The big services firms, the McKinseys, KPMGs, and Accentures will play, too, because it makes their sales cycles faster and easier. If you go with the incumbent, you’ve confirmed they are the best choice. Sourcing team[s] can learn and validate their work. And a provider who lost can find out ways to improve next time,” explains Makranyi.

Calling all consultants, accountants, lawyers, agencies — here’s your chance to compete on a level playing field for enterprise accounts.

 

A version of this article appeared in Biznology, the digital marketing blog.

Marketers, Are You Going OOH With Data? Let Consumers Know Why

Mobile, social, and other digital media are increasingly connected to OOH advertising. One of my pet peeves is that when I’m in my home or office, or out and about, I receive real-time reminders about using my geolocation (really, a proximity). And that’s all they say. Period.

My precise location is here. Well, it was here — when I wrote this.

One of my peeves is that when I’m in my home or office, or out and about, I receive real-time reminders that this application, or that plug-in, or this website, would like to detect and use my geolocation (really, a proximity). And that’s all they say. Period.

It’s most usually a short “push notice” — combined with an “accept,” “allow,” or “OK” button to indicate my consent. Most of the time I click in the affirmative, and move on. But as a consumer, I am sometimes left curious as to why. Which is why I’m frustrated.

Notices: Give Me a Push, With a Reason to Pull

My preference would be for a slightly longer notice explaining why my location would be helpful — for the digital property to induce or invite me to send my acceptance more readily.

  • Is my known location being used to improve my user experience, by unlocking a functionality that is location-dependent?
  • Is it to serve interest-based ads on the site or app that are location-relevant?
  • Is such data shared with anyone else — and if so, why?
  • Is it a combination of these?

Sometimes, the need for geolocation is a seemingly obvious request. To use an app for maps, traffic, weather or news pertinent to my location is certainly agreeable. I get it. But if there are reasons beyond user utility, a consumer ought to know what those other purposes are. And I’m not talking about a paragraph buried in Terms and Conditions or Privacy Policies — as important as those disclosures are.

Take advertising. I actually opt for data collection to enable more relevant ads. I understand why such ads exist — and use far more free services, content, and conveniences that are paid for by sponsors and advertisers, who gain access data about me, than I would otherwise pay for myself. Most Americans — and probably most global citizens — like free stuff and increasingly understand this pragmatic, useful exchange. It just doesn’t need to be behind a curtain. There should be no mystery.

This is where self-regulation (disclaimer, I work for the Digital Advertising Alliance, DAA) and privacy-by-design step in: Just tell me why you want to use it! And let me make an informed decision regarding my consent.

Location Data Has Sensitivity — So Transparency and Choice Must Be Heightened

Location data can be sensitive. Advertising may be a helpful use — but what of stalking, civil rights, employer monitoring, government surveillance? And even advertising has a “no” factor, if an algorithm inadvertently discriminates, or a “creep” concern if you feel you’re being unwittingly followed (that is, your device) around a shopping mall or grocery store. (Even if I get a coupon offer.)

So, if we are — as we should — going to be transparent with a push notice, make it short, sweet — and explain in short copy why it is helpful to consumer experience. It only takes a phrase, or a bullet point or two, to explain how and why such data collection serves such outcomes.

That was a key point that Senny Boone, SVP of accountability for the Association of National Advertisers, explained at a recent presentation, which was sponsored by Geopath, a location-based marketing trade organization; and PMD Media, a targeted outdoor and digital advertising firm.

“Business needs to grow. New growth is based on new data and new information provided by consumer interaction, behavior, and insights,” she said, noting the rising importance of place-based information. “Consumers seek more data privacy as business and technology provide less privacy protection and more data tracking — or that is the perception.”

So are we in a conflict with the consumer here? Is this loss of privacy perception accurate?

We shouldn’t be in conflict — if we believe in transparency, she said, and have privacy and a consumer focus in our brand culture.

If you adhere to codes brought forth by our trade associations — both advertising and out-of-home — which largely have synced up in line with DAA Principles, then you are in good company, Boone said.

Give Me One Reason to Stay Here and I’ll Turn Right Back Around

This is particularly true regarding geolocation data, where enhanced notice through push notifications are required — but with a rationale as part of the push. Only then can meaningful consumer consent be given. Last month, two BBB National Programs enforcement cases, successfully resolved, highlighted the need for such enhanced notice. One case involved a fitness app specifically seeking to use location data for interest-based advertising. Takeaway: Use the enhanced notice for location data consent to explain why.

Boone went on to say that mobile, social, and other digital media are increasingly connected to out-of-home (OOH) advertising. She pointed to the Outdoor Advertising Association of America code that says:

We support responsible use of data for advertising purposes. We recognize that mobile phone and digital technology bring benefits to consumers seeking information, way-finding, entertainment, and connection to others. Increasingly, mobile-social-and-online media are connected to OOH advertising. We encourage member companies to work with suppliers that provide appropriate notice and control for the collection of precise location data from mobile phone devices used for advertising purposes. Anticipating technological changes, OAAA will continue to monitor developments in this area.”

Yes, that digital billboard you’re standing near may be wanting to interact with you. Location-based marketing is only set to grow. So make sure to undertake a data audit, know your location data partners, adhere to laws that may exist for any jurisdiction (GDPR, CCPA, etc.) — and follow industry codes for privacy ethics and best practices.

And tell me why my location is so darn useful to me as a consumer — rather than you as the marketer — when such data is sought. Not only is such explanation respectful and ethical, it serves to educate the market about why relevant ads may be that much more engaging (rather than annoying).

Perception is reality, and right now, we need to do a lot more education to get consumers — pragmatic as we are in our behavior — to get our attitudes to match.

 

 

 

 

How to Use Sentiment Analysis to Transform Your Digital Marketing Strategy

The goal of sentiment analysis is to increase customer acquisition, retention, and satisfaction. Moreover, it helps put the right brand messaging in front of the most interested eyes.

Sentiment analysis is a fascinating concept.

Brands use it to better understand customer reactions, behaviors, and opinions toward their products, services, reputation, and more. The goal of sentiment analysis is to increase customer acquisition, retention, and satisfaction. Moreover, it helps put the right brand messaging in front of the most interested eyes.

Before the digital age, gauging and understanding sentiment was an incredibly cumbersome process. It typically involved sending out surveys manually, going to the streets and asking people, or gathering focus groups in one place at one time. The big data-infused model of sentiment analysis we know today hit its stride on the political scene in 2010. Since then, it has morphed into a key tactic in marketing plans. These days, most of the grunt work is automated.

However, even with all of the advances in areas like martech, voice search, conversational commerce on social media, virtual assistants, and big data analytics, understanding how to actually use sentiment analysis to improve the bottom line is a complicated task.

Here are a few key approaches to help you get the value you need.

Know the Terms and Phrases That Indicate Intent

Most businesses today (hopefully) don’t even begin their digital branding and marketing efforts without a list of keywords relevant to their industry and a plan on how to target their audiences. You should have a good idea of the terms and variations that bring you traffic to your website, when used in conjunction with your brand and products. If you run an auto repair shop, people are likely finding you on the web through terms such as: body shop near me, auto repair, replace brake pads, etc.

Google Search Console gives you a great, fairly accurate idea of what’s bringing people to your website:

google search console
Credit: Author’s own

In terms of sentiment analysis, to gain actionable insight, you need to know how people are using these keywords in a way that indicates interest and engagement potential. Now, this is perhaps the biggest gray area in sentiment analysis, because not all positive sentiment equates to sales. Just because there are a lot of positive words around luxury cars doesn’t necessarily mean people are about to buy.

However, there are certain terms and phrases that signal people have entered your buyer’s journey. Let’s say you run an SEO agency and one of the terms you’re tracking for sentiment analysis is “Google update.” If you notice that a lot of people are searching for things like “what to do after a google algorithm update?” or “how to recover from a google penalty?” it’s a good indicator that they might need your services at the moment; you should target them accordingly.

Spot Patterns in Product Reviews

At its core, sentiment analysis is a game of pinpointing patterns and reading between the lines. Simply put, the more genuine and meaningful feedback you get on your product, the better insights you will gain into your customers.

Of course, gathering such high-quality feedback is easier planned than executed; especially for newer or smaller companies. Only 10% of customers will review or rate a business after a purchase, while half of consumers will leave a review only some of the time. However, the number of reviews jump significantly to 68% when a company asks the customer directly to leave one.

In order to find fruitful, up-to-date patterns, you need to make it a marketing process to consistently seek out new reviews. Then, you’ll want to start by searching for common adjectives. These should include words like:

  • great, simple, easy,
  • or awful, difficult, poor, etc.
trustpilot review
Credit: Capterra.com

In the above image, there are a good amount of reviews that include the word “great” for this product. Looking at the context around this term, we notice recurring patterns around components, like features and usability, and “not so” great opinions on customer service.

Finding recurring themes in customer sentiment will give you a better picture into the positive and negative aspects of your business or product. These can indicate the level of trust people have in your brand and how likely they are to give you a recommendation. When you are looking for patterns, try to come up with several adjectives that shed light on both sides of the spectrum.

  • What words are commonly used to describe their experience?
  • Is there an issue that forces multiple people to leave negative reviews?
  • What part delights them the most?
  • What’s preventing you from solving common problems?
  • Which products or solutions are users comparing yours to?

The answers to these important questions can help you understand user sentiment better and build a customer-focused marketing strategy.

Look to Social Media for Unabashed (Unfiltered) Opinions

Oftentimes, social media is one of the best places to get raw opinions, where people don’t hold back —  both in positive and negative lights. Knowing how people feel in an unfiltered environment can be a great way to tell which parts of your business are working very well —  and not so well.

A social listening platform is an important tool to keep in your portfolio for monitoring online mentions and gathering important datasets. Tools like Mention, Talkwalker, and Brand24, not only keep an ear on social mentions, but also turn these comments and hashtags into valuable customer analytics to help your marketing team understand your customers even better.

For instance, the online gaming developer Wargaming used brand monitoring techniques to analyze its customer’s desires and see which products performed best. The company tracked its users’ social media conversations to see what they were looking for, what parts of the games they liked or disliked, and any suggestions they offered for improvements.

Similarly, you can use a social listening tool to combine all your brand mentions into one database, giving your marketing team a bird’s eye view of audience sentiment on social platforms and identify areas to work on.

talkwalker
Credit: Talkwalker.com

While gathering this sentiment is good, the most important thing is knowing what to do with it. About 83% of customers who make a social mention of a brand —  specifically, a negative one —  expect a response within a day, and 18% want one immediately. Unfortunately, a majority of these mentions go unanswered, which can really impact a brand’s image. By utilizing an effective real-time social listening program, you can not only stay on top of social buzz, you can intervene and reply to any negative sentiment right away.

Some of the next steps will be fairly obvious, especially when you’re dealing with negative feedback. For instance, if your customer sentiment from social listening reveals that people are having trouble updating their software or there are issues with the product itself, this indicates that some redesign is necessary. However, don’t get too comfortable when you are getting positive reactions —  these tend to trick companies into thinking that no improvements are needed.

This kind of feedback can support a stronger marketing strategy. Let’s say your business sells pool supplies. While your customers may not be tweeting about your great chlorine chemicals, they are more likely talking about the fun pool floaties and games your website sells. Therefore, it would be helpful to highlight these fun accessories, as well, by listing them more prominently on your page and even including UGC to promote them.

poolfloatz
Credit: Instagram

Use Predictive Analysis to Spot Trends and Automate Actions

Now that you have all these valuable insights, you need to know how you can use them to shape your current and future business strategies.

Plugging your sentiment analysis into a predictive model is crucial for spotting trends, getting a feel for how opinions are progressing, and determining your next steps. Predictive analytics use machine learning and AI technology to not only gather, but analyze loads of consumer data and make accurate projections. These systems gauge historical behavioral data to help determine the best plan of action in the future.

In fact, customer segmentation and targeting (which is the logical next step after you analyze your audience’s sentiments) is one of the areas where applying AI and predictive analytics has the highest chance of working well for business.

applications of AI
Credit: Emerj.com

In order to develop an optimal predictive model for sentiment analysis, ask yourself:

  • What do you want to know?
  • What is the expected outcome? What do you think your customers are thinking?
  • What actions will you take to improve overall sentiment when you get the answers? How will you automate these actions?
  • What are the success metrics for these actions?

The Wrap

Chances are, your customers are already telling you what you need to make improvements to your business. By gathering as much data as possible on customer sentiment, your marketing team can understand just what needs to be done to provide a better experience, tweak campaigns accordingly, and acquire and retain more customers in the process.

Be sure you know what to data to collect, how to mine it, and how to apply it to keep raking in the revenue.

Think of Food Nutrition Labels. Now, There’s Audience Data Labeling

This summer — this “nutritional” label for commercially available audience data, which vendors, agencies, advertisers and publishers can use to understand the sourcing of targeting data and how it is prepared for market — is ready for marketplace use.

Last fall, I reported briefly on an industry initiative related to “data labeling” a bid to provide transparency of data sourcing for audience data used in digital and mobile marketing. DataLabel.org is an initiative of the Interactive Advertising Bureau (IAB) and the IAB Tech Lab. (At the time of inception, the Data & Marketing Association now the Data Marketing Analytics division of the Association of National Advertisers was also at the table.)

This summer this “nutritional” label for commercially available audience data, which vendors, agencies, advertisers and publishers can use to understand the sourcing of targeting data and how it is prepared for market is ready for marketplace use.  (From a June 27 IAB Tech Lab press release🙂

“Data transparency is a table-stakes requirement to ensure responsible and effective use of audience data and companies that provide consistent access to detailed information about their data will attract more business,” said Dennis Buchheim, EVP and general manager at IAB Tech Lab. “Taking part in the corresponding compliance program will further differentiate an organization, affirming their full commitment to the highest standards.”

Transparency in Data Sourcing Matters

I remember hearing IAB CEO Randall Rothenberg admonishing the ad tech ecosystem in early 2017 to get out of the “fake anything” business, and arguably the effects of fraud, brand safety, and other concerns have led many advertising and marketing professionals to scour their data sourcing, permissions, stacking, integrating, and statistical analyzing to make sure that an otherwise reputable company is not engaged with anything untoward on the data front.

DataLabel.org supports this objective, in part, and goes further.  While it does not assign a quality score to any particular data source, it does enable apples-to-apples comparisons in important areas, (Opens as a PDF) which inform where media dollars based on audience data are committed:

Data Labeling label
Source: DataLabel.org

Yes, it’s an agnostic nutritional data label for data sourcing. Through IAB et al, dozens of companies were part of a working group that led to the Data Transparency Standard, Version 1.0 (a PDF download] led by Meredith Digital, Lotame Solutions and Pandora, among its supporting cast.

Does ‘Table-Stakes’ Mean Traction? You Look Good Dressed, in Responsible Data

According to the IAB, “completion of the program requires an annual business audit to confirm that the information provided within the labelling is reliable, that the organization has the necessary systems, processes, and personnel in place to sustain consistent label completion at scale, and that a label can be produced for all in-market segments available. Engagements typically range between [two to five] months, depending upon the size and complexity of the company’s business.”

So now that’s the Data Label is available to the data-driven marketing marketplace, is there real traction to see its use?  From the data provider side, at least, I’d say so.  While some may be taking a wait-and-see approach, some data marketing companies are moving forward with data labeling and transparency certification.

“The digital ecosystem tends to focus on areas like inventory and traffic,” said Chris Hemick, senior product marketing manager, Alliant, whose company is now in the onboarding process. “Alliant is an advocate for bringing the same level of focus to the data marketplace. We firmly believe that IAB’s efforts to spotlight data provider practices around audience creation will be a positive for the entire industry.”

Another data provider, Audience Acuity, echoes these sentiments. “The concept of the Data Transparency Label was introduced in the fourth quarter of last year, after it was developed by the ANA’s Data Marketing Analytics (DMA) division, the IAB Tech Lab, the Coalition for Innovative Media Measurement (CIMM), and the Advertising Research Foundation (ARF),” said Riad Shalaby, CMO of Audience Acuity. “We are aligned with their perspective on this important topic, and we are delighted to be one of the first major data companies in the United States to provide this level of transparency.”

There are many things we, as data marketing professionals, need to concern ourselves with in best practices, ethics, and even legal compliance. Brand safety, ad measurement, piracy, privacy and security, and fake anything are among them. Proper data governance is related to all of these concerns. The more we spotlight our roles as stewards of and for data integrity, the better we can achieve marketplace confidence and trust in the very information that helps make brand-consumer engagement succeed.

What’s the Price on ‘My Data’? Let the Marketplace Set the Rate

A bipartisan bill in Congress would assign the U.S. Securities and Exchange Commission with the task of determining what consumer data is worth; at least when it comes to Big Digital giants. So what’s my data worth?

A bipartisan bill in Congress would assign the U.S. Securities and Exchange Commission with the task of determining what consumer data is worth; at least when it comes to Big Digital giants. So what’s my data worth?

On the face of it, having the government mirror the private sector, and recognize that consumer data is a valuable asset, is actually quite wise. Data is worth something — and accounting rules, risk management, capitalism, and a reverence for asset protection — all point to a need to understand data’s worth and secure it accordingly. But should the government come up with the arithmetic? Really? And why limit this to Big Digital … data drives all economy sectors!

If this is about commerce and productivity, and facilitating next-generation accounting and capitalism, then I’d be all gung-ho. If it’s about setting the stage for just being punitive, then perhaps we can and must do better.

Take privacy. I’m already getting click fatigue — with permission notices on every site I want to visit, as well as the apps I use, it’s no wonder people are questioning if laws like GDPR and CCPA really afford any meaningful privacy protection at all, as well-intended as they may be. Privacy is personally defined — though universal principles need apply. Again, I think we can and must do better.

Recognizing data’s value — as the fuel for today’s economy — means recognizing data’s limitless beneficial uses (and encouraging such uses and further innovation), while putting a no-go ring around unreasonable uses (like throwing elections).

Business Efforts to Calculate Data’s Worth

“My data” is a misnomer. On the data valuation front, we from the direct marketing world — purveyors of personally identifiable information (PII) — have been putting a price on data for years … and understand data’s value, intrinsically. Big reveal: It’s not about me. (Sorry, Taylor Swift.)

Worldata, for example, has been tracking list prices for decades, and dutifully reporting on this. In the world of direct response, there’s “sweat equity” in both response and compiled lists. For response lists, some enterprise built a list of customers (or donors). The value of that list is derived from the shared attribute those customers have – and not, as some privacy advocates would have it, with the sum of one individual after another appearing on that list. With compiled lists, observable data is harnessed and staged also for marketing use – providing a more complete view of prospects and customers. Again, the value is derived from the attributes that data subjects share.

Even in digital data driving today’s media placement for advertising (more accurately, audience placement) — the algorithms deployed in search, social, and display — the values of these formulae are derived from affinities in these proprietary calculations, much of it anonymized from a traditional PII perspective. Yes, there are lots of data — nearly $21.2 billion in U.S. trade alone — but it’s not hoarding; it’s being put to productive use — in effect, 1:1 at mass scale.

With any innovations, there are bound to be mistakes by good companies, and some bad players, too. But it’s amazing to see how the marketplace weeds these out, over time. The marketplace, in time, weeds out the wheat from the chaff. The industry comes up with brand safety, privacy, security, chain-of-trust, and other initiatives to help facilitate more transparency and control. And testing shows which data sources are timely and reliable — and which ones where data quality is in question.

Predict This: Data Unleashed for Responsible Use Unleashes Consumer Benefits

Recently, I heard a current federal official say that data may be fuel — but it’s not like oil. Oil is finite. Data, on the other hand, is a limitless resource — like fusion. And it can be replicated. In fact, he went on to say, the more it is shared for responsible data use, the more consumers, citizens, commerce, and the economy benefit. This is correct. The commercialization of the Internet, indeed, gave us today’s global Digital Economy — giving billions access to information where they are able to derive limitless benefits.

That’s why potential breaches of data do need to be risk-assessed, prevented, understood for a likelihood of harm — with data governance and employee training thoroughly implemented. That’s also why government should investigate significant breaches to detect lax practices, and to instruct enterprises how to better protect themselves from bad actors. Here, I can see a viable SEC role, where all publicly held companies, and privately held too, are called into question – not just one type of company.

Where privacy is concerned … don’t just divide Big Digital revenue by the number of users with social accounts — and start menacing on what data about me online may be worth. That immediately starts off with a false assumption, fails to recognize information’s exponential value in the economy, and denies the incredible social benefits afforded by the digitization of information.

The Digital Advertising Alliance (a client) conducted a study in 2016, and found that consumers assign a value of nearly $1,200 a year to the “free” ad-financed content they access and rely upon via digital and mobile. However, if they were forced to pay that amount – most would not be willing (or able) to pay such a premium.

This research shows why we need to protect and facilitate ad-financed content. But it’s part of a larger discussion. It’s about why the commercialization of the Internet has been a 25-year success (happy birthday, October 24) and we must keep that moving forward. As consumers, we all have prospered! Let’s start our discussion on data valuation here.

 

How Will Your Audience Receive Your New Product?

Product innovation is necessary for every company to grow and evolve in a competitive market. But if your audience “doesn’t get” your new product, success is much less of a guarantee.

Product innovation is necessary for every company to grow and evolve in a competitive market. But if your audience “doesn’t get” your new product, success is much less of a guarantee. Before you unveil your hard-won innovations, here are some ways to ensure you’re targeting the segments of your audience who will be the most receptive — both to the new product and accompanying marketing efforts.

First, Really Know Who They Are

While basic demographics like age, marital status, geographic location, hobbies and other points help you form a picture of your audience, to really know them means gaining specific, unique insights about them. You want to understand more than just who they are on paper by finding out how they think and feel and what they truly need. To do this, you have to integrate survey data with rich behavioral insights gleaned from big data.

Look at how personality profiles developed through a scan of big data reveal the personality characteristics common to the potential target audience for a new robot vacuum:

Credit: GutCheckIt

This audience ranks high for agreeableness, which points to other traits like altruistic, modest, and empathetic. So when communicating with them about the vacuum, messaging that uses a social responsibility angle will likely attract and feel relevant to them.

How your new product appeals to the individual needs and lifestyles of your audience further deepens your understanding of them. Consider in this summary of needs how the robot vacuum could hit home with the audience’s high ideals, drive toward harmony, and interest in self-expression, as well as how the vacuum could appeal to the audience majority who enjoy keeping their home tidy.

Credit: GutCheckIt
Credit: GutCheckIt

Then, Determine How Best to Reach Them

Once you’ve formed a full understanding of your audience’s personality, needs, and lifestyle, combine your learning with a study of the type of media consumed and during which times of day. For example, the vacuum audience learns about new products mainly through social media rather than television or promotional emails. They spend 7-plus hours per week on the web and using apps, mostly in the early evening hours between 5-8 pm.

Credit: GutCheckIt

To reach this audience effectively, online or mobile campaigns work best, with ads that could be shown on traditional TV in the later evening hours between 8-11 pm.

To learn what type of unique insights you could uncover about your brand’s audience before you launch a new product, visit the GutCheck website to learn more.