The Biggest Obstacle to Personalization Is the Creative Element

In a world where everyone is exposed to constant marketing through every conceivable media channel every day, messages that are not relevant to the target will be utterly ignored. And don’t blame the consumers for it, either. You, as a consumer, are trained to ignore irrelevant messages, as well.

In this consumer-centric environment, personalization is something all marketers must practice constantly, not only to increase the level of customer engagement, but also to not be ignored completely. And if your messages keep getting ignored, decreasing click-through rate isn’t just some annoying KPI that doesn’t look good in front of your boss, it may be an existential threat to your organization.

Unfortunately, personalization isn’t easy, simple, or cheap. There are many elements that must work harmoniously, so that each target sees something that is uniquely relevant to “her.”

4 Elements of Personalization

First, you need data about the target. What is she about, and what does she look like? That may require data from all kinds of sources — be they online or offline transactions, browsing history, store visits, reactions to previous campaigns (requiring both campaign and response history data), call-center logs, third-party demographic data, etc. Putting them all in one place, and rearranging them to create coveted Customer-360 View is often the first hurdle. But that is just the beginning. Without customer-centric data, there is no personalization — unless you count on your guesswork.

Then you need to make sense out of collected data. We often call such work analytics, which includes segmentation (or clustering), modeling, personas development (a series of affinity models), etc. Many marketers consider this to be the highest hurdle, as it requires different types of talents. Data scientists tend to think that the modeling work is the pinnacle of personalization, and they may not be wrong. But is it enough? So, what if they have 40 personas meticulously built by top-notch statisticians? How would you use them to differentiate messages for “each” target?

That leads to the third and forth elements in personalization, which are “Display Capability” and “Content and Creative.” Basically, you need to be able to show different creatives to different targets. If you are uniformly displaying the same content to everyone, what is the point in all this, no matter how many personas or affinity models you built?

Display capability is a technical hurdle. And you can procure technologies to overcome it, whether the challenge is dynamic web content, or personalized email delivery. You have to align pieces of technologies to make it happen. If Person A shows up on your website, and her affinity score is higher for “Luxury Travel” category in comparison to “Family Oriented Activities,” you should be able to show a picture of luxury cruise ship sailing in the Caribbean sunset, not necessarily a picture of happy children surrounded by cartoon characters.

As you can see, I am actually mixing three elements in this one example. I am assuming you built a series of personas (or affinity models). Your website should be dynamic so that such models can trigger different experiences for different visitors. Then of course, I am assuming you have ample amount of marketing creatives to differentiate messages. Display technology is a prerequisite in all this. If you don’t have it, go get it.

Your Persona Menu

Building a Customer-360 View is a customer-centric activity, but creating a persona menu is a selfish activity. What do you want to sell? And what kind of person would be interested in such products or services?

If you are selling fashion items, personas such as “Fashionista” or “Trend Setter” would be helpful. If you are pushing cutting-edge products, an “Early Adopter” persona would be necessary. If you are selling various types of insurance or security-related products, you will benefit from personas such as “Security Conscious.”

The important point here is that you should create persona menu based on your product and marketing roadmap. Be imaginative and creative. What kind of persona would be interested in your services? Once the goal is set, we need some samples of people who actually displayed such tendencies or behaviors. If you are building a persona called “Luxury Travel,” gather samples of people who actually have been on a luxury cruise ship or checked into luxury hotels (of course you have to define what constitutes “luxury”). Modelers do the rest.

Now, here is the reason why setting up a proper persona menu is so important. Not only will we define the target audience with it, but also categorize your marketing contents and digital assets with personas.

The most basic usage of any model is to go after high score individuals in a given category. You want to send messages to fashion-oriented people? Just select high score individuals using the Fashionista model.

But personalization is a little more complex that that. Let’s just say this one individual showed up at your website (or your store for that matter). You may have less than one second to show something that “she” would be interested in. Pull up all persona scores for that person, and see in which categories she scores high (let’s say over 7 out of a maximum score of 9). Going back to the previous example, if the target has score of 8 in Luxury Travel, and 4 in Family-oriented Activity, pull out the content for the former.

The Creative Element

Now, why is this article titled “The Biggest Obstacle to Personalization Is the Creative Element”? Because, I often see either lack of enough creative materials or lack of proper content library is the roadblock. And it really breaks my heart. With all the dynamic display capabilities and a series of models and personas, it would be a real shame if everyone gets to see the same damn picture.

I’ve seen sad and weird cases where marketers balk at the idea of personalization, as their creative agency is not flexible enough to create multiple versions of marketing materials. In this day and age, that is just a horrible excuse. What are they dealing with, some Mad Men agency people from the 1950s with cigarettes in their mouths and glasses of Scotch in their hands?

I’ve also seen other strange cases where proper personalization doesn’t happen – even with all good elements ready to be deployed – because departments don’t know how to communicate with one another. That is why someone should be in charge of all four elements of personalization.

How will the persona menu be created with grand marketing goals in mind? Who would procure actual data and build models? How will the resultant model/persona scores be shared throughout the organization and various systems, especially with the dynamic display technologies? How will the content library be tagged with all the relevant “persona” names (e.g., Tag “Luxury Travel” persona name to all digital assets related to “Luxury Cruise Ships”)?

Model scores (or personas) may function as a communication tool that binds different departments and constituents. Personalization is a team sport, and it is only as good as the weakest link. If you invested in building CDP solutions and analytics, go a little further and finish the work with the creative elements.

If you have a bunch of pictures stored in someone’s PC (or worse, some agency guy’s drawer), go build a digital content library. And while you’re at it, tag those digital assets with relevant persona names in your persona menu. Even automated personalization engines would appreciate your effort, and it will definitely pay off.

How to Integrate AI Tech Into Each Step of the Customer Journey

The Customer Lifecycle. The Sales Funnel. The Buyer’s Journey. All of these phrases are similar expressions of the same thing. They’re used to describe the process that it takes for a visitor to become a customer.

The Customer Lifecycle. The Sales Funnel. The Buyer’s Journey. All of these phrases are similar expressions of the same thing. They’re used to describe the process that it takes for a visitor to become a customer.

While the models and names of stages may have changed through the years, many agree that it can be boiled down to four simple components:

Awareness > Consideration > Decision > Loyalty

The No. 1 goal for most businesses is to generate more conversions (which primarily consists of sales). This can be through their marketing efforts, sales tactics, brand communication, conversion rate optimization, and other methods. Of late, many companies have developed critical competencies in using AI to nudge customers towards sales, and have improved their numbers drastically as a result.

AI, machine learning, and big data technology can all work hand-in-hand to improve the customer experience and support an optimized customer journey, which leads to more conversions in several key ways.

Let’s talk about how you can start using AI tech in each stage of the funnel.

Awareness

Marketing strategies these days are often heavily focused on the top of the funnel to build brand awareness and attract new customers. For many businesses, recognition is nearly equivalent to the value of their brand. Elena Veselinova and Marija Gogova Samonikov explain in their book Building Brand Equity and Consumer Trust Through Radical Transparency Practices that brand impact is a continuous process that insures purchases, cash flow, revenue and share value. Brand communication and experience creates and builds a loyal base of customers that do not consider any other brand.

Creating a strong level of brand awareness takes time and strategy. Companies spend millions of dollars on marketing campaigns and advertising to increase their reach and recognition, but AI tech is able to take the guesswork out of these strategies by analyzing huge volumes of consumer data for more targeted campaigns. For example, predictive analytics software can collect, track, and analyze datasets from past customers to determine which strategies or tactics performed well. These datasets are turned into reports with insights to guide marketing efforts and place relevant content in front of the most interested eyes at the right times.

With AI-assisted marketing, advertising strategies can be backed with data to optimize ad placement. Machine learning systems can even identify the best influencers for brands to partner with in order to reach relevant audiences and grow brand familiarity.

Credit: Venturebeat.com

Consideration

The next step of the buyer’s journey is often overlooked by marketers because it can drag on for a long time, depending on the product and the customer’s needs. During the consideration phase, a customer is already familiar with a brand or product but are unsure of whether or not to actually purchase. Customers will typically research the product’s reviews, compare prices to competitors, and look for alternatives during this stage. Due to this, the number of potential customers tends to narrow down considerably as they move from this step to the decision phase.

Brands must work to combat each customer’s concerns and questions standing in the way of a purchase decision. One of the best ways to do this is by offering personalized content that is relevant to each person, making it easy for them to find the information they are seeking.

AI systems can be used to predict a customer’s needs based on consumer data and previous online behavior, and then encourage conversions with a tailored UX or even a completely customized landing page that displays content relevant to that customer.

For example, if a site visitor has viewed a certain product page and played a video demonstrating its features, these actions can trigger an AI system to target them with personalized content that prompts a conversion if they don’t proceed to buy immediately. This content could be something as simple as an email message with more information or a display ad with a special offer for the specific product.

Credit: Personyze.com

Then there are platforms that use conversational AI tech (such as chatbots and voice assistants) to power automated, text- or audio-based interactions between a business and its customers. These platforms can understand speech, decipher intent, differentiate between languages, and mimic human conversations with great accuracy. Increasingly, they are advanced enough to even understand individual context and personalize the conversation accordingly.

Based on data insights, AI tech can curate content that matches up with the issues that are most important to that person, whether it be product features, immediate delivery, long term savings, etc. Customers respond quite well to personalized offers — an Accenture study reported that 91% of consumers are more likely to purchase from a company that sent them targeted deals or recommendations.

Decision

Once a customer moves from consideration to action, AI tools can be used to support a positive sales experience and eliminate any bumps along the way. If a customer encounters an issue while browsing the site, or during checkout or payment, it could be an instant sales killer, if it isn’t handled immediately by something like live chat.

According to multiple studies, one of the most frustrating parts about online customer service is long wait times. By using AI-enabled chatbots, companies can instantly answer common questions and resolve issues or roadblocks affecting the progression of the buyer’s journey. And customers certainly appreciate these quick response times. AI systems can significantly increase conversions with effective personalization and swift customer service.

Credit: AIMultiple.com

Loyalty

The last step of the customer journey is possibly the most valuable. Over half of customers reportedly stay loyal to brands that “get them.” Returning customers also tend to spend more money than new ones, and an oft-reported stat says that on average 65% of businesses’ revenue comes from existing customers.

Businesses (and customers) can benefit greatly from loyalty programs that are backed with machine learning technology. Starbucks famously uses AI tech to analyze customer behavior, improve convenience, and identify which promotions would perform best based on that person’s drink or food preferences, location, and purchase frequency. Their loyalty program uses this data to send out thousands of offers each day for the products their customers are most likely to buy. Their customer loyalty program grew 16% YoY last year as a direct result of their Deep Brew AI engine.

Credit: Starbucks app

While a positive shopping experience and great products are certainly important factors in a customer’s decision to buy again, data-driven marketing campaigns that encourage loyalty can also help a company to grow their numbers of repeat sales. Again, AI-assisted personalization techniques can boost the chances of a customer coming back for more, especially if they receive targeted offers or shopping suggestions based on previous interactions.

Credit: Accenture.com

The Wrap

AI is proving to be the tool of the future for marketers. It allows marketing teams to use predictive insights and analytical data to encourage and assist every micro-decision taken by consumers. AI systems not only help customers move along the buyer’s journey, they can also provide a more meaningful experience along the way, leading to more conversions and brand loyalty down the road.

Don’t Blame Personalization After Messing It Up

In late 2019, Gartner predicted “80% of marketers who have invested in personalization efforts will abandon them by 2025 because of lack of ROI, the peril of customer data, or both.” But before giving up because the first few rounds didn’t pay off, shouldn’t marketers stop and think about what could have gone wrong?

In late 2019, Gartner predicted “80% of marketers who have invested in personalization efforts will abandon them by 2025 because of lack of ROI, the peril of customer data, or both.” Interesting that I started my last article quoting only about 20% of analytics works are properly applied to businesses. What is this, some 80/20 hell for marketers?

Nonetheless, the stat that I shared here begs for further questioning, especially the ROI part. Why do so many marketers think that ROI isn’t there? Simply, ROI doesn’t look good when:

  1. You invested too much money (the denominator of the ROI equation), and
  2. The investment didn’t pay off (the numerator of the same).

Many companies must have spent large sums of money on teams of specialists and service providers, data platforms featuring customer 360, personalization software (on the delivery side), analytics work for developing segments and personas, third-party data, plus the maintenance cost of it all. To justify the cost, some marginal improvements here and there wouldn’t cut it.

Then, there are attribution challenges even when there are returns. Allocating credit among all the things that marketers do isn’t very simple, especially in multichannel environments. To knock CEOs and CFOs off their chairs – basically the bottom-line people, not math or data geeks – the “credited” results should look pretty darn good. Nothing succeeds like success.

After all, isn’t that why marketers jumped onto this personalization bandwagon in the first place? For some big payoff? Wasn’t it routinely quoted that, when done right, 1:1 personalization efforts could pay off 20 times over the investment?

Alas, the key phrase here was “when done right,” while most were fixated on the dollar signs. Furthermore, personalization is a team sport, and it’s a long-term game.  You will never see that 20x return just because you bought some personalization engine and turned the default setting on.

If history taught us anything, any game that could pay off so well can’t be that simple. There are lots of in-between steps that could go wrong. Too bad that yet another buzzword is about to go down as a failure, when marketers didn’t play the game right and the word was heavily abused.

But before giving it all up just because the first few rounds didn’t pay off so well, shouldn’t marketers stop and think about what could have gone so wrong with their personalization efforts?

Most Personalization Efforts Are Reactive

If you look at so-called “personalized” messages from the customer’s point of view, most of them are just annoying. You’d say, “Are they trying to annoy me personally?”

Unfortunately, successful personalization efforts of the present day is more about pushing products to customers, as in “If you bought this, you must want that too!” When you treat your customers as mere extensions of their last purchase, it doesn’t look very personal, does it?

Ok, I know that I coveted some expensive electric guitars last time I visited a site, but must I get reminded of that visit every little turn I make on the web, even “outside” the site in question?

I am the sum of many other behaviors and interests – and you have all the clues in your database – not a hollow representation of the last click or the last purchase.  In my opinion, such one-dimensional personalization efforts ruined the term.

Personalization must be about the person, not product, brands, or channels.

Personalization Tactics Are Often Done Sporadically, Not Consistently

Reactive personalization can only be done when there is a trigger, such as someone visiting a site, browsing an item for a while, putting it in a basket without checking out, clicking some link, etc. Other than the annoyance factor I’ve already mentioned, such reactive personalization is quite limited in scale. Basically, you can’t do a damn thing if there is no trigger data coming in.

The result? You end up annoying the heck out of the poor souls who left any trail – not the vast majority for sure – and leave the rest outside the personalization universe.

Now, a 1:1 marketing effort is a number’s game. If you don’t have a large base to reach, you cannot make significant differences even with a great response rate.

So, how would you get out of that “known-data-only” trap? Venture into the worlds of “unknowns,” and convert them into “high potential opportunities” using modeling techniques. We may not know for sure if a particular target is interested in purchasing high-end home electronics, but we can certainly calculate the probability of it using all the data that we have on him.

This practice alone will increase the target base from a few percentage points to 100% coverage, as model scores can be put on every record. Now you can consistently personalize messages at a much larger scale. That will certainly help with your bottom-line, as more will see your personalized messages in the first place.

But It’s Too Creepy

Privacy concerns are for real. Many consumers are scared of know-it-all marketers, on top of being annoyed by incessant bombardments of impersonal messages; yet another undesirable side effect of heavy reliance on “known” data. Because to know for sure, you have to monitor every breath they take and every move they make.

Now, there is another added bonus of sharing data in the form of model scores. Even the most aggressive users (i.e., marketers) wouldn’t act like they actually “know” the target when all they have is a probability. When the information is given to them, like “This target is 70% likely to be interested in children’s education products,” no one would come out and say “I know you are interested in children’s education products. So, buy this!”

The key in modern day marketing is a gentle nudge, not a hard sell. Build many personas – because consumers are interested in many different things – and kindly usher them to categories that they are “highly likely” to be interested in.

Too Many Initiatives Are Set on Auto-Pilot

People can smell machines from miles away. I think humans will be able to smell the coldness of a machine even when most AIs will have passed the famous Turing Test (Definition: a test of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human).

In the present day, detecting a machine pushing particular products is even easier than detecting a call-center operator sitting in a foreign country (not that there is anything wrong about that).

On top of that, machines are only as versatile as we set them up to be. So, don’t fall for some sales pitch that a machine can automatically personalize every message utilizing all available data. You may end up with some rudimentary personalization efforts barely superior to basic collaborative filtering, mindlessly listing all related products to what the target just clicked, viewed, or purchased.

Such efforts, of course, would be better than nothing.  For some time.  But remember that the goal is to “wow” your target customers and your bosses. Do not settle for some default settings of campaign or analytics toolsets.

Important Factors Are Ignored

When most investments are sunk in platforms, engines, and toolsets, only a little are left for tweaking, maintenance, and expansion. As all businesses are unique (even in similar industries), the last mile effort for custom fitting often makes or breaks the project. At times, unfortunately, even big items such as analytics and content libraries for digital asset management get to be ignored.

Even through a state-of-the-art AI engine, refined data works better than raw data. Your personalization efforts will fail if there aren’t enough digital assets to rotate through, even with a long list of personas and segments for everyone in the database. Basically, can you show different contents for different personas at different occasions through different media?

Data, analytics, contents, and display technologies must work harmoniously for high level personalization to work.

So What Now?

It would be a real shame if marketers hastily move away from personalization efforts when sophistication level is still elementary for the most.

Maybe we need a new word to describe the effort to pamper customers with suitable products, services and offers. Regardless of what we would call it, staying relevant to your customer is not just an option anymore. Because if you don’t, your message will categorically be dismissed as yet another annoying marketing message.

 

The Intersection of Personalization & Privacy: How to Communicate with Consumers

Consumers expect to get whatever they want, whenever they want it, delivered how they want it. You can credit (or blame) Amazon for setting expectations so high, but those same expectations extend to online publishing and marketing.

[Editor’s note: While this is geared toward the publishing audience in language, there are numerous valuable takeaways for marketers.]

Consumers expect to get whatever they want, whenever they want it, delivered how they want it. You can credit (or blame) Amazon for setting expectations so high, but those same expectations extend to online publishing.

Increasingly, publishers must personalize to thrive — a mission that can be at odds with new privacy mandates.

What Exactly Do Consumers Expect?

Virtually every publisher now promises customized content, but that promise can mean a few different things. On the one hand, it’s a promise to deliver a certain type of content that’s tailored to your reader’s individual interests. But it also means a promise to deliver content according to that person’s consumption preferences for device/channel (desktop, mobile, or tablet/website, social, or email).

Publishers deliver on these promises through a variety of features. Notifications that push to a consumer’s preferred device are one popular way to meet audiences on the most personal level. Likewise, social integration (both as commenting platforms and logins) is now seen as essential because it not only customizes the experience, but also makes it friction-less.

But, as publishers are well aware, building these features and executing personalization strategies takes significant resources that aren’t necessarily part of the core business.

Brands Are Doing the Same Thing with More Resources

While brands and publishers typically sit on opposite sides of the media ecosystem, their challenge is the same when it comes to personalization. Publishers and advertisers must both deliver the right message to the right person, at the right time. Tellingly, brands and publishers have tackled this challenge in different ways.

By and large, brands and bigger media companies have taken this kind of work in-house. But most small and medium-sized publishers have gone in the opposite direction, turning to agencies and vendors to navigate the complexities of data analytics, personalization, and monetization.

These are technical and costly undertakings. Small publishers may struggle because of limited expertise, but even big publishers may prefer to invest in content rather than building in-house technology. And just finding, vetting, and holding vendors accountable is a challenge for many publishers.

But regardless of how publishers solve for personalization, the brand context is important because well-resourced brands are setting the bar for consumer expectations here. As privacy compliance adds layers of complexity to personalization, brands and publishers will have to adapt to perform the same mission, albeit with varying levels of resources.

Personalization Is the Crucible of Privacy Chaos

To understand how personalization and privacy intersect, start with a fundamental question: How do I personalize something for you if I don’t know anything about you?

The question illustrates the tension between personalization and privacy. The more consumers share, the greater the level of personalization. Of course, the opposite is also true. If you don’t want to share anything personal, be prepared to accept the generic experience.

While that may sound like common sense, the reality is that publishers are stuck in a bind. You must reconcile the chaos that comes from a patchwork of state-mandated privacy laws — including California’s CCPA, plus laws in 10 other states — with consumer expectations that value privacy on the one hand and expect seamless, personalized experiences on the other. To be clear, there’s no “right answer,” in part because just as personalization preferences vary by individual, so, too, do our feelings about privacy.

Publishers, perhaps better than any other stakeholder, are uniquely positioned to lead this conversation. After all, consumers seek out publishers because they are trusted sources. But when it comes to explaining the tradeoffs between personalization and privacy, publishers usually fall back on their lawyers. That can be a mistake. Instead of relying fully on lawyers, publishers should communicate with their consumers in a clear, authentic voice. Here are some suggestions:

  • Speak in your brand’s voice. Typically, conversations that touch on the tradeoff between personalization and privacy get off to a bad start because privacy policies are written in a foreign language called legalese. Using your brand voice is more effective because it’s authentic. If your brand is edgy or sarcastic, talk about privacy with an edgy or sarcastic tone. Two examples: 1) Fitbit’s privacy policy is written in easy to navigate bullet points for users who may not have the time to take a deep-dive into the brand’s Terms of Service; 2) Apple’s privacy, which is quite in-depth, is written in the same easy-to-understand language Apple uses for its product copy.
  • Tell people what information you want and why you need it. A concept like “personally identifiable information” means a lot to lawyers, but it’s not something consumers think about in their daily lives. Instead, make specific asks for email, social media, or cookies and then explain why you need that information. Be clear that your product might not work as advertised unless the user shares some private information. The key is context. If you want movie screening times “near you,” for example, we need to know your location. Instead of just asking for a user’s location, say something like, “Tell us where you are so we can find a movie near you.”
  • Explain how the consumer benefits in concrete terms. If you’re using language like “so we can best serve you…” you’re being too vague. Instead, state the value proposition directly. Explain how you want to serve the consumer by telling them what they can expect — content tailored to their interests, timely notifications, etc. When you do that, you empower the consumer to make their own informed choices about the tradeoffs between privacy and personalization.
  • If you plan to share someone’s information with a third-party, be upfront about it. Reserving the right to share consumer data with third-party partners sounds like legalese, but it also sounds like you’re hiding something. There are valid reasons to share data with others. Tell consumers why you’re sharing their data, who you’re sharing it with, and how the opt-out works.

Navigating these delicate waters will be challenging, but putting the time and energy into incorporating your brand identity into privacy compliance will pay for itself in the long run. Your users will appreciate the effort and better personalization, and you will (hopefully) have stronger user connections and fewer people opting out.

‘Too Much’ Is a Relative Term for Promotional Marketing

If a marketer sends you 20 promotional emails in a month, is that too much? You may say “yes” without even thinking about it. Then why did you not opt out of Amazon email programs when they send far more promotional stuff to you every month?

If a marketer sends you 20 promotional emails in a month, is that too much? You may say “yes” without even thinking about it. Then why did you not opt out of Amazon email programs when they send far more promotional stuff to you every month? Just because it’s a huge brand? I bet it’s because “some” of its promotions are indeed relevant to your needs.

Marketers are often obsessed with KPIs, such as email delivery, open, and clickthrough rates. Some companies reward their employees based on the sheer number of successful email campaign deployments and deliveries. Inevitably, such a practice leads to “over-promotions.” But does every recipient see it that way?

If a customer responds (opens, clicks, or converts, where the conversion is king) multiple times to those 20 emails, maybe that particular customer is NOT over-promoted. Maybe it is okay for you to send more promotional stuff to that customer, granted that the offers are relevant and beneficial to her. But not if she doesn’t open a single email for some time, that’s the very definition of “over-promotion,” leading to an opt-out.

As you can see, the sheer number of emails (or any other channel promotion) to a person should not be the sole barometer. Every customer is different, and recognition of such differences is the first step toward proper personalization. In other words, before worrying about customizing offers and products for a target individual, figure out her personal threshold for over-promotion. How much is too much for everyone?

Figuring out the magic number for each customer is a daunting task, so start with three basic tiers:

  1. Over-promoted,
  2. Adequately promoted, and
  3. Under-promoted.

To get to that, you must merge promotional history data (not just for emails, but for every channel) and response history data (which includes open, clickthrough, browse, and conversion data) on an individual level.

Sounds simple? But marketing organizations rarely get into such practices. Most attributions are done on a channel level, and many do not even have all required data in the same pool. Worse, many don’t have any proper match keys and rules that govern necessary matching steps (i.e., individual-level attribution).

The issue is further compounded by inconsistent rules and data availability among channels (e.g., totally different practices for online and offline channels). So much for the coveted “360-Degree Customer View.” Most organizations fail at “hello” when it comes to marrying promotion and response history data, even for the most recent month.

But is it really that difficult of an operation? After all, any respectful direct marketers are accustomed to good old “match-back” routines, complete with resolutions for fractional allocations. For instance, if the target received multiple promotions in the given study period, which one should be attributed to the conversion? The last one? The first one? Or some credit distribution, based on allocation rules? This is where the rule book comes in.

Now, all online marketers are familiar with reporting tools provided by reputable players, like Google or Adobe. Yes, it is relatively simple to navigate through them. But if the goal is to determine who is over-promoted or adequately promoted, how would you go about it? The best way, of course, is to do the match-back on an individual level, like the old days of direct marketing. But thanks to the sheer volume of online activity data and complexity of match-back, due to the frequent nature of online promotions, you’d be lucky if you could just get past basic “last-click” attribution on an individual level for merely the last quarter.

I sympathize with all of the dilemmas associated with individual-level attributions, so allow me to introduce a simpler way (i.e., a cheat) to get to the individual-level statistics of over- and under-promotion.

Step 1: Count the Basic Elements

Set up the study period of one or two years, and make sure to include full calendar years (such as rolling 12 months, 24 months, etc.). You don’t want to skew the figures by introducing the seasonality factor. Then add up all of the conversions (or transactions) for each individual. While at it, count the opens and clicks, if you have extracted data from toolsets. On the promotional side, count the number of emails and direct mails to each individual. You only have to worry about the outbound channels, as the goal is to curb promotional frequency in the end.

Step 2: Once You Have These Basic Figures, Divide ‘Number of Conversions’ by ‘Number of Promotions’

Perform separate calculations for each channel. For now, don’t worry about the overlaps among channels (i.e., double credit of conversions among channels). We are only looking for directional guidelines for each individual, not comprehensive channel attribution, at this point. For example, email responsiveness would be expressed as “Number of Conversions” divided by “Number of Email Promotions” for each individual in the given study period.

Step 3: Now That You Have Basic ‘Response Rates’

These response rates are for each channel and you must group them into good, bad, and ugly categories.

Examine the distribution curve of response rates, and break them into three segments of one.

  1. Under-promoted (the top part, in terms of response rate),
  2. Adequately Promoted (middle part of the curve),
  3. Over-promote (the bottom part, in terms of response rate).

Consult with a statistician, but when in hurry, start with one standard deviation (or one Z-score) from the top and the bottom. If the distribution is in a classic bell-curve shape (in many cases, it may not be), that will give roughly 17% each for over- and under-promoted segments, and conservatively leave about 2/3 of the target population in the middle. But of course, you can be more aggressive with cutoff lines, and one size will not fit all cases.

In any case, if you keep updating these figures at least once a month, they will automatically be adjusted, based on new data. In other words, if a customer stops responding to your promotions, she will consequently move toward the lower segments (in terms of responsiveness) without any manual intervention.

Putting It All Together

Now you have at least three basic segments grouped by their responsiveness to channel promotions. So, how would you use it?

Start with the “Over-promoted” group, and please decrease the promotional volume for them immediately. You are basically training them to ignore your messages by pushing them too far.

For the “Adequately Promoted” segment, start doing some personalization, in terms of products and offers, to increase response and value. Status quo doesn’t mean that you just repeat what you have been doing all along.

For “Under-promoted” customers, show some care. That does NOT mean you just increase the mail volume to them. They look under-promoted because they are repeat customers. Treat them with special offers and exclusive invitations. Do not ever take them for granted just because they tolerated bombardments of promotions from you. Figure out what “they” are about, and constantly pamper them.

Find Your Strategy

Why do I bother to share this much detail? Because as a consumer, I am so sick of mindless over-promotions. I wouldn’t even ask for sophisticated personalization from every marketer. Let’s start with doing away with carpet bombing to all. That begins with figuring out who is being over-promoted.

And by the way, if you are sending two emails a day to everyone, don’t bother with any of this data work. “Everyone” in your database is pretty much over-promoted. So please curb your enthusiasm, and give them a break.

Sometimes less is more.

How to Make Actionable Sense of Customer Sentiment Analysis

Creating a better customer experience is a top priority for most businesses, with 72% of companies saying improving CX is their No. 1 goal, according to data from Forrester. However, figuring out what drives a better user experience is a total guessing game, unless you take a deep dive into customer sentiment analysis.

Creating a better customer experience is a top priority for most businesses, with 72% of companies saying improving CX is their No. 1 goal, according to data from Forrester. However, figuring out what drives a better user experience is a total guessing game, unless you take a deep dive into customer sentiment analysis.

Understanding the responses and reactions that customers give out after using your products can help your brand immensely. Of course, conducting market research and surveys, and gathering feedback from customers are all small but essential steps toward improving your product or service, as well as its user experience. However, these reports are mostly a whole lot of confusing numbers and statistics; they offer no action plan or recommendations, or even insights on what to do next.

Making actionable sense of the numbers can be tricky, especially if there are no clear problems or opportunities that were identified through your research.

So, what should you do? Let’s go step-by-step.

Pinpoint Common Threads in Customer Reviews

While it’s typically a company’s first reaction to try to remove negative reviews that could deter future customers, these actually may be your best resource for fixing hidden issues.

About 25% of consumers have left a review for a local business because of a bad experience, but this doesn’t mean that 100% of these reviews are helpful to either companies or other customers. It’s best to turn to a reliable system here that can sift through emotionally exaggerated (and practically useless) or downright fake reviews and uncover valuable information that could point you toward better solutions.

A review platform, such as Bazaarvoice, allows brands to collect genuine ratings and reviews from customers, respond to their questions and concerns about their products, display moderated content created by customers on social media, and even implement a product sampling program based on the reviews you’ve collected.

Similarly, an interaction management tool, like Podium, gets you in the game earlier, helping you connect and interact with prospects on multiple channels. It enables team collaboration on lead generation and nurturing, as well as solving customer problems, leading to a consistent customer experience.

Customer Sentiment Analysis image
Credit: Podium.com

More customers tend to leave reviews with brands that use customer review management tools. This results in more data for your sentiment research, eventually ensuring better targeting and success of your product marketing campaigns.

Watch out for repeated keywords throughout these reviews, such as issues with customer service, packaging, delivery, or pricing. Looking for patterns in your customer reviews lies at the core of identifying the problems and coming up with solutions.

Use Smart Segmentation

Customers never fit into the one-size-fits-all category. Even if you cater to a small niche or if your product has a very specific use, there will be subsets, segments, and cohorts, all influenced by varying demographics and regulations, who could affect opinions of your business. This is why smart segmentation is important when reviewing customer sentiment analysis.

Again, these segments may need different targeting strategies, depending on whether your company is a B2C or B2B entity.

B2C

B2C marketers need to look at the:

  • age:
  • location:
  • income: and
  • in-the-moment needs of their customers.

B2B

B2B marketers, on the other hand, need to address non-personal variances, such as:

  • company size:
  • budget; or
  • objectives.

By pairing demographic and quantitative data, customer sentiment may make more sense and provide even deeper insight than before. For instance, customers who are motivated by finding the best deal may say that your shipping costs are too high; whereas, customers with FOMO may be ready to pay extra for next-day delivery. When you have multiple datasets of behavioral data that you can compare against one another, your team can understand how to cater to various customer segments by understanding their motivations.

Note that customer “segments” vary from “profiles” or “personas.” They are not as specific, and typically only focus on one or two variables rather than a list of unique qualities. There are countless ways to segment your audience, so be sure to find the segmentation model that best fits your business.

Customer Sentiment Analysis photo
Credit: MeaningCloud.com

Identify Engagement Intent

Understanding the “why” behind your customer’s actions will shed some light on their sentiment reactions. Your expectations always influence your experience, so a customer’s engagement intent could play a part in their response.

The rise of search as a marketing channel has made it clear that there are essentially four engagement intent categories that consumers fall into today:

  • informational;
  • navigational;
  • commercial; and
  • transactional.

Each of these steps correlates well with the traditional AIDA sales funnel model.

Informational

The first is searching for information on a particular subject that may or may not be a problem for them. These are typically prospects who are just entering the marketing funnel. They simply want to know more, so if your website does not offer the information they are looking for, their interest in your brand or product will not develop at all.

Navigational

People in the navigational category are looking for a specific product, service, or piece of content. This group knows what they want, and they will be easily frustrated if they can’t find it.

Commercial

The commercial investigation intent group is interested in buying, but they just aren’t quite ready yet or aren’t convinced that your product offers the best solution for them. They fall just above the action segment of the sales funnel and are often looking for the last bits of information before they make a purchase.

Transactional

And finally, the transactional group has the intent to buy. They have already made their decision to buy a specific product; however, any hiccups in the buying or checkout process could deter them.

Identifying Engagement Intent

Of course, identifying their engagement intent is a little tricky, especially after the interaction has been completed. But with some digging and martech tools, there are ways to figure out the motivations behind every brand-customer engagement.

One of the clearest ways to identify engagement intent is through carrying out intent research, attribution modeling, and analyzing their behavior on your digital property. If they just read a post on your blog, chances are they were looking for more information on a topic related to your industry. If they clicked an ad and filled up a form on your landing page, they are probably interested in availing themselves of your service.

Once their intent has been identified and understood, it will be much easier to understand their sentiment post brand engagement or product usage.

Experiment With Changes

Finally, the only way to make customer analysis actionable is to, well, take action. However, just switching things up without constantly analyzing the results will only put you back at Square One.

Many marketers rely on A/B/n or multivariate testing strategies to compare different changes, whether it be in the design or layout to an entire product or service experience. However, A/B testing can be a long and arduous process that yields murky results. It may even mislead you, if you over-rely on seasonal or contextual variables. Unsurprisingly, AI technology has been a huge help in the A/B testing realm by improving the accuracy and reliability of the process, resulting in few conversion opportunities lost.

AI-based algorithms are able to gather and analyze massive amounts of data at a time. They can compare results of multiple tests against each other simultaneously at various interaction points along the buyer journey.

Tools like Evolv use machine learning (ML) to find which experiences and customer journey paths work best (make profits) for you and nudge customers down those paths accordingly. You can set up experiments on your landing pages with goals and KPIs, and let the algorithm tweak the UX for each customer by presenting various combinations. The data from these experiments help you understand how satisfied the customer is with the interaction, and also develop new hypotheses to keep testing further or make decisions related to product development or service delivery.

The Way Ahead

By understanding the root causes behind your customer’s reactions and feelings, you can go as far as to influence sentiment, improve brand loyalty ,and encourage advocacy. Always be looking for overlaps and commonalities among complaints. This will help you avert PR disasters, deliver exceptional customer service, and stay ahead of the competition.

Use sentiment analysis to understand where your customers are coming from by segmenting them and uncovering their intents at every interaction. Finally, track the effects of all your initiatives and take action responsibly to ensure they stay delighted at all times.

What Did You Do on Data Privacy Day 2020? Do Tell Us.

Each year, Jan. 28 is known as “Data Privacy Day” in the United States and globally — also Data Protection Day in other jurisdictions. As business organizations — and marketers — we see that it’s a day when consumers are reminded to exercise their “privacy rights.”

Each year, Jan. 28 is known as “Data Privacy Day” in the United States and globally — also Data Protection Day in other jurisdictions.

As business organizations — and marketers — we see that it’s a day when consumers are reminded to exercise their “privacy rights” and take advantage of tips and tricks for safeguarding their privacy and security. In our world of marketing, there are quite a few self-regulatory and co-regulatory tools (U.S. focus here) that enable choices and opt-outs:

  • To opt out of commercial email, direct mail, and telemarketing in certain states, consumers can avail themselves of DMAchoice. For telemarketing, they can also enroll on the Federal Trade Commission’s Do Not Call database.
  • For data collected online for interest-based ads, consumers can take advantage of Digital Advertising Alliance’s WebChoices and Network Advertising Initiative consumer control tools, which are accessible via the ubiquitous “AdChoices” icon. DAA also offers AppChoices, where data is collected across apps for interest-based ads. [Disclosure: DAA is a client.]
  • Now that California has a new consumer privacy law, consumers there can also take advantage of DAA’s new “Do-Not-Sell My Personal Information” Opt Out Tool for the Web. Its AppChoices mobile app also has a new CCPA opt-out component for “do not sell.” Publishers all over the Web are placing “Do Not Sell My Personal Information” notices in their footers, even if others outside California can see them, and offering links to their own in-house suppression lists, as well as DAA’s. Some publishers are using new the Privacy Rights icon to accompany these notices.

Certainly, businesses need to be using all of these tools — either as participants, or as subscribers — for the media channels where they collect, analyze, and use personal and anonymized data for targeted marketing. There’s no reason for not participating in these industry initiatives to honor consumer’s opt-out choices, unless we wish to invite more prescriptive laws and regulations.

We are constantly reminded that consumers demand high privacy and high security — and they do. We also are reminded that they prefer personalized experiences, relevant messaging, and wish to be recognized as customers as they go from device to device, and across the media landscape. Sometimes, these objectives may seem to be in conflict … but they really are not. Both objectives are good business sense.

As The Winterberry’s Group Bruce Biegel reported while presenting his Annual Outlook for media in 2020 (opens as a PDF), the U.S. data marketplace remains alive and well. For data providers, the onus is to show where consumer permissions are properly sourced, and transparency is fully authenticated and demonstrated to consumers in the data-gathering process. It’s a rush to quality. Plainly stated, adherence to industry data codes and principles (DAA, NAI, Interactive Advertising Bureau, Association of National Advertisers, among others) are table stakes. Going above and beyond laws and ethics codes are business decisions that may provide a competitive edge.

So what did I do on Data Privacy Day 2020? You’re reading it!  Share with me any efforts you may have taken on that day in the “public” comments below.

Dating Tips That’ll Help Marketers Get Their Client Relationships Unstuck

Committing to improvement is a good idea any time of year, but there’s something poetic about marketers revitalizing along with the calendar. So let’s talk about what we can learn from the intersection of marketing personalization, dating, and client relationships. Are you a good date?

Committing to improvement is a good idea any time of year, but there’s something poetic about marketers revitalizing along with the calendar. So let’s talk about what we can learn from the intersection of marketing personalization, dating, and client relationships. Are you a good date?

I’ve been dating and doing client service (separately) for long enough to know they’re actually pretty similar. When you first get together, it’s all magical. Every text and call makes your heart skip a beat; things you’ve done a million times before feel fresh and exciting. You think about them constantly. However, the newness of the relationship soon starts to fade; you’ve got the scope of work signed and things are just humming along. So you start to rely solely on email and that scheduled “touch base.” Pretty soon, things get stagnant and your priorities shift.

This is a make-it or break-it moment. Will you put in the work to keep everyone at the level of full-heart-eye emojis, or will you get stuck in a routine? Lessons from the dating world can help you get those client relationships unstuck.

Inventory your client relationships.

  • Are you speaking their language by using their preferred method of communicating?
  • Are you still keeping in touch the way you used to at the exciting start of things?
  • Are you genuinely listening and engaged in conversation?

You want this relationship to last, so ask yourself how you could do even better. What if you rolled into your client’s office with cupcakes and cookies — and hung around to enjoy them with your clients? I make a habit of it, because who doesn’t love a treat? High-touch, high value … great date!

But it goes much further than just being the guy that shows up with flowers.

  • Are you proactively suggesting new ideas?
  • Are you forwarding them news that has an impact on their business?
  • Are you identifying materials and work product that went out of your agency that wasn’t up to your standards and then offering to make it right?
  • On the flip side, are you having those tough conversations about parts of the relationship that aren’t working that are faults on their side?

Those big personal investments are the secret to getting client relationships unstuck and, for me, it’s just the natural result of being a friendly, curious person — and it’s the No. 1 reason why my clients are usually clients and friends for life. Sure, this is business, but being open and letting your personality help forge relationships is what guarantees people remember you. I’ve always believed that the way you engage with your clients should stick with them just as much as the measurable outcomes of your work.

In 2020, build your relationship checklist. I’m talking a real, tangible checklist! Keeping track helps you assess whether you’re doing enough to sustain a happy relationship, and it’s a great way to make sure that all of your clients feel special.

Here’s the bottom line: In client services, as in dating, success depends on showing that you care, and putting the work in to keep it fresh. Whether you’re in client services or courting a dreamboat, you have got to nurture the relationship beyond day-to-day work.

Here’s the net-net: it may be a new century, but the personal touch in any relationship stands the test of time.

Why Include Direct Mail In Optichannel Marketing?

Direct mail is highly effective on its own; however, when you combine it with other marketing channels, it gets even better. Demand Metric, in partnership with PFL, conducted a benchmark study. The optichannel marketing research is meant to understand the importance of multichannel marketing.

Direct mail is highly effective on its own; however, when you combine it with other marketing channels, it gets even better. Demand Metric, in partnership with PFL, conducted a benchmark study, “Multichannel Marketing Maximizing Program Engagement and ROI”. The optichannel marketing research is meant to understand the importance of multichannel marketing and the power of intentional, coordinated marketing efforts.

The goal of the study was to collect data to identify best practices and help marketers know how to reach specific audiences, and when to use particular tactics within their multichannel campaigns. The results indicate that direct mail needs to be a part of your optichannel marketing strategy.

Key findings:

  • When direct mail is personalized and tightly integrated into the channel mix and campaign technology: Average response rates improve significantly, with a 62% increase in those reporting good or very good response rates. The ROI of multichannel campaigns improves significantly, with an 80% increase in those reporting good or very good ROI.
  • Just over half of this study’s participants include direct mail in their multichannel campaigns, and 80% of them report that direct mail improves multichannel campaign performance.
  • The executive, or C-Suite, audience is the most sought after by study participants. Events and direct mail are the most effective channels to reach them.
  • While postcards are the most frequently used direct mail format, the dimensional format does the best job of representing the brand.
  • More channels produce higher response. Respondents using seven or more channels in their mix are 26% more likely to indicate their multichannel programs produce good or very good response.

Respondents use a multitude of channels that include:

  1. Email: 91% usage
  2. Social Media: 81% usage
  3. Events: 73% usage
  4. Display Ads/Remarketing: 60% usage
  5. Direct Mail: 56% usage
  6. Search/PPC: 51% usage
  7. Outbound Business Development/Sales Development: 47% usage
  8. Content Syndication: 35% usage
  9. Other: 5% usage

Most marketers are using between three and five channels on any given campaign, but results show that you should consider adding more channels. When marketers use seven or more channels, they report a 77% “very good” or “good” response rate. The report also found that marketers are not consistently using the most effective channels. The top three most effective channels are: events at 83%, integrated and personalized direct mail at 78%, and Search/PPC at 73%. What are you using?

Direct Mail Needs More Attention From Marketers

The report shows that marketers are most familiar with postcard and letter formats, and report that they use those formats most. Postcards are the least expensive direct mail format. Many marketers favor postcards because there is nothing to open: the message is easily visible. Dimensional mail formats are a close third in usage. This format includes pieces that are not flat, like the other types, but have an element of depth to them. A dimensional mail piece is often sent in boxes or tubes, and its very form invites opening it. These pieces evoke natural curiosity and tend to drive higher response rates. Have you tried dimensional mail?

According to study participants, direct mail clearly enhances multichannel campaign performance. In this study, 52% report a moderate to major improvement in campaign performance when direct mail is one of the channels. When direct mail is part of the channel mix, campaigns have slightly better response rates. Personalized direct mail generates significantly better response rates to multichannel campaigns. Are you using direct mail enough?

As you can see, adding direct mail to your optichannel marketing campaigns is significant. The more personalized and integrated it is, the better your response rate is going to be. Are you ready to get started with more personalized direct mail?

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.