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.

Know What to Automate With Machine Learning

There are many posers in the data and analytics industry. Unfortunately, some of them occupy managerial positions, making critical decisions based on superficial knowledge and limited experiences. I’ve seen companies wasting loads of money and resources on projects with no substantial value — all because posers in high places bought into buzzwords or false promises. As if buzzwords have some magical power to get things done “auto-magically.”

I’ve written articles about how to identify posers and why buzzwords suck. But allow me to add a few more thoughts, as the phrase “Machine Learning” is rapidly gaining that magical power in many circles. You’d think that machines could read our minds and deliver results on their own. Sorry to break it to you, but even in the world of Star Trek, computers still wouldn’t understand illogical requests.

Beware of people who try to employ machine learning and no other technique. Generally, such people don’t even understand what they are trying to automate, only caring about the cost reduction part. But the price that others end up paying for such a bad decision could be far greater than any savings. The worst-case scenario is automating inadequate practices, which leads to wrong places really fast. How can anyone create a shortcut if he doesn’t know how to get to the destination in the first place, or worse, where the destination is supposed to be?

The goal of any data project should never be employing machine learning for the sake of it. After all, you wouldn’t respect a guitarist who can’t play a simple lick, just because he has a $5,000 custom guitar on his shoulder.

Then, what is the right way to approach this machine learning hype? First, you must recognize that there are multiple steps in predictive modeling. Allow me to illustrate some major steps and questions to ask:

  1. Planning: This critical step is often the most difficult one. What are you trying to achieve through data and analytics? Building the most eloquent model can’t be the sole purpose outside academia. Converting business goals into tangible solution sets is a project in itself. What kind of analytics should be employed? What would be the outcome? How will those model scores be applied to actual marketing campaigns? How will the results would be measured? Prescribing proper solutions to business challenges within the limitation of systems, toolsets, and the budget is one of the most coveted skill sets. And it has nothing to do with tools like machine learning, yet.
  2. Data Audit: Before we chart a long analytics journey, let’s put a horse before the cart, as data is the fuel for an engine called machine learning. I’ve seen too many cases where the cart is firmly mounted before the horse. What data are we going to use? From what sources? Do we have enough data to perform the task? How far in time do the datasets go back? Are they merged in one place? Are they in usable forms? Too many datasets are disconnected, unstructured, uncategorized, and unclean. Even for the machines.
  3. Data Transformation: Preparing available data for advanced analytics is also a project in itself. Be mindful that you don’t have to clean everything; just deal with the elements that are essential for required analytics to meet pre-determined business goals. At this stage, you may employ machine learning to categorize, group, or reformat data variables. But note that such modules are quite different from the ones for predictions.
  4. Target Definition: Setting up proper model targets is half-art/half-science. If the target is hung on a wrong spot, the resultant model will never render any value. For instance, if you are targeting so-called “High Value” customers, how would you express it in mathematical terms? It could be defined by any combinations of value, frequency, recency, and product categories. The targets are to be set after a long series of assumptions, profiling, and testing. No matter what modeling methodology eventually gets employed, you do NOT want targets to be unilaterally determined by a machine. Even with a simple navigator, which provides driving directions through machine-based algorithms, the user must provide the destination first. A machine cannot determine where you need to go (at least not yet).
  5. Universe Definition: In what universe will the resultant model be applied and used? Model comparison universe is as important as the target itself, as a model score is a mathematical expression of differences between two dichotomous universes (e.g., buyers vs. non-buyers). Even with the same target, switching the comparison universe would render completely different algorithms. On top of that, you may want to put extra filters by region, gender, customer type, user segment, etc. A machine may determine distinct sets of universes that require separate models, but don’t relinquish all controls to machines, either. Machine may not aware of where you would apply the model.
  6. Modeling: This statistical work is comprised of sub-steps such as variable selection, variable transformation, binning, outlier exclusion, algorithm creation, and validation, all in multiple iterations. It is indeed laborious work, and “some” parts may be done by the machines to save time. You may have heard of terms such as Deep Learning, Neural Net, logistic regression, stepwise regression, Random Forest, CHAID analysis, tree analysis, etc. Some are to be done by machines, and some by human analysts. All those techniques are basically to create algorithms. In any case, some human touch is inevitable regardless of employed methodology, as nothing should be released without continuous testing, validation, and tweaking. Don’t blindly subscribe to terms like “unsupervised learning.”
  7. Application: An algorithm may have been created in a test environment, but to be useful, the model score must be applied to the entire universe. Some toolsets provide “in-database-scoring”, which is great for automation. Let me remind you that most errors happen before or after the modeling step. Again, humans should not be out of the loop until everything becomes a routine, all the way to campaign execution and attribution.
  8. Maintenance: Models deteriorate and require scheduled reviews. Even self-perpetuating algorithms should be examined periodically, as business environments, data quality, and assumptions may take drastic turns. The auto-pilot switch shouldn’t stay on forever.

So, out of this outline for a simple target modeling (for 1:1 marketing applications), which parts do you think can fully be automated without any human intervention? I’d say some parts of data transformation, maybe all of modeling, and some application steps could go on the hands-free route.

The most critical step of all, of course, is the planning and goal-setting part. Humans must breathe their intention into any project. Once things are running smoothly, then sure, we can carve out the parts that can be automated in a step-wise fashion (i.e., never in one shot).

Now, would you still believe sales pitches that claim all your marketing dreams will come true if you just purchase some commercial machine-learning modules? Even if decent toolsets are tuned up properly, don’t forget that you are supposed to be the one who puts them in motion, just like self-driving cars.

Coronavirus and Marketing Automation: Let’s Be Careful Out There

I’m no stranger to writing about crisis management. And while we’re in uncharted waters here with the COVID-19 Coronavirus, there are some things that marketers forget about doing in times of crisis, including the emails they have set up in their marketing automation tools.

I’m no stranger to writing about disaster preparedness and crisis management. I live in an area where we get hit with a hurricane every few years. And while we’re in uncharted waters here with the COVID-19 Coronavirus, there are some things that marketers forget about doing in times of crisis, including the emails they have set up in their marketing automation tools.

I will leave it up to the medical professionals to discuss what needs to be done to protect yourself from the virus, other than to say it’s a very fluid and dangerous situation, so please take is seriously.

That said, marketers and business owners, here are some things you need to consider regarding your current and ongoing email campaigns:

Let’s talk about your tone: I received the above email March 12, and it’s completely tone deaf. The subject line for the email I got from Spirit Airlines says it all: “Never A Better Time To Fly.” And while I certainly understand that Spirit still needs to fill seats on its planes, maybe it could have come up with a better subject line considering the times?

In my favorite gaffe email of the day, also from March 12 (and I’m not taking political sides here; in fact, I get emails from both parties), our president literally invited me to dinner.

Which brings me to my second point: Please take a look at your marketing automation campaigns. It may be time to cancel some, tweak some of the copy in others, add some new ones, etc. We tend to set-em-and-forget-em, but unless you want to put a negative ding on your brand image, have a look at what you’re sending out — especially in these unprecedented times.

I hope this helps. I wrote this quickly given the fluid situation surrounding COVID-19; there are many more things you can do as a marketer in times of crisis. Please be safe!

 

 

4 Ways Artificial Intelligence Can Impact Your Conversion Rates

At this point, there is little doubt that artificial intelligence is the future of business. The Salesforce “State of Marketing” report found that more than a fifth of businesses currently use AI for marketing purposes, including programmatic buying, personalization, and real-time offers.

At this point, there is little doubt that artificial intelligence is the future of business. The Salesforce “State of Marketing” report found that more than a fifth of businesses currently use AI for marketing purposes, including programmatic buying, personalization, and real-time offers.

artificial intelligence graphic
Credit: Salesforce

Further, AI is the fastest-growing sales technology, according to the Salesforce “State of Sales” report.

Outside of sales and marketing, companies are frequently using artificial business intelligence for tasks like reporting, dashboards, and data warehousing and analytics.

While applying AI to these business operations is certainly beneficial, it does beg the question of how exactly this technology will impact the future of conversion optimization, as well as the most important person in a business: the customer.

At the end of the day, the thing that really matters in business is the numbers. AI technology for analyst reports and predicting turns in the market is all well and good, but if it isn’t boosting sales, then what is the point?

The good news is that AI is showing promising results in terms of conversion rates, proving once again that big data is paving the way to a more profitable future for many companies. Here’s how.

1. Enriches Customer Experience

The concept of improving the customer experience (CX) is a big challenge for many reasons. CX is not merely limited to the user-friendliness of a website or the customer service that is provided; it is a combination of all of these elements. Yet another report from Salesforce found that consistency is a core element in a positive customer experience, and 70% of customers say connected processes based on earlier interactions and contextualized engagement are important for them to do business with a company.

This means that in order to improve the CX for customers, brands must adjust every part of the experience to create a coherent message.

Studies have found that customers are willing to pay more for a better experience with a business. It also has a strong effect on their likelihood to repurchase and refer the product or company to friends.

artificial intelligence graph
Credit: Temkin Group

But what exactly makes up “customer experience” and where does AI fit in?

CX is essentially the accumulation of every interaction a customer has with a business, from introduction, to purchase, to customer service. As experienced business owners know, one small kink in the journey can send people running. AI and machine learning technology can help create a more optimized experience for each customer, from start to finish.

For example, when fashion brand FlyPolar experienced a near 400% decrease in sales in the span of just four months, the business executives knew that something wasn’t right. Because most of its customers purchased online, FlyPolar used AI software to optimize its website landing pages. By using machine learning technology, this AI program “learned” which designs performed best and delivered positive results.

After several weeks of testing, the AI system identified the core roots of the conversion problems and provided the proper insights for solutions. FlyPolar created a simpler four-step conversion funnel on its website, with optimized CTA button placement throughout the landing pages. By using machine learning algorithms, FlyPolar increased its checkout page traffic by 16% and its order value by 13% in just three weeks.

This case study shows that AI technology can quickly and easily identify the root of the problem, arguably one of the most difficult parts of optimizing the CX.

The prediction capabilities of AI-powered systems can also make it easier for your customers to find exactly what they are looking for; which, in turn, improves their experience with your website. Traditional searches base results on matching keywords or similar phrases, which may or may not be accurate. In contrast, present-day search programs use ML to “learn” consumer behavior and accurately return the items that match their queries, based on their previous behavior.

ML-based search takes numerous data points into consideration, including past view and click rates, ratings, and even inventory levels to provide customers with appropriate and targeted results.

It should be no surprise here that Amazon is one of the leading retailers to utilize this kind of technology. Amazon’s recommendation engine uses item-to-item collaborative filtering to provide search results that are based on multiple data points, rather than just keyword matches. Not only does the algorithm take each customer’s past searches, purchases, and product views into consideration, but also the ratings and popularity of each item.

artificial intelligence example
Credit: Amazon

Since Amazon debuted an AI-based recommendation engine, its profits started growing exponentially. By basing search results on multiple criteria, Amazon is able to push certain products while providing shoppers with the results that fit their needs, providing a better experience for the customer with each query.

2. Enhances Personalization

Buying online is no longer a one-size-fits-all experience. In fact, customers are becoming more and more unyielding that businesses customize just about everything to fit their needs. According to Accenture’s “Personalization Pulse Check” report, three out of four customers report that they would be more likely to purchase from a brand that offers personalization and recognition than businesses that do not.

Personalization is also directly related to higher profits. Researchers have found that businesses utilizing big data systems to create personalized experiences for their customers report up to 10% higher revenues.

AI is able to take the guesswork out of personalization. One of the best examples of this strategy in action comes from Starbucks, which reported a 300% increase in customer spending thanks to its highly-customized marketing program. Customers regularly receive personalized offers and incentives to earn more points toward a free drink reward. Every customer’s offer is based on past behavior, including how often each customer purchases and which types of items the customer tends to buy.

Starbucks’ AI-powered personalization system sends out around 400,000 variants of emails with incentives that are almost entirely unique for each recipient. Due to the hyper-personalization that Starbucks offers, many customers find it easy to fulfill the requirements for these rewards. This does wonders to increase consumer participation, purchase frequency, and ultimately, customer loyalty.

artificial intelligence in loyalty programs
Credit: Starbucks App

Of course, loads of consumer data are needed in order for online companies to provide this high level of personalization. Each customer’s preferences, demographics, and behavior must be tracked and analyzed in order for brands to properly adjust their strategies to fit an individual consumer.

The results from integrating personalized messaging and marketing speak for themselves: 63% of marketers report that an increase in conversion rates was the top benefit they saw from personalization.

AI-powered personalization can be used to help customers move their way through the buyer’s journey, as well. Using ML, these programs use predictive analysis to incentivize shoppers with personalized messages, email campaigns, retargeted ads, and more.

The algorithms can study consumer behavior so that ads and other messages are sent at the right time and trigger the ideal response. For example, an algorithm that tracks customers’ click rates and scrolling habits can predict when new customers are likely to abandon their carts and send a well-timed message or personal offer to keep them engaged.

artificial intelligence-generated offer
Credit: Acquisio.com

3. Improves Results of A/B Testing

Most marketing teams and web designers rely on A/B testing to determine the best layouts, color schemes, and messaging to grab their customers’ attention. However, there are obvious limits to the “old-fashioned” testing approach. Gathering the research takes time, and there is not always a clear winner from the results.

In fact, the traditional form of this strategy may not even be effective. Jeremy Miller, marketing director at Sentient, said during an interview:

In traditional A/B testing formats, you have your control vs. an experiment. You run that experiment against your traffic, and whichever design performs better is the one you deploy … but people have found that six out of seven experiments don’t result in a positive outcome, so you actually have to put a lot of energy and resources to try to determine how you can actually increase conversions using A/B testing.”

AI can solve the three biggest problems with traditional A/B testing: time required, insight, and limited variables. By reducing these weaknesses, marketing teams have the ability to make informed design changes with the results and data to support them. Instead of taking a linear approach to testing, AI can compare thousands of variables at the same time and instantly compare the results to determine the best combination.

For example, online lingerie company Cosabella used an AI-driven testing approach when it was redesigning its website. Rather than comparing designs two at a time, like a traditional A/B test would, Cosabella was able to carry out an A/B/n experiment with 160 different design elements, simultaneously. With that many variables, it would have taken up to a year of A/B testing to gather results; with AI, the process took only seven weeks.

artificial intelligence testing
Credit: Cosabella.com

Through this testing process, Cosabella was able to determine the aesthetics that resulted in better conversions. It found that customers bought more when CTA buttons were pink, rather than black. The company also determined that family values resonated with its customers, so it did away with “free shipping” banners and replaced them with “Family Owned Since 1983.” After these short seven weeks of testing, Cosabella reported a 38% increase in conversions and a 1,000% lift in newsletter signups.

4. Speeds Up Customer Service

The faster a company can respond to customer inquiries or issues, the better. For this reason, the demand for live chat grew by 8.29% last year. Unfortunately, most businesses do not have the resources to keep their customer service departments running 24/7, leading to long response wait times for disgruntled customers.

By automating customer service with AI-powered chatbots, businesses can not only solve the issue of wait time, but also the quality of the response and assistance that customers receive.

In 2012, Amtrak’s customer service department serviced 30 million passengers each day. Obviously, with such high numbers, it was difficult to handle individual inquiries in a timely manner, so Amtrak decided to jump on the chatbot train with its AI-powered customer service rep “Julie.”

Julie was able to resolve most of these issues by pre-filling forms through scheduling tools and guiding customers step-by-step through the online booking process. Because most of these problems were handled online, the number of calls and emails decreased dramatically. At the end of the first year, Julie had answered over 5 million questions, increased booking rates by 25%, and generated 30% more revenue, thanks to upsell options included in the messaging.

artificial intelligence chat
Credit: NextIT.com

In terms of conversions, live chatbots can not only resolve issues in an instant, they can increase the chances that a customer decides to buy. When a customer’s issue is solved quickly, they are twice as likely to repurchase from that brand. Live chat is also the preferred method of communication for resolving problems or issues; however, it is important to note that the quality of the messaging far outweighs the speed of the response.

According to Kayako’s report on live chat service, 95% of customers say that receiving a thorough response that answers their question or resolves the problem is more important than just getting a quick reply. This is a major issue that many companies have with AI chatbots; they are simply programmed to give automated, scripted responses, which 29% of customers report as simply frustrating and unhelpful.

This is where AI-based chatbots save the day; they can adjust their messaging based on FAQs, as well as the customer’s phrasing and responses. This process leads to better and more natural replies from bots that delight customers and give them the timely information they need.

An AI chatbot is not a one-time fix to the issue of customer service. It is a strategy that must be properly monitored, adjusted, and perfected over time in order to deliver the best results.

The Wrap

Many conversations these days are revolving around AI and its impact on the future of business. And, quite honestly, it seems like the answer to just about every current business planning issue out there. Predictive analytics can tell you when things are about to change. Machine learning can understand your customers on a personal, granular level, and big data can keep track of every metric for accurate reporting.

However, one of the clearest benefits of AI is the direct impact it can have on conversions. It eliminates the guesswork from improving the CX of webpages and delivers timely and accurate testing results needed to increase the likeliness of conversions. Big data systems and AI make hyper-personalization possible to customize the experience for each visitor. Finally, chatbots can use ML to instantly engage with customers, resolve issues immediately, and close sales.

Success all boils down to how a business makes the customer feel. Most of the time, this is what determines whether or not a customer will purchase. Studies have found, unsurprisingly, that when customers feel special, important, and satisfied, they are more likely to buy from those brands. AI gives brands the power to do just that.

Why Many Marketing Automation Projects Go South

There are so many ways to mess up data or analytics projects, may they be CDP, Data Lake, Digital Transformation, Marketing Automation, or whatever sounds cool these days. First off, none of these items are simple to develop, or something that you just buy off the shelf.

As a data and analytics consultant, I often get called in when things do not work out as planned or expected. I guess my professional existence is justified by someone else’s problems. If everyone follows the right path from the beginning and everything goes smoothly all of the time, I would not have much to clean up after.

In that sense, maybe my role model should be Mr. Wolf in the movie “Pulp Fiction.” Yeah, that guy who thinks fast and talks fast to help his clients get out of trouble pronto.

So, I get to see all kinds of data, digital, and analytical messes. The keyword in the title of this series “Big Data, Small Data, Clean Data, Messy Data” is definitely not “Big” (as you might have guessed already), but “Messy.” When I enter the scene, I often see lots of bullet holes created by blame games and traces of departed participants of the projects. Then I wonder how things could have gone so badly.

There are so many ways to mess up data or analytics projects, may they be CDP, Data Lake, Digital Transformation, Marketing Automation, or whatever sounds cool these days. First off, none of these items are simple to develop, or something that you just buy off the shelf. Even if you did, someone would have to tweak more than a few buttons to customize the toolset to meet your unique requirements.

What did I say about those merchants of buzzwords? I don’t remember the exact phrase, but I know I wouldn’t have used those words.

Like a veteran cop, I’ve developed some senses to help me figure out what went wrong. So, allow me to share some common traps that many marketing organizations fall into.

No Clear Goal or Blueprint

Surprisingly, a great ,many organizations get into complex data or analytics projects only with vague ideas or wish lists. Imagine building a building without any clear purpose or a blueprint. What is the building for? For whom, and for what purpose? Is it a residential building, an office building, or a commercial property?

Just like a building is not just a simple sum of raw materials, databases aren’t sums of random piles of data, either. But do you know how many times I get to sit in on a meeting where “putting every data source together in one place” is the goal in itself? I admit that would be better than data scattered all over the place, but the goal should be defined much more precisely. How they are going to be used, by whom, for what, through what channel, using what types of toolsets, etc. Otherwise, it just becomes a monster that no one wants to get near.

I’ve even seen so-called data-oriented companies going out of business thanks to monstrous data projects. Like any major development project, what you don’t put in is as important as what you put in. In other words, the summary of absolutely everyone’s wish list is no blueprint at all, but the first step toward inevitable demise of the project. The technical person in charge must be business–oriented, and be able to say “no” to some requests, looking 10 steps down the line. Let’s just say that I’ve seen too many projects that hopelessly got stuck, thanks to features that would barely matter in practice (as in “You want what in real-time?!”). Might as well design a car that flies, as well.

No Predetermined Success Metrics

Sometimes, the project goes well, but executives and colleagues still define it as a failure. For instance, a predictive model, no matter how well it is constructed mathematically, cannot single-handedly overcome bad marketing. Even with effective marketing messages, it cannot just keep doubling the performance level indefinitely. Huge jumps in KPI (e.g., doubling the response rate) may be possible for the very first model ever (as it would be, compared to the previous campaigns without any precision targeting), but no one can expect such improvement year after year.

Before a single bite of data is manipulated, project champions must determine the success criteria for the project. In terms of coverage, accuracy, speed of execution, engagement level, revenue improvement (by channel), etc. Yes, it would be hard to sell the idea with lots of disclaimers attached to the proposal, but maybe not starting the project at all would be better than being called a failure after spending lots of precious time and money.

Some goals may be in conflict with each other, too. For instance, response rate is often inversely related to the value of the transaction. So, if the blame game starts, how are you going to defend the predictive model that is designed primarily to drive the response rate, not necessarily the revenue per transaction? Set the clear goals in numeric format, and more importantly, share the disclaimer upfront. Otherwise, “something” would look wrong to someone.

But what if your scary boss wants to boost rate of acquisition, customer value, and loyalty all at the same time, no matter what? Maybe you should look for an exit.

Top-Down Culture

By nature, analytics-oriented companies are flatter and less hierarchical in structure. In such places, data and empirical evidences win the argument, not organizational rank of the speaker. It gets worse when the highest-ranking officer has very little knowledge in data or analytics, in general. In a top-down culture, no one would question that C-level executive in a nice suit. Foremost, the executive wouldn’t question his own gut feelings, as those gut-feelings put him in that position in the first place. How can he possibly be wrong?

Trouble is that the world is rapidly changing around any organization. And monitoring the right data from the right place is the best way to keep informed and take actions preemptively. I haven’t encountered any gut-feeling — including my own — that stood the test of time better than data-based decision-making.

Now sometimes, the top-down culture is a good thing, though. If the organizational goals are clearly set, and if the top executive does not launch blame games and support a big data project (no pun intended here). Then, an indefinite amount of inter-departmental conflicts will be mitigated upfront (as in, “Hey, everyone, we are doing this, alright?).

Conflicts Among Teams — No Buy-in, No Use

But no amount of executive force can eliminate all infighting that easily. Some may say “Yeah, yeah, yeah” in front of the CEO or CMO, but sabotage the whole project behind the scene. In fact, I’ve seen many IT departments get in the way of the noble idea of “Customer-360.”

Why? It could be the data ownership issue, security concerns, or lack of understanding of 1:1 marketing or advanced analytics. Maybe they just want the status quo, or see any external influence on data-related matters as a threat. In any case, imagine the situation where the very people who hold the key to the of source data are NOT cooperating with data or analytics projects for the benefit of other departments. Or worse, maybe you have “seen” such cases, as they are so common.

Another troublesome example would be on the user side. Imagine a situation where sales or marketing personnel do not buy into any new way of doing things, such as using model scores to understand the target better. Maybe they got burned by bad models in the past. Or maybe they just don’t want to change things around, like those old school talent scouts in the movie “Moneyball.” Regardless, no buy-in, no use. So much for that shiny marketing automation project that sucked up seven-figure numbers to develop and deploy.

Every employee puts their prolonged employment status over any dumb or smart project. Do not underestimate the people’s desire to keep their jobs with minimal changes.

Players Haven’t Seen Really Messy Situations Before

As you can see, data or analytics projects are not just about technologies or mathematics. Further, data themselves can be a hindrance. I’ve written many articles about “good” data, but they are indeed quite rare in real life. Data must be accurate, consistent, up-to-date, and applicable in most cases, without an excessive amount of missing values. And keeping them that way is a team sport, not something a lone tech genius can handle.

Unfortunately, most graduates with degrees in computer science or statistics don’t get to see a real bloody mess before they get thrown into a battlefield. In school, problems are nicely defined by the professors, and the test data are always in pristine conditions. But I don’t think I have seen such clean and error-free data since school days, which was indeed a lifetime ago.

Dealing with organizational conflicts, vague instructions, and messy data is the part of the job of any data professional. It requires quite a balancing act to provide “the least wrong answers” consistently to all constituents who have vastly different interests. If the balance is even slightly off, you may end up with a technically sound solution that no one adopts into their practices. Forget about full automation of anything in that situation.

Already Spent Money on Wrong Things

This one is a heart-breaker for me, personally. I get onto the scene, examine the case, and provide step-by-step solutions to get to the goal, only to find out that the client company spent money on the wrong things already and has no budget left to remedy the situation. We play with data to make money, but playing with data and technology costs money, too.

There are so many snake oil salespeople out there, over-promising left and right with lots of sweet-to-the-ears buzzwords. Yeah, if you buy this marketing automation toolset armed with state-of-the-art machine-learning features, you will get actionable insights out of any kind of data in any form through any channel. Sounds too good to be true?

Marketing automation is really about the “combination” of data, analytics, digital content, and display technologies (for targeted messaging). It is not just one thing, and there is no silver bullet. Even if some other companies may have found one, will it be applicable to your unique situation, as is? I highly doubt it.

The Last Word on How to Do Marketing Automation Right

There are so many reasons why marketing automation projects go south (though I don’t understand why going “south” is a bad thing). But one thing is for sure. Marketing automation — or any data-related project — is not something that one or two zealots in an organization can achieve single-handedly with some magic toolset. It requires organizational commitment to get it done, get it utilized, and get improved over time. Without understanding what it should be about, you will end up automating the wrong things. And you definitely don’t want to get to the wrong answer any faster.

Industry Q&A: What’s Up With B2B Marketing in Argentina?

I was teaching B2B digital marketing to master’s degree students at San Andres University in Buenos Aires again this summer. The students were pretty enthusiastic about the concepts and tactics I shared. So, I decided to look into what’s going on in B2B marketing in Argentina, overall.

I was teaching B2B digital marketing to master’s degree students at San Andres University in Buenos Aires again this summer. The students were pretty enthusiastic about the concepts and tactics I shared. So, I decided to look into what’s going on in B2B marketing in Argentina, overall.

Martin J. Frias
Martin J. Frias

Thanks to an introduction from the longtime agency pro and university instructor Freddy Rosales, I had the chance to meet Martin J. Frias, who filled me in. Here’s what I learned.

Ruth Stevens: How do B2B marketers in Argentina approach new customer prospecting these days?

Martin Frias: Twenty years ago, we would buy databases from trade publications and data vendors, and use them to cold call, trying to reach senior executives. The problem in those days was getting past the gatekeepers.

Stevens: What has changed since then?

Frias: Three things. First, technology. Buyers now have anonymous access to product information. Sellers — even smaller brands — are using marketing automation like InfusionSoft, Marketo, and a local provider called Doppler emBlue to conduct event-triggered campaigns. Second, inbound marketing, meaning content posted by sellers on LinkedIn and blogs. Third, a larger role for marketing, as active members of inside sales and lead qualification teams. But I must tell you, not all firms are moving toward this kind of modern marketing. Most are still doing the same old push email and events.

Stevens: So, what’s still missing?

Frias: A shared vision between sales and marketing about the entire demand generation and sales process. The two sides need to agree on what is a lead, how to define qualification, and identify the tools needed to operate — from marketing automation, to CRM, to ERP. In short, sales and marketing need to take joint responsibility for guiding the buying process.

Stevens: I am hearing that WhatsApp is a favorite tool here. Please explain.

Frias: Yes, WhatsApp offers an enterprise network tool that integrates with marketing automation, so you can manage omnichannel messaging via WhatsApp, Facebook, Instagram, and others. But you have to be careful. It can backfire. Many business buyers consider WhatsApp an exclusively personal medium, and they resent receiving business communications through it. Also, I think businesses may worry that their targeted communications could fall into the hands of competitors, thanks to WhatsApp’s extraordinary ease of sharing.

Stevens: Are there any prospecting data sources available now?

Frias: You can buy data, or you can buy access. For example, there’s an IT community platform here with a half a million subscribers. Marketers generally don’t trust the databases that are for sale. At my agency, we use LinkedIn Sales Navigator; whereby, we can contact 5 million Argentinean professionals, mostly those in middle management. We use LinkedIn’s Social Selling Index, company size, industry, and title for segmentation, and we attract the targets with content.

Stevens: Is there a professional association for B2B marketers in Argentina?

Frias: No. I wish there were. There is a post-grad program in B2B marketing offered at ITBA, one of our leading engineering schools. The tech industry is really the leader in B2B marketing here. Other key industries, like oil and gas, manufacturing, and construction, are more interested in brand positioning and awareness, and less about lead generation. So, they focus on their websites, value propositions, sales collateral, trade shows, and business events — like golf outings, and sponsoring sports events. They’re not using content, marketing automation, and lead management.

Stevens: Please tell me about yourself and how you became active in B2B marketing here in Argentina.

Frias: I started at Oracle Hyperion, heading a lead generation team in the financial services area. Then I worked at several other firms. Now, I have a 15-person agency called Pragmativa. We offer full B2B demand generation services, including website design, search marketing, display advertising, content, social media, and marketing automation. So, we’ll run a client’s prospecting, and manage their data. The one thing we don’t do, because I don’t believe in it, is cold-call telemarketing. Despite frequent requests from clients.

Stevens: Anything else you’d like to share?

Frias: Yes, I have a B2B marketing blog, in Spanish, and welcome followers.

 

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

WWTT? Adidas Social Media Campaign Generates Offensive Tweets

On July 1, as part of its #DaretoCreate campaign, Adidas UK promoted the new home kit for Premier League team Arsenal on Twitter. But sadly it didn’t go as planned, thanks to racist, anti-Semitic, and classless Internet trolls.

On July 1, as part of its #DaretoCreate social media campaign, Adidas UK promoted the new home kit for Premier League team Arsenal on Twitter. But sadly it didn’t go as planned, thanks to racist, anti-Semitic, and classless Internet trolls. The basis of of the social media campaign to hype up the new kit was simple: When Twitter users liked a tweet (now-deleted) from @adidasUK, the account would share an AI-automated tweet with the message “This is home. Welcome to the squad.” along with an image of the new Arsenal jersey and a link where they could purchase it. On the jersey, where players’ names are displayed, would be the individual’s Twitter handle.

And this is where it falls apart. Some handles were racist, anti-Semitic, referenced the 96 Liverpool Football Club fans that were crushed to death at a match in 1989, and more.

https://twitter.com/ZachAJacobson/status/1145883221994831872

The Adidas UK Twitter account deleted the original and all offensive tweets, and Twitter has tracked down the accounts and suspended them. But the harm is still done.

In regard to the snafu, Adidas made the following statement:

“As part of our partnership launch with Arsenal, we have been made aware of the abuse of a Twitter personalization functionality created to allow excited fans to get their name on the back of the new jersey. Due to a small minority creating offensive versions of this, we have immediately turned off the functionality and the Twitter team will be investigating. We are in contact with Twitter, the innovation provider, to establish the cause and ensure they continue to monitor and action violating content as a matter of urgency.”

A Twitter spokesperson also commented on issue:

“We regret that this functionality has been abused in this way and are taking steps to ensure we protect the health of the interactions with this account. We have already taken action on a number of accounts for violating our policies and will continue to take strong enforcement action against any content that breaks our rules.”

And aside from the wildly offensive nature of these tweets, it’s an utter shame that the excitement of a new home kit has been tarnished a bit for Arsenal, who also shared that they do not condone any of the messages that were shared.

In a tweet from PR expert Andrew Bloch, which has since been deleted (that seems odd), Bloch writes:

Adidas’ #DareToCreate campaign provides yet another valuable reminder to brands on why you should never let the internet customise anything.’

And he’s not wrong. The New England Patriots learned that the hard way back in 2014 when their Twitter account automatically retweeted images of custom digital Pats’ jerseys, featuring Twitter handles that in some cases were extremely racist and offensive. And according to Fortune, there have been other mishaps made by Coca-Cola, Nutella, and Walker Crisps.

So yes, perhaps Andrew Bloch nailed it on the head, or perhaps if brands are going to host this kind of social media campaign, automation has to be turned off and a lot of common sense and human review has to be turned on. True, you lose the quick turnaround and have to invest more time and resources … but then you also might avoid such embarrassment.

I’ll be curious to see if this social media snafu damages the relationship between Adidas and Arsenal … but in the meantime, marketers tell me what you think in the comments below!

3 Ways to Better Manage Marketing Automation So the ‘Shiny Object’ Doesn’t Stab You

I presented at the All About Marketing Tech Virtual Conference & Expo on the topic of targeting and automation. One of the themes I hit upon was about how companies are hindering their marketing automation success with needless complexity.

On Thursday, I will be presenting at the All About Marketing Tech Virtual Conference & Expo on the topic of targeting and automation. One of the themes I plan to hit upon is about how companies are hindering their marketing automation success with needless complexity. This topic falls squarely in the “land of shiny objects,” which is a recurring theme in many of my posts.

This theme in my posts and the 1:10 p.m. ET session, “Using Automation + Targeting to Engage and Convert,” focuses on how tempting technology can be to the marketing practitioner and how it can lead to the desire to do too many things — to detrimental effect. However, there are three things you can do to manage automation better.

Step 1 in Marketing Automation

First, make sure you have a customer strategy. If you do not have a solid strategy, then you will be automating a bunch of tactics. Unless these tactics sit under a cohesive strategy, they may work against each other.

For example, a price-focused customer acquisition program may hurt long-term brand development or pricing power. When you add automation to this scenario, it will supercharge the tactic and potentially cause greater harm.

Step 2

Second, make sure you have a test-and-learn agenda. Automation is a very data and metrics-driven process and it is managed by humans, using those same data points and metrics.

Successful marketing automation involves iterative learning to drive growth. Therefore, knowing what you are trying to achieve through automation and running multiple tests to better understand the underlying dynamics is critical.

What tends to happen, however, is that too many objectives are pushed through the automation system and the ability to learn is muddled by an excess of data and a dearth of focus.

The advice I often give is:

“Because you can do something through automation, it does not mean you should.”

Creating a learning agenda you can manage and identifying the critical metrics needed for evaluation are critical first steps before automating a marketing function.

Step 3

Third, make sure you have a pivot plan. A pivot plan anticipates how you will modify your automation program and lists the levers at your disposal.

For example, if results are not coming in as expected, you may alternate content, alternate segments or redefine the automation goals.

Doing all three at once will most likely leave you as clueless as when you began. While this seems like marketing management 101, it is easy to lose sight of this with automation. Automation generally promises rapid decision-making over volumes of interactions and self-learning capabilities.

As a result, it is tempting to get out of the way and let it do its magic. In the near to mid-term, despite automation’s usefulness, this will not substitute for strategic and management thinking.

Conclusion

I am in no way discouraging the use of marketing automation. It is not only the future, but it is also the present and is driving positive results.

Successful marketers need to start experimenting with the technology now.

However, marketing automation is also not so wonderous and awe-inspiring that we forget that it needs management and strategy. That, in turn, means balancing lofty automation goals with what you can managerially digest.

Automation — With a Little Help From Good Machines

Some claim that human behaviors are just algorithmic responses developed over past 70,000 years or so. Now, armed with data that we are casually scattering around, machine-based algorithms outperform human brains in most areas already, and such evolution will continue.

We should be mindful when dropping buzzwords (refer to “Why Buzzwords Suck”). As more and more people jump on the bandwagon of a buzzword, it tends to gain magical power. Eventually, some may even believe that buying into a “word” will solve all their problems.

But does it ever work out that way? Did anyone make a fortune buying into the Big Data hype yet? I know some companies did; but, ironically, the winners do not even utter such words. I’ve never seen any news release from Google or Amazon that they are investing in “Big Data.” For them, playing with large amounts of data have been just part of their businesses all along.

Now the new buzz is about AI, machine learning and automation, in general; and it will be a little different from buzzwords from the past. Whether we like it or not, that is the direction that we are already headed in the world where each decision will be increasingly more dependent on deterministic algorithms.

Some even claim that human behaviors are just algorithmic responses developed over past 70,000 years or so. Now, armed with data that we are casually scattering around, machine-based algorithms outperform human brains in most areas already, and such evolution will continue until most humans will become largely irreverent in terms of economic value, they say. Not that it would happen overnight, but the next generation may look at our archaic way of things the way we look at our ancestors who were without computers.

First, the Marketing Case for AI

If such is our fate, why are contemporary humans so willingly jumping onto this automation bandwagon where machines will make decisions for us? Because they are smarter than average humans? What does “smart” even mean when we are talking about machines? I think people generally mean to say that machines remember details better than us, and calculate a complex series of algorithms faster and more accurately than us.

Some may say that humans with experiences are wiser with visions to see through things that are not seemingly related. But I dare to say that I’ve seen machines from decades ago finding patterns that humans would never find on their own. When machines start learning without our coaching or supervision — the very definition of AI — at a continuously increasing rate, no, we won’t be able to claim that we are wiser than machines, either. In the near future, if not already.

So, before we casually say that AI-based automation is the future of marketing, let’s ask ourselves why we are so eager to give more power to machines. For what purpose?

The answer to that philosophical question in the business world is rather simple; decision-makers are jumping onto the automation bandwagon to save money. Period.

Specifically, by reducing the number of people who perform tasks that machines can do. As a bonus, AI saves time by performing the tasks faster than ever. In some cases — mostly, for small operations — machines will perform duties that have been neglected due to high labor costs, but even in such situations, automation will not be considered a job-creating force.

Making the Marketing Case for Humans Using Data

Some may ask why I am stating the obvious here. My intention here is to emphasize that automation, all by itself, doesn’t have the magic power to reveal new secrets, as the technology is primarily a replacement option for human labor. If the result of machine-based analytics look new to you, it’s because humans in your organization never looked at the data the same way before, not because it was an impossible task to begin with. And that is a good thing as, in that case, we may be talking about using machine power to do the things that you never had human resources for. But in most cases, automation is about automating things that people know how to do already in the name of time and cost savings.

Like any other data or analytics endeavors, we must embark on marketing automation projects with clear purposes. What would be the expected outcome? What are you trying to achieve? For what types of tasks? What parts of the process are we automating? In what sequence?

Just remember that anyone who would say “just automate everything” is the type of person who would be replaced by machines first.

At the end of that automation rainbow, there lie far less people employed for given tasks, and only the logical ones who see through the machines would remain relevant in the new world.

Nonetheless, providing purposes for machines is still a uniquely human function, for now. And project goals would look like those of any other tasks, if we come back to the world of marketing here. Examples are:

  • Consolidate unorganized free-form data into intelligent information — for further analyses, or for “more” automation of related tasks. For instance, there are thousands of reasons why consumers call customer service lines. Machines can categorically sort those inquiries out, so that finding proper answers to them — the very next logical step — can also be automated. Or, at least make the job easier for the operator on the call (for now). Deciphering image files would be another example, as there has been no serious effort to classify them with sheer manpower in a large scale. But then again, is it really impossible for humans to classify large numbers of images? How about crowdsourcing? Or let an authoritarian government force a stadium-full of North Koreans to do it manually? We’d use machines, because it would be just cheaper and faster to do it with machine learning. But who do you think corrected wrong categorization done by machines to make them better?
  • Find the next, best product for the buyer. This one is quite a popular task for machines, but even a simple “If you bought this, you would like that, too” type of product recommendation would work far better if input data (i.e., product descriptions and product categories) were well-organized — by machines. Machines work better in steps, too.
  • Predict responsiveness to channel promotions and future value of a customer. These are age-old tasks for analytics teams, but with sets of usable data, machines can update algorithms and apply scores, real-time, as new information enters the system. Call that AI, if algorithms are updated automatically, all on its own. Actually, this would be easier for a machine to pick up than fixing messy data. Not that they will know the difference between easy and difficult, but I’m talking about in terms of ease of delegation, from our point of view.
  • Then ultimately, personalize every interaction with every customer through every touch channel. I guess that would be the new frontier for marketers, as approaching personalization on such massive scale can’t be done without some help from good machines. But I still stand by my argument that each component of personalization efforts is something that we know how to do (refer to “Key Elements of Complete Personalization”). By performing each step much faster with machines, though, we can soon reach that ultimate level of personalization through consolidation of services and tasks. And the grand design of such a process will be set up by humans — at least initially.

This Human’s Final Thoughts on AI

These are just some examples in marketing.

If we dive into the operational side, there will be an even richer list of candidates for automation.

In any case, how do marketers stay a step ahead of machines, and remain commanders of them?

Ironically, we must be as logical as a Vulcan to control them effectively. Machines do not understand illogical commands, and will ignore them without any prejudice (but it would “feel” like disrespect to us).

Teaching Humans to Automate

I heard that some overzealous parents started teaching computer programming to 4- or 5-year old children, in addition to a foreign language and piano lessons. That sounds all Cool and the Gang to me, but I wondered how they would teach such young kids how to code.

Obviously, they wouldn’t teach them JavaScript or Python from Day 1. Instead, they first teach the kids how to break down simple tasks into smaller steps. For example, if I ask you to make a grilled cheese sandwich, you — as a human being — will go at it with minimal instruction. Try to order an imaginary machine to do the same. For the machine’s sake, it won’t even know what a grilled cheese sandwich is, or understand why carbon-based lifeforms (especially gluttonous humans) must consume such large quantities of organic materials on a regular basis.

Teaching Machines to Human

If you try it, you will find that the task of writing a spec for a machine is surprisingly tedious.

Just for a little grilled cheese sandwich, you have to:

human automation, the grilled cheese story
Photo by: Christoher Del Rosario (www.christopherdelrosario.com) | Credit: Getty Images by Christopher Del Rosario / EyeEm
  • instruct it on how to get to the breadbox,
  • how to open it,
  • how many slices of bread should be taken out,
  • how to take them out without flattening them (applying the right amount of pressure),
  • how to open the refrigerator,
  • how to locate butter and cheese in the mix of many food items,
  • how to peel off two slices of cheese without tearing them,
  • how to ignite a stove burner,
  • how to find a suitable pan (try to explain “suitable,” in terms measurements and shape),
  • how to preheat the pan to a designated temperature (who’d design and develop the heat censor?),
  • how to melt butter on the pan without burning it,
  • how to constantly measure and monitor the temperature,
  • how to judge the right degree of “brown” color of grilled cheese,
  • etc. etc..

If you feel sick reading all of this, well, I didn’t even get to the part about serving the damn sandwich on a nice plate yet.

Anyway, Human Marketers, Here’s the Conclusion

I am not at all saying that all decision-makers must be coders. What I am trying to emphasize is the importance of breaking down a large task into smaller “logical” steps. Smart machines will not need all of these details to perform “known” tasks (i.e., someone else taught it already). And that is how they get smarter. But they would still work better in clear logical steps.

For humans to command machines effectively, we must think like machines — at least a little bit. Yes, automation is mostly about automating things we already know how to do. We use machines to perform those tasks much faster than humans. To achieve overall organizational effectiveness, break down the processes into smaller bits, where each step becomes the stepping stones for the next. Then prioritize which part would be the best candidate for automation, and which part would still be best served by human brains and hands.

For now, that would be the fastest route to full automation. As a result of it, many humans may be demoted to jobs like reading machine-made scripts to other humans on the phone, or delivering items that machines picked for human consumers in the name of personalization. If that is the direction where human collectives are headed, let’s try to be the ones who provide purposes for machines. Until they don’t even need such instructions from us anymore.