Models Are Built, But the Job Isn’t Done Yet

In my line of business – data and analytics consulting and coaching – I often recommend some modeling work when confronted with complex targeting challenges. Through this series, I’ve shared many reasons why modeling becomes a necessity in data-rich environments (refer to “Why Model?”).

The history of model-based targeting goes back to the 1960’s, but what is the number one reason to employ modeling techniques these days? We often have too much information, way beyond the cognitive and arithmetical capacities of our brains. Most of us mortals cannot effectively consider more than two or three variables at a time. Conversely, machines don’t have such limitations when it comes to recognizing patterns among countless data variables. Subsequent marketing automation is just an added bonus.

We operate under a basic assumption that model-based targeting (with deep data) should outperform some man-made rules (with a handful of information). At times, however, I get calls as campaign results prove otherwise. Sometimes campaign segments selected by models show worse response rates than randomly selected test groups do.

When such disappointing results happen, most decision makers casually say, “The model did not work.” That may be true, but more often than not, I find that something went wrong “before” or “after” the modeling process. (Refer to “Know What to Automate With Machine Learning”, where I list major steps concerning the “before” of model-based targeting).

If the model is developed in an “analytics-ready” environment where most input errors are eradicated, then here are some common mishaps in post-modeling stages to consider.

Mishap #1: The Model Is Applied to the Wrong Universe

Model algorithm is nothing but a mathematical expression between target and comparison universes. Yes, setting up the right target is the key for success in any modeling, but defining a proper comparison universe is equally important. And the comparison group must represent the campaign universe to which the resultant model is applied.

Sometimes such universes are defined by a series of pre-selection rules before the modeling even begins. For example, the campaign universes may be set by region (or business footprint), gender of the target, availability of email address or digital ID, income level, home ownership, etc. Once set, the rules must be enforced throughout the campaign execution.

What if the rules that define the modeling universe are even slightly different from the actual campaign universe? The project may be doomed from the get-go.

For example, do not expect that models developed within a well-established business footprint will be equally effective in relatively new prospecting areas. Such expansion calls for yet another set of models, as target prospects are indeed in a different world.

If there are multiple distinct segments in the customer base, we often develop separate models within each key segment. Don’t even think about applying a model developed in one specific segment to another, just because they may look similar on the surface. And if you do something like that, don’t blame the modeler later.

Mishap #2: The Model Is Used Outside Design Specification

Even in the same modeling universe, we may develop multiple types of models for different purposes. Some models may be designed to predict future lifetime value of customers, while others are to estimate campaign responsiveness. In this example, customer value and campaign responsiveness may actually be inversely related (e.g., potential high value customers less likely to be responsive to email campaigns).

If multiple response models are built for specific channels, do not use them interchangeably. Each model should be describing distinct channel behaviors, not just general responsiveness to given offers or products.

I’ve seen a case where a cruise ship company used an affinity model specifically designed for a seasonal European line for general purposes in the name of cost savings. The result? It would have been far more cost effective developing another model than having to deal with the fallout from ineffective campaigns. Modeling cost is often a small slice in the whole pie of campaign expenses. Don’t get stingy on analytics and call for help when in doubt.

Mishap #3: There Are Scoring Errors

Applying a model algorithm to a validation sample is relatively simple, as such samples are not really large. Now, try to apply the same algorithm to over 100 million potential targets. You may encounter all kinds of performance issues caused by the sheer volume of data.

Then there are more fundamental errors stemming from the database structure itself. What if the main database structure is different from that of the development sample? That type of discrepancy – which is very common – often leads to disasters.

Always check if anything is different between the development samples and the main database:

  • Database Structure: There are so many types of database platforms, and the way they store simple transaction data may be vastly different. In general, to rank individuals, each data record must be scored on an individual level, not transaction or event levels. It is strongly recommended that data consolidation, summarization, and variable creation be done in an analytics-friendly environment “before” any modeling begins. Structural consistency eliminates many potential errors.
  • Variable List/Names: When you have hundreds, or even thousands of variables in the database, there will be similar sounding names. I’ve seen many different variable names that may represent “Total Individual Dollar Amount Past 12-month,” for example. It is a common mistake to use a wrong data field in the scoring process.
  • Variable Values: Not all similar sounding variables have similar values in them. For example, ever-so-popular “Household Income” may include dollar values in thousand-dollar increments, or pre-coded value that looks like alphabets. What if someone changed the grouping definition of such binned variables? It would be a miracle if the model scores come out correctly.
  • Imputation Assumptions: There are many ways to treat missing values (refer to “Missing Data Can Be Meaningful”). Depending on how they were transformed and stored, even missing values can be predictable in models. If missing values are substituted with imputed values, it is absolutely important to maintain their consistency throughout the process. Mistreatment of missing values is often the main cause for scoring errors.

Mishap #4: Nature of Data Is Significantly Shifted

Data values change over time due to outside factors. For instance, if there is a major shift in the business model (e.g., business moving to a subscription model), or a significant change in data collection methods or vendors, consider that all the previous models are now rendered useless. Models should be predictors of customer behaviors, not reflections of changes in your business.

Mishap #5: Scores Are Tempered After-the-Fact

This one really breaks my heart, but it happens. I once saw a user in a major financial institution unilaterally change the ranges of model decile groups after observing significant fluctuations in model group counts. As you can imagine by now, uneven model group counts are indeed revealing serious inconsistencies caused by any of the factors that I mentioned thus far. You cannot tape over a major wound — just bite the bullet and commission a new model when you see uneven or inconsistent model decile counts.

Mishap #6: There Are Selection Errors

When campaign targets are selected based on model scores, the users must be fully aware of the nature of them. If the score is grouped into model groups 1 through 10, is the ideal target “1” or “10”?

I’ve seen cases where the campaign selection was completely off the mark, as someone sorted the raw score in an ascending order, not a descending order, pushing the worse prospects to the top. But I’ve also seen errors in documentation or judgement, as it can be really confusing to figure out which group is “better.”

I tend to put things in 0-9 scale when designing a series of personas or affinity models to avoid confusion. If score groups range from 0 to 9, the user is much less likely to assume that “zero” is the best score. Without a doubt, reversed score is far worse than not using the model at all.

Final Thoughts

After all, the model algorithm itself can be wrong, too. Not all modelers are equally competent, and machine-learning is only as good as the analyst who originally set it up. Of course, you must turn that stone when investigating bad results. But you should trace all pre- and post-modeling steps, as well. After years of such detective work, my bet is firmly on errors outside the modeling processes, unless the model validation smells fishy.

In any case, do not entirely give up on modeling just because you’ve had a few bad results. There are many things to be checked and tweaked, and model-based targeting is a long series of iterative adjustments. Be mindful that even a mediocre model is still better than someone’s gut feelings, if it is applied to campaigns properly.

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.

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.

Are You Prepared to Handle the Oncoming Martech Consolidation?

For those marketers who rely on marketing technologies while navigating an industry landscape that changes almost daily, here are four considerations to make when adapting to the oncoming martech consolidation.

In previous posts, I have often referred to the vast martech landscape as the land of shiny objects. This was a term of derision and admiration. The landscape is filled with amazing innovations. It also can overwhelm even the most tech-savvy marketers and cloud strategic thinking.

We marketers were often so enthralled by what we could do, we often lose sight of what we should do. Today, as the economic impact of COVID-19 grows, the effect on marketing technology spend will be significant. The martech landscape has been built on billions of speculative investments from private equity. However, most of these products were barely profitable, if at all, before COVID-19. Most of them are now burning significant cash, and they were never capitalized with a pandemic in mind.

Soon, investors will be making hard choices. Many martech solutions will be sold at huge discounts, some will close. I believe the much-anticipated industry consolidation is around the corner. This is not the way we wanted martech consolidation to happen, but this is the painful reality. For those marketers who rely on these technologies while navigating an industry landscape that changes almost daily, here are four considerations to make when adapting to the oncoming martech consolidation:

  1. Hire “The” technology expert. Many martech companies have implantation consultants; the best ones are often held closely and deployed on the most complex projects. This could be your opportunity to hire them. If new hires are not in the budget, perhaps a contracting agreement might work. In either case, if you have invested in the technology, why not invest a bit more for the right talent who will help you get the most out of your investment?
  2. If you are using a niche technology, reach out to your account rep. Find out how they are doing and what their plans are. If you have a good relationship with your rep, they will hopefully share any changes afoot, availability of on-going product support, the possibility of a sale or even closure.
  3. If you need to invest in new technology, look for solution providers with a broad base of active clients. (Notice the word “Active”). In some cases, one or two large clients can support a solution provider just fine. However, if typical license fees are $60,000 per year and the solution provider has a staff of 20 people, a broad base of clients will be critical for survival. (It’s just math.)
  4. The exceptions to No. 3 are cases where the solution provider has recently been acquired by a larger concern, especially post COVID-19. In such cases, someone with deep pockets thought enough of the technology to buy and invest in its survival. Although deep pockets do not always translate into smart money, it is enough of a reason to consider the technology seriously.

Those of us who have been keeping track of the martech universe know that the growth was unsustainable (There were over seven thousand solutions in the market as of 2019). The hope was that the best products would survive and eventually lead to industry consolidation. It seems that COVID-19 will abruptly end the natural evolution of the industry, for the time being. Innovations and investments will return, but exactly when is anyone’s guess.

In the meantime, we need to be kind and helpful to those who will be affected. In doing so, we may benefit from their wisdom, which was often drowned out in the previously noisy clamor of martech.

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.

How to Use Sentiment Analysis to Transform Your Digital Marketing Strategy

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

Sentiment analysis is a fascinating concept.

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

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

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

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

Know the Terms and Phrases That Indicate Intent

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

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

google search console
Credit: Author’s own

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

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

Spot Patterns in Product Reviews

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

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

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

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

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

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

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

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

Look to Social Media for Unabashed (Unfiltered) Opinions

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

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

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

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

talkwalker
Credit: Talkwalker.com

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

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

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

poolfloatz
Credit: Instagram

Use Predictive Analysis to Spot Trends and Automate Actions

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

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

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

applications of AI
Credit: Emerj.com

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

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

The Wrap

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

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

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.

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.