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.

Understanding What a Customer Data Platform Needs to Be

Marketers try to achieve holistic personalization through all conceivable channels in order to stand out among countless messages hitting targeted individuals every day, if not every hour. If the message is not clearly about the target recipient, it will be quickly dismissed. So, how can marketers achieve such an advanced level of personalization?

Modern-day marketers try to achieve holistic personalization through all conceivable channels in order to stand out among countless marketing messages hitting targeted individuals every day, if not every hour. If the message is not clearly about the target recipient, it will be quickly dismissed.

So, how can marketers achieve such an advanced level of personalization? First, we have to figure out who each target individual is, which requires data collection: What they clicked, rejected, browsed, purchased, returned, repeated, recommended, look like, complained about, etc.  Pretty much every breath they take, every move they make (without being creepy). Let’s say that you achieved that level of data collection. Will it be enough?

Enter “Customer-360,” or “360-degree View of a Customer,” or “Customer-Centric Portrait,” or “Single View of a Customer.” You get the idea. Collected data must be consolidated around each individual to get a glimpse — never the whole picture — of who the targeted individual is.

You may say, “That’s cool, we just procured technology (or a vendor) that does all that.” Considering there is no CRM database or CDP (Customer Data Platform) company that does not say one of the terms I listed above, buyers of technology often buy into the marketing pitch.

Unfortunately,the 360-degree view of a customer is just a good start in this game, and a prerequisite. Not the end goal of any marketing effort. The goal of any data project should never be just putting all available data in one place. It must support great many complex and laborious functions during the course of planning, analysis, modeling, targeting, messaging, campaigning, and attribution.

So, for the interest of marketers, allow me to share the essentials of what a CDP needs to be and do, and what the common elements of useful marketing databases are.

A CDP Must Cover Omnichannel Sources

By definition, a CDP must support all touchpoints in an omnichannel marketing environment. No modern consumer lingers around just in one channel. The holistic view cannot be achieved by just looking at their past transaction history, either (even though the past purchase behavior still remains the most powerful predictor of future behavior).

Nor do marketers have time to wait until someone buys something through a particular channel for them to take actions. All movements and indicators — as much as possible — through every conceivable channel should be included in a CDP.

Yes, some data evaporates faster than others — such as browsing history — but we are talking about a game of inches here.  Besides, data atrophy can be delayed with proper use of modeling techniques.

Beware of vendors who want to stay in their comfort zone in terms of channels. No buyer is just an online or an offline person.

Data Must Be Connected on an Individual Level

Since buyers go through all kinds of online and offline channels during the course of their journey, collected data must be stitched together to reveal their true nature. Unfortunately, in this channel-centric world, characteristics of collected data are vastly different depending on sources.

Privacy concerns and regulations regarding Personally Identifiable Information (PII) greatly vary among channels. Even if PII is allowed to be collected, there may not be any common match key, such as address, email, phone number, cookie ID, device ID, etc.

There are third-party vendors who specialize in such data weaving work. But remember that no vendor is good with all types of data. You may have to procure different techniques depending on available channel data. I’ve seen cases where great technology companies that specialized in online data were clueless about “soft-match” techniques used by direct marketers for ages.

Remember, without accurate and consistent individual ID system, one cannot even start building a true Customer-360 view.

Data Must Be Clean and Reliable

You may think that I am stating the obvious, but you must assume that most data sources are dirty. There is no pristine dataset without a serious amount of data refinement work. And when I say dirty, I mean that databases are filled with inaccurate, inconsistent, uncategorized, and unstructured data. To be useful, data must be properly corrected, purged, standardized, and categorized.

Even simple time-stamps could be immensely inconsistent. What are date-time formats, and what time zones are they in?  Dollars aren’t just dollars either. What are net price, tax, shipping, discount, coupon, and paid amounts? No, the breakdown doesn’t have to be as precise as for an accounting system, but how would you identify habitual discount seekers without dissecting the data up front?

When it comes to free-form data, things get even more complicated. Let’s just say that most non-numeric data are not that useful without proper categorization, through strict rules along with text mining. And such work should all be done up front. If you don’t, you are simply deferring more tedious work to poor analysts, or worse, to the end-users.

Beware of vendors who think that loading the raw data onto some table is good enough. It never is, unless the goal is to hoard data.

Data Must Be Up-to-Date

“Real-time update” is one of the most abused word in this business. And I don’t casually recommend it, unless decisions must be made in real-time. Why? Because, generally speaking, more frequent updates mean higher maintenance cost.

Nevertheless, real-time update is a must, if we are getting into fully automated real-time personalization. It is entirely possible to rely on trigger data for reactive personalization outside the realm of CDP environment,  but such patch work will lead to regrets most of the time. For one, how would you figure out what elements really worked?

Even if a database is not updated in real-time, most source data must remain as fresh as they can be. For instance, it is generally not recommended to append third-party demographic data real-time (except for “hot-line” data, of course). But that doesn’t mean that you can just use old data indefinitely.

When it comes to behavioral data, time really is of an essence. Click data must be updated at least daily, if not real-time.  Transaction data may be updated weekly, but don’t go over a month without updating the base, as even simple measurements like “Days since last purchase” can be way off. You all know the importance of good old recency factor in any metrics.

Data Must Be Analytics-Ready

Just because the data in question are clean and error-free, that doesn’t mean that they are ready for advanced analytics. Data must be carefully summarized onto an individual level, in order to convert “event level information” into “descriptors of individuals.”  Presence of summary variables is a good indicator of true Customer-360.

You may have all the click, view, and conversion data, but those are all descriptors of events, not people. For personalization, you need know individual level affinities (you may call them “personas”). For planning and messaging, you may need to group target individuals into segments or cohorts. All those analytics run much faster and more effectively with analytics-ready data.

If not, even simple modeling or clustering work may take a very long time, even with a decent data platform in place. It is routinely quoted that over 80% of analysts’ time go into data preparation work — how about cutting that down to zero?

Most modern toolsets come with some analytics functions, such as KPI dashboards, basic queries, and even segmentation and modeling. However, for advanced level targeting and messaging, built-in tools may not be enough. You must ask how the system would support professional statisticians with data extraction, sampling, and scoring (on the backend). Don’t forget that most analytics work fails before or after the modeling steps. And when any meltdown happens, do not habitually blame the analysts, but dig deeper into the CDP ecosystem.

Also, remember that even automated modeling tools work much better with refined data on a proper level (i.e., Individual level data for individual level modeling).

CDP Must Be Campaign-Ready

For campaign execution, selected data may have to leave the CDP environment. Sometimes data may end up in a totally different system. A CDP must never be the bottleneck in data extraction and exchange. But in many cases, it is.

Beware of technology providers that only allow built-in campaign toolsets for campaign execution. You never know what new channels or technologies will spring up in the future. While at it, check how many different data exchange protocols are supported. Data going out is as important as data coming in.

CDP Must Support Omnichannel Attribution

Speaking of data coming in and out, CDPs must be able to collect campaign result data seamlessly, from all employed channels.  The very definition of “closed-loop” marketing is that we must continuously learn from past endeavors and improve effectiveness of targeting, messaging, and channel usage.

Omnichannel attribution is simply not possible without data coming from all marketing channels. And if you do not finish the backend analyses and attribution, how would you know what really worked?

The sad reality is that a great majority of marketers fly blind, even with a so-called CDP of their own. If I may be harsh here, you are not a database marketer if you are not measuring the results properly. A CDP must make complex backend reporting and attribution easier, not harder.

Final Thoughts

For a database system to be called a CDP, it must satisfy most — if not all — of these requirements. It may be daunting for some to read through this, but doing your homework in advance will make it easier for you in the long run.

And one last thing: Do not work with any technology providers that are stingy about custom modifications. Your business is unique, and you will have to tweak some features to satisfy your unique needs. I call that the “last-mile” service. Most data projects that are labeled as failures ended up there due to a lack of custom fitting.

Conversely, what we call “good” service providers are the ones who are really good at that last-mile service. Unless you are comfortable with one-size-fits-all pre-made — but cheaper — toolset, always insist on customizable solutions.

You didn’t think that this whole omnichannel marketing was that simple, did you?

 

Don’t Blame Personalization After Messing It Up

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

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

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

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

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

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

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

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

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

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

Most Personalization Efforts Are Reactive

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

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

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

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

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

Personalization Tactics Are Often Done Sporadically, Not Consistently

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

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

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

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

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

But It’s Too Creepy

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

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

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

Too Many Initiatives Are Set on Auto-Pilot

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

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

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

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

Important Factors Are Ignored

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

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

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

So What Now?

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

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

 

The Importance of Always Having a Solid Email Marketing Program

As we all adjust to what may be our new normal, digital marketing becomes ever more vital. Now is not the time to go dark, even if you can’t meet with prospects and partners face-to-face as you normally do. Email marketing should already be a part of your digital arsenal.

As we all adjust to what may be our new normal, digital marketing becomes ever more vital. Now is not the time to go dark, even if you can’t meet with prospects and partners face-to-face as you normally do. A solid email marketing program should always be a part of your digital arsenal, no matter what’s going on in the world.

Email Marketing Keeps You Top of Mind

We’ve all found those prospects who are a perfect fit in every way — except they’re not ready to buy. Sometimes it’s a priorities issue. In other cases, it’s a mismatch between need and budget cycle.

When you find those prospects, stay in touch via email until their need becomes pressing enough to push those other issues aside. Nothing beats email when it comes to drip marketing.

Email Messages Are Easy to Personalize

No, we’re not talking about “Dear [your name here],” though that certainly is one type of personalization. We’re talking about a more meaningful way to connect with your audience by tailoring email to their interests. These can be self-identified or based on past behavior. You can do this on your website, too, though doing it with your email marketing is usually a little easier to wrangle. Getting your email and your website to work together this way is even better. Which is an excellent segue to our next point.

Email Is the Great Connector

Email doesn’t just connect you to your target audience. It connects various pieces of your marketing tool kit. Email can introduce prospects to your social media presence and vice versa, allowing you to meet them where they already are. Well-executed email marketing efforts can drive traffic to your website, which is likely where the conversion from possibility to prospect occurs. In both cases email is improving not only your reach but your engagement.

You Own Email

Social media channels can fall out of favor in the blink of an eye. That doesn’t mean we shouldn’t invest meaningfully in those platforms that work best with our audience. But those “borrowed” platforms should not be more than a part of our overall strategy. Owning email marketing means never having to worry about what social network will fizzle or when the next search engine algorithm update might upend years of SEO gains. (Well, we still have to worry about these things, but we don’t have to worry about them being catastrophic to our marketing.)

The key to all of this email magic is relevance. No surprise there. That’s the key to all marketing today, traditional or digital. If you want to reap the benefits of email marketing’s power, don’t show up in someone’s inbox just to show up. Have something relevant to say that they want to hear.

 

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

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

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

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

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

What Exactly Do Consumers Expect?

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

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

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

Brands Are Doing the Same Thing with More Resources

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

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

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

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

Personalization Is the Crucible of Privacy Chaos

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

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

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

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

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

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

Empower Your 2020 Political Direct Mail With These Tips

It’s that time again for political direct mail planning. Are you the one planning to win your election? Did you know that a USPS commissioned survey in 2018 found that 68% of voters believe direct mail to be the most credible source of political outreach?

It’s that time again for political direct mail planning. Are you the one planning to win your election? Did you know that a USPS commissioned survey in 2018 found that 68% of voters believe direct mail to be the most credible source of political outreach? (Opens as a PDF) You need to build a strategy that raises awareness, builds a following, and motivates voters. What is the best way to do that? Using a combination of direct mail, social media, Google ads, and YouTube ads to engage voters both offline and online will enhance your results.

Because 73% of Americans prefer the first contact to be by mail, you need to be in the mailbox before early voting ballots go out. Are you prepared with a realistic timeline? You should also know that 55% of voters use mail to decide how to vote. If you are not in the mailbox you are missing out on a huge opportunity. Yes, direct mail is expensive, but it more than pays for itself with big ROI.

So what should you include in your mail piece?

  • Stance on important issues
  • Contrast with an opponent
  • A list of endorsements
  • Important voting information, such as deadlines
  • A picture
  • Color
  • Personalization

You may think that the best way to win is to mail to every registered voter. But really, your best bet is to mail to only active voters. These are the people who will mail in ballots or show up at polling stations. You need to convince them to vote for you: Do not waste your money on the others. What size mailer should you send to them? Use a large piece, such as an 9 x 12, because oversized pieces have been shown to increase response rates by 10.4%. They really stand out in the mailbox.

As you are designing and writing copy, keep in mind that your text should be concise and easily scanned. The best designs use bolding, italics, color, and contrast to draw the eye to important content. The easier you make it for people to quickly understand what you are saying, the more effective your mail piece will be. Direct mail is better understood, remembered, and acted upon when you use best practices. After you design a piece, send a PDF to your mail service provider to review for potential postal regulation issues before you print. You do not want to waste money on postal penalties.

Remember, unlike a business that sells products or services, which has the ability to sell them over a long period of time, political mail needs to convince people quickly to either support or not to support a candidate or a proposition. You can also add texture to your mailers to give people a reason to hold your mail piece longer. A very popular one is the soft touch coating, which feels like velvet. People can’t help but pet the paper. Lastly, make sure that you use personalization on your mail pieces. It makes people feel more important and makes your message more personal to them. Are you ready to get started?

‘Crassmas’ Messages Show the Strengths of Snail Mail, the Weaknesses of Poor Digital Personalization

Even if the old-fashioned way of choosing, inscribing, and snail mail posting greeting cards has given way to “eCards,” the good intention is the same. It’s a reminder that someone is actually thinking of you. Which is why I was annoyed when I recently received cards from friends sent using the Jacquie Lawson platform.

Seasonal greeting cards are many things to both senders and recipients.

Starting at the top, they can be very personal communications of greetings, reminders of friendships often left to lapse during our busy year. At the bottom, they can be nothing more than purely commercial direct mail — with a bough of holly or a reindeer to give them a seasonal scent.

Either way, they are big business (estimated at 6% of the $7.5 billion greeting card market).

And even if the old-fashioned way of choosing, inscribing, and snail mail-posting them has to a great extent given way to “eCards,” the good intention is the same: If absence makes the heart grow fonder, the reminder that someone is actually thinking of you and expending time, effort, and money to send a greeting should be at least heartwarming, even if the non-digital examples have become somewhat anti-environmental.

Which is why, despite this un-Christmas like critique, I became really annoyed when I recently received cards from friends sent using the Jacquie Lawson platform. However brilliant the superb graphics (and they are truly beautiful) the gross commercialism of the accompanying messages totally detracted from the personal richness of the senders’ intent.

The notice in my inbox was straightforward enough. It said that my named friend had sent me an ecard. The “Correspondent” was simply, “Jacquie Lawson ecards,” a name I may or may not have known. And when, for no good reason, I had not opened the original missive, the day after Christmas I received a reminder. (Identification of the generous sender in the illustrations has been surpressed: what might her husband say?)

personalization absent
Credit: Peter J. Rosenwald

What Bothered Me?

These notices, instead of keeping the focus on my friend’s message to me and the hope that it would be something pleasurable, instead were Jacquie Lawson branding-dominant. Using the next-to-last paragraph of the reminder, right after “You can view your card here” to invite the reader to “learn more about us here” may be someone’s idea of a good promotional ploy. But to me, it was a rather good example of turning Christmas into “crassmas.” Can you imagine receiving a seasonal gift with a promotional message in the box?

Lest we have missed the Jacquie Lawson come-ons and just enjoyed the animated card, after the greetings message from the sender, at the bottom of the card this line with its links reminds us not of our friend’s greeting but of, you guessed it, Jacquie Lawson.

personalization absent, branding present
Credit: Peter J. Rosenwald

Perhaps this is a singular example, but there has been a growing tendency this past year for marketers to forget that “personalization” — the heart of truly successful targeted marketing — needs to stay focused not on the super technologies that make personalization and the accompanying graphics possible, but rather on not letting anything get in the way of truly personal interactions.

Sure, Jacquie Lawson has every right to promote the beautiful work done by her team and, no doubt, I’ll be receiving plentiful invitations to know more about it and purchase new designs from the company.  That’s the business we are in.

But in this New Year, let’s not let our desire for growth and profits outweigh the personalization sensitivities of our messages

Direct Mail Planning for Your 2020 Marketing Goals

As we start the new year, direct mail planning is essential. The strategies we used in 2019 need updates to be more effective in 2020. According to the DMA, direct mail had an average response rate of 9% for house lists and 4.9% for prospect lists. How do your response rates compare?

As we start the new year, direct mail planning is essential. The strategies we used in 2019 need updates to be more effective in 2020. According to the DMA, direct mail had an average response rate of 9% for house lists and 4.9% for prospect lists. How do your response rates compare?

Looking at more than just response rates, how did your other metrics do this year? Starting with the worst performing ones, devise a different strategy to increase performance in 2020.

About 66% of mail is opened and reviewed. Direct mail not only cuts through the daily marketing clutter, but has been proven to drive digital activity and influence online purchasing decisions. Are your direct mail campaigns as effective as they can be? In the digital marketing arena email fatigue, ad blindness, and the increase in ad blocking, have combined to result in stagnating and sometimes declining performance. Direct mail should be combined with these types of channels in order to boost overall performance. Are you taking a multichannel approach for 2020?

Direct Mail-to-Multichannel Marketing

We live in an interconnected world, your customers expect you to communicate with them through the various channels they use. The companies that do this effectively see the best results. When you use campaigns that include both digital and direct mail, you, on average, receive 39% more attention than a digital-only campaign. Research shows messages delivered via direct mail can be powerfully motivating, with 92% of people driven to digital activity and 87% influenced to make an online purchase. Are you planning how to be more effective at this for 2020?

Personalization

Are you using the true power of direct mail? Personalization through variable printing is powerful. You can alter copy, offers, and even images, based on each person in your list. There are even ways to utilize more information, such as demographics, geographies, psychographics, and behavioristic data to go beyond a regular piece to a truly specialized one. When you are able to do this, you drive response much higher than before. What are you going to try in 2020?

Retargeting

Want to try something new for 2020? What about retargeted direct mail? What do we mean by that? You can retarget online activity by reaching out with direct mail. Some of the most common ways retargeting works are: for abandoned shopping carts; people who visited your website, but did not purchase; contacting lapsed customers; or creating new customers. So how do you get the mail addresses for online contacts? You can take a list of email addresses and append mail ones, or you can take a list of IP addresses that have visited your site and append mail addresses to it, or you can use mobile devise owner information to append mail addresses.

Mail Formats

For 2020, we need to think outside of the box and try new things. If all you have sent are letters or postcards, try a new format to gain more attention. Consider sending larger pieces, because they get higher response rates. If you are selling a high-priced item, consider using dimensional mail in order to drive more sales. These are more costly to send, but the ROI is much better.

Get creative and have some fun planning out your 2020 direct mail strategy! Are you ready to get started?

The Secrets Behind 3 Great Optichannel Experiences

How can any business build a positive brand relationship with its consumers? The only way to do that in 2020 is to create awesome optichannel customer experiences. People don’t remember your marketing; they remember how it feels to do business with you.

In 2020, every consumer will be interacting with marketing content across a thousand channels all the time — by some estimates, they already see as many as 5,000 ads each day. It’s a cacophony of impersonal, untargeted media that barely makes an impact. But if everyone is bombarded by marketing media constantly, how can any business build a positive brand relationship with its consumers? The only way to do that in 2020 is to create awesome optichannel customer experiences. People don’t remember your marketing; they remember how it feels to do business with you. And the optichannel experience is what leaves them with a positive or negative feeling.

Here are three companies that have made a science of optichannel customer experiences, and what your brand can learn from them.

Leverage Identity Like Neiman Marcus

Customer identity crosses into the retail-online threshold, but not enough brands use it to improve the customer experience. Neiman Marcus does.

It starts as soon as customers enter the store. Interactive directories and “Memory Mirror” smart monitors allow them to have a digitally enhanced fitting room experience. Meanwhile, the retailer’s app enables users to take pictures of outfits in the real world and then use augmented reality to match them with similar looks from its catalog. This comes together to create an award-winning omnichannel retail experience that empowers consumers and removes barriers along the buying journey.

Neiman Marcus also leverages that information to personalize the e-commerce, email and direct mail experience of every customer. “Identity is the core of personalization,” says VP of Customer Insight and Analytics Jeff Rosenfield, “and if you don’t get it right, you’re not talking to the entirety of that customer.”

The retailer put these ideas into practice with several CX features. For example, when you search for specific sizes on the Neiman Marcus website, your visits will start using those sizes by default. Email and printed direct mail pieces then feature items you looked at, and sales offers are tied to your user data.

What Makes Neiman Marcus’s Optichannel Strategy Successful

Identifying visitors and targeting them with optichannel marketing across social networks, online ads, direct mail, and email is within every business’s reach. You just need to dive into the data to make it happen.

The first step is to resolve customer identity. Ideally, you should have a way for them to log-in to the website and a good incentive for them to stay logged in. Loyalty programs and member discounts are great ways to do this. The insights you glean from logged-in user sessions should be collected and used to optimize your overarching strategy as well as that individual’s user experience.

Cookies and user session data will allow you to note where they went and what they did on your website. Even in a retail store, you can still note what customers bought or what they asked your salespeople about and add it to a customer profile. When that customer interacted with your brand, what did they do? Did they focus on one product category? One set of sizes? Are they moved by certain discounts or occasions?

Identifying these kinds of user behaviors and supplementing them with demographic data creates a predictive-marketing tool you can use to improve your campaigns. Follow-up emails can feature products in their favorite categories and discounts on the things they looked at most. Instead of sending the same mail piece to every address on your file, you can use customer segmentation based on demographics and behavior to create targeted mailings for each segment that specifically leverage their buying factors.

These tactics are viable in industries with more complex sales cycles than retail, too.

Bring the Magic to Life Like Disney World

Walt Disney World gives its customers the automated equivalent of white-glove concierge service across every touchpoint of the optichannel journey. Families move from booking on a mobile-responsive website to planning trip details on the My Disney Experience app to a next-generation resort stay powered by “magic band” technology. The magic bands use NFC tech to act as tickets, wallets, line-cut free passes and more.

Each step is personal and empowering. Disney recognizes its customers from the first touch to the last and uses everything it knows to deliver an ultra-convenient vacation experience. The resort truly creates optichannel magic by empowering its customers across every channel.

What Makes Disney World’s Optichannel Strategy Successful

You may not be able to give every customer a piece of technology as cool as magic bands, but you can connect the dots of their activities across channels and use the data to deliver white-glove concierge experiences of your own.

Try to remove as much frustration from the buyer’s journey as possible. Whether a “visit” happens on a website, phone or face-to-face, try to capture where they came from and what they did. Use that data to identify what they want and to make every future experience with your brand easier and more magical.

What that looks like will vary by brand, but the key is to understand the customer journey and smooth out the steps that cause friction. Is it hard for customers to find items they looked at previously? Try to bring them back up if they revisit the site, or perhaps promote them via targeted web and social media ads. Can you position follow-up emails so they speak to the products they looked at and remove buying obstacles? Can you identify special offers based on user behavior that will make it easier for them to say yes? Are there come customer behaviors that indicate a sales phone call would be welcome?

Make Local Personal Like MB Financial

There are more than 430,000 small businesses in Chicago, where MB Financial had 86 local branches. However, MB was not connecting with any of those businesses. To these prospects, the bank was just another old, faceless institution. So it set out to put the real managers from those branches on its  “MB Is Me” optichannel campaign to create personal connections and generate leads.

The campaign ran print, radio and digital media ads throughout the area featuring four messages: MB Financial delivers the personal attention you want, the banking services you need, business advice you can use, and business connections you wouldn’t expect.

Those ads set the stage, but the real conversion piece was a localized direct mail campaign that featured the local branch managers talking directly to the small business owners they served. Using customer propensity models — like response lift modeling — the bank identified 30,000 small businesses that were likely prospects and sent postcards to each of those businesses from the manager of the closest branch.

The postcards were versioned for each branch’s business area. They featured professional photos of the branch manager, a personal message, and an invitation to call their direct phone numbers. There was also an offer to get up to $550 in bonus cash for opening an account and/or line of credit.

The optichannel campaign built trust in MB Financial’s commitment to small business banking needs, and the direct mail piece converted a 205% increase in sales leads.

What Makes MB Financial’s Optichannel Strategy Successful

This is the only campaign we’ve discussed that specifically focuses on lead generation and customer acquisition, but it shows the power of optichannel experiences in generating qualified leads.

By extending optichannel strategies to outbound marketing, MB Financial created personal connections in a faceless marketing environment. Customer modeling, personalized creative and strategic channel execution all work together to form your next customer’s impressions.

Every prospect experiences your brand as an optichannel phenomenon. The campaigns they see shape the reaction they will have to your direct marketing.

MB Financial tied those pieces together. It didn’t need to be personalized to the individual level, just versioned so every prospect business was able to personally connect with and recognize their local branch managers.

From public messaging to targeted engagement to a personal experience: That’s how optichannel marketing continues to change the game.

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.