Sponsored Content: Amplero – Search Engine Land News On Search Engines, Search Engine Optimization (SEO) & Search Engine Marketing (SEM) Mon, 19 Feb 2018 22:16:25 +0000 en-US hourly 1 https://wordpress.org/?v=5.6.4 Three ways retailers can deliver meaningful 1:1 shopping experiences amidst the retail apocalypse through AI marketing /5-ways-retailers-delivering-meaningful-11-shopping-experiences-amidst-retail-apocalypse-292360 Tue, 20 Feb 2018 12:30:43 +0000 /?p=292360 If you were paying attention, 2017 might have seemed like the year that industry pundits’ predictions about the death of retail finally came to fruition. The traditional Main Street took some major blows last year. Shopping mall mainstays like Wet Seal, Payless ShoeSource, BCBG Max Azria and Gymboree were among the casualties. Even behemoths like […]

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If you were paying attention, 2017 might have seemed like the year that industry pundits’ predictions about the death of retail finally came to fruition. The traditional Main Street took some major blows last year. Shopping mall mainstays like Wet Seal, Payless ShoeSource, BCBG Max Azria and Gymboree were among the casualties. Even behemoths like Toys R Us weren’t immune — in August 2017, the retail giant filed for Chapter 11 bankruptcy protection and is set to close more than 100 stores in the year ahead. The year before, victims included Aeropostale, Pacific Sunwear, Sports Authority and American Apparel.

But despite these and other signals of a big shift, retail isn’t dead; it’s just easing out of its old skin and taking a new digital form. You don’t need to be a retail insider to know that what’s largely driving this transformation is a sea change in customer expectations and behavior heralded by the combination of mobile purchasing and Amazon.com. The thrill of a visit to the mall and an armful of shopping bags has been replaced by the “Confirm Your Order” button and the promise of a box on every doorstep.

There’s no question the retail landscape is changing, but is there merit to all the doom and gloom? Amazon’s own founder, Jeff Bezos, said it best in a recent Fast Company interview: “Our customers are loyal to us right up until the second somebody offers them a better service.”

Hope isn’t lost, but business as usual will no longer cut it. Just ask Radio Shack, Aerosoles or Teavana (a few more of 2017’s casualties). The truth is that retailers who can offer a compelling, individualized experience and build lasting relationships with their customers are well-positioned to thrive in the so-called retail apocalypse.

And here’s why. The fact is, retail marketing executives have a few aces up their sleeves, whether they know it or not.

  • Existing brand love: You have history with your customers. They know and love you. You’ve cultivated a brand that’s infused with meaning and identity that your customers are still willing to attach themselves to. There’s a reason Lululemon still sees more sales per square foot than any other apparel retailer (and most jewelry and electronics retailers as well) despite a price point that’s three to four times the price of alternate options. Brand love still holds sway in this new retailscape.
  • Pricing trust: If a single e-commerce platform shows you 11 versions of a product from 11 different vendors at 11 shifting price points, it’s difficult to know the true sticker price. And typically on Amazon, the lowest seller wins. When a product becomes a commodity, consumers go to Amazon. But a luxury, boutique or beloved product pulls consumers to the source.
  • Customer insights: Most importantly, you know your customers — how they interact with your website, your social channels, your emails and your point-of-sale. There’s no one else with the legacy of data on your current customer base. This is your trump card, and it’s the first essential step to creating a truly differentiated experience and invigorating sales.
  • In-store experience: There’s a big reason Amazon purchased Whole Foods and is pioneering a transformative store experience with the recent launch of AmazonGo. Bridging the physical and digital experience is difficult, and it’s worth doing. Amazon gets it, Starbucks gets it, and BestBuy gets it. These leaders are creating great experiences across online and offline, and they are driving huge business results by making their customers happy.

Trust and love may be the foundations for future growth, but delivering meaningful individualized interactions will be the fuel to spark these long-term relationships. And you don’t have to be Amazon, Facebook or Google to make this leap.

While companies have spent millions capturing data with the promise of delivering a unified customer experience, they struggle to turn these insights into actions. The fact is, until now, it’s been nearly impossible to do true personalization, or at least to do it well. There are a few common pitfalls that lead to the often-clunky attempts at personalization that end up feeling anything but personal.

Want to avoid those pitfalls? Below are three tips for delivering relevant, 1:1 customer interactions:

  1. Tear down marketing silos

Seventy-five percent of consumers expect a consistent experience, wherever they engage, according to the 2016 Connected Shoppers Report from Salesforce.

Whether you call it “omnichannel,” “cross-channel” or an “integrated experience,” it’s time to create a unified customer experience. Consumers see you as a single brand, not a series of separate touch points. In that same way, engagement should feel cohesive — from the first time consumers see your email popup to the moment they step out of a physical store.

To do it, you need a complete view of your customer, which means using a centralized intelligent decisioning layer that can ingest every single one of your data sources to find correlations and optimize against. It means syncing up the digital experience and the brick-and-mortar experience — mapping the entire customer journey to design an optimal customer experience.

  1. Have a little empathy

If your goal is to establish a lasting, meaningful relationship with your customers — and it should be — then you need to see them and treat them as individuals. Automated campaigns have become so focused on improving immediate opens and clicks that they overlook the lifetime value metrics that are the foundation of a long-term consumer relationship, like retention and average revenue per user.

Your customers don’t think of their interaction with you in terms of a single campaign, so don’t treat your relationship as a contained exchange with a beginning, a middle and a predetermined end. Move from transactional to relational. Think beyond the next click and instead optimize for long-term human-centric KPIs, like customer lifetime value, and choose an AIM (Automatic Identification and Mobility) technology that can do it.

If you’re unable to identify at-risk customers unless they’ve abandoned their cart, contacted customer service or called out your brand on Twitter, you’re doing it wrong. Instead, AIM technologies should enable marketers to pinpoint the behavioral signal in the noise to identify and intervene early with customers who are at risk of going to your competitor.

  1. Never stop adapting

Amazon may have used their powerful recommendation engine to win a bigger chunk of consumer spending, but retailers now need to think way beyond simply increasing basket size. By now, most retailers have thousands of contextual behavioral data points they can use to optimize every interaction.

Your tools need to deliver more than insights—they need to be able to execute cross-channel action across hundreds of customer attributes and thousands of experience permutations. They need to bridge optimization across your site, email, social, SMS, paid media, and native applications.

Unfortunately, today’s enterprise marketing clouds primarily rely on rules-based decisioning and personalization that create human bottlenecks in your campaign process. For example, a clothing retailer might create a rule that says: if a customer of a particular segment purchases a certain pair of shoes, then deliver a message to promote this matching belt. There’s only one or two paths a customer can take. Using this method, a retailer is limited in the number of attributes and segments they can target or optimize. Beyond that, they become too labor-intensive, and so granular, they actually exclude a larger part of your customer base.

Meanwhile, recommendation engines provide incremental short-term value for the immediate transaction and can increase basket size through add-ons, but are unable to optimize customer lifetime value across channels or predict customer churn. They’re also often limited to SKU-based affinity categories and focused on linear transactional models versus optimizing across the whole customer relationship.

However, retailers using AIM technologies at the core of their marketing efforts are now able to dynamically anticipate and take action on a customer’s needs and tastes based on the full breadth of their cross-channel data footprint. While there’s no shortage of hype around AI, almost 85% of companies believe AI will allow their companies to obtain or sustain a competitive advantage, according to “Reshaping Business With Artificial Intelligence,” from MIT Sloan.

AIM technologies are finally enabling marketers to optimize for the entire customer relationship, not just short-term metrics like click-through rates or linear purchase conversions, which fosters repeat purchases and deeper brand loyalty.

And, as retailers continue to face challenges in 2018, the ability to build lasting customer relationships at scale will be the difference between surviving and thriving.

To learn more about how today’s customer-obsessed retailers are using AI to transform the brand/consumer relationship, download the full 2018 Retail AI Marketing Guide, “Retail Rising.”

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AI marketing and the journey through the uncanny valley /ai-marketing-journey-uncanny-valley-288350 Mon, 18 Dec 2017 12:30:59 +0000 /?p=288350 “Things usually get worse before they get better.” “It’s always darkest before the dawn.” Whether it’s the valley of the shadow of death in the 23rd Psalm or the Dangerous Trench on the way to Shell City in the Sponge Bob movie, we’re used to the concept of feeling that things are getting worse, even […]

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“Things usually get worse before they get better.”

“It’s always darkest before the dawn.”

Whether it’s the valley of the shadow of death in the 23rd Psalm or the Dangerous Trench on the way to Shell City in the Sponge Bob movie, we’re used to the concept of feeling that things are getting worse, even though we know we’re headed in the right direction.

This experience can be represented by a U-shaped curve, literally forming the shape of a valley between two peaks. In technical terms, the curve represents a nonlinear relationship between two variables. A specific example is the uncanny valley — the hypothesis of the unease, frustration, or even revulsion we feel as something approaches the behavior and appearance of a human without getting all the way there. In this case, the two variables are the humanlike nature of the object and the emotional response to it. This can be experienced with robots and AI assistants, and with 3D animation. Perhaps you know someone who gets illogically angry when Siri or Alexa fails to understand their commands, or maybe you get uncomfortable watching humanoid robots or CGI-animated humans in TV and movies.

Although 78 percent of marketers are adopting or expanding artificial intelligence marketing in 2018, marketers are also uneasy about the uncanny valley. They are concerned that by implementing AI marketing, they will lose control of the customer experience, possibly bewildering or even revolting their customers. While this is a reasonable concern, it could prove to be an unfounded and risky position — because marketers have already forced their customers into the uncanny valley through the use of marketing automation and aggressive personalization. And to quote another truism, when you’re going through hell, keep going. Because you don’t want to stay there.

Your customers are already in the uncanny valley

Could it be true that we’re already subjecting our customers to experiences that create bewilderment and revulsion? You don’t have to have 3D avatars or robots in your customer experience to create these eerie, negative feelings in your customers. The uncanny valley is represented by a sudden decrease in empathy when a human-like being ceases in some way to be human. Here are some specific examples to indicate that your customers may already be in the uncanny valley:

Broken context

Example: An AI assistant or chatbot initially passes for human but fails to understand the context of a question that would be simple for a human to understand, revealing that it is not human. Here is just one of many anecdotes from Reddit:

In just seconds, this user went from loving their Echo to figuratively (literally?) flipping the table in frustration.

Not quite lookalikes

Example: A cursory read of our example user’s Facebook history could tell you that he is a foodie, a vegetarian and a fan of subscription boxes. Recently, this user got targeted by a new artisanal food subscription service that was relevant in many respects, except for the fact that they exclusively offer cured meats. It’s reasonable in some respects that an artisanal cured meat subscription service would target him. Except that as a vegetarian, this user found their ad bewildering and invasive, causing him to lose interest and scroll quickly past. In his scrolling fervor, he accidentally registered a click on the ad, leading to weeks of cured meats in his feed.

Bad timing

Another example from that same user: Currently, his bank is aggressively targeting him with a competitive mortgage offer. Three weeks ago, there were credit, fund consolidation and other signals that he was preparing for a home purchase. At that point, it was stone cold silence from the bank. But now that he has signed a mortgage with another bank and closed escrow, he is getting targeted after the fact with an offer that he would have considered three weeks ago. Now, it’s just aggravating.

Three ways to ascend from the uncanny valley

Ascending from the uncanny valley is possible, but it takes buy-in from executives and a concerted effort by the entire marketing organization. Fortunately, Artificial Intelligence Marketing (AIM) provides a new approach for interacting with customers, allowing for consistent relevant experiences across all channels and continuous optimization at scale. Marketers shouldn’t fear the uncanny valley. They should focus on crossing through to the other side. Here’s how:

1. Keep context: Match your level of sophistication across channels

Ideally, your website, app and chatbot work together to provide integrated, personalized service. Your customers should be able to access the same contextual features whether in the mobile app, at the brick-and-mortar store or when chatting with Alexa. If a user clicks through to your site from a specific offer email, that offer should automatically persist on the website. While your customers often encounter the same creative elements across your app, social, display, email and website, they’re disappointed when experiences are disjointed and out-of-context.

Unfortunately, many brand experiences can only be delivered to users in a single channel due to the inherent limitations of current marketing clouds, making a promise of sophisticated interaction that can’t be delivered in other channels.

2. Reduce uncertainty: Use visual cues to signal behavior and ability

If you do have varying levels of sophistication for some of your communication channels, you can give your customer cues to set appropriate expectations. If you have created a bot or an app with sophisticated abilities, imbue it with human personality. In contrast, a limited chatbot doesn’t need a name, a highly humanized voice or an avatar. And if your mobile app focuses on a subset of features, be clear about what they are in the app name and description.

3. Responsiveness: Reduce the lag between insight and action

If you gain insight about an individual customer, how quickly can you adjust your interactions to be responsively relevant? A human conversation involves both parties responding in real time to conscious and subconscious cues. If your campaigns and audience segments are static, or if your channels are siloed, it can take too long to move at the speed of the customer. However, with AI marketing that has dynamic decisioning at its core, new data and behavioral signals can immediately be acted upon without human intervention. The result is a more responsive customer interaction that adapts as your customer evolves.

To get to the other side

Helping your customers ascend out of the uncanny valley can seem like a monumental task, but with AI marketing, it is now feasible. Consumer brands that make the leap and move away from the rules will be the first to reap the benefits of consistent, cross-channel interactions that are optimized at scale by AI.

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How AI is disrupting major industries /ai-disrupting-major-industries-276349 Wed, 07 Jun 2017 11:35:47 +0000 http:/?p=276349   Advances in artificial intelligence tend to provoke polarizing reactions for most people. One, a dystopian anxiety where humans huddle in fear of their metallic overlords. Or two, a utopian futurist society where humans are freed from mundane labor and complex challenges are solved by machines. Wherever you fall on the AI anxiety spectrum, the […]

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Advances in artificial intelligence tend to provoke polarizing reactions for most people. One, a dystopian anxiety where humans huddle in fear of their metallic overlords. Or two, a utopian futurist society where humans are freed from mundane labor and complex challenges are solved by machines.

Wherever you fall on the AI anxiety spectrum, the fundamental truth is that we are entering a major transformative cycle across nearly every industry.

While AI is currently dominated by increased media attention and business hype, the way consumers and brands interact is quietly poised to make a tectonic shift over the next two years through marketing solutions built with AI in their core DNA.

In a recent Wakefield/Demandbase study, 80 percent of marketing leaders say that AI will “revolutionize” marketing by 2020. Meanwhile, IDC research forecasts worldwide revenues for cognitive and artificial intelligence (AI) systems will reach $12.5 billion in 2017, an increase of 59.3 percent over 2016. In addition, global spending on cognitive and AI solutions will continue to see significant corporate investment over the next several years, achieving a compound annual growth rate (CAGR) of 54.4 percent through 2020 when revenues will be more than $46 billion.

On the other end of the spectrum, you have billionaire Mark Cuban stating at SXSW that “the world’s first trillionaires are going to come from somebody who masters AI and all its derivatives and applies it in ways we never thought of.”

Every enterprise from Google and Netflix to Walmart and Nordstrom is making major investments in AI. But why the shift now?

The proliferation of customer data volume via mobile devices, Internet of Things (IoT) technology like FitBits, beacons and smart appliances, and even open, public data sources have far outstripped human capability to process, generate insights and drive action.

Meanwhile, today’s consumers increasingly expect highly contextual, fluid interactions with brands, which traditional marketing automation and personalization tools can’t provide at meaningful scale. The train may be driven by data, but marketers are currently tied to the railroad tracks.

Although companies like Netflix, Google and Uber are often cited as the disruptive AI vanguard, traditional industries are increasingly adopting AI technology to help bridge the digital transformation gap and compete against industry outsiders threatening to grab market share.

Finance and the machine

The shift is already underway within banking. With banking services scrambling to meet the consumer demand for personalized online and mobile interactions, bankers believe AI will play a key role in bridging that digital gap.

Four in five bankers believe AI will “revolutionize” the way in which banks gather information, as well as how they interact with their clients, said the Accenture Banking Technology Vision 2017 report, which surveyed more than 600 top bankers and also consulted tech industry experts and academics. The report forecasts that AI will become the primary way banks interact with their customers within the next three years.

In addition, 49 percent of banking executives say the traditional transaction/ branch-based banking model will be dead by 2020, according to The Economist‘s 2016 report, “Retail Banking: In tech we trust.”

While the concern is that artificial intelligence will take away the human component to banking, the truth is that emerging AI technologies — particularly on the marketing side — allow banks to simplify interactions and only deliver the most relevant experiences for customers. Conversely, it allows financial services marketing teams to move away from mundane data and campaign processes, in favor of spending more time on creativity and innovation.

Free-to-play gaming fuels AI growth

While gaming has leveraged varying levels of artificial intelligence since its inception to simulate human characteristics in non-player characters and drive good game play, the challenges associated with the free-to-play gaming model has brought AI marketing technologies to the forefront to improve the holistic player experience.

Formerly, studios typically invested in producing big titles and gaming franchises that required an upfront purchase to play. In the traditional platform model, 90 percent of their revenue came from 28 percent of their users. However, in the mobile free-to-play market, only 6 to 10 percent of mobile gamers will make premium in-app purchases. In 2016, the average iPhone user spent $27 on games, out of a total of $40 on apps.

While studios already cared about user engagement in the previous model, the new pricing model requires a deeper understanding of how users commit to a game. Techcrunch reports that nearly one in four people abandon mobile apps after only one use.

Within this environment, leveraging in-game data to deliver personalized, real-time experiences to encourage playing time and decrease churn has become mission-critical, which has driven gaming companies to look to core AI solutions to continuously optimize the entire player experience.

AI prevents subscriber churn among mobile carriers

In the ultra-competitive and saturated telecommunications industry, major carriers are constantly stealing mobile subscribers from one another to bolster growth. Because of this, the primary players — Sprint, Verizon, T-Mobile, and AT&T — are heavily investing in emerging AI solutions to help predict and prevent customers leaving for another carrier (called churn within the industry).

Traditional offline predictive models are unable to provide an in-depth view at the velocity needed to actually prevent churn. New AI platforms are allowing mobile carriers to understand the behavioral signals for a customer at risk for churn weeks earlier and take preventative action to course correct the customer relationship with the brand (provide new data or pricing incentives, encourage engagement, or create enticing renewal offers).

Because of the pace of the industry, the sheer volume of data available and its position as a conduit for all mobile experiences, telecommunications is a leading candidate for the industry poised to see some of the greatest return on investment from AI technology.

Humans welcome

While the prevailing concern around automation focuses on job replacement, we’re actually seeing a surge in creativity and innovation as overloaded marketers and data scientists are freed from the manual labor of wrangling data and building complex targeting rules. Customers should expect a creative surge over the next two years from brands they interact with as the focus shifts from traditional marketing technology to core AI solutions.

And no matter where you fall on the AI anxiety spectrum, increased creativity ultimately results in not just a better customer experience, but a better human one.

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AI at the core. Humans at the helm. /ai-core-humans-helm-274450 Mon, 08 May 2017 11:33:45 +0000 http:/?p=274450 Any marketer will tell you that applying AI to Marketing is a hot trend right now and has the potential to disrupt the industry. Just last week, Oracle announced that it is delivering artificial intelligence across its customer experience cloud. Oracle’s announcement follows a long line of press releases from major marketing clouds such as […]

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Any marketer will tell you that applying AI to Marketing is a hot trend right now and has the potential to disrupt the industry. Just last week, Oracle announced that it is delivering artificial intelligence across its customer experience cloud. Oracle’s announcement follows a long line of press releases from major marketing clouds such as Salesforce, IBM and Adobe. The space is so hot, in fact, that there seems to be a competition to have the best name for your AI, with the likes of Einstein, Watson and Sensei regularly competing for top billing.

If you look beyond the cute names, however, you will find that there is real technology at play. AI Marketing solutions aren’t just a new way of describing the collaborative filtering recommendation engines that gained popularity last decade (e.g., Certona, MyBuys and Baynote). Nor are they the A/B/N or multi-variate testing tools that grew up earlier in this decade (e.g., Optimizely, Maxymizer and Monetate). They are, assuredly, all of that and much more, taking advantage of advances in AI technologies such as image/facial recognition, natural language processing/generation and machine learning to fundamentally change the way modern marketing is done.

Today’s marketing clouds fall short

The challenge with traditional marketing automation solutions is that they are only as scalable or as intelligent as the marketers running them. While such solutions have shown the ability to automate marketing execution for specific “if/then” scenarios, these solutions fall woefully short when trying to apply them broadly across a large B2C enterprise for three key reasons:

1. In order to get the desired targeting granularity, marketers have to write and maintain dozens of “if/then” rules across hundreds or even thousands of campaigns

2. All of the targeting rules have to be set in advance of ever having run the campaign, so initial success relies solely on the experience and “best guess” capabilities of the marketer configuring the campaign

3. Running A/B/N tests to optimize campaign efficacy remains a very manual, labor-intensive process, often requiring a data scientist to get involved and spend weeks doing uplift or propensity modeling, only to come up with a recommendation for improvement that, while helpful, only positively impacts a small portion of a marketer’s total audience.

As a result of these challenges, marketers spend their time programming campaign rules, managing holdout groups and analyzing test results instead of being strategic or creative. While it is important for today’s marketers to be “data-driven,” the pendulum has swung too far: automated and programmatic campaigns have become so focused on improving short-term opens and clicks that they miss out on optimizing the longer-term ARPU (average revenue per user) and retention KPIs (key performance indicators) that directly impact topline revenue.

AI marketing’s early days — for engineers and mathematicians only

For several years, disruptive B2C brands have recognized the role that AI can play in engaging customers and driving revenue. Amazon has developed arguably the world’s most famous recommendation engine, relying on item-to-item collaborative filtering to drive product suggestions for well over a decade now. Entertainment and media companies like Netflix and Spotify have followed suit, relying heavily upon personalization to keep customers engaged on an ongoing basis. And retail-in-a-box providers such as Stitch Fix or Trunk Club couple techniques such as natural language processing and clothing recommendations with input from a human stylist to decide which items to send to which consumer.

Until recently, however, using artificial intelligence to drive marketing and customer experience required building a team of hundreds of engineers and data scientists to spend years building and perfecting an optimized model for the particular use case at hand. With the rise of major cloud providers such as Amazon and Microsoft, as well advancements in big data tools and infrastructure, machine learning capabilities are more readily available than ever before, with off-the-shelf offerings like TensorFlow, Azure ML and Spark MLlib.

From developers to marketers with bolt-on AI

Using off-the-shelf ML tools has eliminated the need to fully code an AI from scratch; however, they still require significant customization, and one must be either a hard-core engineer or an advanced mathematician (or both) to fully take advantage of these tools. But this is changing as more and more players look to provide marketers with tools that fit seamlessly into their current marketing solutions.

This evolution to provide marketer-friendly AI tools can most easily be seen in the press releases of the major marketing clouds. In the month of April alone, Salesforce had 70 distinct press mentions talking about how Einstein is helping to power their Marketing Cloud. Point solutions are also getting into the game, as witnessed by mobile marketing company Kahuna’s recent repositioning away from being a mobile marketing company and doing an overnight transformation into an AI-powered cross-channel marketing platform.

Salesforce Einstein applies AI models to solve specific, narrow use cases across the various clouds. Image courtesy of Salesforce.com.

In the vast majority of cases, existing marketing technology providers (both major marketing clouds and point solutions) are following a similar path. They are acquiring large teams of data scientists who build and deploy models to target narrow use cases in order to improve marketing engagement and effectiveness. To see this in action, one only has to look at Salesforce and what they are doing with Einstein. Salesforce has acquired at least 10 AI companies, compiling a team of more than 175 data scientists. Those data scientists have, in turn, bolted AI models onto their existing legacy marketing cloud infrastructure to help address some of the key pain points across the marketing spectrum, such as optimizing the right time to send a consumer an email or to predict a customer’s likelihood to open or click a particular email.

Bolt-on AI solutions can add incremental value over current business-as-usual marketing, as Salesforce notes on their company blog:

Marketing Cloud Einstein has been in beta for almost a year and we have seen some tremendous results. One of my favorite examples is the e-commerce and coupons company ShopAtHome. By redefining customer engagement around predictive scores, the company generated a 23 percent lift in email clicks, and a 30 percent increase in email opens.

— Courtesy of Salesforce Blog Post “Welcome to the World of Intelligent Marketing

In today’s world of the connected customer, however, simply optimizing opens and clicks isn’t enough. Today’s connected customers expect a longer-term, value added relationship with brands, so enterprises must optimize for longer-term KPIs such as 45-day average revenue per user or 60-day retention.

For good reasons, bolt-on AI solutions that are built on legacy infrastructures can’t support this type of approach. The average B2C enterprise has on average 100+ attributes for each of their customers across persona data, (name, address and so on), behavioral/usage data (e.g., game play data, voice consumption data) and marketing interaction data. By combining these attributes, there are more than 2100 different permutations of assembling that data for testing and targeting purposes. That may look like a small number when written in an article like this, but when you consider the fact that the length of the universe in seconds is 244, it becomes apparent that bolting AI onto existing legacy infrastructure to solve narrow use cases is not going to be sufficient in today’s world of the connected customer.

Applying AI at the core of the marketing stack

In order to optimize longer-term KPI optimization for customer lifetime value metrics such as revenue and retention, one needs to re-imagine a modern day marketing cloud with AI at the very core of the tech stack.

Applying AI at the core of a modern-day marketing cloud has the potential to disrupt in a world of aging marketing cloud architectures. For example, Amplero’s own Artificial Intelligence Marketing (AIM) Platform incorporates machine learning and multi-armed bandit experimentation at the core, creating marketing automation and optimization tools that quickly run thousands of recursive tests, to continuously optimize every customer interaction and adapt to rapidly evolving consumer behaviors across nearly any marketing channel, all at a scale that is not humanly possible.

AI applied at the core is capable of driving autonomous marketing and continuous optimization:

  • Auto-machine learning marketing quickly integrates and activates disparate data environments and points marketing solutions into revenue-producing campaigns.
  • Continuous testing through multi-arm bandit experimentation mathematically optimizes KPI lift by exploiting pockets of KPI lift (value) while continually exploring new possibilities.
  • Adaptive optimization ensures timely response to changes in customer or market dynamics without the need for human intervention.

In addition to driving improved campaign execution and optimization, marketing clouds that apply AI at the core generate a tremendous amount of insights about campaigns, customers and context which can be proactively pushed back to the marketer via alerts to help guide future marketing strategy and creative. These learnings are automatically applied back into the AIM platform to continuously optimize KPI performance.

Amplero Artificial Intelligence Marketing Platform. In the diagram above, a row with dark purple bars shows that a particular campaign is being played more frequently than those that are light purple, given their positive impact on KPI lift.

While Amplero is an early leader in applying AI at the core of the marketing stack, other companies taking similar approaches include Optimove, Adgorithms and Motiva. Collectively, companies applying AI at the core are using machine learning to plan, personalize and optimize every interaction across the customer journey.

We are clearly entering a new era of marketing clouds — the era of Artificial Intelligence Mareting. With humans at the helm and AI at the core, the possibilities truly are endless.

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