5 Predictions for AI in Marketing in 2019

 


Forbes – This article originally appeared on Forbes.


There’s no denying that artificial intelligence is rapidly moving from fiction to reality. In marketing, especially, interest in AI has intensified as marketers have realized the potential that intelligently harnessing the ever-growing streams of customer data has on transforming customer experiences.

AI has become increasingly important not only to marketers but to organizations as a whole in order to stay ahead in today’s dynamic market landscape. According to a recent MIT global survey of 600 executives, nine out of ten companies already use AI to improve their customer journeys. More importantly, 70% of those companies have seen it directly improve revenue. Netflix has projected that its AI-powered personalized recommendations save it $1B in revenue annually by avoiding canceled subscriptions through engaging customers with relevant content.

Fueling AI’s adoption has been marketers demanding to see — and receiving — proof that AI delivers what it promises. Early adopters have shown how AI in marketing makes them more efficient, productive, and effective by simplifying, accelerating, and scaling marketing initiatives while removing many complexities inherent in marketing today. Similarly, our recent benchmark analysis of 3.8B marketing interactions across channels and verticals found that AI-powered marketing campaigns increase customer engagement by 7x and revenue 3x by helping marketers be more targeted, more relevant in their content, and more effective with how they engage each individual customer.

Now, as we step into 2019, here are five predictions for what the year holds for AI in marketing.

 

1. AI will intelligently orchestrate the full customer journey. Marketers’ first venture into AI was at the top of the funnel for audience expansion and audience targeting. Moving forward, AI will increasingly power the full customer life cycle, from customer acquisition to engagement to retention. Forrester recently predicted that “more than 20% of marketing platforms will use AI to optimize midcycle engagement” for B2B marketing. That number will be far greater for B2C. Furthermore, AI will move from influencing just the edges of the customer experience, such as dynamic content insertion, to orchestrating its core.]

  • What this means for marketers: AI-powered decision engines can solve the increasingly complex customer journey by optimizing, personalizing, and guiding every aspect of customers’ self-guided journeys with your brand. Yet only solutions with AI at their core that can unify all incoming customer data and use its insights to orchestrate decisions in real time can achieve this.

 

2. CMOs will challenge their teams to scale, and it will happen through AI. As marketing organizations’ demands and revenue targets continue to grow, CMOs will place increasing emphasis on leveraging technology to meet those demands. CMOs will challenge their teams to run more campaigns, add more personalization, and diversify their strategies by being smarter with their resources. Marketing organizations will quickly realize the only technology that can effectively streamline and scale how they work is AI.

  • What this means for marketers: AI can be a partner that amplifies and accelerates your marketing strategies by automating and optimizing hyper-targeted, multi-stage, cross-channel marketing programs. Not only will this allow your team to move faster and produce more, but it will also enable you to find incremental opportunities for growth.

 

3. Marketers will get creative with how they use AI. As marketers begin to understand how AI can augment and amplify their efforts, we’ll see them leverage AI in innovative ways and start breaking status quo marketing practices. After the initial AI learning curve and period of laying the groundwork with strategy, processes, and people, marketers will start pushing the boundaries of their programs and experimenting with new strategies, taking a bigger leap every time.

  • What this means for marketers: Beyond offloading mundane marketing tasks to AI, embrace the opportunity to reimagine your customer engagement strategies. With AI, new campaign ideas can go live in no time without significant resources and be readily optimized and pivoted.

 

4. Marketers will fully embrace AI because it stops being black box. Marketers want control of their marketing programs and need to be able to explain their marketing investments. But the first generation of AI marketing asked marketers to take a leap of faith. That’s why the recent Business Insider Intelligence report found that “when asked to choose which trending technology they felt most unprepared for, 34% of global marketing executives chose AI.” The growth in AI in 2019 and beyond will be fueled by “explainable AI” in which marketers can see what factors influence predictions and can control the parameters that guide AI.

  • What this means for marketers: AI adoption will only accelerate, and it’s important to get started implementing AI now or risk falling behind and being leapfrogged in the future. Look for AI solutions that give you the level of control and the visibility into AI algorithms and workflows you need.

 

5. Marketing AI companies providing real ROI will emerge from the noise. Last year, Gartner analysts predicted that “by 2020, AI technologies will be virtually pervasive in almost every new software product and service.” We’re already seeing this prediction play out with a proliferation of marketing software providers — emerging and incumbent — adding AI to their product strategy and pivoting their messaging to claim AI capabilities. Yet not all marketing AI is created equal. This year, we’ll see more emphasis on separating real AI marketing solutions from fringe AI solutions. Gartner has already started helping marketers identify noteworthy AI solutions by introducing the first Cool Vendors report on AI for Marketing.

  • What this means for marketers: As the “AI” label becomes standard, marketers will find it increasingly challenging to identify real AI solutions capable of effectively solving their problems. Marketers will need to be more vigilant with testing vendors’ claims to weed out masked AI solutions.

 

Lastly, one final prediction to add to the mix:

2019 will be a pivotal year in which marketers finally realize the ROI of AI. We’ll see AI’s mounting, tangible benefits move it from hype to mainstream. Will 2019 be the year you embrace AI?

 


Download ROI of AI Marketing: 4 Levers for Cross-Channel Success


 

Getting Started with AI in Marketing

AI Marketing in Action: Best Practices for Getting Started with AI

After being heralded as “the next big thing” for years, AI has arrived, bringing with it the power to add game-changing leverage, focus and control to your marketing efforts. Of course, as with any new technology, the move to AI entails a learning curve, and its perception as a “black box” has left many unsure how to begin applying AI to their marketing strategies.

For those ready to embrace this truly transformational approach, AI puts the tools in marketers’ hands to harness a complete and constantly updating customer profile to engage with customers with incredible precision, relevance, effectiveness and speed. At the same time, it helps marketers be more efficient, productive and effective by simplifying, automating, and accelerating marketing workflows. In previous posts we explored how AI helps determine the right marketing actions for each individual customer at every step by continuously optimizing:

  • WHO are the right customers to target at any moment by using Predictive Audiences to determine customers who are ready to purchase, at risk of churning, ripe for upsell and more.
  • WHAT content will most likely influence each customer by using Predictive Recommendations to surface the most relevant offers, products or articles for each individual customer based on what they seek at that moment.
  • WHEN each customer will be most responsive by using Predictive Engage Time to optimize send times for when each customer is most likely to actually engage with your brand, not simply open a message.
  • WHERE is the channel they’ll be most receptive by using Predictive Channel-of-Choice  to adapt which channel to deliver the message based on their preferred channel in the current context.

And the numbers don’t lie; as our recent benchmark study found, compared with traditional marketing efforts AI-powered campaigns:

  • Drive up to 7X greater customer engagement and 3X revenue
  • Deliver 2X greater impact on engagement for mobile push compared to email
  • Achieve 50% lift as AI continues to accelerate engagement and impact and learning from customer interactions

 

PUTTING AI INTO ACTION: CONSIDERATIONS FOR SUCCESS

So how do we put AI into action? Like any successful initiative, getting results with AI begins with being clear about what you aim to accomplish and making sure you have the right people, processes, platform and implementation plan in place. Accept that there will be a learning curve, and begin by laying the foundation before you begin to scale.

 

  1. Be Clear About Your Desired Outcomes

It seems obvious in hindsight, but to succeed you need to be clear about what “success” actually means. AI doesn’t determine your strategy for you, but it can applied to any variety of marketing strategies. The end goal will determine how to incorporate AI across marketing activities, and which of the many metrics it delivers you’ll optimize to meet your desired outcome.

 

  1. Assess How AI Will Integrate With Existing Technologies and Workflows

AI can only accelerate your marketing efforts when it fits seamlessly into your existing technologies and processes. Look for AI partners that tightly integrate into your tech stack and ecosystem. Then, ensure AI is core of your efforts, not simply added at the margins. Many marketers treat AI as an “add-on” to their existing workflow, but that’s a recipe for mediocre ROI.

AI also needs to integrate into existing workflows. Begin by determining how AI will fit into your business processes, and identify the organizational gaps that must be addressed.

 

  1. Approach AI as a Cross-Channel Strategy

AI isn’t limited to a single channel or platform. Because AI connects data across channels, it allows you to turn your focus to delivering a holistic and highly focused cross-channel customer experience. Use AI as an opportunity to unify your cross-channel strategies and multiply their impact.

 

  1. Ensure AI and People Are a Partnership

AI’s purpose is to accelerate and amplify marketing activities. To do that, AI requires human guidance to make the right decisions; people bring context and that essential human touch. Outline the interplay required between your team members and AI, detailing the direction and input they’ll need to provide, and you’ll be on a much stronger footing to make the most of this transformational tool.

 

  1. Understand AI Is a Process

Every new tool and approach has a learning curve, and AI is a long-term play in which you must first crawl, then walk, before you are ready to run. If you begin with initially applying AI to a single, more basic marketing program and then ramp up gradually, you’ll have a solid groundwork to build upon. Because of AI’s self-learning, continuously improving capabilities, the sooner you start, the quicker you’ll reap the benefits of AI.   

 

  1. Think Big: AI Can Help Achieve Transformational Impact

AI enables you to orchestrate customer experiences with nuance and scale not possible with existing tools. While AI has the potential to truly revolutionize your marketing, it demands that you scale your imagination and your ambition accordingly. It’s an invitation to think outside of the bounds of your comfort zone, and to search out partners that can scale with your needs and help guide your own AI evolution.

 

THE TAKEAWAY

AI-powered marketing is the only way today’s marketers can effectively engage their large, diverse, and rapidly evolving customer bases. And now is the time to embrace it, as AI will quickly move from being a competitive advantage to being an essential, table-stakes tool. While the process of fully integrating AI into your strategy and workflow can seem daunting, the longer you put off AI, the longer it will take you to catch up with your competitors adopting AI today.

And know that there is no one-size-fits-all strategy for AI. Start small with a single-use case, and then scale up. You’ll quickly see that AI will not only cut down your manual tasks, infuse customer insights across your marketing activities and help you launch faster, but it will free you up to get back to the heart of marketing: Employing your creativity and your strategic intuition to craft next-level customer experiences.

For the full set of findings as well as real examples of marketers who have used AI to drive revenue by making better, faster decisions around the “Who, What, When & Where” of cross-channel marketing, download The ROI of AI in Marketing: 4 Levers for Cross-Channel Success.

 


Download ROI of AI Marketing: 4 Levers for Cross-Channel Success


 

ROI of AI Marketing - Artificial Intelligence helps marketers know the right channel and the right time to send messages to each individual

AI Marketing in Action: Delivering Perfectly Timed Content on the Right Channel Improves Engagement 4X

Consumer attention is getting scarcer. We are all increasingly connected across multiple devices on which we are bombarded with near-constant messages, notifications and ads. In this hyper-saturated environment, how do you stand out in customers’ inboxes and across channels?

While you invest significant resources to create rich content and experiences tailored to specific customers, if your message is delivered at the wrong moment or context, it’s a wasted opportunity. And in today’s lightning-fast marketplace, opportunities are the last thing you can afford to waste. Timing and channel can’t be an afterthought.

But as engagement and sales are no longer limited by time of day or to any one device or channel, how can marketers determine the optimal time and channel on which to engage each customer as they switch fluidly between a growing number of devices and channels throughout the day?

Optimizing send times has long been standard practice for marketers. Traditional send time optimization requires manually analyzing historic trends and running A/B test campaigns at various times of day to determine optimal send times. But there are problems with this approach. For one, it treats your customer base as a uniform group and neglects the dynamics of today’s always-on, mobile, multi-device consumer. More importantly, it focuses on optimizing for opens, rather than downstream actions that drive revenue, such as website engagement and conversions.

If you’re not learning from customers’ behavior patterns as they use different devices, apps, and channels for different purposes at different times of the day, you’re taking a guess as to how to best engage with them. Can you afford to do that?

Fortunately, AI is here to help.

AI empowers marketers with incredible leverage, helping them better engage customers across every channel. By generating insights from every aspect of customers’ behavior and previous engagement with the brand, it makes precise recommendations as to how to engage with them depending upon your desired outcome.

It optimizes WHO marketers should be targeting, selects WHAT content they engage with, WHEN to engage with them and WHERE is the best channel. This “AI Marketing in Action” series explores AI’s impact on these 4 Levers of cross-channel marketing and quantifies its impact on each lever. Our findings are based on a recent benchmark study that analyzed 3.8B marketing interactions from campaigns across various channels and verticals.

In our last posts, we explored how AI helps you determine WHO are the right customers to target and WHAT is the best content to bring customers at any moment for each of your customer strategies. Now, we’ll explore how AI helps determine WHEN and WHERE customers see that content.



AI-POWERED PREDICTIVE ENGAGE TIME AND PREDICTIVE CHANNEL-OF-CHOICE

Predictive Engage Time and Predictive Channel-of-Choice work in close partnership to ensure that your messages get noticed and acted upon.

Today’s perpetually connected customers are much more likely to have many frequent bursts of activity around the clock than a recurring habit of opening their emails at certain times of day or clicking onto sites or apps at specific hours. Unlike traditional Send Time Optimization, Predictive Engage Time Optimization uses AI to examines campaign clicks, website browsing behavior, total engagement time, engagement depth and transactions to determine windows of time in which each user is most likely to engage. From analyzing the full downstream activities, AI identifies and optimizes send times to when each customer is most likely to engage with your brand, not simply open a message.

Predictive Channel-of-Choice steps in to determine the best channel to engage your customers. It similarly uses AI to learn on which channel each individual customer is most likely to engage at a given moment by performing an ongoing, deep analysis of customer engagement and transaction behaviors. As campaigns run, it automatically adapts the channel on which each customer receives your message accordingly.

SHOW ME THE FACTS

Of course, these optimizations aren’t of much use if they don’t provide real-world ROI. Our recent benchmark study found that Predictive Engage Time drives 3X engagement over email and nearly 5X over mobile push. Why the significant lift? Because research shows users prefer to engage deeply at certain hours of the day, while casually browsing throughout others. Adapting individual send times to when users are most likely to engage in downstream activity pays off in real-world results.

Beyond that, combining Predictive Audiences with Predictive Engage Time drives up to 3.8X over email and 7.2 over mobile push.

 

THE BOTTOM LINE

With Predictive Engage Time and Predictive Channel-of-Choice, marketers can ensure that their messages capture customers’ attention without their constant monitoring. Their messages are continuously optimized to reach each individual customer at the moment and channel where they are most likely to take action. And that translates directly into engagemnt and revenue.

 

For the full set of findings, as well as real examples of marketers who have used AI to drive revenue by making better, quicker decisions about the “Who, What, When & Where” of cross-channel marketing, download The ROI of AI in Marketing: 4 Levers for Cross-Channel Success.

 


Download ROI of AI Marketing: 4 Levers for Cross-Channel Success


 

The ROI of AI - understand the 4 levers to create ROI from AI in Marketing. This blog post talks about WHAT to send using predictive recommendations.

AI Marketing in Action: Delivering More Relevant Content with Predictive Recommendations Increases Engagement Up To 5.7X

We’ve all experienced the power of uniquely tailored, relevant content and recommendations. Top-tier platforms such as Netflix, Amazon and Spotify utilize powerful content and product recommendation engines to create the kind of seamless, delightful user experience that keeps customers coming back again and again.

Most of us, however, don’t have access to tools as sophisticated as those of Amazon, Netflix or Spotify. Yet, we still want to provide our customers with useful, relevant, actionable information that simplifies their purchase decisions. But exactly WHAT that information is constantly changes throughout the customer journey. This makes selecting the content, products, and offers which will “break through” a very difficult and fast-moving target. Multiply that by your number of customers and potential touchpoints, and you’ll see that choosing content for each customer interaction quickly becomes next to impossible.

Of course, there are tools to help personalize content. Recommendation systems and content optimizers have existed for years now, but they are typically based on manually entered rules and inflexible templates. They are also driven largely by marketers’ hypotheses about what would resonate with their customers rather than what customers’ behaviors, interests and lifecycle stages reveal. Consequently, they don’t keep pace with today’s rapidly changing consumers.

And as we all know, irrelevant content is the #1 reason consumers disengage with brands. Can you really afford to base your revenue on guesswork?

That’s where AI is here to help.

AI helps marketers work smarter, faster, and more intuitively as they engage customers along their customer journey. It does so by optimizing WHO marketers should be targeting, selecting WHAT content they engage with, WHEN to engage with them, and WHERE is the best channel. This “AI Marketing in Action” series will explore AI’s impact on these 4 Levers of cross-channel marketing and quantify its impact on each lever. Our findings are based on a recent benchmark study that analyzed 3.8B marketing interactions from campaigns across various channels and verticals.

Our last post explained how AI helps you determine WHO are the best customers to target at any moment for each of your customer strategies. Now, we’ll explore how AI helps determine WHAT those customers see.



AI-POWERED PREDICTIVE RECOMMENDATIONS

Predictive recommendations surface the most relevant content—be it an offer, a product, an article—for each individual customer at a given moment. It ensures every message you deliver is unique and personalized to what each customer seeks at that stage of their journey with your brand.

It does so by analyzing—in real-time—a complete view of user profile, interest and behavior data in relation to your brand content. It then continuously tests itself, self-learning with every customer interaction and optimizing content for revenue-generating actions.

How much work is this for me? With Predictive Recommendations, you simply guide each message’s content by setting basic parameters and defining whether to base content on user actions, their affinities, other customers’ purchases and browsing behaviors, or relevant trending items.

The recommendation engine picks up from there, pulling together all user behavior event-streams from sites, apps and other sources as well as historic CRM data and your brand’s content and product information. AI then continuously analyzes the dynamic relationship between your users’ product interactions, historic brand engagement, and latest customer activity across multiple channels. As each campaign runs, AI selects the most relevant content for each customer based on their current context, crafting individualized messages for each of them without your having to lift a finger.

SHOW ME THE FACTS

Having an Amazon, Netflix, or Spotify-grade predictive recommendation engine to optimize your content is a real bonus, but let’s not forget where the rubber hits the road: ROI. And the numbers don’t lie: Our recent benchmark study found that Predictive Recommendations drive a 2.5X – 5.X lift in engagement. “

Predictive Recommendations also drive nearly 3X revenue relative to their use in the marketing mix.

THE BOTTOM LINE

With Predictive Recommendations, marketers can listen to customers’ needs, reply with the relevant, actionable content customers seek, and stop wasting critical opportunities to connect. More importantly, making your customer communications truly 1:1 not only improves your relationship with customers but also drives incremental ROI.

For the full set of findings, as well as real examples of marketers who have used AI to drive revenue by making better, quicker decisions about the “Who, What, When & Where” of cross-channel marketing, download The ROI of AI in Marketing: 4 Levers for Cross-Channel Success.

In upcoming blog posts we’ll explore AI’s impact the remaining two levers of marketing:

  • “The When” with Predictive Engage Time: Optimize the delivery of the campaigns to the times when each individual customer is most likely to engage
  • “The Where” with Predictive Channel-of-Choice: Deliver the campaign on each individual customer’s channel-of-choice

 


Download ROI of AI Marketing: 4 Levers for Cross-Channel Success


 

The ROI of AI in Marketing requires understanding WHO to target and segment with the help of predictive audiences and segmentation

AI Marketing in Action: Selecting Who To Target with Predictive Audiences Increases Conversions 28%

Marketing success starts with identifying the right customers to target for each customer strategy and marketing campaign. But what does that process look like for you today? Do you define customer segments based on specific demographic and behavioral parameters, pass the requirements over to your data team and then wait a week, or two, or more to get back customer lists before launching campaigns? Then, how often are your lists refreshed?

While you wait for your customer lists, your customers may churn, purchase from your competitor or simply lose interest in your brand because you failed to engage them. By the time you have your lists, a portion of customers may no longer even fall into those segments. In today’s world of fleeting attention you can’t afford to wait around. You have to get ahead of your audience. But how do you determine if customers engaging (or not engaging) with your brand today are simply browsing, looking for more information or are ready to convert? How can you tell if they are thinking of churning or are ripe for upsell?

That’s where AI is here to help.

At its core, AI helps marketers be smarter and faster about how they engage customers along the customer journey by optimizing WHO they should be targeting, with WHAT content, WHEN to engage them and WHERE is the best channel. This “AI Marketing in Action” series will explore AI’s impact on the 4 Levers of cross-channel marketing, the “Who, What, When & Where,” and quantify its impact on each lever based on a recent benchmark study that analyzed 3.8B marketing interactions from campaigns across channels and verticals. Lets begin by exploring AI’s impact on the WHO.



 

AI-POWERED PREDICTIVE AUDIENCES

AI helps you determine who are the best customers to target at any moment for each of your customer strategies by translating a holistic view of your customers – all the historic data as well as real-time behaviors – into actionable customer scores.

How involved is this process? You simply define your desired goal – such as driving first purchases – and AI algorithms surface the best customers to target. Each customer’s likelihood to respond is scored based on a complete customer view – including their profile, product interactions, historic brand engagement and their latest customer activity across channels. Scores continuously update and are immediately ready to use across campaigns.

Bonus points: You have full visibility into the attributes that influenced the score. You can also see the performance of predictions before using them in your campaigns.

SHOW ME THE FACTS

The real question boils down to: do Predictive Audiences achieve higher ROI than rule-based, static segmentation? Analyzing customers who used both approaches the answer is, yes.                             

Our recent benchmark study found that Predictive Audiences drive 28% lift in conversion events such as orders, subscription upgrades and form fills. In fact, high propensity users are 5X more likely to convert than low propensity users.  

 

 

Why do these Predictive Audiences outperform? Because people’s propensity towards a desired action, affinities and lifetime value are based on a complex combinations of variables, which can’t be defined by set rules. For example, figuring out whether someone is ready to sign up for a subscription is determined not only by a specific milestone during their trial but other variables such as engagement patterns, recent activity, content consumed, time spent on site, email interactions, location, and potentially a host of other variables. And those variables can change over time. Predictive audiences listens and reacts.

THE BOTTOM LINE

You no longer need to wait for your data team to create and maintain segments. With AI, you always have the right audiences ready to engage. More importantly, moving from an audience strategy that’s reactive to one that’s proactive drives incremental ROI.

For the full set of findings, as well as real examples of marketers who have used AI to drive revenue by making better, quicker decisions about the “Who, What, When & Where” of cross-channel marketing, download The ROI of AI in Marketing: 4 Levers for Cross-Channel Success.

In upcoming blog posts we’ll explore AI’s impact the other 3 levers of marketing:

  • “The What” with Predictive Recommendations: Determine the right piece of content, offer or product to show each customer
  • “The When” with Predictive Engage Time: Optimize the delivery of the campaigns to the times when each individual customer is most likely to engage
  • “The Where” with Predictive Channel-of-Choice: Deliver the campaign on each individual customer’s channel-of-choice

 


Download ROI of AI Marketing: 4 Levers for Cross-Channel Success


 

Gartner Names Blueshift a Cool Vendor in “AI FOR MARKETING”

While AI may sound like another buzzword, it’s proven to provide tangible, measurable ROI to marketers. That’s why yesterday leading analyst firm, Gartner, released its first ever report titled, “Cool Vendors in AI for Marketing,” by Andrew Frank, Mike McGuire, Bryan Yeager, Benjamin Bloom. This Cool Vendors report evaluates interesting, new and innovative vendors, products and services in AI for marketing, and Blueshift was selected and featured as an innovative new company!

Gartner’s report points out, “AI’s capacity to transform marketing is obscured by a fog of hype, but the breakthroughs are real. Marketing technology leaders need to engage in AI initiatives or risk being blindsided by disruptive AI-enabled competition.”

Blueshift has built it’s AI-first platform to help marketers drive growth and customer engagement by using AI to both automate and make better, quicker decisions about the “Who, What, When & Where” that will generate the greatest ROI from their cross-channel marketing. Specifically, by harnessing and analyzing streams of real-time customer, campaign, website, transaction, and product data it enables marketers with:

  • Predictive Audiences (“Who”): Selecting the best customers to target for a marketing campaign.
  • Predictive Recommendations (“What”): Determining the right piece of content, offer or product to show each unique customer based on where they are in the customer journey.
  • Predictive Engage Time Optimization (“When”): Optimizing campaign delivery to the times when each unique customer is most likely to engage.
  • Predictive Channel-of-Choice (“Where”): Delivering campaigns on each unique customer’s channel-of-choice.

By combining these elements, Blueshift helps transform multi-channel campaigns, currently designed with cumbersome manual rules, into AI-enabled orchestration based entirely on a customer’s self-directed journey. This enables marketers to seamlessly scale personalized campaigns.

In the report, Gartner highlights that Blueshift achieves this because “its solution combines capabilities typically found in disparate marketing technologies, such as customer data platforms, marketing automation and personalization engines, into a central real-time, AI-driven offering. As a result, marketers can define parameters and develop dynamic creative instead of spending hours building out intricate multi-channel journeys with rigid templates.”

It is a great honor to be named a Gartner “Cool Vendor” for AI in Marketing. Our vision has been to put AI into the hands of every marketer. We are excited that Blueshift’s AI-Powered platform is helping cutting edge marketers transform customer engagement at every step of a multi-channel journey.

Gartner’s report is a great resource for CMOs and marketers seeking to harness large volumes of fast-moving data and the latest technologies to drive customer engagement and marketing ROI.

Disclaimer:

Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.


Watch the webinar AI-Powered Brands

Learn how nimble, high-growth consumer brands use AI-Powered marketing to spark customer engagement and ignite revenue. Watch The AI-Powered Brand Webinar.


 

Use AI techniques to personalize and optimize your communications to reduce subscriber churn.

Stop Subscriber Churn in Its Tracks with These 4 AI-Powered Campaigns

Churn is a natural part of every business. However, no organization likes their users to churn. So, what should you do (especially after you’ve been trying tactic after tactic to no avail)? Use AI techniques to personalize and optimize your communications.

According to a recent “State of Marketing” report, there’s a 50% chance that customers will switch brands if brands don’t anticipate their needs. The goal of marketers is to build strategies and campaigns that will (1) keep active customers engaged, (2) re-engage “at-risk” customers, and (3) bring churned customers back into the fold.

So, what can growth marketers at subscription companies do to reduce churn?

1. Intervene before it’s too late!

“Newsletters are stupid,” said by Alex Shultz, Facebook’s VP of Growth. He believed that most companies today are sending marketing emails that are just spam. Why? Because that same newsletter will be sent to everyone in their email list — to someone who has been enjoying your product for three years and to someone who has just signed up to your site yesterday. No distinction at all, no personalization, no understanding of who that individual person is. Schultz said that companies should be focusing on notifications and triggers-based emails, SMS, and push notifications in reaching out to their customers.

Growth marketers will keep their customers engaged when they provide delightful, relevant content while personalizing it based on their customer’s expressed and perceived preferences. One way to do that is by relying on 1:1 marketing, with no two users receiving the same message at the same time.

IN SHORT: Keep customers interested in your products or services by sending personalized offers or incentives using targeted email or push notifications. (Hint: NEVER rely on a single channel to drive repeat engagement.)

2. Strike while the iron is hot. Quick, send them that enticing incentive!

According to a Pegasystems survey on customer engagement, 56% of top-performing companies are investing in AI to personalize and continuously learn from customer interactions. Today’s AI-powered productivity tools make it possible to anticipate customer needs which allow marketers to tailor highly-personalized campaigns to keep customers engaged.

One example is by using trigger-based email marketing, an example of a customer-centric, behaviour-based marketing approach. Recent Forrester Research showed that trigger-based email marketing campaigns can generate 4x more revenue and 18x greater profits. Subscription upsells are one way of retaining active customers by sending them personalized messages containing information on products they might be interested in. Offering incentives to customers to switch to the next subscription tier can also help in convincing them to upgrade their subscription.

IN SHORT: AI can easily help growth marketers gauge their customers’ willingness to accept an upsell.

3. Out of sight, out of mind? Go remind them.

Abandoned carts may mean that the customer has forgotten he added an item to his cart or he really didn’t want the item.

Growth marketers can still turn abandoned carts into revenue. Remember, these customers have already expressed an interest in the products and are engaged with the brand, all they need is a little nudge to complete their purchase.

With the help of AI, growth marketers can send targeted recommendations that are similar to the one that the customer already added to his cart. These recommendations may include product information on new arrivals, price drops on items with which the customer has engaged, or “back-in-stock” notifications. Even if the customer didn’t really want the abandoned item in his cart, his interest may be piqued by the new recommendations. These triggers are especially good for mobile push notifications since they are “newsworthy”.

4. Win them back.

Win-back campaigns involve the re-activation of churned or “about-to-churn” customers. Winning back churned customers so they can be active again is never an easy task. Move back from using traditional marketing campaigns which lack sufficient real-time data and insight.

AI helps in analyzing vast volumes of customer data especially in identifying the characteristics of high-value past customers. But for AI tools to work effectively in your win-back campaign, you need to feed it with the right data and algorithms.


Growth Marketers Guide CoverA well-designed AI system can streamline an organization’s complex processes. Leveraging marketing AI can provide a significant, tangible lift to an organization’s customer engagement efforts. You can learn more about how Growth Marketing drives increased user engagement by downloading our whitepaper.

 


 

At Blueshift we believe it's important to give back to your community, especially the neighbourhood you work in.

Culture at Blueshift : Supporting local neighborhoods through volunteering

Every year we enjoy summer with outdoor fun events, whether it’s hiking Angel Island in SF Bay, getting there by Sail boat or rafting down the American River in the Sacramento area. This year our team members wanted to do something different and give back to the local community we are part of. So we signed up with San Franciso Rec & Parks. They matched us with their upcoming events and hosted us in St. Mary Square in China Town. We arrived early, mulched the whole park and cleaned up the space so our neighbors can enjoy the park. And we enjoyed the park ourselves by eating a delicious lunch and savoring one of those picture perfect days. Can’t wait for the next summer !!

5 Practical AI Techniques for Improving Customer ROI

Seventy-seven percent of marketers believe that real-time personalization is crucial, yet sixty percent struggle to personalize content in real time.

Personalizing content in the form of giving the right recommendations for each user, scheduling the content based on each user’s behavior, and then selecting the right communication channel require processing large amounts of data to understand customer preferences at a personal level. This strategy is accomplished most effectively and efficiently using artificial intelligence (AI) systems.

AI systems process large streams of data in real time and develop models that understand customer intents and preferences. For instance, AI can be used to score users on their likelihood to churn or purchase in the near term. It can understand a customer’s propensity toward various categories, balance content freshness with popularity, and recommend the next best content or product for every customer. It can also interpret the data to understand the optimal time and channel to engage each customer.

Here are the top five practical AI techniques for improving customer ROI:

1) User Intent Predictions

Modeling user actions on your website or app enables you to predict behaviors and attributes that are correlated with near term actions, like purchases or churn.

For instance, an e-commerce website with a 5% session conversion rate has 95% of all sessions abandoned. But all abandoned sessions are not alike. And it’s imperative that marketers understand the true composition of this group. Typically, some are serious, likely buyers, while the rest are casual visitors who are just browsing your site. A predictive engine, can help you build a model that separates your likely buyers – that can often comprise 25% of this group – from the rest. These high intent users are typically more than 2 times more likely to respond to email messages than the average recipient and yield 7-12 times the ROI from paid advertising such as display retargeting.

2) User Affinity Predictions

Unlike user intent (which is near term), user affinity models give you an idea of the user’s persona and lifetime value. It is done by using the concepts of affinity marketing to categorize users into affinity groups based on their demographics, preferences and behavior.  Using these attributes, one segments customers by the product categories and brands they prefer, similarity in attributes to known customers such as preferences for certain authors or their preferred price bands for specific types of products.

3) Product and Content Recommendations

Once you know the right set of users to target (e.g., high value users with high purchase intent), you need to understand the right content or product selection for each user. Techniques such as collaborative filtering, which makes predictions about a customer’s preferences based on the preferences of similar customers, and unsupervised clustering, which uses data analysis algorithms to find “hidden” patterns or groupings in customer data sets, can help determine the right set of products for each user.

4) Personalized Promotions and Offers

Since promotions directly impact the bottom line, you should not only model who will be receptive to the promotions, but also drive a change in a user’s behavior by offering the promotion. The former is known as affinity modeling or response modeling, while the latter is known as uplift modeling. In uplift modeling, you try to find users who would not have transacted with you without an offer and—from among these users—find the ones who have a high likelihood of responding to your offer.

5) Creative and Model Optimization

When you have a set of “always on” running campaigns, it’s important to set aside a budget for exploring new ways to auto-optimize creative or data science models using a challenger and champion paradigm.

Traditional A/B testing models offer a quick path to finding an initial set of champions among creatives and data science models. You can go beyond that by using Bayesian optimization algorithms that test a new set of challengers against current winners. Auto optimization platforms can do this every time a new variant is added to the system by running these tests automatically to yield results that optimize return while minimizing variability. This is similar to the concept of efficient frontier in portfolio theory.

You Need an AI-Powered Marketing Platform with Access to Customer Data

Marketers can unlock the full potential of AI by using a platform that gets advanced access to your customer data and applies these AI techniques to improve your marketing campaigns throughout the buyer’s journey. Learn more about the Blueshift Platform here.

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