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


 

From Acquisition to Retention: Top AI Techniques for Today’s Marketing Leaders

Top AI Techniques for Today’s Marketing Leaders (From Acquisition to Retention)

In our recent survey, more than 60% of marketers revealed that they are planning to increase their usage of AI going forward — proof that more and more marketers are realizing AI’s potential to enable greater marketing success.

However, very few marketers are taking advantage of advanced AI capabilities that can not only enable them to do more than just acquire new prospects but also convert them into loyal and more valuable customers.

Incorporating AI into All Aspects of Marketing

How marketers integrate AI into their strategies can determine their overall marketing success. They should consider AI as an integral component of their entire marketing game plan instead of treating it as a mere tool to bolt on when needed. Even before they consider specific AI techniques to implement, they should first look at their strategy for using AI by asking the following questions:

  • How can AI help us better find and engage new prospects?
  • How can AI enable us to engage customers?
  • How can AI turn our existing customers into more valuable customers?

Here are our recommendations on using AI for these three key aspects of marketing.

1. New Prospect Acquisition

Acquiring new prospects can be a very difficult endeavor no matter the size of the business. Increasing [brand] visibility and generating quality leads are two of the biggest marketing challenges businesses face.

Traditionally, marketers employ highly manual, time-consuming, and blunt approaches to win new customers. To get a decent list of prospects, for example, they use demographic data such as industry and job title to purchase lists, get referrals, and harvest their website visitors. After pulling this data into their systems, they then have to start engaging with these prospects to further narrow down this list to pinpoint and target the right customers.

AI can give marketers the capability to obtain relevant and useful information quickly using behavioral data. In fact, almost half of surveyed marketers (43%) use AI primarily to acquire new prospects using audience expansion techniques such as the following:

Look-alike audience expansion.
AI can be applied to the demographics, preferences and behavior of your existing customers to develop predictive scores of your best customers. You can then use this set as a seed list on a large network like Facebook to acquire similar audiences there.

Targeting and re-targeting users.
Almost 40% of surveyed marketers use AI techniques to better target audiences on large networks such as Facebook and Google. AI can be applied to prospect behavior to develop predictive scores that can then be used to re-target prospects with specific offers on the large networks. Similar techniques can be used to re-activate existing customers and prevent them from churning.

Percentage of marketers using AI in their marketing today and how they are using it

Tip: While these AI-powered techniques are helpful, marketers should put the same or even more emphasis on the other stages of the Buyer Journey. Which brings us to the next point.

2. Delivering Personalized Customer Engagement

One of the biggest challenges for marketers today is around delivering personal and content-rich experiences at every touchpoint in the customer journey. AI has a lot to offer today to enable marketers to intelligently and insightfully engage their customers on a highly targeted basis, with recommendations and micro-segmentation.

Marketing Stats - Percentage of Marketers using advanced AI in thier marketing

Using AI-enabled technologies like collaborative filtering, for example, marketers can predict customers’ interests based on the preferences and behaviors of others similar to them. But despite the proven capabilities of collaborative filtering, only 6% of marketers are using it. Similarly, only 16% of marketers are using predictive techniques to learn user preferences or affinities for various products and services based on their behavior and to segment them using these affinities.

Tip: To win today’s customers, marketers should provide them with highly personalized content. To be able to do so, they should apply advanced AI technologies like collaborative filtering and predictive affinities to the engagement phase of the Buyer Journey.

3. Activating Your Customer Data to Retain More Customers

According to Harvard Business Review, “acquiring a new customer is anywhere from 5 to 25 times more expensive than retaining an existing one.” So if marketers want to save on costs while keeping their sales up, they should focus on creating more value out of their existing customers. To do so, they should harness both historical and real-time customer data—but herein lies the problem.

The majority of marketers (54%) are using less than half of the customer data they have. As a result, they are not taking advantage of the potential in this data to help them improve customer engagement and increase share-of-wallet. The study results also show that those marketers who have been able to get advanced access to their data are 2.4 to 2.8 times more likely to deployed AI techniques like predictive affinities and collaborative filtering.

Marketing Stats - Markters with greater access to customer data are up to 3xs more likely to use advanced AI in their marketing

Tip: To make the most of their AI investments and deploy leading-edge AI use cases, marketers (particularly non-technical marketers) should have advanced access to data and should activate more customer data.


Data Fuels AI-Powered Marketing

Marketers will only unlock the potential of AI if they can first get advanced access to their data and then apply AI techniques to improve engagement all through their buyers’ journey.
Download Blueshift's report on the state of AI, Marketing, and Customer Data - lots of marketing stats

Get the full report here

How recommendations help you stay relevant in content overload

How Recommendations Help You Stay Relevant in the Era of Content Overload

One thing we can guarantee about the future: we’re never going to run out of content.

Take TV for example. Where once we had a handful of channels broadcasting one program at a time, we now have multiple streaming platforms, countless cable channels, on demand, and DVRs.

Or music: You’re not limited by your carefully curated CD collection anymore. You can choose from almost any song ever recorded on Spotify.

For the content consumer, it’s an embarrassment of riches. For businesses that rely on advertising or subscription revenue, it’s a challenge.

Attention spans are shrinking. With endless options, consumers will move on in matters of seconds if what they see or hear doesn’t capture their interest.

To stay relevant in the media industry — bringing targeted audiences, charging top dollar for your ads and maintaining a healthy growing subscriber base — your content needs to be relevant.

And, of course, every consumer’s tastes are different. The key to relevance is personalization recommendations.

For example, after revamping its mobile website to deliver a personalized, Facebook-like experience, USA Today saw a 75-percent increase in time spent per article.

Recommendation Models Used By Successful Advertising and Subscription Businesses

As content executive Paul Lentz points out, publishers have been using data to target specific content at specific audiences since the print era.

In today’s digital era, a few successful media companies have developed recommendation techniques to engage and retain users with almost supernatural precision.

  • After experimenting with content-based and collaborative filtering, the New York Times settled on a best-of-both-worlds approach that models the content and adjusts it according to viewing signals from readers, models reader preferences, and uses the resulting data to make recommendations.
  • Netflix’s recommendation engine divides users up into “a couple thousand” taste groups. Netflix claims the engine is worth $1 billion a year and is responsible for more than 80 percent of the shows users choose.
  • Spotify’s Discover Weekly playlists have become a favorite feature among users for introducing them to new songs and reminding them of old favorites. The “magic” of the algorithm, the man behind the playlist says, comes from comparing your listening habits to those with similar taste and “filling in the blanks.”

What does each of these approaches have in common? Each media company leveraged a massive database of user data to make comparisons among users, identify trends in their preferences, and anticipate their behavior.

You can do the same with Blueshift’s AI-powered marketing platform. Blueshift can help you

Learn how to configure recommendations in a single click or bring your own algorithms to BlueShift Personalization Studio.

How Data AI and Automation Can Drive On-Demand Growth

How Data, AI, and Automation Can Drive On-Demand Growth

In the previous post, we outlined 5 tips for success in the on-demand economy. What each of these five tips comes down to is understanding what your users and service providers need, when they need it, and responding quickly and fluidly. In other words, anticipating what they want when they want it, and being there when they’re ready. This is how you build a community of enthusiastic, passionate users and service providers, eager to tell others about your brand.

How can you do all this? With the power of data.

We like to say that on-demand marketplaces are data-driven businesses. Every on-demand platform uses data in some way, and the successful companies are data-intensive operations.

But it’s not enough just to have the data about your customers and service providers. You need to derive useful insights from the data to provide immediate results and delight customers in real time. But with so much data coming in so quickly, our human brains can’t keep up. Artificial intelligence (AI) can.

Here are few ways successful on-demand service companies can use data and artificial intelligence to drive growth.

    1. Personalized onboarding. In the delivery business, once users sign onto a platform, 80 percent never leave. Artificial intelligence can pour through your customer data to personalize the onboarding process, ensuring customers feel cared-for even before they use your service.
    2. Personalized triggers and notifications. What users love about on-demand services is that they can find and track service providers effortlessly. AI platforms can automatically update customers and suppliers on delivery times, offer personalized choices based on where they are in their user journeys.
    3. Repeat transactions. Users who use the service again and again are the holy grail for on-demand businesses. With artificial intelligence, you can automatically target upsell and cross-sell opportunities for both buyers and service providers based on data. You can also segment them based on who is likely to re-engage or likely to churn, so you can target them with a tailored strategy.
    4. Curated content and offerings. Consumers demand relevant content, tailored to their interests and delivered to their device of choice. AI helps you personalize newsletters, web and in-app content, and recommendations based on user interests and affinities calculated from historic and up-to-the moment behavior
  1. Location-based personalization. As we said, most on-demand businesses start locally. With AI, you can segment your users based on where they are — but even more importantly, the context of why they’re there. You can provide location-specific offerings and recommendations across any channel, including the web, your mobile app, and SMS.

Explore How AI Can Transform Your On-Demand Service

On-demand businesses grow when they use data to understand the needs of their customers and service providers and connect them seamlessly. With a 360-degree view of your users and real-time insights powered by artificial intelligence, Blueshift can take the data crunching off of your hands so you can focus on strategy, growth, and retention.

To learn more about how Blueshift helps on-demand businesses grow, click here.

Send-time Optimization

Send Time Optimization or Engage Time Optimization?

Marketers should adapt their send time to each user individually, and send campaigns closer to the times when they are more likely to engage in downstream activity.

As you might have read in our previous blog post “Re-Thinking Send Time Optimization in the age of the Always On Customer“, Blueshift focuses on “Engage Time Optimization” rather than what marketers traditionally call as “Send Time Optimization”. Since we’ve posted this article, we’ve elaborated a bit on the details of the development of that feature on Quora (When is the best time (day) to send out e-mails?). Through this post however, we would like share more of those insights, and advocate for focusing on optimizing downstream user engagement metrics rather than initial open rates.

The idea of “Send Time Optimization” is not new, and has been around for quite some time. One of the more recent reports on this was posted by MailChimp in 2014, but articles and discussions on this topic go back as far as 2009 and older. The data science team at Blueshift followed the hypothesis that if there is a specific hour of the day, or day of the week that an audience is more likely to engage, that should reflect in increased open (or even click) rates when messaged at different times.

Open Rates vs Click Rates

In order to observe this effect (or the absence of it), we analyzed over 2 billion messages that were sent through Blueshift. Some of the results are presented in the graphs below for one of our biggest clients.

Through the Lens of Open Rates

“irrespective of the segment that was targeted, the audience size and the send time, the open rate is the highest in the first two hours after the send”

We looked at the open rate (%, shown on the Y-axis) in the first 24 hours after the send was executed (in hours, shown on the X-axis).

open_rates

What you see are 18 email campaigns from one client over the period of one month (totaling over 20 million emails). On the top left, we see campaigns sent out on Monday, next, Tuesday, and so on – through Saturdays on the bottom right. There were no campaigns on Sunday for this client during this month. These campaigns were sent to audiences ranging from tens of thousands of users in specialized segments (e.g. highly engaged  customers) to large segments of 2–3M users. The send times varied from 5AM – 12PM (in parenthesis in the legend).

What you can see from this graph, is that even though the campaigns were sent out on different days of the week and at different hours, the initial response in term of open rates is very predictable for the first hours. The conclusion from these plots is that irrespective of the segment that was targeted, the audience size and the send time, the open rate is the highest in the first two hours after the send. Depending on the actual time of the send you can achieve a slightly higher open rate in the first hour, but you might loose more ‘area’ in the following hours, accumulating to more or less the same open rates after some hours.

Through the Lens of Click Rates

Naturally, the question comes to mind if there is any measurable effect when we look at clicks, which can be considered as a deeper form of engagement by the users that received the message:

click_rates

But as you can see from these second set of graphs where the Y-axis represents the click rate (%), we observed a very similar behavior: the actual response rate in terms of clicks does not significantly change when a campaign is sent at a different time.

We came to the same conclusion when repeating this experiment for opens and clicks for other clients in our dataset as well. After doing more in-depth analysis on our datasets, we observed that users that were targeted in email campaigns at certain times, showed engagement (e.g. visits to the website or app) at other times. Users prefer to engage deeply at certain hours of the day while casually browsing through out. Marketers should adapt their send time to each user individually, and send campaigns closer to the times when they are more likely to engage in downstream activity. You can find more info about this “Engage Time Optimization” in this post.

 

Engage Time Optimization

Re-Thinking Send Time Optimization in the age of the Always On Customer

Many email service providers tout Send Time Optimization as an add-on feature and promise marketers that they can tailor their marketing campaigns to the exact time their customers are expected to open their emails. It’s tempting to take that at face value and think it’s a silver bullet to improving your customer engagement. Our internal research, after analyzing over a billion emails sent through the Blueshift platform over last year, has shown that in the age of smartphones and always on connectivity, the notion of “Send Time Optimization” needs some serious re-thinking.

Stop Optimizing to “Open Rates”

“look at full downstream activity and measure what windows of time their customers are more likely to follow through and complete specific goals”

Today’s perpetually connected customers are much more likely to have many more frequent bursts of activity around the clock than a recurring habit of opening their emails at a certain time of day or clicking onto sites or apps at specific hour. Then what does it mean to do “Send Time Optimization” for marketers? Instead of optimizing for immediate opens, marketers need to focus their attention and look at full downstream activity and measure what windows of time their customers are more likely to follow through and complete specific goals than when they open or click emails. The true measure of success should be specific conversion goals or sum total of time spent on your site or apps.

As a results-driven marketer ask yourself: “Would you rather have someone who opened a message, or someone who converted/made a purchase?”

Enter => Engagement Time Optimization

Blueshift’s recently released Engage Time Optimization computes windows of time for each user where they are more likely to engage fully, rather than optimizing for immediate opens or clicks. We look at the sum total of time spent by each customer over a long period of time and rank each hour in the day based on time spent and how deep in the conversion funnel they got to. You can access “hour affinity” for each user through the segments panel under “User Affinity” tab inside our application dashboard.

Re-Thinking Send Time Optimization in the age of the Always On Customer - look at engage time optimization to optimize your campaign sends to further down the purchase funnel

 

You can use these “hour affinities” like any other user affinity attributes during the segment creation and tailor campaigns to specific audiences. For example you can create segments of users who prefer “morning” hours by picking 5am to 8am or those who prefer “evening” hours by picking 5pm to 8pm or any other combination. We believe this offers a powerful alternative to traditional “Send Time Optimization” feature by tailoring the campaigns to the customers based on their full funnel behavior than on immediate opens or clicks.

 


If you’d like to see a demo or request more information on Engagement Time Optimization, contact us via our site or email us at hello@getblueshift.com.