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.

7 Key Programs Online Retailers Must Launch to Drive Customer Engagement

7 Key Programs Online Retailers Must Launch to Drive Customer Engagement

Engaging today’s customers is harder than ever. According to a recent Marketo report, the majority of buyers expect all their interactions with a brand to be personalized — and they think that marketers can enable this if and only if they have a deeper understanding of their unique needs.

Many forward-thinking online retailers have already revamped their customer engagement strategies to cater to customer demand for personalized engagement. Take Amazon, for example – the e-commerce giant keeps winning on customer satisfaction because of its powerful individual customization strategies.

To be able to engage like Amazon does, you need to determine the right methods to employ to drive successful and sustainable customer engagement.


7 marketing programs that ignite customer engagement:


#1 Abandoned-Browse Recommendations Using Collaborative Filtering

The goal is to engage a prospective buyer who dropped by your online store — someone who searched for and browsed some tops and dresses, for example — but left without buying anything. Using collaborative filtering, you should email her within a day to show her similar tops and dresses that other customers liked. This will encourage her to go back to your online store to browse some more and, hopefully, make a purchase.

#2 Post-purchase Recommendations Using Collaborative Filtering

Never stop engaging a new customer. Encourage her to buy again. Collaborative filtering allows you to make recommendations based on an item she already purchased. Within three days of her receiving her shipment, show her similar or related products that she might be interested in based on product choices of other purchasers of the same item.

#3 Category Affinity

Offer your shoppers choices from a category of products or services that interest them. For example, you might have noticed that a shopper keeps searching for nursing wear and related items. You can take advantage of this affinity by recommending popular content from this category. Send her weekly updates to keep her engaged.

#4 New and Trending Content from Relevant Categories

Another proven strategy to engage customers is to notify them of newly added items and items that are converting the best in their areas of interest. Send them weekly “what’s hot” emails to showcase new and trending products that they might want to purchase. This strategy can also be effective for welcoming and engaging new buyers.

#5 Re-Targeting

What if your buyers recently searched and added some items to their cart but did not complete their purchase? Within three days of their activity, send them push notifications or emails to encourage them to complete their purchase. Show them again the items they wished to buy and similar items from the same category that might interest them.

#6 Replenishment Reminders

For products that get re-purchased regularly, such as staples and grocery items, remind customers to “buy it again.” Employ purchase frequency analysis to determine when to best send buyers replenishment reminders. Amazon does this very effectively for a range of items from groceries to batteries

#7 Content Update Alerts

Your buyers, particularly those who subscribe to “back in stock” alerts, would love to know about any changes in price and availability of their “favorite” items. Are they back in stock? Is their favorite item on sale? How many items are left? Alert them in real time as content updates happen.


Grow Your Audience using AI

These seven key programs will help you do more than just sell your products — they will help you grow your customer base and build loyalty. However, you need to leverage the right technology platform to be able to implement these programs effectively. Learn more about using Blueshift’s solution for retail and e-commerce and how other companies have benefitted from this.


To learn more, check out our comprehensive guide here for everything you need to know about the subject.

Growth-Marketers-Guide-to-Customer-Experience-720x180-blogfooter

Top 3 Reasons wy Mrters Struggle with Data. Learn what they are from Blueshift and learn how to overcome them.

Why Do Marketers Struggle with Data?

Our recent study shows that activating data is crucial to successful artificial intelligence (AI). However, 92% of the companies we surveyed said that they struggle with one or more of either data access, unification or analysis that prevents them from making better use of their customer data (see figure 1).

Old news to you? Perhaps, but the ever-increasing importance of data in marketing will ensure that those who cannot harness their data are going to be left behind. The problem becomes clearer when we drill down into each of these areas to better understand the situation and look for potential solutions.

Figure 1: Top challenges faced by  marketers

Data Access

Data access is the first and most fundamental step to working with data. It gives marketers the ability to use data from multiple sources in near real-time. Today’s marketers need quick access to data stored or generated from multiple systems both inside and outside their organizations. Here are some examples:

  • Customer record data from the CRM system
  • Purchase data from e-commerce and traditional point of sale systems
  • Campaign response history from the marketing system
  • Customer service data from the support system
  • Visitor and browsing data from the website
  • Ad clicks from advertising networks and social media networks
  • Social media activity and interactions from those systems

With so many sources of data, it’s not surprising that 46% of all marketers consider data access one of their top challenges. Besides the numerous sources of data, two other factors impede data access:

  1. Systems that are monolithic and focused on specific tasks but not designed for easy data access. Legacy systems, in particular, were designed to work as fully contained units where information extraction was episodic and infrequent rather than continuous.
  2. Organizational processes that emphasize guarding the data and limiting access to it rather than sharing it. Usually IT departments serve as guardians and gatekeepers to data access.  In our study, marketers who were given advanced access to data were 1.6x more likely to be using a majority of their customer data over those who had to go through IT to get access to this data.

All of this results in balkanized data that is hard to get to and difficult to access in real-time.  Solutions such as data warehouses and data lakes were designed for a “store and analyze” approach which is ineffective in today’s real-time environment.

Data Unification

Data unification refers to the ability to bring together data from multiple sources and connect them together. A unified view of customer data is a good example of this because it enables marketers to better engage with customers by offering them personalized experiences on not only their profiles (gender, preferences, purchases, etc.) but also their behavior (website visits, catalog views, mobile phone activity, current geographical location and social media activity).

McKinsey & Company refer to this as a 3D-360° customer view (see Figure 2) that comprises data from many sources that get updated often and needs to be used in almost real-time to see its value.  41% of marketers in our study considered this to be one of their top challenges.

Figure 2: 3D-360° View of Customer

Data Analysis

Analysis is perhaps the hardest of these functions so it’s probably no surprise that 54% of all respondents in our study considered this to be a top challenge.  As a 20-year data-driven marketer, I attribute the data analysis challenge to three things: lack of a strategic approach to the analysis, missing data analysis skillset in many marketing teams, and deployment of the wrong technology.

It all starts with strategy.  Very often marketing teams jump into data analysis expecting to dive into the data in the hopes of an answer revealing itself.  And very often, such efforts take a lot of effort and don’t produce the results expected.  It’s the “dumpster diving” of data analysis. Instead, it is far more effective to start with one or more hypotheses, frame the questions you want to answer and then start the analysis.

There’s no substitute for having the right people. Data-driven marketing has been the domain of a relatively small number of people and good talent is hard to find.  This leads to marketing leaders confusing people who know how to create reports and run operations with those who can effectively frame the problems they are trying to solve and use analytical techniques to solve those problems

Use the appropriate technology to solve the problem rather than using the latest technology hoping to get the answers.  This starts with setting up the problem correctly, identifying use cases and then using the right technology that collects and helps analyze the data.

McKinsey & Company go beyond these three general areas to identify several other more specific issues in their recent report, Ten Red Flags Signaling Your Analytics Program Will Fail.

The Way Forward

Data has been called the “fuel of the new economy.” And just like during the oil rush of the late 19th century, everyone’s drilling. Smart organizations will take a thoughtful and fresh approach to getting the most out of their customer data by:

  • Opening up access to the data so marketers can utilize most of their customer data
  • Providing a unified view of the data by dynamically accessing the data from multiple systems rather than trying to consolidate everything in a single system
  • Analyzing the data by first developing a strategy and process for developing hypotheses and testing each hypothesis by asking the right questions.

Learn how to overcome your customer data struggles in this new independent research, “Activating Customer Data for AI-Powered Marketing”. Get your copy now.

Download Blueshift's report on the state of AI, Marketing, and Customer Data - lots of marketing stats

5 Predictions on How AI Will Reshape Marketing

5 Predictions on How AI Will Reshape Marketing

According to the Tata Consultancy research cited in the Harvard Business Review, companies implement AI primarily to increase the effectiveness of information technology, marketing, financial trading, and customer service. Though AI promises many things in the field of Marketing, today’s deployments are far more pedestrian and focused on anticipating customer purchases or improving the efficiency of various marketing activities like monitoring social media.

However, AI’s impact in marketing over the coming years will be wide-ranging and far more significant than it is today. Here are the 5 key changes that we see taking place.

#1 Customer experience becomes more personal, less robotic

AI will make the Customer Experience more personal, and paradoxically less robotic.

This may sound counterintuitive, but customers are quite comfortable with bots. Research by Pega Systems shows that 55% of all customers surveyed were comfortable with a business using AI to interact with them. 19% were uncomfortable and 26% were neutral. Many respondents felt that bots can help improve customer service particularly in retail, healthcare, and telecommunications. Anyone who has had to navigate through a phone tree when calling their local phone provider will empathize with this sentiment.

However, customers would rather not encounter bots that cannot give them a contextual experience. Aftersales support, for example, is an area where customers would rather deal with a human being. They want real conversations.

Recent innovations in AI are aimed at addressing such gaps. The rapid innovation in technologies like Siri and Alexa are harbingers of things to come.

#2 From driver-assist to self-driving marketing campaigns

Just as AI technology in cars is moving from driver-assist to self-driving cars, AI in marketing will evolve from improving tasks like segmentation to helping orchestrate an entire campaign.

Most AI technologies for Marketing are designed to enable marketers to streamline manual and repetitive tasks. In the future, this will change. We will see AI playing an important role in orchestrating entire campaigns. The campaign builder technologies of today will be replaced by AI-powered campaign builders that will use data to personalize messages and offers, choose the best channel mix and the best times to execute those campaigns.

Forward-thinking marketers are already taking advantage of AI to connect customer experience from the top of the funnel down to the bottom. In our recent survey, for example, marketers revealed that they are using AI to overcome their top challenges in orchestrating customer experience at the top of the funnel. More than 40% of surveyed marketers are using AI for audience expansion, while 39% are using it for audience targeting.

#3 More time on strategy, less on manual operations

AI will give marketers more time to spend on strategy and less on manual operations.

Collaborative filtering and offer optimization are just some of the many aspects of marketing that AI can optimize. Any problem that involves the analysis of large amounts of data to see patterns, AI is a good tool to tackle that problem.

This leaves marketers with the not only the time but also convenient access to results from the analysis of large amounts of data. Marketers should interpret these results and factor them into their future strategy.

#4 More creative story-telling with expanded reach

Marketers will once again become creative storytellers, with AI helping them scale their storytelling to millions of users.

According to MarTech Advisor, 95% of content has no impact on engagement and is largely wasted. Marketers should combine the “old school” skill of storytelling with new AI-powered techniques like sentiment analysis to both create and deploy the most engaging content that people will consume.

AI can also be applied to distribute this content in a highly personalized manner based on the preferences and content consumption behavior of the recipient.

#5 Thrive or die based on the use of AI

Brands will either thrive or die based on how well they adopt and use AI in marketing.

Recent research by Deloitte shows that AI has sparked a renaissance in modern retail. An industry that was not so long ago in a “retail apocalypse” has bounced back and grown faster than overall GDP every year since 2009. Brand leaders have invested in AI to better personalize the retail experience for their customers.

Most industries will face a similar challenge and will need to figure out how to adopt AI or be at a competitive disadvantage. Data access and activation will be a key determinant of success. Our research shows that businesses that use more than 75% of their customer data in their marketing and 1.4 times more likely to exceed revenue goals than those who don’t.


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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.