Is Identity Resolution the New, Must-Have Martech Solution?

There’s a bit of growing confusion and buzz in the martech space around the topic of identity resolution. It’s the new elixir being pitched as the critical additive to make your marketing technology stack work better, faster, and deliver better results. But is it?

There’s a bit of growing confusion and buzz in the martech space around the topic of identity resolution. It’s the new elixir being pitched as the critical additive to make your marketing technology stack work better, faster, and deliver better results. But is it?

For those of you familiar with the marketing technology space, every new solution comes with a blend of real value, hyperbole and needless complexity. Identity resolution is no different. Here I will try to unpack this relatively “new” capability and put it into perspective for marketing leaders. (Why did I put new in quotes? Keep reading to find out.)

What is Identity Resolution?

Identity resolution uses artificial intelligence (AI) to connect customer interactions and achieve a single customer view. The concept of capturing all customer interactions (marketing, engagements, sales, post sales), at the individual level, has been around for many years. However, achieving this goal has been very hard.

The reason is that customers interact with your brand across multiple channels (online and offline) while using multiple devices. Additionally, some interactions are anonymous or only provide limited identifiers. This interaction variability results in very complicated, disjointed customer data.

Until recently, most efforts at achieving a single customer view involved creating rules engines by which each interaction could be matched with other interactions and assigned to a single customer. Due to differences in the technology stack, channels employed, and the customer experience, rules engines had to be custom-built for each organization. This was expensive; enter AI.

Identity resolution uses AI in generating matching logic vs. using a team of analysts. The basic idea is to train the AI algorithm using known matches and then validate future correct matches the algorithm makes. This is why I refer to it as a “new” capability. In reality, it is only new because rules engines have been replaced by AI. For most marketers this change is only relevant if the match rates are better and the solution is cheaper than existing efforts are at achieving a Single Customer View.

What’s the Hype and Confusion About Identity Resolution?

While the addition of AI is innovative, it does not always translate into better match rates. Other major challenges with single customer view, such as the accurate collection of relevant data, still remain. AI, like any other analytic solution, also suffers from bad data and can put out spurious results. Therefore, verifying and validating AI matches is a task in and of itself.

The next issue to keep in mind is that identity resolution is probably not going to be sold as a separate solution in the near future. Within a short period of time, it will be integrated into larger martech solutions such as CRM or marketing clouds. Waiting to implement identity resolution could mean leaving the difficult task of systems integration to the cloud solution providers. However, the trade-off will be losing first mover advantage.

What Is the Value?

Single customer view has been the holy grail in marketing for good reason. With it, marketers can better understand the impact of interactions across the full customer experience life cycle. As an added benefit, marketers could also generate data-driven justifications for modifying or redesigning large segments of the customer experience. This will result in significant growth opportunities for your brand.

Despite the hype and confusion, identity resolution presents a great opportunity to finally achieve a single customer view. In theory, the introduction of AI should make identity resolution a desirable solution with better match rates and lowered costs. This means the evaluation of identity resolution tech is somewhat straight forward (though not necessarily easy).

The core evaluation question becomes, “Is the identity resolution solution cheaper and better at creating a single customer view vs. current efforts?”

4 Ways Artificial Intelligence Can Impact Your Conversion Rates

At this point, there is little doubt that artificial intelligence is the future of business. The Salesforce “State of Marketing” report found that more than a fifth of businesses currently use AI for marketing purposes, including programmatic buying, personalization, and real-time offers.

At this point, there is little doubt that artificial intelligence is the future of business. The Salesforce “State of Marketing” report found that more than a fifth of businesses currently use AI for marketing purposes, including programmatic buying, personalization, and real-time offers.

artificial intelligence graphic
Credit: Salesforce

Further, AI is the fastest-growing sales technology, according to the Salesforce “State of Sales” report.

Outside of sales and marketing, companies are frequently using artificial business intelligence for tasks like reporting, dashboards, and data warehousing and analytics.

While applying AI to these business operations is certainly beneficial, it does beg the question of how exactly this technology will impact the future of conversion optimization, as well as the most important person in a business: the customer.

At the end of the day, the thing that really matters in business is the numbers. AI technology for analyst reports and predicting turns in the market is all well and good, but if it isn’t boosting sales, then what is the point?

The good news is that AI is showing promising results in terms of conversion rates, proving once again that big data is paving the way to a more profitable future for many companies. Here’s how.

1. Enriches Customer Experience

The concept of improving the customer experience (CX) is a big challenge for many reasons. CX is not merely limited to the user-friendliness of a website or the customer service that is provided; it is a combination of all of these elements. Yet another report from Salesforce found that consistency is a core element in a positive customer experience, and 70% of customers say connected processes based on earlier interactions and contextualized engagement are important for them to do business with a company.

This means that in order to improve the CX for customers, brands must adjust every part of the experience to create a coherent message.

Studies have found that customers are willing to pay more for a better experience with a business. It also has a strong effect on their likelihood to repurchase and refer the product or company to friends.

artificial intelligence graph
Credit: Temkin Group

But what exactly makes up “customer experience” and where does AI fit in?

CX is essentially the accumulation of every interaction a customer has with a business, from introduction, to purchase, to customer service. As experienced business owners know, one small kink in the journey can send people running. AI and machine learning technology can help create a more optimized experience for each customer, from start to finish.

For example, when fashion brand FlyPolar experienced a near 400% decrease in sales in the span of just four months, the business executives knew that something wasn’t right. Because most of its customers purchased online, FlyPolar used AI software to optimize its website landing pages. By using machine learning technology, this AI program “learned” which designs performed best and delivered positive results.

After several weeks of testing, the AI system identified the core roots of the conversion problems and provided the proper insights for solutions. FlyPolar created a simpler four-step conversion funnel on its website, with optimized CTA button placement throughout the landing pages. By using machine learning algorithms, FlyPolar increased its checkout page traffic by 16% and its order value by 13% in just three weeks.

This case study shows that AI technology can quickly and easily identify the root of the problem, arguably one of the most difficult parts of optimizing the CX.

The prediction capabilities of AI-powered systems can also make it easier for your customers to find exactly what they are looking for; which, in turn, improves their experience with your website. Traditional searches base results on matching keywords or similar phrases, which may or may not be accurate. In contrast, present-day search programs use ML to “learn” consumer behavior and accurately return the items that match their queries, based on their previous behavior.

ML-based search takes numerous data points into consideration, including past view and click rates, ratings, and even inventory levels to provide customers with appropriate and targeted results.

It should be no surprise here that Amazon is one of the leading retailers to utilize this kind of technology. Amazon’s recommendation engine uses item-to-item collaborative filtering to provide search results that are based on multiple data points, rather than just keyword matches. Not only does the algorithm take each customer’s past searches, purchases, and product views into consideration, but also the ratings and popularity of each item.

artificial intelligence example
Credit: Amazon

Since Amazon debuted an AI-based recommendation engine, its profits started growing exponentially. By basing search results on multiple criteria, Amazon is able to push certain products while providing shoppers with the results that fit their needs, providing a better experience for the customer with each query.

2. Enhances Personalization

Buying online is no longer a one-size-fits-all experience. In fact, customers are becoming more and more unyielding that businesses customize just about everything to fit their needs. According to Accenture’s “Personalization Pulse Check” report, three out of four customers report that they would be more likely to purchase from a brand that offers personalization and recognition than businesses that do not.

Personalization is also directly related to higher profits. Researchers have found that businesses utilizing big data systems to create personalized experiences for their customers report up to 10% higher revenues.

AI is able to take the guesswork out of personalization. One of the best examples of this strategy in action comes from Starbucks, which reported a 300% increase in customer spending thanks to its highly-customized marketing program. Customers regularly receive personalized offers and incentives to earn more points toward a free drink reward. Every customer’s offer is based on past behavior, including how often each customer purchases and which types of items the customer tends to buy.

Starbucks’ AI-powered personalization system sends out around 400,000 variants of emails with incentives that are almost entirely unique for each recipient. Due to the hyper-personalization that Starbucks offers, many customers find it easy to fulfill the requirements for these rewards. This does wonders to increase consumer participation, purchase frequency, and ultimately, customer loyalty.

artificial intelligence in loyalty programs
Credit: Starbucks App

Of course, loads of consumer data are needed in order for online companies to provide this high level of personalization. Each customer’s preferences, demographics, and behavior must be tracked and analyzed in order for brands to properly adjust their strategies to fit an individual consumer.

The results from integrating personalized messaging and marketing speak for themselves: 63% of marketers report that an increase in conversion rates was the top benefit they saw from personalization.

AI-powered personalization can be used to help customers move their way through the buyer’s journey, as well. Using ML, these programs use predictive analysis to incentivize shoppers with personalized messages, email campaigns, retargeted ads, and more.

The algorithms can study consumer behavior so that ads and other messages are sent at the right time and trigger the ideal response. For example, an algorithm that tracks customers’ click rates and scrolling habits can predict when new customers are likely to abandon their carts and send a well-timed message or personal offer to keep them engaged.

artificial intelligence-generated offer

3. Improves Results of A/B Testing

Most marketing teams and web designers rely on A/B testing to determine the best layouts, color schemes, and messaging to grab their customers’ attention. However, there are obvious limits to the “old-fashioned” testing approach. Gathering the research takes time, and there is not always a clear winner from the results.

In fact, the traditional form of this strategy may not even be effective. Jeremy Miller, marketing director at Sentient, said during an interview:

In traditional A/B testing formats, you have your control vs. an experiment. You run that experiment against your traffic, and whichever design performs better is the one you deploy … but people have found that six out of seven experiments don’t result in a positive outcome, so you actually have to put a lot of energy and resources to try to determine how you can actually increase conversions using A/B testing.”

AI can solve the three biggest problems with traditional A/B testing: time required, insight, and limited variables. By reducing these weaknesses, marketing teams have the ability to make informed design changes with the results and data to support them. Instead of taking a linear approach to testing, AI can compare thousands of variables at the same time and instantly compare the results to determine the best combination.

For example, online lingerie company Cosabella used an AI-driven testing approach when it was redesigning its website. Rather than comparing designs two at a time, like a traditional A/B test would, Cosabella was able to carry out an A/B/n experiment with 160 different design elements, simultaneously. With that many variables, it would have taken up to a year of A/B testing to gather results; with AI, the process took only seven weeks.

artificial intelligence testing

Through this testing process, Cosabella was able to determine the aesthetics that resulted in better conversions. It found that customers bought more when CTA buttons were pink, rather than black. The company also determined that family values resonated with its customers, so it did away with “free shipping” banners and replaced them with “Family Owned Since 1983.” After these short seven weeks of testing, Cosabella reported a 38% increase in conversions and a 1,000% lift in newsletter signups.

4. Speeds Up Customer Service

The faster a company can respond to customer inquiries or issues, the better. For this reason, the demand for live chat grew by 8.29% last year. Unfortunately, most businesses do not have the resources to keep their customer service departments running 24/7, leading to long response wait times for disgruntled customers.

By automating customer service with AI-powered chatbots, businesses can not only solve the issue of wait time, but also the quality of the response and assistance that customers receive.

In 2012, Amtrak’s customer service department serviced 30 million passengers each day. Obviously, with such high numbers, it was difficult to handle individual inquiries in a timely manner, so Amtrak decided to jump on the chatbot train with its AI-powered customer service rep “Julie.”

Julie was able to resolve most of these issues by pre-filling forms through scheduling tools and guiding customers step-by-step through the online booking process. Because most of these problems were handled online, the number of calls and emails decreased dramatically. At the end of the first year, Julie had answered over 5 million questions, increased booking rates by 25%, and generated 30% more revenue, thanks to upsell options included in the messaging.

artificial intelligence chat

In terms of conversions, live chatbots can not only resolve issues in an instant, they can increase the chances that a customer decides to buy. When a customer’s issue is solved quickly, they are twice as likely to repurchase from that brand. Live chat is also the preferred method of communication for resolving problems or issues; however, it is important to note that the quality of the messaging far outweighs the speed of the response.

According to Kayako’s report on live chat service, 95% of customers say that receiving a thorough response that answers their question or resolves the problem is more important than just getting a quick reply. This is a major issue that many companies have with AI chatbots; they are simply programmed to give automated, scripted responses, which 29% of customers report as simply frustrating and unhelpful.

This is where AI-based chatbots save the day; they can adjust their messaging based on FAQs, as well as the customer’s phrasing and responses. This process leads to better and more natural replies from bots that delight customers and give them the timely information they need.

An AI chatbot is not a one-time fix to the issue of customer service. It is a strategy that must be properly monitored, adjusted, and perfected over time in order to deliver the best results.

The Wrap

Many conversations these days are revolving around AI and its impact on the future of business. And, quite honestly, it seems like the answer to just about every current business planning issue out there. Predictive analytics can tell you when things are about to change. Machine learning can understand your customers on a personal, granular level, and big data can keep track of every metric for accurate reporting.

However, one of the clearest benefits of AI is the direct impact it can have on conversions. It eliminates the guesswork from improving the CX of webpages and delivers timely and accurate testing results needed to increase the likeliness of conversions. Big data systems and AI make hyper-personalization possible to customize the experience for each visitor. Finally, chatbots can use ML to instantly engage with customers, resolve issues immediately, and close sales.

Success all boils down to how a business makes the customer feel. Most of the time, this is what determines whether or not a customer will purchase. Studies have found, unsurprisingly, that when customers feel special, important, and satisfied, they are more likely to buy from those brands. AI gives brands the power to do just that.

WWTT? JPMorgan Chase Opts for AI-Written Marketing Copy

Earlier this week, JPMorgan Chase announced that it had inked a five-year deal with Persado, a company with a product that produces AI-written marketing copy. Copywriters, don’t start hyperventilating … robots haven’t come for your jobs, yet.

Earlier this week, JPMorgan Chase announced that it had inked a five-year deal with Persado, a company with a product that produces AI-written marketing copy. Copywriters, don’t start hyperventilating … robots haven’t come for your jobs, yet.

According to a press release from Persado, in 2016 JPMorgan Chase started a pilot program with the company’s Message Machine product. Using the tool, Chase took marketing copy for its Card and Mortgage businesses and reworked it. The end result was a lift in clickthrough rates as high as 450% from AI-written marketing copy created using Persado’s tool, compared to a previous CTR range of 50% to 200%.

AI-written marketing copy from Persado outperforms the control.
Credit: JPMorgan Chase, sourced from Philadelphia Inquirer

Persado’s Message Machine uses a database of over 1 million tagged and scored words and phrases to create the AI-written marketing copy, but that doesn’t mean its use by Chase, and other brands, will render the need for “human touch” obsolete. In an article from The Philadelphia Inquirer, Erich Timmerman, executive director for media relations at JPMorgan’s tech-oriented office in San Francisco was quoted:

“The goal is to get to copy that resonates. Edits and review have always been integral to the process.”

Throughout Persado’s press release, Chase’s CMO Kristin Lemkau is quoted, giving high praise to Persado’s product, and stating: “Machine learning is the path to more humanity in marketing.” While I’m personally not sure where I stand on that last statement made by Lemkau, I also feel like you can’t argue with what is working for the financial services company.

Chase clearly took its time to dedicate itself and its marketing to go through the pilot program, and it liked the results. And to note, the financial services company is not the only major brand working with Persado — the client list also includes Dell, Air Canada, Staples, and more. But, according to an Ad Age article, Chase is the first marketer working with Persado to employ its AI writing across all platforms.

I think this move is an interesting one for Chase, and since it was made following plenty of testing, I think it makes sense. Do I think AI-written marketing copy will always win out? No. But I think we can learn something about copywriting from AI.

And at the end of the day, there are bigger things to stress about  … like French’s partnering with Coolhaus Ice Cream to create yellow mustard flavored ice cream. Worry less about a robot coming for your job, and more about why someone thought this was a good idea.


Financial Institutions Can Put Artificial Intelligence to Much Better Use

I’ll start with a potentially controversial statement. Banks are misallocating their investment in artificial intelligence and predictive analytics by putting it into consumer-facing chatbots, rather than using it internally to empower their staff to understand and better serve the customer.

I’ll start with a potentially controversial statement. Banks are misallocating their investment in artificial intelligence and predictive analytics by putting it into consumer-facing chatbots, rather than using it internally to empower their staff to understand and better serve the customer.

Most customers don’t like speaking with bots and usually call their bank when they have an issue that requires processing that’s beyond what artificial intelligence can currently offer. In fact, AI’s reputation has been damaged virtually beyond recovery by the endless loop most customers encounter when they call the bank, not able to get to where they want to go.

Moreover, you don’t see pictures of chatbots pinned up in banks with “Employee of the Month” emblazoned across the bottom. Nor was any new business won on the strength of a chatbot’s performance. Finally, customers don’t stay with banks because they developed a great working relationship with a chatbot. Truth of the matter, chat hasn’t reached the level where it’s consistently reliable for addressing the customer concerns that rise to the level of making a call to a financial institution.

All that said, artificial intelligence is a highly powerful tool. How it’s being used is simply being misallocated. So the question becomes, is there a way banks can use it to enhance human engagement with clients? The answer is, “Yes.” Although banks and other financial institutions are in a completely different line of business than, say, a luxury retailer or car dealership, what they have in common is that critical need to engage customers at various points in a given transaction. This applies to banks and other financial institutions at least as much as it applies to other businesses. Reaching out to, connecting with and maintaining relationships with customers, and doing it well, is a key consideration. Done well, banks have a better chance of securing a higher lifetime value from their clients when they get it right. And it’s much harder for bankers or advisers to know about the hundreds of products that are available to them; far more so than, say, a car salesman at a dealership, or an associate in the dress department at Saks. AI’s best use is providing them — the customer-facing bank advisers — with the tools to have the right information for the right client, so they can spend more time on the customer relationship.

There are ways in which the power of predictive analytics can be brought to bear immediately, creating a more substantial and recognizable benefit for both financial services providers and their customers. A knowledge-driven approach to cross-selling and upselling is one such strategy.

There’s a vast range of training, tools and processes that can positively influence engagement efforts. But predictive analytics can push these initiatives into a much higher gear, providing a uniquely powerful impact when it comes to solidifying those all-important bonds with customers. Through better analysis and use of data that’s already available to most financial institutions in petabytes, it’s possible to learn more about customers, and consequently offer them more relevant service, support and product options. The right, internal approach to applying predictive analytics, therefore, results in benefits for both customers and the financial services providers they work with — a true win-win situation.

Historically, banks — especially large ones — tend to lean more toward conservative, careful approaches to new strategies and technology than quick movement and adoption. Given the mound of compliance mandates that govern their every engagement, this is understandable. But it but can be a significant drawback. This is where predictive analytics can sharpen their game. Many institutions have demonstrated a resistance to adopting this specific tool, or have used it in a very limited way. But they’re missing out on the benefits. And understanding the inherent pitfalls in predictive analytics is key to achieving success in deploying it.

How Financial Institutions Can Effectively Deploy Predictive Analytics

It’s a given that cross-selling and upselling help create more lifetime value from customers. But finding strong connections between products and clients is still a complicated process; particularly when you have to juggle moving parts, such as customer credit scores, income, credit utilization, and the like. Figuring out what products you can sell to whom, and predicting what those outcomes will be, constitutes a successful cross-sell. When done correctly and ethically, cross-selling can ultimately strengthen the customer relationship into a lifetime value — read, profitability — for the bank. This is because they’re able to match a product that was needed with a demand that they’ve identified.

It’s 20/20 hindsight, but we all know about the debacle of Wells Fargo’s unethical cross-selling and upselling, and how much trouble it got into as a result. With upselling, predictive analytics can really make a difference in the campaign to upsell. And unlike the Wells Fargo situation, this approach is sustainable. Looking through vast amounts of consumer data can help banks to understand how relationships have historically evolved between the bank and its consumer over time. On the consumer side, the spotlight is on how their data is being used. Only by robust analysis of customer behavior — ideally where multiple products are being offered — can banks regain their customers’ trust that their data is being used to benefit them.

Predictive analytics platforms can conduct this type of analysis, leaning on demographic information, as well as purchasing and financial data that institutions already have from past customer activity. All in real-time. Such an analysis would be prohibitive in terms of time, were trained experts to do the crunching. The predictive analytics tool can then offer sharply defined, personalized, relevant recommendations for staff members to share, while they continue to provide the critical human element in the cross-selling and upselling processes.

Where does this data come from? The sheer volume of payments data that banks gather, whether credit card, utilities, rent or many more — can inform what financial product the customer might be looking for and can afford, creating a sharper, more relevant offering. And that’s where artificial intelligence and predictive analytics can play a role that helps bankers sharpen their game and engage more successfully with their customers, without throwing them on the mercy of the bots. Incidentally, it also proves the notion that artificial intelligence is less about displacing humans and more about helping them perform higher-value work.

Securing profitable customers — back to the lifetime value concept — is job No. 1 for banks, whether small or large. Successfully cross-selling — truly matching a product with an identified need — goes a long way to strengthen that customer relationship. The current financial services landscape is ripe for improvement through the use of predictive analytics. Many institutions are already using advanced analytics, tied to marketing and basic interactions — but few have developed strong processes that focus on understanding customer habits and preferences. From there, they can use predictive tools to become more relevant, valuable — and humanly available — to their clients. The institutions that manage to do so will have an advantage in building stronger, longer-lasting relationships and will enjoy the increased value that comes from them.

With thanks to Carol Sabransky, SVP of Business Development, AArete, who made substantial and insightful contributions to this article.

5 Trends in Customer Experience Software for 2019

I asked people who use customer experience software to share their thoughts on how the software, and its use, will evolve in 2019. Here are five trends to look for this year.

I asked marketers using customer experience (CX) software to share their thoughts on how the technology and its use, will evolve in 2019. Based on this research, I expect CX technologies will evolve in 2019 to support greater system and data connectivity, improve customer insights, and increase message relevance and process automation.

Here are five trends to look for this year.


M&A activity reflects a trend toward unified customer data platforms and all-in-one solutions for marketing, sales, support, analytics and CX. That’s great for businesses, because soon they won’t need to spend millions on integrations and IT for a single, holistic view of the customer.

We’ll see the introduction of tools and improvement in design to help vertical markets perform integrations more easily, which can find ways to transfer customer information from one application to the other.


Improved integration will help collate disparate customer data to provide a holistic view of customer activity across all departments — sales, marketing, customer support, etc. As CX software improves, organizations will start valuing CX data more than actual goods or services sold, because CX data will have a stronger correlation with long-term revenue generation and profitability.

Customer experience software users say 2018 was the year of data for CX software — from GDPR-mandated data cleanups to a wave of new data from relational and transactional customer interactions, CX software companies focused on data collection. From real-time data collection facilitated by chatbots and AI, 2018 saw a new way for the CX world to gather, store and leverage customer data for more customized engagement. This focus on data will be the foundation for what’s to come in 2019 — a greater focus on data collection, data analysis, and acting on data to increase customer retention and better connect with customers.

CX software will get a chance to show what it can do. We can expect the use of AI to grow and enable companies to sort and evaluate the data collected faster than in previous years. As CX software continues to gather more information, it will also continue to improve the software’s processes and provide better analysis.


We see the embedding of more “marketing-like” approaches — more analytics of customer behavior and more automation of responses and customer outreach. CX pros are starting to do this as well, moving away from working only with survey responses from a small number of customers. This is parallel to the development that started some 20 years ago in marketing automation when businesses moved on from small-scale surveys in market research to using all their business data to better understand customers. CX solutions are looking at all data on customers, using it to understand their needs and wants, and building automatic processes to meet those needs.

Businesses are realizing that customers today rate their experience based on the sum of all the interactions with business — not just on call wait time, or Internet ease of use. All of these things come together as customers move seamlessly from one channel to another — they see this as one overall experience. As such, successful CX solutions are embedding tools that can work with the entire customer journey — from its “discovery,” based on journey analytics, to the orchestration of a better overall CX through customer journey management.


We’ll see organizations leveraging technology to make customer journeys frictionless, personalized — and ultimately, more profitable. Companies will be able to better target customers with more in-depth information, personalized messaging and tailored recommendations that better align with needs. At the end of the day, it will all come down to how well companies use CX software to learn about their customers and better serve them.

Customers will become more skeptical of companies that fail to personalize emails and content. Consumers respond better when they feel like they’re people, not just another number on a list. The future of successful CX software implementations is those that take the time to focus on personalized, relevant information of value that helps makes customers’ lives simpler and easier.

Artificial Intelligence/Machine Learning

The shiny toys of Augmented Reality (AR), Artificial Intelligence (AI), and data analytics only account for the aesthetic aspects of CX. Many companies are looking at the sexy components of a well-built website while overlooking why customers fell in love with brands like Netflix and Amazon. These top sites and companies gave the people exactly what they wanted from the onset. The next step for companies in 2019 is to find the right balance to effectively blend UX and CX to suit the customers’ needs like never before.

With that being said, the automation side of CX is incredibly powerful. We’ve seen improvements in AI software within the past year and it will be fun to see how much it develops this year. The next couple years will revolutionize CX. Now that companies have built the technology, the only thing left is to fine-tune it. Build on the useful technology put in place.

Machine learning (ML) and AI are already being used to identify data points with the most impact. We will see this translate into providing meaningful action points which leverage the data points.

We will see CX software using AI to predict CX for new products, based on past data with greater accuracy.

Lastly, we will see marketers leveraging AI to learn about their target customers and prompt them to take action to meet their needs.

How AI Is Changing Google Ads

To clarify the change, Google Ads is now using AI to create text ads for advertisers, and these auto generated ads will automatically go live in your account after 14 days. Yes, you read that correctly: A computer program is going to draft advertisements for your business.

Artificial intelligence (AI)  is taking over the Internet one platform at a time. Many experts have been anticipating what AI will mean for search, particularly voice search, but not as many people are thinking about Google Ads.

Google Ads is a platform that can benefit from AI because it helps identify which ads to show users to get the best return on investment (ROI). As AI gets more intelligent, marketers can use it to get ahead of their competition.

About Online AI

To stay ahead of the competition, we must start thinking about the changes that will come, so we can prepare for them now. The best way to do that is to learn as much as possible about AI, and then predict its influence.

AI is controlled by machines. It’s human intelligence simulation using computers.

So far, AI has made life a lot easier. It’s able to predict the actions of humans and this helps marketers plan what they should do to generate more leads and sales.

Yes, this means marketers can create strategies to make it more likely their consumers will convert simply by using AI information.

What many people don’t know is that Google Ads has already implemented AI into it’s advertising platform. It’s the brains behind Google Ads’ automation tools.  Of course, we’re in the early stages of AI and Google has many other plans to better identify exactly what people want from their searches.

Recent Google Ads AI Update

Back in April, Google announced they would start offering ad suggestions on the Google Ads Recommendations page. This announcement was released via email without much hype.  In fact, many advertisers probably didn’t even notice it.

To clarify the change, Google Ads is now using AI to create text ads for advertisers, and these auto generated ads will automatically go live in your account after 14 days. Yes, you read that correctly: A computer program is going to draft advertisements for your business.

If that scares you, and frankly it should, then I have some good news.  Luckily, it is possible to opt out of this new feature so the computer generated ads do not get automatically added to your account.  This is highly recommended so you retain control over your advertising versus letting Google run your campaigns.

Google is certainly trying to help make advertising easier, but it’s always important to understand the inherent conflict of interest.  Google’s goal with Google Ads is to generate as many clicks as possible because that’s how Google makes money. More clicks equals more money.

However, for businesses more clicks does not necessarily equal more money because not all clicks are created equal.  That’s why conversion tracking is so critical to your success with Google Ads and it’s also why you shouldn’t let Google’s AI draft all of your ads.  Sure, test some of their ads, but don’t completely give up control if you want your ad campaigns to be successful.


AI is improving every day and when used properly it will help advertisers generate more leads and sales. However, AI is not a “set it and forget it” solution.  It’s up to advertisers to use their human brains to analyze the quantitative and qualitative data to make smarter decisions. That will never change no matter how much AI improves in the coming years.

Want more Google AdWords tips to improve your performance? Click here to grab a copy of our Ultimate Google AdWords Checklist.

The ‘Algorithmification‘ of Everything

If I had asked any of my schoolmates what an “algorithm” was, their eyes would have glazed over and they would probably have asked me what I had been smoking. Fast-forward a few decades and we’ve got the algorithmification of everything, including marketing.

If I had asked any of my schoolmates what an “algorithm” was, their eyes would have glazed over and they would probably have asked me what I had been smoking. Fast-forward a few decades and we’ve got the algorithmification of everything, including marketing.

Those glazed looks would’ve happened a long time ago, long before Facebook was a glimmer in Mark Zuckerberg’s eye and he had started to bring together the more than 2 billion people who log in at least once a month. That Facebook population is now what Evan Osnos of the New Yorker says, were it a country; “ … would have the largest population on Earth … [and] as many adherents as Christianity.” When they log in, they are shaking hands with unnumbered algorithms and putting into those invisible fingers their faith and their data to be parsed, analyzed and manipulated, and hopefully not stolen.

What is an algorithm? Programmers like to say it is a word used by them when they don’t want to bother explaining what they do.

And because algorithms have become so ubiquitous, we seldom give them a thought — except when our IT colleagues start telling us why making any small change in our marketing program will take weeks or months and cost a bundle, or until something goes badly wrong as Facebook and others have discovered about their hacked data.

Our legislators, not usually well-versed on technology matters, have now started making a lot of noise about regulations: They are closing the server door after the data has bolted — an unlikely way to solve the essential problems.

Automation has always been the Holy Grail for marketers; not surprising when the ability, speed and relatively low cost of using artificial intelligence (AI) to number-crunch and manage segmentation of media and analysis of data gets better and better every year. eMarketer reports that: “About four in 10 of the worldwide advertisers surveyed by MediaMath and Econsultancy said they use AI for media spend optimization. This is another application of AI that is increasing among marketers as their demand-side platforms add AI features to increase the probability that a given programmatic bid will win its auction.”

Where is it headed? No one knows for sure. It’s all in the hands of the algorithms and they appear to be multiplying like rabbits. If you revere Darwin, as I do, you’ll expect them to get better and better. But before you totally buy into that, you would do well to read Melanie Mitchell’s thoughtful New York Times article “Artificial Intelligence Hits the Barrier of Meaning.”

There are more and more times when we applaud the use of the algorithms and can see that if properly created, they offer many benefits for almost every area of our marketing practice, as well as other areas of our lives. We really don’t have to panic (yet) about the machines and their algorithms taking over. As Neil Hughes wrote here last month; “The reality is that machines learn from systems and processes that are programmed by humans, so our destiny is still very much in our own hands.”

Machines screw up just like we do; and all the more so, because they are doing just what we told them to do.

All this machine thinking doesn’t come without dangerous side effects. Sometimes, when we try to communicate with inflexible AI systems supposedly designed to simplify and ease customer interactions, the “I” in “AI” becomes an “S,” replacing “A-Intelligence” with “A-stupidity.”

If, as defined, “an algorithm is a procedure or formula for solving a problem, based on conducting a sequence of specified actions,” we can only optimistically hope that the specified actions will take into account individual customer differences and make allowances for them. The moments when they don’t are when we start screaming and swearing; especially if we are on the customer end of the transactions.

As The New York Times wrote in a recent article: “The truth is that nobody knows what algorithmification of the human experience will bring.”

“It’s telling that companies like Facebook are only beginning to understand, much less manage, any harm caused by their decision to divert an ever-growing share of human social relations through algorithms. Whether they set out to or not, these companies are conducting what is arguably the largest social re-engineering experiment in human history, and no one has the slightest clue what the consequences are.”

However important algorithmification may seem to us, our marketing efforts and our use of AI and its algorithms are not very significant in the greater scheme of things outside of our limited business perspective. But don’t dismiss their growing impact on every facet of our future lives. As data guru Stephen H. Yu opined in his recent piece “Replacing Unskilled Data Marketers With AI”:

“In the future, people who can wield machines will be in secure places — whether they are coders or not — while new breeds of logically illiterate people will be replaced by the machines, one-by-one.”

You had better start to develop a meaningful relationship with your algorithms — while there is still time.

Is AI Rocket Science? Dr. Merlin Stone Shatters the Madness and Illuminates AI’s Real-world Powers

World-renowned AI guru Dr. Merlin Stone shares the power of AI to make life better. He says how AI is improving everyday life by revealing universal truths and facilitating optimal decision-making for the best possible outcomes in business, health, education and politics.

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Credit: Pixabay by ElisaRiva

Buckle up for our final fireside chat with AI wizard, Dr. Merlin Stone who quells the fear that AI is taking over life on here on earth. He discusses benefits of AI in everyday life. Spoiler Alert: Don’t pack your bags for Mars just yet!

Peter: As we wrap up in Part III, any other ideas coming through for the future? We mentioned Kubrick in 2001 A Space Odyssey, which was fantastic; especially the way that film was created before the days of computer-generated imagery (CGI) – all hand-built models of the revolving Space Station, and a Pan Am connecting flight docking in perfect synchronization accompanied by the Blue Danube in the background – wonderful!

Integrated AI Makes Life Easier

Dr. Stone: And yes, think of HAL as your AI support… I have Alexa like everybody else, but I don’t use it like most; more to play the radio and tell jokes!  I also use it to forecast the weather. Other people use Alexa with more sophistication as home automation devices, for instance. So I think the time will come certainly, when all the different systems we have like Amazon Prime with the firestick hanging out of the edge of the television will come together and be more integrated; it’s all still a bit chaotic, but nonetheless it’s a lot better than we used to have, which we tend to forget.

Yes, but that stuff is already happening. So the day’s not far off when we come into the house; I won’t have to use remote control, I can command the television to turn on, show me An Evening with Amy Winehouse; it will do all the stuff at YouTube with Firefox and all the rest of it without me having to do anything else, which I have to do right now.

Does AI Have a Mind of Its Own?

Peter: In 2001, we all liked HAL to start with until he literally developed a mind of his own, and I think that’s the trouble with most people’s worry about Artificial Intelligence; Terminator style take-over by the machines. And now of course, we have the Futurist of the moment, Elon Musk, whose plans around all his businesses are geared to provide finance to break free of this world and get to Mars.

Dr. Stone: Why go to Mars – the wrong temperature and the wrong gas!

Peter:  He’s got thousands of people who’ve paid a deposit to go out; and it’s a one-way trip!

Dr. Stone: There’s one born every minute then!

Peter: I think he has some interesting ideas, though, about neural connections, so that you bring AI in to play to support your own thinking?

Dr. Stone: Actually, there are a lot of simple AI examples to explore before we go deeper into the future. For instance, our local supermarket yesterday; we wanted to be a little more adventurous for dinner, and I know that they often have delicious off-cuts of salmon; tasty, but not expensive, so I could have, in this new world, ask Alexa what salmon they have you on the shelf. Instead of having to go there and look in person.

Peter:  That’s home delivery isn’t it?

Dr. Stone:  Sure, but I want to walk and pick it up. Why can’t the shop do what it’s doing and combine that with what Home Delivery does?

Peter: That’s going to be down to sensors and database access. Not rocket science.

Holographs as Tomorrow’s Teachers?

Dr. Stone: The UK is bad at on shelf pricing; you go to France and many of the stores have got digital pricing. Well let’s see what Carrefour can bring to Tesco. I think a lot of this stuff is really, really simple. Most people aren’t looking to the world to get wildly excited by any of this. They just want some basic stuff dealt with, and the basic stuff is their lives, their shopping their health, work life, education.

Yes, education. Awful. It’s really a global issue now; however much you invest, it doesn’t seem to work terribly well. It’s an extraordinarily labor intensive process – obviously with kids you can understand why that is, but all the same, it should be so much better by now.

Peter: It would be frightening though, to think of a world of the future where kids are taught by holographic images of maybe the best teachers in the world…

Dr. Stone: I gave the same lectures this year that I gave last year right. Why?  Even in the 60s the same lecturer who was interested in artificial intelligence, just videoed his lectures and then he showed them year after year: the introduction to philosophy. He’d watch the reactions, but he wouldn’t have to stand up and talk.

Peter: But didn’t that evolve?

Dr. Stone:  Many universities are still Chalk & Talk’- astonishingly so – and the numbers in class get bigger and bigger because it’s become a business, but it’s amazing.  People benefit most from education when they are learning, not being just taught. But we still do it. We have lots of aids to that process, you know, learning management systems, module posting devices and all the rest of it, but it’s still not terribly different from the way it was 20 to 30 years ago. In most universities, it’s a lecturer standing up in class.

Peter:  It’s like the National Health Service (NHS) again; it’s the lack of IT in schools.

Dr. Stone: When I was hospitalized, I’d been diagnosed lupus, and the guy next to me had shot his liver by drinking too much, and I heard the doctor say to him that he must eat. So the nurses would come around and slam the tray down in front of him. He wasn’t capable of feeding himself! So I said to the consultant when he came through the curtain: “Excuse me but I’m a management consultant. You’re feeding this guy next door by just putting the tray down in front of him and leaving, and I’ve heard you say he needs to eat!” And his reply was: “Oh, he’s in the wrong ward.” Why was he in the wrong ward? It’s crazy. And then the guy opposite, who’d been in a bad accident, was on oxygen with tubes going through his nose and he was breathing through his mouth. So where’s the oxygen?  So I asked the nurse why they were giving oxygen through a nasal tube, when he’s breathing through his mouth? “Oh, he doesn’t like the mask!” So, you’re wasting your time.

So I think some of the discussion about AI is about pointing a finger at these extraordinarily basic faults that exist. It’s not the world of 2001; no, it’s not Mars. You are talking about making most people’s lives better. It’s ‘is there salmon in the supermarket now? It is when do I leave to work; if you’re older, when to drink a glass of water before you get up? Often very simple things. These are the things that are important in people’s lives just to make life a little bit easier. More fulfilling.

Peter: Are you excited about all this?

Dr. Stone:  Yes, sure, I do my bit. I write a lot about it…

Peter:  We talked about the EU and the Euro and everything else – do you just throw your hands up in horror?

Dr. Stone: I think until the Euro goes, the EU will be a basket case because it doesn’t have the desire to make sure everybody’s well off; it just wants to penalize debt; the system’s bad; there’s no sensible big picture that I can see.

Peter: That’s because of humans, particularly politicians … Is AI going to be the answer to a lot of these problems or challenges?

Who Do You Trust: Humans or Machines?

Dr. Stone: Yes, it goes back to what I said earlier. That is that the data, the truth, is probably what unifies the discussion. So if you have somebody telling the truth, it’s a big statement about what’s going on. And people trust that person.

In fact, we had call on a radio program the other day, where they asked people, when the Big Blue computer was being tested for a cancer diagnosis, who would you trust, the machine or a human specialist? And the audience, interestingly, was split almost exactly 50/50.

Peter: Really?

Dr. Stone: Sure, having seen the evidence, that was the outcome. And of course it plays to the difference between a doctor and a consultant surgeon: the doctor diagnoses what you have, while with the consultant tells you that you have what he diagnoses. In other words, by definition, he’s biased hopelessly – he can only look at certain signs, whereas the idea is if all the data is there, it’s a much more open ended diagnosis.

One of my research interests; in fact I’m writing an article about it, is on lying and disinformation. When you read history, military stuff and all the rest of it, for instance a famous book published in the 1970s called The Psychology of Military Competence, you discover that seven out of ten of the incompetencies were information related. Shooting the messenger; denial; you can imagine what they were. It’s the story of Hitler, Napoleon….. Or some of Churchill’s behavior; he put Great Britain back on the gold standard after the 1st World War and trashed our economy probably 10 years before the rest of the world. So there are plenty of economic examples as well. Again Bush, Blair and Iraq would be a recent military example.

Peter: We’ve run out of time now – Many thanks for your insights. We could chat into the small hours, but what comes across in all 3 parts of our discussion, is actually a very positive view of the future, where, quite simply, AI is used to interpret Big Data and make our day-to-day lives much better and more efficient.  We will all be better educated, even our politicians who will be well informed to make sensible, BIG decisions in the best interest of the people.  I think we all look forward to that day!

Video Q&A: How Will AI Help Marketers Improve Retargeting & Conversion?

There’s a lot of loose talk around the potential for AI to change the nature of the marketing game, but beyond the buzz it can be hard to tell exactly how marketers will be using it to improve their businesses. In a series of video Q&A’s, marketing AI practitioner and Trust Insights co-founder Christopher Penn will explain how marketers can actually use AI.

Penn will be leading the keynote session on AI applications in marketing at the FUSE Digital Marketing Summit this November. Learn more about attending here.

Check out Penn’s previous videos:
Q: How Will AI Help Marketers Tap Their Data Wells?
Q: What Marketing Processes or Tasks Will AI Eliminate?

Q: How will AI help marketers with retargeting and sales conversion?

This is a really interesting question because one of the things that marketers struggle with is what causes a conversion. What factors, what measures, what metrics, what learners, what dimensions lead to conversion or contribute to conversion? A big part of this is the foundation of attribution analysis. What pieces of data have driven conversions in the past? And then, with things like retargeting, you’re trying to focus on predicting what things are likely to cause conversions in the future. The way AI and machine learning help with this is dealing with what are called “weak learners.”

A weak learner is any dimension or metric whose predictive power is just barely above random chance. It’s called a weak learner because it’s a weak signal. It’s not a signal that by itself is a very strong signal. So for example, the number of times someone has retweeted your tweets, right? For a fair number of businesses, that’s going to be a really weak learner. In fact, it may or may not even be statistically relevant. But at the very least that is probably going to be a weak learner.

There are also things like how many times someone has opened an email, the number of social channels someone follows you on, the pages they visited on a website, the amount of time they spent on a page.

When you think about all the data that we have access to as marketers and then we consider that most of these metrics are pretty weak, you get to start getting a sense of the scope of the problem.

We have all this data and none of it is the one answer that we’re looking for. The answer that says, “This is the thing we need to do more of.” It would be nice if it didn’t work that way. It would be nice to know you should always send email on Tuesdays, that’s going to cause all your conversions. Doesn’t happen.

So how does AI help with this? Through techniques that aggregate weak learners together and make them function as a stronger learner, we can get a sense of what combinations of dimensions and metrics matter most.

Hear Penn’s full answer to the question of how AI will enhance marketers’ ability to convert sales and retarget customers in the above video.

See Christopher Penn present the keynote session Using AI & Deep Learning to Generate Marketing Results at the FUSE Digital marketing Summit.


Do Buzzwords Get in the Way of Progress?

Have you read a column in the past week, month or year that’s void of buzzwords? Probably not. In the age of 5,000-plus choices of what partners, technologies or agencies to choose from, I find it uncanny how the marketplace is fraught with complex ways to explain simple things.

Have you read a column in the past week, month or year that’s void of buzzwords? Probably not. In the age of 5,000-plus choices of what partners, technologies or agencies to choose from, I find it uncanny how the marketplace is fraught with complex ways to explain simple things. Blame it on analysts who define industries? Blame it on a competitive marketplace and people trying to stand out with that killer phrase that describes what they do? Blame it on retailers striving to explain and justify what they do to their corporate leaders? Or startups striving to associate new ideas to mainstream challenges? Or blame it on consultants for making the simple complex and charging for it.

What it doesn’t help are retailers. In a perfect world, retailers live their brand. They look for simple ways to communicate with a broad spectrum of customers, and need creative yet practical approaches to words. You’re a merchandiser, an e-commerce company, and a lifestyle brand, and it can be a cultural challenge to balance buzzword frenzy with simple words the market needs to hear about your company. My main problem with buzzwords — and I’ve been as guilty as anyone in the use of them, just read a few of my columns — is using terms in loose context can minimize the impact of the term and make it actually more confusing. Therefore, in the spirit of no buzzwords, this column is just that: real talk for real retailers.

Lets start with a few buzzwords:

  • Disruptive technology: This begs the question of how disruptive your disruptive technology has to be for you to claim that it’s truly disruptive vs. just moderately irritating.
  • Ecosystem: This buzzword got big in mid-2014, 2015 as Luma Partners really promoted its Lumascape. Next thing you know every vendor is using it and every internal IT team began following suit to describe their “data lake strategies” and “technology road map.” I’m not sure I’ll ever get used to referring to my business interdependencies using the same terminology we use to talk about global warming and our attempt to save the planet.
  • Millennials: Are millennials really a buzzword? They might be. They’ve become more than just another generational grouping. As more millennials enter the workforce, replacing the retiring baby boomers, we will continue to spend a lot of time talking about the impact they’re having on the intersection between business, technology and our interpersonal lives. Maybe more importantly, we will continue to try to figure out why they break up with each other via text.
  • Thought leadership: This buzzword was prevalent for many years, and I still don’t really know what it means — or maybe I thought I did and really didn’t. I was awarded Thought Leader of the Year in 2016, and had trouble describing the award outside of … unfortunately, it seems to be entrenched and positioned to bother us for another year. I’ve been trying desperately to think of a new term that could supplant it, but question if I’m enough of a thought leader to make that happen.
  • Storytellling: I have to confess that I’ve coached and advised leaders to use stories to convey important things about their businesses because a good story resonates better than death by Powerpoint presentation. Now we’ve got storytelling classes, storytelling departments, and even storytelling gurus. Once gurus come into the picture, we’ve officially hit buzz status
  • Artificial intelligence/machine learning: These are likely the most overused, misunderstood and confusing buzzwords. How many times have you heard, “We have AI.” While this area of discipline and technology advances will reshape much of what we know today, any buzzword that conjures up impending doom of the human race isn’t helping in a dynamic business world.
  • Big data: I have trouble with anything that starts with “big” as a modifier of an industry trend. What’s big, and is there bigger? Much like the term disruptive, big data is an overused phrase that doesn’t serve many outside of its sellers. Google, Facebook,, Microsoft, Apple have big data. If you really want to understand big data in our society, there’s a great book: “The Human Face of Big Data.” Warning, this book is big, literally. In the end, the term does little to help you contextualize marketing problems or your own internal data challenges.

We’re in a world of endless information. Buzzwords in my opinion distort real talk and make complex concepts harder for the masses to address in situational marketing. Have fun with it by infusing a NO Buzzword culture or, better yet, force the offender to fully explain the term in the context of your business. And remember the goal of words is not to show how smart you are versus; they are a way to level set on complex ideas.

Make the complex simple!