How to Integrate AI Tech Into Each Step of the Customer Journey

The Customer Lifecycle. The Sales Funnel. The Buyer’s Journey. All of these phrases are similar expressions of the same thing. They’re used to describe the process that it takes for a visitor to become a customer.

The Customer Lifecycle. The Sales Funnel. The Buyer’s Journey. All of these phrases are similar expressions of the same thing. They’re used to describe the process that it takes for a visitor to become a customer.

While the models and names of stages may have changed through the years, many agree that it can be boiled down to four simple components:

Awareness > Consideration > Decision > Loyalty

The No. 1 goal for most businesses is to generate more conversions (which primarily consists of sales). This can be through their marketing efforts, sales tactics, brand communication, conversion rate optimization, and other methods. Of late, many companies have developed critical competencies in using AI to nudge customers towards sales, and have improved their numbers drastically as a result.

AI, machine learning, and big data technology can all work hand-in-hand to improve the customer experience and support an optimized customer journey, which leads to more conversions in several key ways.

Let’s talk about how you can start using AI tech in each stage of the funnel.

Awareness

Marketing strategies these days are often heavily focused on the top of the funnel to build brand awareness and attract new customers. For many businesses, recognition is nearly equivalent to the value of their brand. Elena Veselinova and Marija Gogova Samonikov explain in their book Building Brand Equity and Consumer Trust Through Radical Transparency Practices that brand impact is a continuous process that insures purchases, cash flow, revenue and share value. Brand communication and experience creates and builds a loyal base of customers that do not consider any other brand.

Creating a strong level of brand awareness takes time and strategy. Companies spend millions of dollars on marketing campaigns and advertising to increase their reach and recognition, but AI tech is able to take the guesswork out of these strategies by analyzing huge volumes of consumer data for more targeted campaigns. For example, predictive analytics software can collect, track, and analyze datasets from past customers to determine which strategies or tactics performed well. These datasets are turned into reports with insights to guide marketing efforts and place relevant content in front of the most interested eyes at the right times.

With AI-assisted marketing, advertising strategies can be backed with data to optimize ad placement. Machine learning systems can even identify the best influencers for brands to partner with in order to reach relevant audiences and grow brand familiarity.

Credit: Venturebeat.com

Consideration

The next step of the buyer’s journey is often overlooked by marketers because it can drag on for a long time, depending on the product and the customer’s needs. During the consideration phase, a customer is already familiar with a brand or product but are unsure of whether or not to actually purchase. Customers will typically research the product’s reviews, compare prices to competitors, and look for alternatives during this stage. Due to this, the number of potential customers tends to narrow down considerably as they move from this step to the decision phase.

Brands must work to combat each customer’s concerns and questions standing in the way of a purchase decision. One of the best ways to do this is by offering personalized content that is relevant to each person, making it easy for them to find the information they are seeking.

AI systems can be used to predict a customer’s needs based on consumer data and previous online behavior, and then encourage conversions with a tailored UX or even a completely customized landing page that displays content relevant to that customer.

For example, if a site visitor has viewed a certain product page and played a video demonstrating its features, these actions can trigger an AI system to target them with personalized content that prompts a conversion if they don’t proceed to buy immediately. This content could be something as simple as an email message with more information or a display ad with a special offer for the specific product.

Credit: Personyze.com

Then there are platforms that use conversational AI tech (such as chatbots and voice assistants) to power automated, text- or audio-based interactions between a business and its customers. These platforms can understand speech, decipher intent, differentiate between languages, and mimic human conversations with great accuracy. Increasingly, they are advanced enough to even understand individual context and personalize the conversation accordingly.

Based on data insights, AI tech can curate content that matches up with the issues that are most important to that person, whether it be product features, immediate delivery, long term savings, etc. Customers respond quite well to personalized offers — an Accenture study reported that 91% of consumers are more likely to purchase from a company that sent them targeted deals or recommendations.

Decision

Once a customer moves from consideration to action, AI tools can be used to support a positive sales experience and eliminate any bumps along the way. If a customer encounters an issue while browsing the site, or during checkout or payment, it could be an instant sales killer, if it isn’t handled immediately by something like live chat.

According to multiple studies, one of the most frustrating parts about online customer service is long wait times. By using AI-enabled chatbots, companies can instantly answer common questions and resolve issues or roadblocks affecting the progression of the buyer’s journey. And customers certainly appreciate these quick response times. AI systems can significantly increase conversions with effective personalization and swift customer service.

Credit: AIMultiple.com

Loyalty

The last step of the customer journey is possibly the most valuable. Over half of customers reportedly stay loyal to brands that “get them.” Returning customers also tend to spend more money than new ones, and an oft-reported stat says that on average 65% of businesses’ revenue comes from existing customers.

Businesses (and customers) can benefit greatly from loyalty programs that are backed with machine learning technology. Starbucks famously uses AI tech to analyze customer behavior, improve convenience, and identify which promotions would perform best based on that person’s drink or food preferences, location, and purchase frequency. Their loyalty program uses this data to send out thousands of offers each day for the products their customers are most likely to buy. Their customer loyalty program grew 16% YoY last year as a direct result of their Deep Brew AI engine.

Credit: Starbucks app

While a positive shopping experience and great products are certainly important factors in a customer’s decision to buy again, data-driven marketing campaigns that encourage loyalty can also help a company to grow their numbers of repeat sales. Again, AI-assisted personalization techniques can boost the chances of a customer coming back for more, especially if they receive targeted offers or shopping suggestions based on previous interactions.

Credit: Accenture.com

The Wrap

AI is proving to be the tool of the future for marketers. It allows marketing teams to use predictive insights and analytical data to encourage and assist every micro-decision taken by consumers. AI systems not only help customers move along the buyer’s journey, they can also provide a more meaningful experience along the way, leading to more conversions and brand loyalty down the road.

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
Credit: Acquisio.com

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
Credit: Cosabella.com

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
Credit: NextIT.com

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.

 

Even AI Needs Clean Data in Order to Be the Shiny Object

Users are quickly realizing that investing in AI is not the end of the road. Then again, in this analytics journey, there really is no end anyway; much like the scientific journey, it is a constant series of hypothesis, testing, and course corrections.

Users are quickly realizing that investing in AI is not the end of the road. Then again, in this analytics journey, there really is no end anyway; much like the scientific journey, it is a constant series of hypothesis, testing, and course corrections. And now, I’ll explain why that means even AI needs clean data.

If there is a book out there — many have asked me about it — it would look more like a long series of case studies, not some definitive roadmap for all. Why? Because prescribing analytics is much like a doctor’s work. It depends as much on the unique situation of the patient as on the list of solutions.

That is the main reason why one cannot just install AI and call it a day. Who’d give it a purpose, guide it, and constantly fine-tune it? Not itself, for sure.

Then there is a question about what goes into it. AI — or any type of analytics tool, for that matter — depends on clean and error-free data. If the data are dirty and unusable, you may end up automating inadequate decision-making processes, getting wrong answers really fast. I’d say that would be worse than not having any answer at all.

So far, you may say I am just stating the obvious here. Of course, AI or machine learning require clean and error free data. The real trouble is that such data preparation often takes up as much as 80% (if not more) of the whole process of applying data-based intelligence to decision-making. In fact, users are finding out that the algorithmic part of the equation is the simplest to automate. The data refinement process is far more complicated than that, as it really depends on the shape of the available data. And some are really messy (hence, the title of my series in this fine publication, “Big Data, Small Data, Clean Data, Messy Data”).

So, why aren’t data readily usable?

  • Data Are in Silos: This is so common that “siloed data” is actually a term that we commonly use in meeting rooms. Simply, if the data are locked up somewhere, they won’t be much of use for anyone. Worse, each silo may be on a unique platform, with incompatible data formats from others.
  • Data Are in One Place, But Not Connected: Putting the data in one place isn’t enough, if they are not properly connected. Let’s say an organization is pursuing the coveted “Customer 360” (or more properly, “360-degree view of a customer”) for personalized marketing. The first thing to do is to define what a “person” means, in the eyes of the machine and algorithms. It could be any form of PII or even biometrics data, through which all related data would be merged and consolidated. If the online and offline shopping history of a person aren’t connected properly, algorithms will treat them as two separate entities, devaluating the target customer. This is just one example; all kinds of analytics — whether they be forecasting, segmentation, or product analysis — perform better with more than one type of data, and they should be in one place to be useful.
  • Data Are Connected, But Many Fields Are Wrong or Empty: So what if the data are merged in one place? If data are mostly empty or incorrect, they will be worse than not having any at all. Good luck forecasting or predicting anything with data fields with really low fill rates. Unfortunately, we encounter tons of missing values in the case of “Customer 360.” What we call Big Data have lots of holes in them, when everything is lined up around the target (i.e., it is nearly impossible to know everything about everyone). Plus, remember that most modern databases record and maintain what are available; but in predictive analytics, what we don’t know is equally important.
  • Data Are There, But They Are Not Readily Usable, as They Are in Free-Form Formats: You may have the data, but they may need some serious standardization, refinement, categorization, and transformation processes to be useful. Many times I encountered hundreds, at time over a thousand, offer and promotion codes. To find out “what marketing efforts worked,” we would have to go through some serious data categorization to make them useful. (Refer to “The Art of Data Categorization”) This is just one example of many. Too often, analytics work is stuck in the middle of too much free-form, unstructured data.
  • Data Are Usable, But They Are One-Dimensional: Bits and pieces of data, even if they are clean and accurate, do not provide a holistic portrait of target individuals (if the job is about 1:1 marketing). Most predictive analytics work requires diverse data of a different nature, and only after proper data consolidation and summarization, we can obtain a multi-dimensional view. So-called relational databases and unstructured databases do not provide such a perspective without data summarization (or de-normalization) processes, as entities of such databases are just lists of events and transactions (e.g., on such and such date, this individual clicked some email link and bought a particular item for how much).
  • Data Are Cleaned, Consolidated, and Summarized, But There Is No Built-in Intelligence: To predict what the target individual is interested in, data players must rearrange the data to describe the person, not just events or transactions. Why do you think even large retailers, like Amazon, treat you like you are only about the very last transaction, sending the “likes” of the last item you purchased, ignoring years of interaction history? Because their data are not describing “you” as a target. And you are not just a sum of past transactions, either. For instance, your days in between purchases in the home electronics category may be far greater than those in the apparel category, yet showing higher average spending in the first category. This type of insight only comes out when the data are summarized properly to describe the buyer, not each transaction. Further, summarized data should be in the form of answers to questions, acting as building blocks of predictive scores. Intelligent variables always increase the predictive power of models, machine-based or not.
  • Data Variables Include Intelligence, But It Is Still Difficult to Derive Insights: Lists of intelligent variables are just basic necessities for advanced analytics, which would lead us to deeper and actionable insights. Even statisticians and analysts require a long training period to derive meanings out of seemingly beautiful charts and effectively develop stories around them. Yes, we can see that certain product sales went down, even with heavy promotion. But what does that really mean, and what should we do about it? For a machine to catch up with that level of storytelling, the data best be on silver platters in pristine condition first. Because changing assumptions based on “what is not there” or “what looks suspicious” is still in the realm of human intuition. Machines, for now, will read the results as if every bit of input data is correct and carries equal weight.

There are schools of thought that machines should be able to take raw data in any form, and somehow spit out answers for us mortals. But I do not subscribe to such a brute-force approach. Even if there is no human intervention in the data refinement process, machines will have to clean data in steps, like we have been doing. Simply put, a machine that is really good at identifying target individuals will be separately trained from the one that is designed for prediction of any kind.

So, what does clean and useful data mean? Just reverse the list above. In summary, good data must be:

  • Free from silos
  • Properly connected, if coming from disparate sources
  • Free from errors and too many missing values (i.e., must have good coverage)
  • Readily usable by non-specialists without having to manipulate them extensively
  • Multi-dimensional as a result of proper data summarization
  • In forms of variables with built-in intelligence
  • Presented in ways that provide insights, beyond a simple list of data points

Then, what are the steps of data refinement process? Again, if I may summarize the key steps out of the list above:

  1. Data collection (from various sources)
  2. Data consolidation (around the key object, such as individual target)
  3. Data hygiene and standardization
  4. Data categorization
  5. Data summarization
  6. Creation of intelligent variables
  7. Data visualization and/or modeling for business insights

Conclusion

I have covered all of these steps in detail through this column over the years. Nevertheless, I just wanted to share these steps on a high level again, as the list will serve as a checklist, of sorts. Why? Because I see too many organizations — even the advanced ones — that miss the whole category of necessary activities. How many times have I seen unstructured and uncategorized data, and how many times have I seen very clean data but only on an event and transaction level? How can anyone predict the target individual’s future behavior that way, with or without the help of machines?

The No. 1 reason why AI or machine learning do not reach their full potential is inadequate input data. Imagine putting unrefined oil as fuel or lubricant for a brand new Porsche. If the engine stalls, is that the car’s fault? To that point, please remember that even the machines require clean and organized data. And if you are about to have machines do the clean-up, also remember that machines are not that smart (yet), and they work better when trained for a specific task, such as pattern recognition (for data categorization).

One last parting thought: I am not at all saying that one must wait for a perfect set of data. Such a day will never come. Errors are inevitable, and some data will be missing. There will be all kinds of collection problems, and the limitation in data collection mechanisms cannot be fully overcome, thanks to those annoying humans who don’t comply well with the system. Or, it could be that the target individual simply did not create an event for the category yet (i.e., data will be missing for the Home Electrics category, if the buyer in question simply did not do anything in that category).

So, collect and clean the data as much as possible, but don’t pursuit 100% either. Analytics — with or without machines — always have been making the most of what we have. Leave it at “good enough,” though machine wouldn’t understand what that means.

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.

Conclusion

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.

Why You’re Probably Not Ready for Machine Learning, and How to Get There

The truth is, there’s a gap there. Companies have spent years developing data capabilities to support the various flavors of direct, automated and digital marketing. Now that effort needs to shift toward corralling even larger data sets for even more business-benefit in machine learning.

Last week on our Marketing Garage podcast, we interviewed a Fortune 500 company, Reckitt Benkiser, about the success they’ve had using machine learning to guide their marketing strategy across a number of well-known consumer brands.

As we went through the interview, I couldn’t help but wonder why more brands aren’t using machine learning in that way. Then at FUSE Digital Marketing, I got to hear Christopher Penn, co-founder of Trust Insights and host of the Marketing Over Coffee podcast, talk about where marketing data is, and where it needs to be for machine learning to add value.

The truth is, there’s a gap there. Companies have spent years developing data capabilities to support the various flavors of direct, automated and digital marketing. Now that effort needs to shift toward corralling even larger data sets for even more business benefit.

That’s why Reckitt Benkiser has both their own internal head of e-commerce analytics and still brings in help from Fractal Analytics to tap into AI insights. And why more marketers need to lay the data ground work to make machine learning work for them in the future.

Artificial Intelligence Is Math, Not Magic

“All machine learning begins with stats and probability,” said Penn. “Machine learning is math, it’s not magic.”

And when it comes to applying that math to today’s marketing data, he identified five core problems:

  1. Volume: There’s so much data being crated today that any attempt to quantify it just sounds like you’re making up words. For example, Penn said “this year it is estimated, as a civilization, we’re going to create about 30 zettabytes of data.” Your brain cannot count to a “zettabyte.” (Go ahead and try, I’ll wait.)
  2. Variety: There are more kinds of data than ever.
  3. Velocity: It’s coming at us faster than ever.
  4. Veracity: Data has to be verified. If it’s not accurate, it’s harmful to your business.
  5. Value: Penn compared data to oil. Raw, it’s sticky and not very useful. It has to be refined and used to create value.

Machine learning can help marketers overcome all of those problems, but you need to get your data and organization into position to process it and put it to work.

One note: There is a difference between artificial intelligence and machine learning. According to Penn, AI is when you develop an algorithm that allows a computer to some extent to “think” for you towards achieving a goal. Machine learning is when you let the computer create an algorithm to solve for the goals you give it based on large pools of data. (And “deep learning” is when you have many machine learning algorithms with deep pools of data working in conjunction.) They’re sort of different levels of the same idea.

The Enterprise AI Journey

Penn says there are five stages of enterprise data usage.

  1. Identification: You can use data to identify what happened.
  2. Diagnostic: You can use data to diagnose problems and why it happened.
  3. Predictive: You can predict what will happen.
  4. Prescriptive: You can use data to determine what should happen.
  5. Proactive: A machine can use data to make it happen for you.

Most companies are somewhere around stages 2 and 3. Getting to that fifth stage is an enterprise AI journey of establishing the capabilities to move form identifying data to proactively using it. Penn sees that as a seven-step process:

  1. Data Foundation: You have the base tools to house and process data.
  2. Measurement and Analytics: You are able to accurately measure and analyze what you’re doing.
  3. Insights & Research: You have the tools to turn the measurement and analytics into business intelligence.
  4. Process Automation: You have the tools to automate these processes, so turning your data foundation into business intelligence happens automatically with minimal personnel intervention.
  5. Data Science: You install the capability to recognize next-level data science insights.
  6. Machine Learning: You have the data science  and tools in place to integrate AI insights into your business.
  7. AI Powered Enterprise: You solve for AI first, breaking down business challenges into process-oriented steps that the AI can solve with minimal human intervention. “How can we use AI to do this for us on an iterative, continuously optimizing process o create business value?”

Again, almost no one has gotten to step 7, or even 6. Th most advanced brands today around around step 5, and most are still in the first half of the journey.

Should You Buy or Build AI?

If you feel like your data and processes are ready for AI, Penn says whether to buy or build yourself comes down to time, money and strategy.

If you have money but no time, a vendor can help. Look to buy or hire a service.

If you have time but not money, look to build in-house. A lot of AI in use today is using open source software. There’s no fee, just a knowledge cost. Budget about two years per person to get up to speed. (For many marketers in 2018, this could be prohibitive.)

Strategy: Before doing either, make sure you’re going to be able to use what you get to add value to the company. Bot of those options are significant investments.  Do you have the strategy to support AI whichever you choose? One good benchmark is to look at the state of your digital transformation. A complete digital transformation is essential to do this. Without being digital, you can’t be AI.

How to Prepare Your Company for AI

Once you’re ready and committed, Penn said you need three kinds of people you need to have to enable your AI strategy.

To go back to the oil analogy, data is messy stuff that’s useless on its own without refinement. So you need:

  • Developers to extract the data
  • Data scientists to refine the data
  • Marketing technologies to figure out how to use that data

Beyond those roles, certain skills become more important in a digital, AI-driven business. So look to train or hire overall talent with skills like these:

A slide from FUSE Digital Marketing 2018 where Christopher Penn laid out the essential skill to thrive in a machine learning world.
A slide from FUSE Digital Marketing 2018 where Christopher Penn laid out the essential skill to thrive in a machine learning world. | Credit: FUSE Digital Marketing 2018 by Christopher Penn

It also takes some different ways of thinking, according to Penn.

To be successful in an AI enterprise, marketers must learn to think algorithmically. Learn to think like a machine, process-focused on how to solve that problem.

Also, it’s important to understand that machines need oversight. Algorithms can spit out a bias if you don’t actively watch for it. It happened to Amazon, it’s happened in police use, and it can happen to you to if you don’t watch.

And finally, AI-focused organization must be built around outcome-focused people. It’s easy to get lost in AI if your goals aren’t clear, measurable and accountable.

Who Does the Machine Serve?

In the future, Penn said there will be two kinds of jobs: “Either you will manage the machines, or the machines will manage you. We want to be the people who manage the machines, not the kind of people who are managed by them.”

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.

The Technology Behind Black Friday Deals

Every marketer or web designer knows that a website has under three seconds to grab a users attention. Any form of friction will result in your target and audience going elsewhere. But, once again there is an increasing number of tools that are helping brands test the user experience of their digital products and services.

As our inboxes start to bulge with the deluge of marketing messages, many are already beginning to experience Black Friday fatigue. While marketers and business leaders pat themselves on the back for another successful campaign, tech-savvy consumers are creating mail rules to send any message containing “Black Friday” directly to their junk folder.

The problem is that customers now expect a little more than a generic email with Black Friday in the subject. The big question shoppers are asking is, what’s in it for me? If Netflix and Spotify know how entertainment preferences and Amazon knows what items we might want to buy, why are we still getting a marketing message containing products that we have little interest in?

Technology is now at the heart of every customer touch point. While consumers prepare to satisfy their insatiable desire for a bargain, the reputation of the world’s biggest brands is heavily reliant on technology.

For the busiest shopping day of the year to be a success, a website must work quickly and seamlessly without interruption. Digital audiences have little regard for how complex systems will work when faced with ten times more visitors. They just expect it to work.

Any break in connectivity or change in the speed of a transaction on an already overloaded website is catastrophic. Essentially, its the modern equivalent of bringing the shutters down of your store on the busiest day of the year. Behind the scenes, many have invested in content delivery networks (CDN) to reduce load times. But this is only one example of how technology is the real day of the day.

IBM AI Powered Ads

These rises in expectations from consumers are forcing marketers to think a little differently and embrace new technologies. For example, IBM recently announced that its latest AI-powered interactive ads will be used by LEGO Systems. The ads are aiming to recommend the right gift set to the right holiday shoppers using technology. Many brands will be watching from the sidelines to see if hyper-personalization and actionable insights can deliver tangible results rather than just more hype.

Lego is joining a long list of household names such as Lufthansa, Best Western, and TruGreen that are all bravely navigating unchartered digital waters to secure an early competitive advantage. Only time will tell if this emerging technology impacted their Black Friday sales.

71% of brands are still relying on generic marketing messages. But there is a realization in the industry that they need to do something different to stand out from the advertising clutter online. The problem is that we are only just learning how these new technologies can make the dream of personalized, targeted messaging across every touchpoint a reality.

Eye Level Is Buy Level

Every marketer or web designer knows that a website has under three seconds to grab a users attention. Any form of friction will result in your target and audience going elsewhere. But, once again there is an increasing number of tools that are helping brands test the user experience of their digital products and services.

H&M turned to Tobii Pro Sprint’s eye tracking platform to create a clear path of purchase for online shoppers and to validate design decisions on their site. The company are also using similar eye-tracking technology on online banking portals, music, and video streaming services, work portals, and believe it can transform any digital product or service

By tracking eye movements when testing websites, businesses can discover how users visually navigate the digital interface. The platform not only highlights what our eyes notice or what they ignore, but also areas where users encounter friction points as well as the things they see or ignore, the areas they get stuck on, and where they naturally gravitate to.

The eye tracking company are on a mission to ensure that technology works in harmony alongside natural human behavior. The technology enables companies to pinpoint potential causes or indicators of usability issues when working on software applications and websites.

The use of smartphones to make purchases online in the holiday season is unsurprisingly up 44% compared to last year. Welcome to a new digital age of the always-on mobile shopper. Digital natives are increasingly checking their mobile devices for bargains throughout the day.

Shoppers are increasingly attracted by discounts, sales, personalization and the convenience of doing it all from their mobile device in any location. How marketers leverage technology to create unique mobile experiences that deliver these minimum requirements is paramount.

Delivering the wow factor and remaining online will determine how successful retailers are during the holiday season. Meanwhile, marketers are faced with both challenges and opportunities of how to engage with the always-on customer without being creepy.

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.

direct mail marketing
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!