Marketing Is Turning to AI for Customer Acquisition

Companies started using artificial intelligence and machine learning about five to seven years ago, but those early efforts weren’t targeted nearly enough.

That is finally starting to change as marketers are turning to the technology to solve very specific issues, like refining their customer retention efforts, targeting competitor’s customers, or creating profiles of their ideal prospects or customers.

Wilson Raj, global director of customer intelligence at SAS, says the technology can help marketers do the following:

  • Refine segmentation for better personalization.
  • Enable timelier and more relevant customer experiences by recognizing past patterns, current engagements, and predicted behaviors and then surface in-moment offers based on those insights.
  • Boost revenue through next-best-action recommendations. Machine learning can help spot patterns or changes in customer behavior more swiftly, enabling marketing to respond in real time by adjusting offers.

The first step in using AI/ML for competitive marketing is to understand what the technology can and cannot do today and how it is evolving, says Christian Wettre, general manager of Sugar Sell and Sugar Market at SugarCRM.


The use of the technology for competitive marketing is in the early stages, according to Wettre. But as more companies have success with it, the pace of adoption will quicken. So Wettre and others expect the technology to penetrate the mass market in the next couple of years.

Though the terms are often used interchangeably, there is a distinction to be made between artificial intelligence and machine learning. ML is an advanced subset of artificial intelligence, enabling CRM systems to learn to find insights without being told exactly what to look for, Raj explains.

Rohan Chandran, chief product officer at Data Axle (formerly Infogroup), agrees. While extremely basic AI has been around for some time, ML and deep learning have become industry buzzwords only in the past few years. AI performs the “grunt work,” like triggering email campaigns. ML drives more advanced use of AI, such as lead qualification scoring.

“You have this training data system and the feedback loops that come in from what actually happens as you use the data; then that system recursively learns and evolves and gets better and better,” Chandran explains.

Before deploying machine learning, determine if it will add value to the process, Chandran adds.

Sugar has been careful about how it approached AI and ML, Wettre says. “We’ve taken a walk-first approach. As we’ve applied the science to the marketing and CRM universe, everything has to do with the ideal customer profile.”

Once the ideal customer is identified, the company uses that knowledge to attempt to attract and convert prospects to customers in a more efficient manner, Wettre continues.

SugarCRM and other companies use AI/ML to continually refine their respective ideal customer profiles (ICPs), according to Wettre. “Building an ICP model is often done on a customer data platform.”

In a traditional scenario, companies will wait until prospects or customers fill out information on web pages before they react, Wettre explains. But with ICPs created with AI/ML, they can move forward with very little information, perhaps even just a web view, and combine that with information from other sources to score customers or prospects on how similar they are to the ideal profile and use that to determine marketing and sales efforts.

Raj says the technology can also help marketing with offer and click optimization on the web or mobile apps. It can, for example, dynamically tailor web content based on visitors’ past search history or website or mobile app interactions. It can also help forecast potential profitability, finding patterns in past behavior to predict lifetime value of prospects or customers at the beginning of their life cycles. That can then be used for improving resource allocation and campaign management and calculating the ROI of marketing investment.


Chandran points to social media commentary as a way for companies to learn which of their competitors’ customers might be prepared to make a switch, adding that AI- and ML-powered sentiment analysis can analyze the commentary to determine the most dissatisfied customers who would be the best targets for marketing outreach.

Using AI and ML enables marketers to not just scour social media feedback but also information from other digital touchpoints and in-store visits to help determine customers’ emotional attitudes, Raj says. ML can analyze “all that stuff at scale” and then “go beyond traditional segmentation approaches to almost give you a fuller view of that consumer.”

“The airline industry is a great example of this in action,” Chandran says, noting that people take to Twitter quickly to complain about very specific aspects of their travel experience.

If a customer of one airline complains on Twitter about an experience with that airline, a competitor can use the information to offer a discount on a future flight between the same airports. Longer term, airlines can use customer social media information to determine how to attack competitors’ weaknesses, Chandran says. “This is where you can highlight not just what your own unique positioning is but specifically what customers are reacting and responding to and tailor your marketing to that.”

Similarly, an airline can better gauge what competitors are doing right and determine if they want to duplicate those efforts.

But it’s not just the travel industry that stands to benefit. In the fast-food industry, Wendy’s monitors the social media and more traditional marketing of Burger King and McDonald’s to help it in its social media marketing campaigns, according to Chandran.

Successful use of AI and ML in marketing comes down to having comprehensive data, Wettre says. “The more behavioral patterns you have about a customer demographically—how the customer has behaved and reacted over time previously—the richer you can create those models and the smarter your AI models are going to be.”

Companies that perform best can collect as much data as possible and then use their own data scientists or technology to make sense of it, according to Wettre. “Understanding the data science isn’t easy. It’s hard to apply it correctly. It’s hard to get statistically meaningful information out of these models.”

Combining competitive intelligence with “look-alike” modeling to determine which prospects are most like current customers, marketers can better target the prospects most likely to convert to customers, Raj adds.

“You can get much more accuracy if you know that this person or unknown [website] visitor is acting very much like a current customer with these same attributes,” he explains. “Now we can treat this unknown user like a known user and dynamically offer content or interactions in a more powerful way. If the unknown user responds, then you get deeper; if the user doesn’t, then try another look-alike model. In the past, we just served up some general offers, but now we can get crisper in terms of the content, media, kinds of products or services, and pricing based on that look-alike model.”

AI and ML enables companies to take this approach on a micro-segmentation level, being extremely precise in the types of content, offers, or interactions they offer prospects, Raj adds. A company can offer a young prospect in the Northeast a very different offer from an older prospect in a different area of the country or even for another young prospect in a nearby state or nearby city.

Raj identifies McCormick, the spice company, as one firm that has done an excellent job with this, developing different content for several hundred flavor profiles that it can serve up to prospects visiting its website.

“It’s like a fingerprint for your profile,” Raj says. “You’re using machine learning and hyper-segmentation for the next best actions and recommendations.”

While large companies like airlines or major fast-food vendors have the resources to invest in these tools, smaller companies have unique challenges: They need AI and ML solutions that are affordable but also easy to transition to or work with their legacy marketing systems, Chandran says. “That is when it will hit the mass market.”

But even some smaller enterprises have already had success with AI and ML technology, Raj says. He points to Raiffeisenbank in Belgrade, Serbia, which used the technology in a successful campaign for a credit product. Using machine learning combined with historical customer data, risk scores, and details on timely bill payments to third parties, the bank greatly refined the customers it targeted for a credit offer, generating a 14 percent success rate, compared to just a 1 percent success rate for previous campaigns.


Sometimes companies expect too much from AI and ML, Wettre says. “The worst practice is when someone falls in love with the idea of a computer answering the questions—that you can turn [AI/ML] loose and it will tell you what to do. It’s not that easy. If you don’t back it up with other investments or with the right vendor, you’re not going to get the results you are looking for,” he suggests.

Chandran adds further that AI and ML solutions aren’t one size fits all. A solution that works for Data Axle likely won’t be suitable for a much smaller company in a different industry, he says.

To maximize the benefits from AI and ML, companies need to continually train the technology, and then monitor it to ensure it is learning as expected, Chandran says, pointing to the Tay Microsoft chatbot, which some Twitter users “attacked” shortly after it launched in 2016. As it learned from previous utterances, the chatbot was soon swearing and spewing racist terminology and eventually had to be shut down.

Wettre recommends starting small, adjusting the use of the technology until successes start occurring, then expanding from there. SugarCRM has followed that concept on its own use of the technology.

Some of SugarCRM’s customers hoped for a quick win with the technology but have learned to use a more structured, step-by-step approach to expanding use of AI and ML.

“Focus on who is a win-win,” Wettre says. While the initial win might be for the company, if the customer sees the relationship as a win, the customer will continue to return.

“Make sure you have the right resources,” Wettre adds. “If you’re going to do generalized AI, you have to invest in very, very skilled people. It’s not a trivial thing to do. These are expensive employees. And it’s a fairly costly thing to do.”

“One of the things that people forget for machine learning is there has to be an objective,” Raj says. “For example, if I want to be able to score [profiles] so that I can acquire these kinds of customers, the first thing I need is to establish a goal for what I want to do with machine learning.”

And then, too, keep in mind that while the technology is excellent at automating tasks and offering predictive modeling, even with sentiment analysis, it still falls short in terms of understanding emotions, Raj adds.


However, experts agree that whatever limitations AI and ML have now, the technologies will continue to improve as they gather more data points. AI and ML will become even more important as the COVID-19 pandemic wanes and companies look to rebuild their client bases and reacquire wayward customers, according to Chandran.

“By 2022, hopefully, micro-segmentation will be mainstream,” Raj says. “Beyond just personalization, I can see machine learning helping with other complex activities. For example, making the necessary budget adjustments in real time in broad campaigns or more specific campaigns; maybe doing a quick ROI analysis on campaign results, and then authorizing changes in resourcing and planning in real time.”

Raj adds that those changes would be based on shifts in demand using prospect and customer data, behavioral data, and information from suppliers. 

Phillip Britt is a freelance writer based in the Chicago area. He can be reached at


This article is written by Phillip Britt and originally published here


In digital marketing, marketing automation refers to a set of processes and techniques that streamlines all the activities involved in presenting your business to prospective clients and customers. It involves the use of special technology and software to manage all your marketing needs and content including emails, social media and website posts, campaigns, pitches, and many more.

Automating your marketing processes involves collecting relevant data, experiences, and preferences and using this information as a workflow to target the right customers on the internet. These messages could either be emails (promotional and not spam), SMS/MMS, direct messages, and general content seen on social media, each one tailored to a particular customer’s journey with your brand at any stage of the B2C and B2B  relationship. Instead of sending out tailored emails one-by-one to thousands of prospective leads, hoping to make a few conversions, the software can do all the gritty work for you and increase your chances by a statistical 20%.

Essentially, marketing automation increases efficiency, maximizes profit, and streamlines the entire marketing process for any brand or business.

Blocking out unnecessary complexity

With technology advancing so rapidly, the digital marketing industry is becoming far too complicated and difficult to maneuver for a lot of people. The whole point of automating your marketing processes is to make your life easier and streamline the foundation of your entire business operation. Of course, it shouldn’t be a complicated thing to do. Through a single interface of any chosen software, you can schedule email drops, manage all your subscriber information and interactions, and create excellent campaigns through a single button click.

Essentially, a good deal of what is required has already been worked into some pretty advanced platforms and technologies. Also, there are specialized teams of people who work to take all the marketing issues off your hands.

According to Madhu Gulati, an Indian-American entrepreneur and CEO, a businessperson shouldn’t have to spend thousands of hours every day trying to navigate the world of marketing, often having a frustrating time making any solid progress. For many people, even with all the outstanding technology available today, the processes are still as complicated as ever.

Madhu is the founder and CEO of Marrina Decisions, a US-based marketing operations agency focused on helping B2C and B2B firms coordinate their marketing automation and operational activities, all targeted at increasing visibility, demand generation, sales and marketing funnel preparation, building solid customer networks, and increasing ROI exponentially.

 “Technology without humans causes problems because technology is only as good as the humans who manage it,” says Madhu. “It does not have to cause headaches – it should take them away. So, in pursuit of happiness, I found a bunch of people who love technology as much as I do. Together, we decided to enjoy our 90,000 hours at work and help you enjoy yours too. You leave the technology to us and get back to what you’re here to do.”

A standard marketing automation team uses software such as Adobe’s Marketo Engage, Eloqua, Pardot, Sales Force Marketing Cloud, and/or several more to manage marketing processes by identifying the right customers through behavior tracking, building and scaling campaigns with great ease, and determining how each step in the system impacts revenue.

At Marrina Decisions, some of the services include marketing campaign managed servicesMarketo-managed servicesMarketo optimizationemail marketing servicesMarketo migration and quick launch, and other data services. When working with a proper team of trained marketing automation certified experts, you gain access to other additional services including “auditing of existing systems, mapping out migration strategies, campaign design and scoring, evaluation of data cleanliness, reviewing client competency for migration, testing and conducting of performance reviews, and finally, inventory on all of your assets, campaigns, processes, and data to be migrated.”

 “Marketing should be one of the most easily enjoyed aspects of business and entrepreneurship,” said Madhu, a past employee of Market2Lead and Marketo, now the CEO of Marrina Decisions, partner of Adobe. “Sadly, the complexities that arise with technology or simply the ‘fear of tech’ often makes it a dreadful process for so many people. Automating your systems or letting a fully functional team handle it for you is often the quickest step to unlocking the full benefits of digital marketing for quick ROI.”


This article is written by MICHAEL PERES and originally published here

Five technology trends in the pharma industry

Technology advancement has brought about a revolution in the medical industry and it has led to significant growth of the sector.

Technology advancement has brought about a revolution in the medical industry and it has led to significant growth of the sector. Robust technology in the medical field is considered as the main pillar in the present times due to its effective role in delivering high-end and swift healthcare services. The pharma industry is all about discovering, developing, producing, and marketing pharmaceutical drugs for using them as medications to cure and vaccinate patients from various kinds of ailments.

The Pharma industry is evolving rapidly and is filled with innovations and technological developments. Here is a glimpse of the top five trending technologies in the pharma industry that are driving its growth-

⦁ Application of AI (Artificial Intelligence) in the healthcare industry

Artificial Intelligence enables finding patients for clinical trials and letting them further find pharma companies for their treatment. It also smoothens the customer chatbots service that allows patients to instantly resolve their queries regarding medicines, treatment, and payment.

Various Artificial Intelligence (AI) tools also assist in providing positive results to patients. It offers digital healthcare solutions with better outcomes and advanced treatments. Artificial Intelligence-based technology keeps a good track of a patient’s health conditions, from monitoring the body temperature, pulse, and various other activities that help decrease the burden of the medical professionals and also ensures that the individual’s stats are under constant check.

⦁ Virtual detailing or E detailing or Remote detailing

During the pandemic, remote detailing hugely increased and is one of the ways of brand communication for pharmaceutical companies.

A medical device called In-Contro MR digitizes promotional content and can be accessed through mobile, laptop, etc. which makes it more interactive for HCPs. You can simply connect with your doctor and get your health queries solved through a webcam via this technology of virtual detailing. It is accessible for face-to-face meetings, web meetings, digital marketing campaigns, etc., and provides satisfactory services to healthcare professionals.

⦁ Digital or Online consultation– Telemedicine & Telehealth

Telehealth played an important role in connecting patients with the doctors for consultation. Telemedicine is an advanced method of health care and proved to be extremely helpful during pandemics. It is a health-related service and the information reaches through telecommunication technologies and electronic information. It enables doctors to treat patients remotely and allows patients to connect with doctors and seek advice despite the geographical barriers using a smartphone or computer.
Undoubtedly, it’s a new horizon in the public health domain and has helped boost the pharma industry.

⦁ A non – invasive patient screening or Predictive Diagnostics

A Predictive diagnostics or non-invasive screening provides a risk score for patients. It significantly reduces the negative impact and prevents the problems of the medical providers.

Predictive Diagnostics can help to detect any early signs and symptoms of a patient’s health conditions in the general ward or ICU ) Intensive care unit and also identify if the person at home is at risk and to save them from hospital admission.

One of the recent technological trends, it is eventually picking up the pace and is changing the face of the pharma biz in India.

⦁ E-learning in healthcare

E-learning is a well-accepted and upcoming technology in the pharma industry. It is reliable and helpful for healthcare practitioners as it upskills and reskills them along with updating them about the newly introduced trends in the medical industry.

Owing to the ever-evolving scenario of the healthcare segment in India especially the telehealth and pharma industry, there has been a huge demand for medical-specific e-learning courses.

Not only is e-learning cost-effective but helps extend the required knowledge and practical exposure with reference to the latest technological development in the medical industry. It offers the perfect platform for medical professionals to invest in upskilling or reskilling themselves for their personal and professional growth.

Summing up

The government is making all the possible efforts to extend high-end and accessible medical facilities to all the people across the country. The future of the pharma industry looks brighter with emerging technologies like Artificial Intelligence and virtual detailing. People are now well aware of advanced technologies and are making good use of their availability. With the introduction of numerous technological innovations, the country and the world is heading towards a healthy and happy future of human existence.


This article is written by Hiren Dhuvad and originally published here

2 eye-opening chatbot stats, backed with data from 400 websites

Chatbots are by no means a new technology, but the biggest surge to utilizing them for business is yet to come.

The focus with chatbots has long been on improving efficiency and saving costs in customer service. However, innovative chatbot implementations prove that chatbots have high potential in generating revenue by converting leads and sales online. Another prominent area is recruitment, where chatbots are used to attract passive candidates and screen them in an instant.

These two eye-opening chatbot stats will make you consider the potential of chatbots in a new light. The numbers are backed by a study of 400 companies in 25 industry categories. Find more information and a link to the study report at the end of this article.

Let’s start off with a big one…

2 eye-opening chatbot stats, backed with data from 400 websites

Chatbots increase website conversion rate by 10-100%

Data shows that adding chatbots on a website is an effective and reliable way to increase conversion rate. Conversions in this case mean for instance contact requests, demo bookings, sign ups to events and webinars, newsletter subscriptions, job applications, and so on.

Conversions are often directly linked to a business outcome (e.g.,sales qualified leads, SQLs), but also softer conversions (marketing qualified leads, MQLs) create valuable engagement between the website visitor and the firm.

Increases in the company website’s conversion rate are somewhat of a holy grail to results-oriented marketers regardless of vertical. Therefore, such solid evidence of chatbot gains serves as an encouraging signal to marketers who haven’t explored the tech so far.

In the study, the 10-100% increase was calculated on top of a 2% baseline conversion rate, which represents a simplified average, mostly achieved with traditional contact forms.

OK, what about the other amazing chatbot stat?

10-30% of chatbot conversations result in a conversion or lead

This statistic is extremely tangible and therefore perhaps even more impressive than the first one. Think of it this way: Out of 100 people who start chatting with a bot, on average10-30 convert into a qualified lead or perform another defined action.

That is huge.

Getting website visitors to start conversations is a real gold mine. The power of chatbots is largely based on two-way interactivity, and the most effective chatbots even make chatting fun. Users proceed towards conversion step by step, so the hurdle of conversion is much lower than with contact forms, where several pieces of information need to be filled in at once.

The conversational nature of chatbots adds an entirely new level of engagement on any website. Chatbot conversations are best served context-specific, personal, helpful, and, of course, fast and easy for the user.

The future of chatbots is bright

The two chatbot stats in this article are only a small part of the evidence supporting the view that today’s chatbot technology comes with high results potential. What’s more, the benefits of chatbots are no longer only tied to soft metrics, but also cover key success factors like conversion rate and online sales.

The global chatbot market is projected to reach USD 9.4 billion by 2024. By then, automated website conversations — whether they’re about support or sales — will be rather required by customers than just a nice to have addition.

Briefly about the data

The chatbot statistics presented in this article are based on a study of 400 websites that use chatbots for various purposes, including lead generation and sales, customer service, and recruitment. Studied chatbots were hand-built decision tree bots with no advanced AI.Access the full report with industry-specific data for 25 verticals.


This article is written by Otto Antikainen and originally published here

How AI can build a more empathetic future for marketing

The desire for companies to connect with the emotions of their customers is by no means a new phenomenon. For marketers, the ability to understand the emotional sentiments behind consumer behaviour has been a priority for decades. Yet it has been notoriously difficult to track these sentiments accurately over time.

Artificial intelligence (AI) offers a solution to this problem. Consumers today expect a tailored experience and AI has unique capabilities to help marketers by understanding sentiment, and reaching them at just the right time. More and more companies, therefore, are likely to embrace AI and its potential in helping them form strong individualised relationships with consumers, at a time when it is more necessary than ever to do so.

Covid-19 and empathy

Securing an emotional connection with your consumers has always been a priority for marketers. It has been well documented that consumer behaviour is being increasingly dictated by emotion over information, and this trend accelerated greatly during 2020. In a year of ever-changing circumstances and disruption, the importance for brands to demonstrate empathy has been clear. Marketers that neglect to appreciate the unique circumstances their consumers find themselves in and fail to communicate in an empathetic and transparent manner, will risk entrenching negative perceptions of their brand in the minds of consumers.

In order to avoid these potential pitfalls, marketers need to be able to master the increasing volumes of consumer data available to them, so it can then be used to inform communication choices and ensure they are as tactful and individualised as possible.

Using data to drive empathy

AI is recognised as an important solution to this problem. A survey recently conducted by Iterable found that 83% of marketers were likely to include the integration of AI technology as a part of their 2021 strategy, and it is easy to see why.

In order to engage in an empathetic manner with their customers, companies first need to gather relevant data from all cross-channel engagements, including email, mobile messaging and all other communication channels. Once this is achieved, the data needs to be normalised so it can be of use in identifying the motivations of individuals.

The challenge today is to not only manage this increasing volume of consumer data but to do so over a sustained period of time. In this uncertain era, customer sentiment can change on a daily basis; data management needs to reflect this reality to be of use to marketers. We have to move beyond manual snapshots of how customers feel about a brand and build a broader, real-time view.

AI as a solution

The process of collecting and managing all the relevant engagement data for such a task would be nearly impossible without the use of AI. By leveraging behavioural data from customers in real time, it enables marketers to take a holistic view of consumer engagement with their brand, allowing them to make every stage of the lifecycle process as personalised as possible.

AI can go beyond addressing those customers with negative sentiments. Whilst it certainly helps mitigate churn, its ability to harness data can help marketers identify those customers with positive sentiments and the most potential to become loyal ambassadors. Nurturing these sentiments can be just as beneficial as reducing churn.

Changes to data

When implementing an AI-based solution, it is important to consider how our relationship with data is set to change in the coming years. Both regulation and attitudes towards third-party data are shifting, with Google set to phase out third-party cookies in the near future. This will lead to an increased focus on both zero-party-data, which a customer shares proactively with a brand, and first-party data, which is collected directly from customers. Whilst this may appear to be challenging for marketers, it is not as drastic a change as once feared. Despite the growth in concern regarding data protection, consumers have demonstrated a willingness to share data in return for a personalised experience, if done so in a transparent fashion.

AI can play a vital role in establishing this trust. By leveraging zero and first-party data accurately, it can help ensure that communication reflects this growing desire for transparency. For this to succeed, AI technology has to operate in a way that consumers can understand. A lack of transparency makes it harder to act in an empathetic manner.

Final thoughts

The increased desire for an empathetic approach from businesses will not go away in 2021. These trends were growing in prominence prior to last year, and the pandemic has only accelerated these changes. For businesses to satisfy this need, they must continue to humanise their interactions with customers. Consumers want to be able to trust the brands they feel connected to, and AI technology offers the best route towards achieving this goal. Marketing is having to operate within delicate circumstances at present. However, by utilising AI we can leverage more from our increasing volumes of data and use it to engage with customers in a manner which is suitably personalised and empathetic.


This article is written by  Jeffrey Vocell and originally published here