Pharmas look at ways to empower Virtual sales – technology driving this massive shift in a traditional sales role

Marketing & Strategy: The sudden and ongoing impact of COVID-19 on professional face-to-face interaction, along with the already evolving transition to more virtual and doctor-led engagements, has worked to accelerate the decline in face-to-face pharmaceutical sales representative visits, replaced by the growing viability of new digital channels.

A survey of almost 400 GPs by the Australian Doctor Group (ADG) on their contact with pharmaceutical field staff early in the pandemic, found a significant drop in face-to-face engagement, with the number of doctors who reported in-person field staff visits falling from 81% to 5% early in the pandemic.

While this decline could easily be explained by government and industry led risk management initiatives, perhaps of greater significance is the post-COVID reluctance of GPs opting to re-establish their previous levels of engagement with sales representatives. Only 50% of GPs stated a preference for this type of pharmaceutical company-initiated approach in the futureMany GPs have reacted positively to the digital interactions they have had and don’t want to give up on those benefits.

“The future model will be very much on-demand,” said Tsumi Smith, Head of Multichannel Marketing at AbbVie.

“But it appears there is still a long way to go. As pharma companies, we often say we are customer-centric and customer-first, but we often don’t listen enough and respond to what doctors truly want and need. There’s still a space for us to inform doctors about new products because they do want to hear about it. But it needs to be on their terms.”

Peter Stephenson, Managing Director of West 53rd St Digital Services and former Asia Pacific Digital Transformation Lead at MSD, believes the pharmaceutical company of the future will have a really strong analytics and data capability. They will understand customer behaviour on different channels, their content preferences, where they are in the prescribing continuum, and they will be able to match their content and support to whatever point the doctor is in their career.

“The challenge is being able to access data from the different ecosystems of engagement to get a single customer view and to then understand how to adapt and customise the channels to meet each customer’s needs,” he stated.

It is not all doom and gloom for pharmaceutical industry. This white paper shows that through the adoption of new technologies and by accepting greater doctor input into when and how engagements take place, pharmaceutical company support and education will remain as relevant and important as ever to healthcare professionals (HCPs).

Download the new white paper here.

Download 2020 white paper here.


This article is written by Health Industry Hub and originally published here

Why should you use a chatbot in your mobile marketing strategy?

Soti Vayena, Senior Digital Marketing Manager at WayMore, a business unit of AMD Telecom, looks at the part chatbots can play in an effective mobile strategy. 

Today’s consumers are mobile-first, with mobile phone users far outnumbering laptop and PC users.

This progress has radically changed the way consumers communicate, replacing traditional interactions with online messaging. Undoubtedly, businesses have been forced to adapt to a new type of communication, in order to provide a seamless buyer’s journey and an excellent experience. They need to be present cross-channel and cross-device, in order to always be available to solve customer queries and questions. That is not always easy due to human limitations. And that is exactly where chatbots come in.

Chatbots, a great tool in conversational marketing, are designed to hold a conversation with human users and can be found everywhere, from smartphone apps for personal use to the company’s business website.

Chatbots are mobile optimized and they provide high user engagement. And users seem to have developed a great liking for them, signalling that they are here to stay!

  • 56 per cent of people who would rather message than call customer service and an estimation
  • 85 per cent of customer interactions to be handled without a human in a few years
  • At least 30 per cent of consumers are enthusiastic about chatbots

WayMore helps companies optimize their marketing campaigns through intelligent products, like chatbots that elevate your customers’ experience, increase customer engagement, embrace scalability, and empower your brand.

The statistics above confirm that chatbots are a popular technology and are rightfully referred to as the future of marketing. The time has come to integrate them into your mobile marketing strategy.

Benefits of integrating chatbots into your mobile marketing strategy
By 2024, the chatbot market is expected to rise to $1.3bn (£920m). If you haven’t yet integrated a chatbot into your mobile marketing strategy, now is the time to do so. In the next few minutes, we will discuss why every business needs them in 2021. Let’s jump in!

Increased customer satisfaction with instant responses
Customers want answers and most of the time, they want them NOW. It’s common after all in the age of instant gratification.

Αs a marketer, your primary goal is to ensure your customers are satisfied. You should make them feel cared and valued throughout the complete sales cycle. Chatbots can instantly manage customer requests with accuracy. To add to this, their demand by customers has increased. By integrating them into your strategy you ensure that customers get the instant assistance they desire, which leads to increase in satisfaction.

Round-the-clock availability, less costs
The majority of customers expect a business to be available 24/7. Traditional customer support teams work for eight hours. So, to run a round-the-clock support system you need three teams working different shifts. Adding the resources needed to give support to customers around the clockcan prove way too costly for many companies. This is where chatbots excel, avoiding these costs.

As chatbots can answer any query or command instantly, your business can stay awake to customer issues throughout the day and night, without spending too much on customer support.

Better problem solving, more scalability
As stated, human beings have a certain limitation when it comes to handling multiple things at the same time. Often when handling beyond three to four tasks, we commit errors. Chatbots, on the other hand, will not. They can handle hundreds of queries at the same time, without errors.

Chatbots are capable of carrying on conversations with nearly an unlimited number of people simultaneously, offering a robust solution in handling customer queries. For a business that needs to handle many customer calls on similar issues or orders, chatbots are the ideal solution.

Chatbots can be configured to understand what customers are asking and to intelligently answer them. Through automation you can handle more customers than you could previously, scaling your business, while simultaneously looking out for the convenience of your customers.

Making sense of customer data
There is a lot of talk around big data in the marketing world. By communicating with customers, chatbots can help you collect a great amount of valuable customer data. With the help of AI, chatbots are able to learn from previous interactions and act on the collected data more quickly and effectively. They can predict intentions, provide recommendations and answers to keep existing customers engaged with brands and products, and also increase conversions.

Moreover, they provide customers with complete product details if needed, and suggest relevant content. As research has suggested consumers are even interested in receiving chatbot recommendations and advice this way.

Personalizing marketing
Customers demand personalized experiences. And chatbots are great personalization marketing tools. By generating highly individualized messages to help customers in their journeys, chatbots can boost conversion rates in an unprecedented way.

With the help of AI, chatbots can provide media or product recommendations, personalize the channels consumers like to be contacted through, as well as the time they receive a message. AI adapts according to the feedback, personalizing communication and content accordingly.

Manage lead generation effectively
As more and more people chat with your messaging bot, artificial intelligence can start making smart decisions and provide dynamic communications automatically to all the users.

Through chatbots, you can engage your prospective customers using personalized messaging throughout their journey. Chatbots can qualify leads and guide them smoothly through the buyer’s journey. By asking them a couple of relevant questions, you can nurture them and provide customized solutions based on their answers, introduce your products to educate them, and eventually inspire them to make a purchase. This way leads feel appreciated rather than irritating.

Designing a chatbot for your business
With all that being said, now is the time to pick software to build your chatbot and integrate it into your mobile marketing strategy. Υou can choose between a simple predefined chatbot or an intelligent chatbot. Using WayMore’s innovative Chatbot Solutions, you can help your business create a powerful competitive advantage to stay ahead of your competition and differentiate your brand. Our solutions provide numerous benefits to both end-users and businesses. On the one hand they build and monetize a valuable audience, and on the other hand end-users enjoy a high-quality customer experience.

Regardless of the size of your business or industry, WayMore provides sophisticated and effective ways to all businesses that wish to instantly offer optimized customer communication to their existing clients. So, take advantage of chatbot innovation technology with its powerful features, provide a personalized experience to your audience, and engage your users before, during, and after critical decision-making points.


This article is written by Soti Vayena and originally published here

AI-Powered Marketing: Leveraging First- & Third-Party Behavioral Data To Improve AI & Organizational Effectiveness

Artificial intelligence (AI) is an invaluable aspect of modern marketing, with many organizations leveraging AI-powered solutions to help collect higher quality data, deliver better buyer experiences and more.

One of those data sets is behavioral data, which enables B2B organizations to identify buyer preferences, their stage of the buyer’s journey and purchasing intent for optimal outreach, engagement and deal closing. Naturally, B2B organizations are leveraging behavioral data to inform their AI-powered solutions and marketing strategies.

“There’s more awareness of what behavioral data is and how you can use it,” said Steven Casey, Principal Analyst at Forrester, in an interview with Demand Gen Report. “There’s been an arc in the development of the martech, with marketers using this kind of data to solve relatively simple problems. Solutions have also been using AI for a while to make recommendations for marketers and sellers, and behavioral data can only enhance that.”

In a recent report from Forrester, Casey highlighted the changing landscape of marketing with the consolidation of AI-powered solutions and the rise of behavioral data, and how it has given way to a new form of AI-powered B2B marketing. The report also explores the importance of data management and how solid data sets can help an organization’s AI tools improve operational efficiency and align marketing and sales strategies.

AI-Managed First-Party Behavioral Data Improves Operational Effectiveness

According to the report, the key to successful behavioral data and AI synergy is the quality of the organization’s first-party data. However, many organizations still struggle to find ways to keep their AI informed and optimized, with 32% of global marketers citing bad data as a hindrance to effectiveness.

“The typical problem people run into is data management,” Casey explained. “They just have too much bad data from too many sources. It’s not clean; it’s not unified. Until you’ve cleaned up the data, it’s just bad fuel for your AI.”

The report highlighted some data sets for organizations to focus on to overhaul their behavioral data, which would allow them to improve their team’s effectiveness, including:

  • Assigned data – By assigning AI to record firmographic and demographic data sets, organizations can identify buyer preferences, needs and goals for a more personalized buying experience;
  • Observed data – Recording specific buyer statements and actions while interacting with a brand’s website, social media page, etc. can improve marketing and sales effectiveness, as AI records key information and provides recommendations for successful buyer interactions; and
  • Inferred (intent) data – This prediction-based data set leverages assigned and observed data to automatically provide predictions and recommended actions to take, allowing organizations to determine buying intent and make strategic decisions accordingly.

“AI-powered marketing thrives on these data sets,” said Casey. “The data shows a behavior, and it helps you see if people at an account did something, or maybe an account or a company did something, and act on it. With this first-party data, I can say ‘Okay, I’m gathering first-party behavioral data, and I’m getting a much better picture of buyers’ true intentions.’”

Expanding Third-Party Data Sets For AI-Powered Initiatives

Third-party behavior data also plays a critical role in AI-powered marketing, enabling organizations to rely on a holistic data strategy to engage buyers.

When building up third-party behavior data for AI-powered marketing, Casey explained that having AI solutions that cast as wide a net as possible is essential. The report highlights how AI can track unique behavior data signals, such as published content and review websites, and provide marketing and sales professionals with an external database that is constantly refreshed and ready to use for email campaigns, ABM engagement, programmatic advertising and more.

AI-powered marketing also encourages organizations to survey their third-party sources to make sure their AI-powered strategies are using behavior data that is accurate, up-to-date and relevant to their marketing initiatives.

“It’s an opportunity for marketers to think more broadly and comprehensively about all the data they gather about their customers,” said Casey. “There are lots of different processes and methodologies for leveraging third-party behavior data. But at its core, it’s a mindset of thinking about what I can learn from the landscape around me that would give me an indication of how best to engage with my own buyers.”

AI & Behavior Data Strategies Encourage Internal Alignment

Casey also made clear that AI and behavior data synergy with specific teams was essential for successful AI-powered strategies, as marketing, sales and customer experience teams will ultimately share and implement the data and solutions.

Various teams can share behavior data among their peers using AI, allowing the solutions to inform marketing, sales and customer service processes using the same, unfettered insights. By leveraging the first- and third-party behavioral data gathered by AI, teams can create a universal truth about buyers that encourages them to collaborate on campaigns and sales deals, optimize workflows and improve decision-making at the employee-level.

“Marketing can raise its profile and overcome some of this old behavior by using AI to make recommendations and surface insights for sellers,” Casey explained. “Leveraging observational data and gathering more insights using AI allows teams to make recommendations based on what your AI has learned while gathering data on similar situations.”

Ultimately, AI-powered marketing is only as good as the data organizations have at their disposal, as the AI solutions, as well as marketers and salespeople, heavily rely on accurate data to support their operations. Behavior data is an interesting and effective alternative to inform AI’s researching capabilities and can help B2B organizations improve internal operations and data gathering from internal and external signals.

“By using behavior data and AI, you are helping your teams be successful,” said Casey. “You are letting your teams know what their customers are doing so that you and your other teams are informed. This is the end goal of AI-powered marketing strategies, as you can leverage first- and third-party data to give marketers an AI partner with that will provide relevant data throughout the buyer’s journey.”


This article is written by Michael Rodriguez and originally published here

How is AI (Artificial Intelligence) Changing the Digital Marketing Game?

Artificial intelligence has taken the digital marketing world by storm. This post sheds light on how AI has impacted digital marketing, with the help of its prominent use cases.

Artificial intelligence (AI) was just an ambiguous term in the realm of digital marketing a few years ago. Today, when AI is delivering exceptional results, marketers no longer feel hesitant to embrace it.

In a survey commissioned by MemSQL, out of 1600 marketing professionals, 61% of them considered machine learning and artificial intelligence as crucial data initiatives. Another 2018 Salesforce survey revealed that an impressive 84% of marketers have already adopted AI — up from 29% in the preceding year.

This year-after-year growth has surpassed other emerging technologies such as marketing automation and the Internet of Things (IoT) that marketers continue to adopt.

Join us as we take a deeper look into how AI is revolutionizing the digital marketing game, as we know it.

Improved User Experience

Great user experience is the mark of a successful digital marketing campaign. Prospects are more likely to convert when they can resonate with the content. It is what turns loyal customers into brand evangelists. And this is where AI can help enhance customer experience.

Marketers can analyze AI-generated data to determine which form of content is the most relevant for their target audience. Factors such as past behavior, historical data, and location can be used to recommend the most valuable content for the users.

An example of this capability can be observed in online shopping experiences. We all know how Amazon shares relevant products to buyers based on views, purchases, and previous searchers. That’s artificial intelligence at work!

Another app has gone above and beyond, allowing users to virtually “try on” clothes without actually visiting the store. This not only translates to higher engagement, but also lower product returns, and less disgruntled customers.

Predictive User Behavior

Artificial intelligence can not only examine past customer behavior but also predict the future behavior of existing and new users. Through data management platforms (DMPs), it can gather third-party data across the internet and not just from the company’s website.

Therefore, businesses can apply these insights to personalize their digital marketing strategy and campaigns. Apart from that, AI can help identify the leads with the most potential to convert. Businesses can then devise compelling digital marketing strategies for their highly qualified prospects.

With new algorithms, the accuracy of data is anticipated to get more efficient. Predicting the ROI and determining sales forecasting will inevitably become a lot more convenient in the future through these innovations.

Real-Time Customer Support

Quick resolution of problems and queries is what drives customer support. For this reason, many companies have introduced AI chatbots on their websites and social media channels to communicate with hundreds of thousands of visitors simultaneously.

According to a Business Insider report, almost 40% of internet users across the globe prefer interacting with chatbots compared to virtual agents. Incorporating chatbots in the digital marketing strategy would allow companies to reach potential customers as well as retain existing ones.

Some of the simplest queries such as order status can be managed by AI chatbots. By reducing the wait time, businesses can considerably increase overall customer satisfaction.

Optimized Email Marketing

As established earlier, personalization is a critical aspect of digital marketing. With the help of AI, brands can run personalized email campaigns. Creating engaging, relevant emails by including product recommendations based on user behavior is a tried and true recipe for success.

AI can help predict what type of images, design, subject lines, and messaging would generate better results during the campaigns. Not just that, brands can deliver the right message to the right users at the right time by leveraging artificial intelligence in email marketing.

Content Marketing

By embracing AI in content marketing, companies can obtain the desired ROI. The technology allows them to determine the type of content that resonates the most with their targeted demographic.

As a result, companies can allocate more resources into creating that form of content. For instance, a study indicates that 40% of millennials engage with video content the most. Hence, if the target audience is dominated by millennials, video content should be the primary focus.

According to the same study, video marketing comes next to blogs, where the latter remains the most prevalent form of content marketing.

Wrapping Up

With the greater accessibility of artificial intelligence, more and more brands are embracing it within their digital marketing strategy. The fact that AI provides timely customer service, relevant recommendations, and enhanced user experience is irrefutable.

For businesses, it offers valuable insights that are key to making informed decisions.


This article is written by Ruhi Van Andel and published here

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

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

AI Can Do That? What Marketers Need To Know About Predictive Features

Image from Freepik

Artificial intelligence is more than a buzzword when it comes to marketing. While there’s still plenty of unfulfilled AI promise (no robotic creative directors in sight), it’s also true that sophisticated marketers are using AI right now — and delivering results.

Sure, we all know about AI-powered tech, like chatbots, voice recognition and automated ad bidding. But there are other, more direct applications of AI in marketing quietly driving the decisions of some brands.

Right now, marketers are using AI to tackle challenges like:

1. High-value customer segmentation: What characteristics define my most valuable customers, and how can I find more of them?

2. Churn risks: Which customers are most likely to churn, and how can I retain them?

3. Product propensity: Which consumers are most likely to accept my latest offer?

Answering these questions requires examining why a consumer behaves a certain way. Whether we seek to identify the common characteristics of high spenders, churn risks or high-propensity audiences, we must paint a picture of the consumer and understand the attributes (known as “features” in data science) that influence the way they behave. Marketers can use AI to build these portraits. Specifically, AI offers a framework to build predictive models that are powered by the attributes that predispose consumers to behave a certain way.

AI For Targeted Marketing

Here is a walkthrough of the predictive modeling process as it relates to marketing to shine a light on how consumer attributes can be used to predict future behavior.

Data scientists apply AI techniques (machine learning and predictive modeling) on consumer data to figure out which consumer characteristics are most likely to be associated with a specific behavior. The resulting characteristics are considered to be “predictive features.”

Predictive features can be created from demographic characteristics (age, gender, profession and marital status), or behavioral characteristics (international traveler, marathon runner or high-end shopper). If you choose them carefully, the right set of features can identify whether (or not) someone is likely to take certain actions.

The “action” depends on marketing goals, and it could be either a desired outcome or one that needs to be prevented: booking a trip, signing up for a particular kind of service, account cancellation and so on.

With a carefully selected set of predictive features in hand, data scientists can use AI techniques to analyze past behavior and find patterns that are predictive of the outcomes they want to drive.

Case In Point: AI’s Predictive Characteristics In The Field

To understand in more detail how this actually works, let’s look at a real-world example of a food delivery company (a former client of my company, an AI- and data-based consumer intelligence solution). With the goal of growing its revenue, this company sought to better understand its highest-value customers, increase the percentage of these valuable customers within its customer base and develop acquisition and retention strategies for them.

To achieve this, the company employed a predictive modeling technique known as recency, frequency and monetary (RFM) modeling. RFM modeling helped define the predictive features of a high-value customer in a manner that created actionable insights for marketing campaigns.

Step 1: The Starting Point

Like most businesses, the food delivery company maintained robust data about its customers and their order history. In data science, this internal data is known as first-party data. Marketers need to start by understanding the first-party data available to them in their organizations and taking an inventory of what they have access to. Depending on the organization, access to data is highly variable. There will be some data that is readily available and unfortunately, there will be data that you will never get your hands on.

First-party data is incredibly valuable, but it does not typically provide the kinds of insights needed for efforts like audience segmentation or programmatic ad buying. For the delivery company, it was relatively easy to define its best customers using first-party data, but these “best customer” characteristics couldn’t be applied to the general consumer population. You cannot create a market segment of middle-aged women who like to order Italian food after 7:00 p.m. (although I could be over-personalizing this).

Step 2: Data Enrichment And Feature Selection

After inventorying the data that is available to you, determine where you have gaps. What sort of information would be useful to better understanding customers and prospects? What data elements will build that elusive 360-degree profile?

In our example, by enriching its first-party data with third-party consumer data (external data acquired through a data marketplace), the food delivery company was able to create a fuller picture of the customer and generate a set of predictive features that defined its best potential customers: young (25-34 years old) couples with kids in which both partners worked and had lengthy commutes.

Step 3: Taking Action On Insights

Once you have enriched your data, you are ready to act. You can use this data as part of your predictive modeling, advanced segmentation and personalization to increase the effectiveness of your efforts.

Predictive features in hand, the food delivery company worked with their marketing partners to identify and target audiences of dual-income professionals who had kids and long commutes, which made it possible to send campaigns directly to the people who were most likely to become high-value customers.

The AI Payoff

The application of AI in marketing is a nuanced process that requires the right volume and type of consumer data. It can be less time-consuming and substantially more effective than traditional methods. Gathering the depth, breadth and scale of data for AI is just one of the challenges (but possibly the biggest) that marketers may encounter. Another common challenge is with internal understanding and skill sets. Does your organization really understand the benefits of AI, and do you have the right people to successfully to implement it? Finding a trusted partner for the near term is one way to overcome these challenges if you lack internal resources.

While AI can seem intimidating, hopefully this explanation demystifies the overlap between marketing, data science and machine learning. Marketers who team up with data scientists to use AI to determine predictive attributes and create predictive models can yield impressive campaign results.


This article is written by Laurie Hood and originally published here

Three Ways AI Can Help You Evolve Your Digital Marketing Strategy

There are only a few other marketing innovations that shocked business owners, marketers and developers over the last decade like artificial intelligence. Artificial intelligence (AI) has made leaps and bounds since the 1950s when it was first theorized and created. Now, in 2021, AI has the potential to help everyone involved in the marketing process perform their jobs exponentially better than they could ten years ago.

You may be wondering, “How exactly does AI improve the way we reach and engage with the people who visit our website?”

Today, we will look at several ways to use AI to get more value from your marketing campaign. With this information, you’ll have options to build an enjoyable, user-friendly experience for your visitors.

Let’s get started.

Use Predictive AI For Personalization

The main reason businesses use AI is for data collection. Instead of manually going through every customer interaction, businesses can use AI programs to compile this information into actionable reports. Consequently, business owners and marketers can make smarter decisions about their campaigns.

One smart way many people are using AI for this business is for personalization. Consumers expect comprehensive experiences from brands. A big part of this puzzle includes personalized offers and content.

Think about the last time you visited Amazon. You likely noticed a page that shows products you may enjoy, along with featured selections under each product. This is an excellent example of how AI personalization improves customer experiences.

Amazon’s algorithm analyzes the products you’ve purchased in the past and recommends products across all categories that fit your needs. Adding a similar feature to your website that works off your customer personas can help you create personalized offers and content selections for users on your website, email, and even social media. The result of a successful personalization campaign is more sales, engagement, and site traffic.

Improve Content Creation With User Insights

Next, let’s discuss how AI helps marketers improve the quality of new content they post on their website. Blogs are an essential part of most businesses, especially when you consider that research suggests over 77% of internet users read blog posts.

E-commerce business owners and marketing teams put even more value into their blogs because most consumers read a company’s blog before making a purchase. There are clear advantages offered through content marketing, but brainstorming topic ideas can pose a challenge.

Using the power of AI, you can brainstorm topic ideas and improve your existing posts faster than ever before. Machine learning can analyze consumer behavior on your blog pages and give you helpful feedback about improving future posts.

For example, if you notice that many of your users leave before they make it a quarter of the way through your post, you can start thinking about ways to extend their time on your site with a video or images for more engagement.

Similarly, you can rewrite the content to see if the problem is with your text copy. This type of testing was possible before, but it’s more effective than ever before with the lightning-fast data collection offered with AI.

Chatbots And Natural Language Processing

Natural language processing (NLP) is a subset of machine learning that can understand, analyze, compile and create human speech. Business owners use it for a variety of reasons to improve marketing. In many cases, NLP is used in chatbot technology to communicate with visitors that land on a website.

Chatbots can understand basic requests from consumers, communicate efficiently and even make changes to their accounts. There’s no doubt that this feature has improved the way business owners sell products to their customers.

Chatbots can put product information on display for people when they land on your website and type a specific keyword or phrase into the system. Tools like chatbots make it easier than ever before to thrive in the world of digital marketing.

NLP also plays a pivotal role in voice search. More consumers use voice-activated devices than ever before. Consequently, website owners now have to use natural language on-site instead of keywords that may sound clunky or forced.

Using long-tail keywords and creating content that answers questions will ensure that Google’s AI program will find your result when someone users their voice-activated device to complete a search. So, even when it’s out of your hands, AI still plays an essential role in your business’s success.

Back To You

Despite the fear that AI will take work from others, it’s clear that we won’t see a significant shift in the tech job market. Instead, marketers will use AI to improve customer experiences by offering personalized and tailored content and top-notch customer support.

All these feats are accomplished with AI that improves on what the marketer is doing and doesn’t replace them entirely. At the end of the day, marketing decisions come from human beings. Using the power of AI, we can make smarter decisions and grow our websites through data-driven reports.

It’s up to you how you’ll implement this technology on your website. But one thing is clear: The future is here. If you plan on running a successful online store in the coming years, AI-powered marketing tools are a must.


This article is written by Chris Christoff and originally published here

AI Won’t Take Your Job

While it’s safe to say computers will never be human, marketers are worrying about their job security as computers become advanced enough to be referred to as AI. Even with half of organizations implementing some shape or form of AI, the technology will never take over for marketers — in fact, it’s actually the opposite.

AI technology will complement, not replace, the marketer. It should be thought of as a “force multiplier” — something that increases output, makes marketers better at their jobs and benefits the business overall. For example, a CMO should not be wasting time in the rows of an Excel sheet; instead, a robot should take on those tedious tasks to free up time and add more value for business alignment.

The Human Element Needed

The role of today’s marketer is to be an expert in automated technology and, perhaps more importantly, connect and empathize with customers at every point of engagement. Humans have vision, strategy, incentives and creativity, all skills a machine could never automate.

Just like the calculator did not erase the need for mathematicians, AI will not make the marketer obsolete. AI is more of a support system that allows marketers to focus on the parts of the job that matter most: Strategy and creativity.

As the industry continues to evolve from traditional spray-and-pray approaches to more personalized methods, the human element is becoming more critical. Computers don’t know how to incite emotional triggers. Today’s personalized marketing requires a broad-based yet targeted approach, which requires not only getting a message right for one person, but also 20 messages right to 20 groups of people. It’s something that cannot be done without a human marketer.

Creating Rules

The last couple decades made it clear that data could help brands personalize their customer experience, improve engagement, convert more people and so much more. As soon as the industry realized the potential of pairing marketing strategy with data-driven decisions, AI became a critical part of tech stacks since it can uncover data and take action based on a set of rules.

In the early days of AI adoption, marketers experimented with leveraging data to solve complex problems, but automation’s promise was not fully realized right away. At first, marketers were stuck with the mundane, repetitive tasks again, like pushing the “next” button while the data seemed to dictate. This took away all the fun and, as marketers grew restless, the rules of data-based decision making became apparent.

Because AI is essentially a computer listening to data and structuring it in a way that makes sense, human rules-based input enabled it to quickly act on the data. This was the true beginning of AI in marketing.

Embracing AI

As technology continues to play a vital role in marketing, tech stacks keep growing and martech platforms get more complex, mundane tasks waste valuable time. That’s why many marketers are starting to turn to more advanced automation that enables them to oversee and configure technology without being the technology themselves.

With automation, marketers can focus on real, business-impacting activities rather than carrying out repetitive tasks. For example, think about launching a single campaign on both LinkedIn and Facebook. More often than not, this is a time-consuming and complex nightmare. However, certain AI technology can combine inputs flawlessly and launch cross-platform campaigns with one interface.

As marketers, we need to accept the fact that some things should be left up to technology. It’s time to embrace machine learning to predict consumer behavior like never before, but always remember that AI is not the silver bullet. It may take care of mundane tasks, but it will always require a human operator to create the rules.

The bottom line: AI is here to help. It’s not to be feared or avoided — let it help you do your job better and smarter.


This article is written by Jason Widup and originally published here