The Power of Effective Marketing Automation You Didn’t Know About

It’s no doubt that we live in a time when technology changed the world and our lives particularly. In a good way. It’s crucial for the businesses to adapt to the changes and improve the existing processes to stay successful and profitable.

At ITCL we think that human approach with the right tools can make the businesses succeed, one of those tools is marketing automation. Cloud based software designed to streamline the marketing efforts on targeting prospects and existing customers. There are so many great use cases for marketing automation available that would be hard to describe in one single article. However, I will try to briefly cover a few topics that might spark your interest in this technology.

To start, one of the most important goals for client-faced businesses is to optimize human resources engagement on repetitive tasks but still have contact with a customer. Let’s imagine that someone is organizing a big conference with multiple well-known speakers that bring quite a big audience. Managing the process of registration and supplying relevant information might not be an easy task. Marketing automation professional services can help to collect, organize and spread the information among the audience of attendees. The whole process, easily. In addition to that, keeping engagement and customer satisfaction at a high level is another task that can be accomplished with providing relevant personalized information to the selected audience. Only useful, easily readable, only relevant information from the business to the customer with minimum efforts from the customer’s side. That would be a well-designed email easily readable across mobile and desktop devices, with information about speaker’s sessions that the customer subscribed to, organized in the right way based on start/end session time, and quick intro per each session. The list of enhancements is limited to imagination only, for example, add the functionality to add to the calendar, create a path plan on the exhibition floor to navigate the customer how to get from one session to another; when is the lunch break and what are the options and food/drink location on the map; provide the recommendations on the other speakers sessions based on customer’s persona/profile; etc. It’s also possible to include relevant advertising in these communications to further benefit the business and customers needs.

The successful setup and execution of this campaign will minimize the organizer time and effort in managing inquiries and responses from/to the clients. And let’s imagine the impact on the subsequent setup of such exhibitions down the road, when everything is pretty much defined and just small details would require adjustments and revisions.

Using Artificial Intelligence to Drive Content Personalization

Business results are not why personalization is important, people are.

A human-centered approach to value creation is key

In an era in which data analysis drives strategy, it’s easy to lose sight of the fact that each unique visitor, page view, goal conversion, sale, and marketing automation ID has a person behind it. And with personas and segmentation initiatives used to create customer profiles and group “like profiles” together, it’s effortless to forget that, in reality, people are a segment of 1 in which one person’s values, motivations, tastes, desires, and interests are unique and altogether different from those of the next person. While demographic, geographic, psychographic, behavioral, and benefit data can all be important in understanding the personalities, circumstances, and consumption patterns of those you create value for, what’s often missing is a deeper level insight into the specific interests, decisions, and intentions that individuals have, all of which dynamically change over time. Gaining a profound understanding of each person’s true interests as well as the information they’re looking to obtain and the problems they’re seeking to solve is key to providing value that ultimately helps them achieve their goals. Since people naturally turn to the internet and search engines in order to conduct research on their passions and predicaments, understanding each person’s digital body language can unveil insights foundational to personalizing value creation for that unique, incomparable, one and only, individual.

AI-powered content personalization facilitates the customer journey

With recent developments in the fields of natural language processing, predictive analytics, and machine learning, sophisticated algorithms that make sense of content pieces, create actionable insights from engagement behavior, and automate content delivery enable marketers to understand deeper level insights about consumer interests and personalize content at scale. One application of artificial intelligence to content personalization is through the use of natural language processing algorithms that crawl through companies’ blog posts, articles, and whitepapers and synthesize topics that are consequently tagged to each piece of content. This process takes unstructured data in the form of written sentences and structures it into topic-level data that gives meaning to each piece of content. Then, when visitors engage with the content pieces, each visitor absorbs the topics that the content pieces are about, therein creating an “interest profile” of each visitor’s favored topics to consume. As each visitor continues to engage with additional pieces of content, the visitor’s interest profile updates based on the recency and frequency with which s/he consumes content on specific topics. Content personalization takes place through algorithms that match the topics tagged to the content pieces with the topics the visitor is interested in, based on weighted probabilities in real time. In this way, firms can serve personalized content based on individuals’ interests in order to drive thought leadership and position the firm as a valuable and trusted resource through which visitors can gain information on topics of interest. For the firms that advance personalization initiatives, business results often include increases in click through rate, improvements in the number of lead conversions that drive unknown to known visitors, and enhancements in content utilization percentages, often amounting to multiples of a positive return on investment.

Natural language processing algorithms tag content at scale, based on keywords and context words

By using natural language processing algorithms, firms can tag hundreds of thousands of pieces of content, almost instantaneously, with topic-level data that drives the content classification side of the personalization engine. The way this is achieved is by running such algorithms through content repositories so that the algorithms synthesize topics, based on keywords and context words, that are consequently tagged to each piece of content. As an example, if your firm were to write a piece of content on the company Apple, the algorithms would recognize that the content piece is about “Apple” the technology firm rather than “apple” the fruit based on metadata, keywords, and context words used in the content. Identifiers such as “Cupertino”, “iPhone”, and “computer” increase the probability that the keyword “Apple” is referencing the company, whereas words such as “tree”, “pie”, and “Granny Smith” would increase the probability that the keyword “apple” is referencing the fruit. If the tagging process is performed correctly, the result will be a content repository in which every content piece is tagged with the appropriate words, weighted based on how relevant each topic is to each content piece. For instance, a content piece that briefly mentions Apple might yield an Apple-tag weighting of 6/100 or no tag at all, whereas a content piece that is focused on Apple might yield an Apple-tag weighting of 97/100.

With this said, using natural language processing algorithms to tag content is not always without challenges. In order for such an initiative to be successful, the algorithms must have a database reference that contains the verbiage that the content employs while the content must have a format that can be understood by machines. Issues arise when niche content that uses esoteric language has no reference in the algorithms’ databases and when content pieces lack enough words for the algorithms to synthesize meaning correctly. Additionally, content pieces might mistakenly be associated with tags that the content is not actually about. The good thing is that all these issues can be mitigated. Additional encyclopedias and databases can be added to ensure coverage of niche topics, under-expressive content pieces can be enriched with descriptive metadata, and patterns of mistakenly tagged data can be scrubbed in aggregate. The end result? A taxonomy of content topics along with correctly tagged content that heightens precision in content understanding and future personalization.

“Interest profiles” drive a deep understanding of people’s interests and intent, fueling content personalization

In the words of an AI executive who transformed the meaning of Oscar Wilde’s quote: “you are what you read”, meaning that the content people consume is indicative of their interests and the decisions they are seeking information on as well as predictive of their future purchase intent. Thus, if we create visitor profiles that capture the interests of people based on their content consumption, we can identify their inclinations and intentions and use this to personalize content going forward. The way this is possible today is by cookie’ing visitors upon site visit and associating the tags from the content they are reading about with their cookie IDs, therein creating “interest profiles” of each visitor’s favored topics to consume, weighted based on how recently and frequently the visitors consume content on specific topics. Using a financial services example, an unknown visitor might visit an asset management firm’s website and read a piece of content on “Small Cap Investments”, then a piece on “Best Investments in Emerging Economies”, then a piece on “Top Equity Investments for 2020”, and finally a piece on “Equity Investments in China.” Each time the visitor consumes a piece of content, s/he absorbs the topic tags the content is about which, in this case, results in an interest profile that indicates an interest in small cap equities within emerging markets, specifically China. With knowledge of the asset class and fund type the visitor is interested in, the asset management firm can personalize content based on an intersection of probabilities in which content with descriptive tags is served to visitors with corresponding interest profiles.

Similar to what we saw with using natural language processing algorithms to tag content at scale, personalization based on interest profiles is not without challenges as well. Issues can arise when a first time visitor does not have an interest profile or when a visitor removes cookies from previous sessions. Such issues can be mitigated by pre-populating interest profiles with neutral topic interests as well as by data stitching one profile to another for a visitor that has multiple cookie ID’s. When performed correctly, the result is a deep understanding of each consumers’ interests and content personalization tailored to each individuals’ preferences.

2 AI-powered personalization use-cases that illuminate potential benefits

Fortune 500 B2B Technology Company

Problem: with over 90 product lines, it was difficult to funnel prospects and customers to the respective products that would help them solve their business problems. Additionally, the firm’s goal was to increase the number of leads generated and make use of the content it had already put dollars behind to create but that had not received much engagement.

Solution: content personalization on the company’s blog that recommended personalized content that aligns with each visitors’ unique interests. As the visitors clicked through the top of funnel content they are interested in, the algorithms gained a better understanding of each individual’s interests and eventually served middle funnel and bottom funnel whitepapers and product pages that aligned with visitors’ interests and contained lead capture forms to convert visitors from unknown cookie ID’s to known contacts.


  • 50% increase in content utilization
  • 280% increase in click through rate
  • 160% increase in leads generated

Global Asset Management Firm

Problem: with an array of fund offerings, it was difficult to funnel investors to the appropriate funds and bounce rates on their blog were substantial. Additionally, the firm wanted to understand the investment interests of retail and institutional investors in order to enable their sales team to determine the appropriate asset classes and fund types to pitch.

Solution: content personalization on the company’s blog that dynamically segmented visitors based on the asset classes and fund types they sought information on. Additionally, predictive analytics data based on the content consumed enabled their sales team to pitch funds that aligned with investors’ interests.


  • 100% increase in content utilization
  • 60% increase in click through rate

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3 Innovative Applications of Digital Personalization in B2C

Digital personalization is about using insights on consumer behavior to deliver tailored value.

The holy grail of marketing would be to actionably understand individuals’ conscious and subconscious desires, interests, and needs, as well as the factors that drive each person’s decision making process. With this, we could more seamlessly predict consumers’ intent and model out value propositions that align with such proclivities in order to deliver them in the right place and at the right time. The modes by which we are able to gain a semblance of this understanding today are by asking consumers about their demands directly, soliciting those who know them well, and observing their environments and behaviors, both physical and digital. And once we have an understanding, personalization is about using our insights to identify patterns and moments that can be used to tailor one or all of the 4 P’s of the marketing mix (the 4 being product, price, place, and promotion) to the target individual.

With increasing creativity and sophistication in information capture, data analytics, and machine learning, we are progressing towards true mass-personalization becoming a reality at an accelerated pace. With this, below is some inspiration on digital personalization in B2C based on the initiatives of 3 leading brands.


With the objective of targeting the profitable, expecting mother segment, Target took the application of statistics to behavioral analysis to a profound level. The retailer’s statisticians sought to identify changes in women’s purchasing patterns that were indicative of pregnancy so that Target could personalize value at this life stage. Based on their insights, Target found a high correlation between an increase in the purchasing of a distinct set of items, including specific supplements, unscented lotions and soaps, cotton balls, washcloths, and beyond, and pregnancy status. With this, they were able to personalize digital marketing campaigns and coupon offerings for products within the Baby category, tailored to each stage of the pregnancy. Target’s achievement in understanding and personalizing value to both new and repeat mothers marked a triumph in using predictive insights to identify an important life moment and deliver tailored marketing that meets the segment’s needs. While the insights do exist in our databases, it is incumbent upon us as marketers to find the needle in the haystack of data in order to better understand and serve our target customers.

Iberia Airlines

Identifying an opportunity to facilitate gift-giving during the holiday season, Iberia Airlines launched an initiative to creatively solicit customers’ travel-related desires and influence purchase by personalizing gift suggestions to those within the travelers’ social circles. To achieve this, Iberia surveyed customers, asking them where they wanted to vacation and with whom, as well as the email addresses of their intended travel companions. Based on responses, Iberia targeted the intended travel companions with emails suggesting the perfect gift for their adventurous friend, subsequently targeting with banner ads and beyond. Through asking the customers directly, Iberia was able to understand their interests and used the insights derived from one consumer to personalize value to another.


Identifying a recurring situational context in which consumers prefer to dine from home, GrubHub automated email marketing campaigns that personalized to the weather conditions of their target audience. Based on geolocation information and weather forecast data, the food delivery service emailed customers when it rained, suggesting that they stay dry and indulge in delicious delivery options within the comfort of their homes. While GrubHub also conducts deep level analysis on customer transaction data in order to identify trending food offerings and understand food preferences in the effort to personalize its value proposition, sometimes understanding and personalization can be derived and initiated based on one or two surface-level insights. Beyond food delivery, we see this simplicity of execution in the marketing of many retail apparel brands who serve dynamic website content based on whether it is a sunny day that calls for a blouse or a brisk day in which a jacket would best fit.

Final Thought

Setting Heisenberg’s uncertainty principle aside, if we could understand the position, mass, direction, and momentum of every particle in the universe at one snapshot in time, we’d be able to model all future interactions with precision. In a similar fashion, the closer we can get to perfectly understanding the factors that drive consumer behavior, the better we’ll be able to model future consumption patterns and personalize our marketing mix to our target audience.

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