AI and Marketing Cloud Technologies: Why The Gap is Still Big?

The science of marketing is becoming more complicated and sophisticated in the last few years which left us with unpredictable scenarios for the future. In marketing industry, I believe the 3 fundamental key areas are human psychology and how the consumer behavior evolved, the commerce trends and finally, and most importantly, the technology.

Today’s technologies, such as clouds, machine learning, data analytics, and artificial intelligence (AI), are changing the way marketers build their strategy on a tremendous level. The biggest question now is: What is the current status of MarTech? Did we arrive at the point where AI is fully reliable for marketers? In order to answer this question, we should look closely at the top tech players in the industry, which are ‘the marketing clouds’.

We have been looking at Marketing Clouds from all sides and still, somehow, we are still able to spot a big gap between the marketer’s needs and the available technologies provided by these platforms.

Marketing Clouds are still slow with enabling AI technologies

Looking at the current scene of marketing clouds would bring some strange conclusions. One of the main common facts that we know about marketing clouds that it all started with different approaches. Marketing clouds have different anatomies, technical structure, capabilities and commercial priority which brought in many obstacles for enhancing automation, predictive models and the enablement of AI.

1. AI means lots of different things to different clouds

AI MarketingWell, each marketing cloud has a unique character that suits it to particular types of clients and points toward its likely future development. The front line competition between two clouds such as Adobe vs. Salesforce is not driven by AI, after all, it is a lot more likely focused on expanding technical functionality, aggregated data sources, and dashboards visuality.

As a marketer, I believe that AI could be implemented in every marketing process but while I review and implement technology stacks I end up with the fact that each cloud has its own different priorities which technically doesn’t fit all the marketing department needs. In other words, the clouds have it is own complicated layers of tech and securities which are not as straightforward as marketers see it.

Finally, we come to another piece of the picture: Marketing clouds is relevantly a new technology, so it shouldn’t surprise you to know the term ‘artificial intelligence AI’ means lots of different things to each cloud. Recently the Oracle’s VP, Jack Berkowitz, agreed definitions were problematic and sought to provide some clarity.

2. Sales vs Innovation

Since we have at least five major enterprise software players are selling marketing clouds – Adobe, IBM, Oracle, Salesforce, and SAP – the commercial factor is playing a key role in this game. The market share is strategically more important for such tech giants than experimental AI enhancement such as ‘dynamic predictive segmentation’ which is still not fully mature yet.

The market share is leading the competition in the cloudland and big clients needs are what they need to secure before moving into more bold steps towards midsize brands and agencies. Here is a hint: Big clients are more concerned about handling a huge amount of consumer data and advertising budgets than performing advanced personalization experience. I hope you get the image!

3. Secure Data vs Automation and Predictive Models

Investors and business leaders are usually concerned about the maintenance of secure data within the organization; these execution difficulties brought a restricted environment with a small room for experimental processing and tighter margin for automation. In conclusion, priority was given to keep up with a secure processing, while customer experience problems took a backseat and a big part of it is still dominated by sales and customer support departments.

The fellow marketers armed with AI are still struggling inside the old fat big organizations to apply their experiments. The limitation is created by the organization mindset is pushing the marketing clouds priorities towards fulfilling the IT needs vs the marketers’ needs. We have seen many promising approaches to fix this gap from Salesforce and IBM in the past year and we hope the predictive models will demonstrate more effectively this year.

4. Technology Stack vs Cloud

Marketing stackIn 2017, we have seen some advanced machine learning techniques implemented but yet marketers are not satisfied. In marketing departments, we are still running manual sheets and relying on a variety of tools to get what we need.  In one survey, 82% of enterprise marketing professionals said they used components from more than one cloud; the other 18% were more or less loyal to one platform.

The modern marketer’s stack consists of 17 products or more

There are over 5300 marketing tools available and the new startups are developing more specialized ML and AI solutions for marketers. This number is crazy and at the same time painful for CMOs who are trying to establish their technology stack effectively. So far we still believe that marketing clouds are not an all-in-one solution and we have to go shopping for several tools to build up what we need. So when the time will come when the cloud beat the technology stack? I wonder!

5. AI requires good Data

Like any sophisticated system, the marketing technology can only go so far with a good data. If you lack good data, you’ll still end up with poor results out. One of the AI functions is training the system and having good signal going in which is still a hard job with the poor correlations of data sources used by marketing professionals. Marketers are dealing with different structured and unstructured data including real-time data sources, AdTech data, sales data, offline data which requires sophisticated data processing in order to run a complex AI technology. Not to mention that the data will have to run in different cycles such as seasonality, trends, and quality assurance processes. Overall, marketing data is not easily sorted and the bigger the numbers, the more complicated the process.

The marketing clouds need first to integrate a system that’s not only smart algorithmically but is also smart about its data sources.

It seems we are still struggling in that phase at this moment!

6. Context vs Data

In marketing, we believe that the context of data is more important than having structured data. For each industry the requirements are different and a machine learning solution must be reliable enough to understand the context of a situation and then give you the proper aid to be able to amplify your capabilities in that situation.

Automation is not technically the final destination for such path. There are many approaches that might drive marketing analysis in different directions which marketing clouds need to consider before taking a bold step. As we know that the clouds are not custom built for such task, we will have to rely on marketing professionals and data scientists to role this. Paying for the cloud support is not the answer to this question based on my experience. We have to build another internal layer of technology to activate the AI functionality offered by the marketing clouds. Yeah, it is as complicated as it sounds!

Are we already comfortable with letting machines make the decisions?

You would often see many leaders in the market announcing that the marketers are ready for letting machines make the decisions. This might be technically true but marketers are still struggling with proving this point to business owners and decision makers in brands and agencies.

AI is a big concept theoretically, but in reality, it’s about executing against a strategy more efficiently. I believe that the definition of AI will evolve over time, as marketers learn more and the technology itself evolves.

The digital marketing has moved from the basics of web analytics and content A/B testing to predicting consumer behavior before it happens. As a marketing consultant, I believe that we are in the moment where AI should start controlling reality. It’s not rocket science, but it’s all the little pieces of enhancements that will come together to help make better decisions.

Marketers should be ready to move from ‘automation’ to ‘intelligent automation’ stage.

Marketers need AI to setup the rules for the huge and complex amount of segments and not just processing these the rules in a timely fashion. Advanced machine learning, pattern recognition, and predictive modeling bring high-value actions and hyper-targeting at the individual level instead of the segment.

The current status of Marketing Clouds and where we are going?

Marketing clouds are evolving fast and the amount of investment in AI is increasing. We expect to see more of advanced functionality this year. Adobe is an experience company, who has invested down the stack, but its bromance with Microsoft shows the limit of its appetite to own it all. In the other hand, IBM with its Watson is investing heavily in its AI-driven customer analytics.

While SAP can be expected to invest in tools to help enterprise customers expand e-commerce across the customer lifecycle, Salesforce is growing fast in adaptation to personal data and personalization with their Einstein technology.

marketing clouds 2017

To conclude, I believe that it is not easy to plot marketers’ needs in layers from emotion to execution. Marketing clouds provider still need to put more of efforts in the right direction in order the activate AI technology in more complicated tasks. We expect clouds that can handle complex data and workflows around first and second party data to thrive. It is essential for clouds to invest in dynamic predictive segmentation and fund AI startups that promise to deliver an advanced management for marketing channels.

In a world where everybody struggles to own the customer data layer. The more the clouds change towards AI, the more they stay in the same size in future and survive the competition.

I hope you take the time to share your experience and discuss your opinion in the comments so we explore different sides of this topic.

Overview: Digital Media in the Middle East, 2017

The Middle East might be one of the most unclear spots for statistics along with African countries. Recently we started to witness some brilliant efforts from institutes and organization releasing effective and accurate statistics for the region.

The Northwestern University in Qatar recently released the survey “Media use in the Middles East, 2017”.  Below are the key findings from this study, which also can be explored in visual detail at the section of data interactive.

Digital Media Use vs Traditional Media

  • As internet penetration rises, Arabic users are less likely to be using traditional media platforms. Most of Arabic users still watch TV, but the rate has declined slightly (98% in 2013 vs. 93% in 2017). Rates of newspaper readership, however, declined more sharply from 47% to 25% in 2017. Radio has also become less popular in the past five years by 10% (59% in 2013 vs. 49% in 2017).

The Usage of Arabic Language

  • The use of Arabic language online has increased. In comparison, use of the internet in English remains essentially flat, 25% in 2013 and 28% in 2017, despite the increase in internet use.

Online Usage in Arabic Countries

  • Internet penetration increased in most of the Arabic countries included in the study surveyed and most top increases were in Jordan, Lebanon, and Tunisia.
  • Time spent online has a strong relationship with the number of years using the internet. Users new to the internet spend about eight hours a week online. This jumps to 14 hours per week for those who have spent two years using the internet and again rises to about 21 hours per week among those who have been online between three to seven years. Those with 10 or more years of internet experience spend about 29 hours per week online.
  • Smartphone ownership increased rapidly in Arabic countries with a majority of users own a smartphone (83% of Jordanians and 65% of Tunisians).

Social Media in the Middle East

  • WhatsApp is the most popular app on the list of social media used by nationals across the region with 67%, followed by Facebook 63% and YouTube 50%.
  • Instagram increased rapidly in the last year according to the survey. The social network usage increased from 4% to 21% in Egypt and from 18% to 32% in Lebanon while in Gulf countries Instagram is becoming one of the most popular apps.
  • Facebook penetration declined across countries by at least 10 percentage points since 2015, except in Lebanon where Facebook use remains stable. In Qatar and Saudi Arabia, penetration dropped by over 20% since 2015 to 22% in Qatar and 55% in Saudi Arabia.
  • Currently, half of the direct messages sent and received are in group chats and half are between individuals. This represents a significant increase in group messaging—an increase of 15 to 42%.
  • Snapchat has increased in popularity in all countries since 2015 (64% Qatar, 51% KSA, 51% UAE, 20% Lebanon, 16% Jordan, 7% Tunisia).
Social Media Middle East 2017
Source: www.mideastmedia.org

Mobile Usage in the Middle East

  • Nearly three-quarters of internet users across the region use Wi-Fi or mobile data services to connect to the internet. However, Wi-Fi use varies by country, from less than half of Jordanians to two-thirds of Tunisians and nine in ten across the other nations (40% Jordan vs. 63% Tunisia, 91% Lebanon, 87% Qatar, 84% KSA, 97% UAE).
  • Nationals who get news via smartphone at least once a day are also more likely to get news via other platforms—both digital and offline—on a daily basis.
  • Just over half of nationals use news apps, and just over one-quarter use them daily. Using news apps is most popular in Saudi Arabia and the UAE and least popular in Qatar (use at all: 85% KSA, 86% UAE vs. 52% Jordan, 49% Tunisia, 42% Lebanon, 33% Qatar).
  • Nearly all Arabic users in Lebanon, Qatar, Saudi Arabia, and the UAE own a smartphone and the same with Jordanians.

Insights on the digital media stats in Egypt

  • Even in the digital age, Egyptians spend more time face-to-face with family in 2017 than 2013. Despite the doubling of internet penetration in Egypt since 2013, the average number of hours Egyptians report spending face-to-face with relatives increased from 9 in 2013 to 20 in 2015 and 31 in 2017.
  • While Facebook penetration is falling in most other Arab countries in this study, it rose significantly among Egyptians. In 2013, 81% of Egyptian internet users said they use Facebook, but that number rose to 93% in 2017.
  • Fewer than 1 in 4 Egyptians used the internet in 2013, but half of all Egyptians are online in 2017 and nearly 6 in 10 own smartphones (57%).

More insights about Egypt at: 2017 Trends Report: Digital Media in Egypt

Highlights for Digital Marketing Professionals

Apparently, any business that does not place digital firmly at the center of its growth strategy runs the risk of increasing irrelevancy and losing potential customers in the Arabic region. Here are some of the top insights I highly recommend for marketers in the MENA region.

  • Invest wisely across the social media channels and avoid putting all the efforts on Facebook. With the recent decline in Facebook usage in the Gulf region and the new updates from the Facebook algorithm, the marketers should revisit their social media strategy.
  • Since each country in the region has different consumer behavior, it is highly required to customize your channels and marketing message for each country/locale.
  • 2018 is the year of live streaming and stories. Maximize the use of Instagram and Facebook stories to generate organic traffic to your social media channels.
  • While Arabic users are not very active with Emails comparing the U.S. users, the companies should try to focus on WhatsApp as an effective way to reach out and engage clients in the region.

We are seeing a digital transformation and ongoing trends that require more investments from marketers and business owners in the region. The social media professionals should always monitor the trends in consumer behavior and the usage of the social network.

Marketing Analyst vs Data Scientist: What’s The Main Difference?

While data doesn’t come in neat little packages, ready to answer the questions marketers concerned about, the demand for sufficient marketing data is the main factor that is shaping the roles of marketing analytics field.
With is a huge potential for connecting the dots between data and marketing activities inside all types of organizations, the rise of data skillsets is booming in 2018. In the past few months, I’ve come across many managers and recruiters asking the same particular question all the time:

“What is the difference between marketing analyst and data scientist?”

Starting with the background: When data science brought many trackable capabilities to the marketing department, marketers had assign dedicated professionals who are accountable for using data to answer the marketing problems and furthermore to understand the performance metrics. Yet, the issue we did face in the last few years is the rapidly growing data resources (online, offline, internal and external). The data aggregation itself was a major challenge for every marketing departments.
In 2017, everyone in the field was talking about the need to grow their data analytics team. With the higher pressure on the efficiency and accountability of marketing KPIs, the marketing department functionality started to evolve and play more important roles within to the organization. We can see now how marketing analytics is not only limited to sales data but also connected with customer support, business automation, and financial department.
However, since diving into data team, in general, is becoming a complicated structure due to the rise of Big Data, Data Mining, AI, and ML, I will try to avoid the hierarchy of business intelligence filed and focus precisely on the marketing needs.

The main two roles in marketing data

Ideally, marketing requires two roles of data: Marketing Analyst and Data Scientist. Why? It’s quite straight-forward to the point of marketing needs, the two function is our way to define the data team role within the marketing department, how they are different?
The most common scenario in marketing departments is the need for an analyst to dominated the marketing analytics and a data scientist to operate the company data aggregation.
Why am I trying to simplify this into two main roles? In companies, we would always see a variety of business needs and marketing functions which requires a customized structure. Apparently, you might see a variety of positions and titles based on the common needs of the organization and nature of the business. An e-commerce business might have a large set of data team while a startup might be only hiring one person, so don’t get confused and let’s first focus on how to distinguish the main differences between the basic two roles every organization need.

The key difference between Marketing Analyst and Data Scientist

The main difference between marketing analyst and data scientist is that marketing analyst should be a native marketing-speaker with professional skills in driving insights to answer the marketer’s needs. On the other hand, a data scientist is a native data-speaker with skills in deriving BI and analytic insights from structured and unstructured data sources.
Marketing Analyst vs Data Scientist
To understand the key differences between the functions of two roles, let’s summarize in 5 main points:
  1. Marketing analyst should have a solid experience with marketing metrics while it is not required in data scientist role.
  2. The data scientist is expected to formulate the critical questions that will help the business and then use the data to solve it, while a marketing analyst is given questions by the marketing team and pursues a solution with that guidance.
  3. The marketing analyst not required to be advanced in programming side while the data scientist should be professional in writing queries. Yet, both roles should work with IT teams to source the right data.
  4. The data scientist role requires a strong data visualization skills and the ability to convert data into a business story. A marketing analyst is more focused on analyzing the marketing metrics.
  5. The data scientist usually work in a multidirectional and free form in order to extract better insights, while marketing analyst usually has a specific direction to work on.

Defining the Marketing Analyst Role

marketing analystThe marketing analyst is similar to any other analyst in terms of methodology but he is truly different when it comes to the functions. So why is that? I strongly believe that marketing analyst is a digital marketer which luckily become a master of analytic tools. You might disagree since you would meet a lot of marketing analysts who didn’t have any experience in marketing. I know this because I suffered from this for awhile and always had conflicts with data-savvy specialists who failed in understanding our marketing needs since they simply lack the marketing background.
However, marketing analysts should be very solid in understanding the function of marketing and its objectives. I am confident to say that marketing experience is crucial more than you might expect. The marketing analysts are located at the heart of marketing team and should speak their language and suffer with them from the same problems.
The main objective of marketing analyst
  • Measure the effectiveness of marketing activities and the online ROI, of various marketing channels used to position a product or service. Given the increasing variety and complexity of marketing channels—reaching this objective is a serious challenge.
  • Bring the data analytics into the heart of all marketing campaigns and tools while setting up the most effective metrics to measure and trends to manage.
  • Turn insights and data patterns into clear indicators and tactics for growth hacking, budget allocation, and performance management.
  • Maintain a reliable and effective connection between the marketing specialists needs and data scientist reports.

Who is the best Marketing Analyst?

  • A native marketer who knows how to play professionally with marketing technology tools and marketing metrics.
  • A scientifically minded person with an appreciation for design. He needs to know the effect of messaging and design on the consumer experience.
  • Analysts by the heart who dominate the dashboards and he have charts ready even for his grocery shopping habits and his girlfriend mood swing.
  • He knows that insights are more important than figures. He loves the data in front of him but he is more in love with knowing the consumer.
  • He is the honest guy who never takes any sides. Neither marketing performance team nor data team.

Technical skills for marketing analyst

  • Strong analytical, conceptual and reasoning skills
  • Professional skills in Web Analytics, Marketing clouds, AdTech, and Automation
  • Experience with Statistical Software, Business Intelligence Platforms, and Data Visualization
  • Intermediate experience with programming language and database querying
  • Experience with market research, segment analysis, consumer behavior and marketing channels

Defining the Data Scientist Role in Marketing Department

Data ScientistBusiness acumen is the main asset desired in a marketing data scientists, after technical skill. It’s so critical because a lot of quantitative candidates I’ve seen are getting so wrapped up in the elegance of the analytics that they forget that they’re hired to answer business problems.

Working with marketing team is somehow challenging for data scientists. The marketing ever-changing periodical strategies can be a roller coaster for data team and they have to adapt and survive quickly. Unlike the majority of businesses where the top element of the data science job is the ability to use computing power to acquire the data, marketing needs could be problematic and tactically challenging over the time.

Who is the best Data Scientist?

  • Tech-savvy with different programming languages and statistics capabilities.
  • A scientist who applies statistical tools, economic tools, and different disciplines is another facet.
  • A coder who aggregate and clean data in the most efficient possible ways with ability to invent new algorithms to solve problems and build new tools to automate work or provide real-time reporting system
  • He is an expert in interpreting the visual display of complex data sets and tells a story.
  • He is sophisticated with analytics programs, machine learning, and statistical methods and quick with preparing data for use in predictive and prescriptive modeling
  • Without asking he is always busy with conducting undirected research, exploring and examining data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
  • He speaks the language of IT and able to communicate requirements and predictions to IT departments through effective data visualizations and reports

Technical Skills

  • Expert in Math (linear algebra, calculus, and probability), Statistics (hypothesis testing and summary statistics), Data visualization (Tableau, Power BI, SAP Analytic Cloud) and reporting techniques
  • Professional with Software engineering skills, Data mining, Data cleaning and munging
  • Professional skills in programming (R, SQL databases, Python or C/C++)
  • Professional with BigQuery, DynamoDB and cloud computing tools
  • Experience with ML tools and techniques (k-nearest neighbors, random forests, ensemble methods)

Collaboration between Marketing Analyst and Data Scientist

Your marketing analyst should deliver the clear results in marketing language while the data scientist should work on doing the math (statically and technically). Technically, a marketing analyst is solid at creating relations between data and marketing needs while data scientist is the true advocate in bringing the data and advanced statistics and bring the most reliable, clean, fastest results to the table.

You have known knowns, known unknowns, and unknown unknowns. Just be careful if both get a conflict. I have seen some violent fights at the office!

Finally, becareful with Data

There are many times where the underlying data that is the basis for what people have calculated is actually wrong. If you make a mistake with the underlying data, that could be a big problem while you analyze.

The premium on being able to understand what data you have, to understand what types of questions can be answered with it, and to make smart decisions is really, really high.

However, there are places where pure data science functions can fall short of what’s required to boost success in the marketplace. This is where marketers thrive.

Looking for your opinion on this and how do you see the difference between the two roles. Contact me if you are looking for marketing analytics consultant.

Yasser Ahmad