Advanced AI Optimization Research for Marketing & Advertising Campaigns

Advanced AI
AI Optimization Research Process for Marketing & Advertising Campaigns

Traditional marketing modes such as Advertisements have become quite expensive. The modern and effective content marketing channels are overcrowded, making it hard to maintain and coordinate the omnichannel presence. To get through these situations, Advanced AI for Marketing can prove useful in providing solutions to optimize marketing campaigns. Many vast enterprises are already implementing the possibilities offered by various machine learning algorithms. Also, deep neural networks help them select the right advertisement to show to the right customer at the right time.

Top technical companies like Google, Amazon, and Alibaba have been implementing superlative machine learning approaches that can demonstrate their effectiveness at optimizing marketing campaign allocation with improvising customer targeting. Here on this topic, you will find out the latest breakthroughs also, the latest and best practices from the leading enterprises that will provide you with the latest advancements introduced by Advanced AI researchers throughout the previous few years.

Field-aware Factorization Machines in a Real-world Online Advertising System

To predict a customer’s response is amongst the core ML tasks in AI-based marketing. Field-aware Factorization Machines or FMMs have been established recently as modernistic methods to face such situations and, in particular, to win over the competition. In this research, those results have been included that are concluded through implementing this method in a production system. It forecasts click-through and conversion rates for displaying advertisements. It also displays how this method effectively wins modern marketing challenges and is lucrative in real-world predictions.

The Summary of This Paper

FMM methods have demonstrated quite impressive results in numerous competitions. However, it is concluded that the training speed for the algorithms of this method is comparatively too low for a production system. The researchers have introduced two solutions that can help with increased training speed to deal with this situation. These techniques are named Premature Warm Start and A Distributed Learning Mechanism. After conducting experiments with the implementation of these two methods, it was suggested that it helped with an increased number of advertisement displays and increased return on investments while also being fast enough for real-world online marketing campaigns.

Deep Interest Evolution Network for Click-Through Rate Prediction

Click-through rate predictions that help us estimate the possibility of user clicks have become the necessity of the marketing systems. Implementing the CTR prediction model is essential to attain the latent user interest behind the user behavior data. User interests evolve, dynamically accompanying the external environment and internal cognition changes. Even though plenty of CTR models can be used for interest modeling, most of them directly consider the representation of behavior in terms of interest. Also, these models mostly lack modeling for latent interest behind a user’s concrete behavior.

The Summary of This Paper

The research suggests that attaining a user’s interests and dynamics is major to advancing the performance of CTR prediction models. Also, it claims that a user’s explicit behavior doesn’t directly demonstrate their latent interest. Therefore, the researchers establish a Deep Interest Evolution Network that models users’ interest evolving process and accordingly improvises the accuracy of CTR predictions in online marketing campaigns. 

Contextual Multi-Armed Bandits for Causal Marketing

The Advanced AI-based model estimates and optimizes the casual effects of automated marketing. With a focus on casual effects, you ensure better ROI by only targeting the right customers who don’t prefer to take organically. The approach draws on the strengths of the casual interface, uplift modeling, and multi-armed bandits. The model optimizes on casual treatment effects instead of pure outcomes; it also incorporates counterfactual generation within the data collection. The research optimizes over the casual business metric following uplift modeling results. Contextual multi-armed bandit methods help scale to various treatments and perform off-policy policy evaluation of the collected data.

The Summary of This Paper

The marketing team of Amazon suggests a new approach to optimizing advertisement campaigns. The approach draws upon casual interface, uplift modeling, and multi-armed bandits. It allows the targeting of marketing campaigns based on casual outcomes rather than only pure outcomes. Ultimately, this presented model approach helps target only those responsive customers who don’t prefer to respond to marketing campaigns just after seeing them. The research optimization confirms that a focus on casual effects can lead to higher investment returns.

The Conclusion 

Customers expect any marketing campaign to understand their interactions with your product. This understanding works as fundamental for building effective marketing campaigns. Numerous Advance AI tools can automate marketing activities and significantly improvise marketing analytics and insights. These researches are some of the working models that bring the maximum user interactions from implementing AI for Marketing and advertising campaigns. By following this topic, you can rest assured that you are informed about the latest breakthrough in AI optimization research for marketing & advertising campaigns.

Top 5 Benefits of Implementing Marketing Technology in Your Business Strategy

Technology is moving faster than ever before and with this customer’s expectations are getting higher too. MarTech marketing technology is becoming key for organizations to meet and exceed customer expectations. MarTech helps organizations fill the digital gap created due to the rapid advancement in technology and evolving consumer behavior. The advancement in artificial intelligence, analytics, and automation helps marketers catch up with recent market trends and speed up the organization’s changes. This blog will discuss the top five benefits of implementing MarTech in your business strategy.

Data unlocking with analytics tools

In this fast-changing digital world, data is the key to sustainable growth for a business. With access to more data than ever before, marketers now can unlock data that most well-funded organizations just a few years ago. The data has now become the resource that needs analysis and refinement to deliver the best for a business. This is why the analytics industry is estimated to grow 10-15 % by 2022.

Analytics tools have now become necessary for companies to add in their marketing technology to utilize data fully. With this, an organization can increase the ROI and effectiveness of its marketing strategy. Due to this, companies are now bringing analytics into the center of the decision-making process to remove any bias and become data-driven to serve their customers better. Change in an organizational culture where data can be utilized to improve and optimize social media marketing, SEO, PPC, and email personalization.

Marketing automation for increasing efficiency

Brands need to maintain their strong presence in consumers’ minds to influence purchasing decisions. But due to so much noise in the digital world, maintaining a strong presence now becomes a challenging task for marketers.Automating the marketing efforts has become important for brands to scale up their efforts and engage consumers in this digital world. Move forward from one size fits all marketing policy to cut through the digital noise. Customers are moving away from products and ads that they don’t find interesting or improve their lives. Automating the market processes allows marketers to connect with consumers in a more personalized way that offers real value.

Marketing automation is improving the productivity of the business by 15-20 percent. Marketing automation is a critical component that helps in increasing and scaling the scope of campaigns. The number of people doesn’t limit a marketing campaign’s success as automation allows in increasing the digital world’s brand footprint and provides better ROI. automation is enabling the organization to sustain its relationship with the existing customers and attract new ones.

Increasing engagement with management tools

Social media has become a great medium for businesses to connect with customers and increase sales. Even after this huge popularity, some businesses are failing to make an impact on these digital platforms. With the right management tools, creating fresh content, responding to customer queries, and increasing brands looks like an easy and achievable task. Marketers need a MarTech stack to fill in the customer expectation gap; automate and optimize customer interactions with brands over social media platforms.

Social media management tools help in publishing and schedule posts when there is maximum engagement. Automatic reposting of high engagement content to increase reach and monitoring competitors to find the latest trends and topics to improve business marketing strategySocial listening becomes an important tool for brands to understand and analyze what people are saying about their brand, products, and competitors. With these valuable and unbiased insights, businesses can improve their marketing and business strategies.

Improving customer experience

Marketers are expected to focus largely or entirely on customer experience with the brand in the next two years. Today’s consumers are more inclined to buy from brands that offer better engagement, relevance, and personalized solutions as per their needs. This seamless customer experience is hard for companies to achieve without a MarTech stack. Artificial intelligence helps organizations analyze and process vast customer data to give that personalized experience to their customers. In a survey, many marketing executives have shown concerns that their organization is not mature when it comes to providing a personalized touch to customers. This is a clear indication of organizations’ slow response and how critical marketing technology is to meet customer expectations. 

Without the MarTech stack, organizations will become an impossible task to track the buyer’s journey. Marketing technologies are empowering the organization to analyze the customer data and forge connections in real-time. A streamlined ecosystem ensures that every customer gets a seamless and personalized customer experience.

Improving ROI and productivity

Organizations are looking to streamline their processes and become more efficient in managing their digital assets with the help of digital asset management. Marketers are creating digital content like images, videos, audio, and other content to target every stage of the buyer’s journey. It is vital to create an easily accessible and organized digital library to track all these digital assets. The DAMs are helping marketers avoid duplication of the content and save valuable time while searching media assets. Digital asset management technology provides a way to give highly relevant content that gives the personalized experience consumer demand.

Experts believe that DAM software is estimated to 30-35 percent growth by 2024.

In addition to providing increased efficiency, this software enables organizations and marketers to track usage and digital assets’ ROI. These valuable insights are empowering CMOs to optimize their marketing strategy to get better ROI from further media creation. 

Final words

MarTech stack becomes essential for brands to achieve rapid progress in this digital consumer landscape. It has become an essential tool for the organization to get valuable insights from the data and to optimize for a better customer experience in real-time. AI and ML enable organizations to improve the efficiency of the marketing campaigns and maintain that personalization touch that today’s customer demands. With that personalized touch, MarTech helps bring the brand closer to consumers, improve the ROI on marketing initiatives, drive more sales, and delight their customers.

MarTech Adoption – How to increase Marketing ROI

Beside your MarTech stack, MarTech adoption is a critical factor in driving success and high return on investment (ROI). Hence enabling MarTech skills and regular use of those skills is a critical first step. Skills come primarily from training and that needs resources especially investment of money.

After spending a lot of $$ on procuring MarTech, resources for training become a luxury that we cannot afford. Failure to train marketing teams on MarTech skills will create a huge barrier to MarTech adoption. If you are not planning to invest in MarTech training you are starting with a poor ROI from the beginning. I caution you against that and I hope you have both training and onboarding as part of MarTech strategy and investment plan.

“Organizations that invest in acquiring and maintaining MarTech skills are eight times more likely to also experience good or very good ROI.”

– A study I read few years ago.

Key Metrics to track MarTech Adoption

Some of the key metrics that come to my mind to measure MarTech adoption and ROI are:

  • Volume metrics: Measure things like impression, leads, form fills, product views etc.
  • Financial metrics: Measure things like opportunities created, revenue generated, revenue influenced or ROI. You need a stack that has data well connected for your insights team to calculate these.
  • NPS of Customer Zero: This is an ongoing tracking of satisfaction across your internal users. I have found this to be an important metric for MarTech adoption.
Customer Zero NPS is very helpful metric for tracking MarTech adoption
Customer Zero NPS – Leading indicator of MarTech Adoption in 2016 for MarTech
  • Productivity metrics: Measure things like time saved or requests handled, adherence to SLAs, time saved, the velocity of deals, speed to lead etc.
  • Stack Complexity/Retire Legacy assets: How many technologies that can be consolidated as we move from a disconnected MarTech stack with redundant technologies to a MarTech stack that is connected like a platform.

You may argue that first two are marketing metrics. MarTech teams should co-own these metrics as this ensures investments in solutions that enable business outcomes. This will also motivate your MarTech teams to integrate across technologies. Integration and Data are two key aspects in the evolution of MarTech maturity. I will be sharing industries’ first MarTech Maturity Model built with over 30 years of collective MarTech experience in the next few weeks. You will be able to use it as a framework to evolve your MarTech program.

Ideas to enable MarTech adoption

Here are some of the ideas that I have used or plan to use in the future to enable MarTech adoption and Marketing enablement:

  • As part of MarTech governance ensure your marketing stakeholder(s) are part of prioritization. They should also share responsibility for enabling ROI. Do this before you decide to invest in that MarTech solution.
  • Involve key marketing stakeholders in vendor selection, implementation and onboarding as a responsible party.
  • As soon as the marketing team onboards on MarTech solution, start creating monthly reports that use 2 or more metrics mentioned above to track MarTech adoption.
  • Roll out onboarding sessions followed by on-going training.
  • Track MarTech adoption with the help of the vendor and report out on a quarterly basis. Have a north star for adoption and request the vendor to come with recommendations to achieve it.
  • Roll-out a full-service digital marketing academy that works round the clock to enable. I will attribute the success of our MarTech program at McKesson to the Digital Marketing Academy. Here are some of the focus areas I recommend for a MarTech or Digital Marketing Academy:
    • On-going training on essential and emerging technologies
    • Certifications
    • Sharing of Best practices (internal and external)
    • Awards for best work in various categories
    • Opportunities to network across division if you are in a large enterprise
    • Have fun included in your program, so that various teams can bond across team silos
Marketing teams like to connect with each other
From Survey after 2016 Digital Marketing Academy at McKesson

I hope this helps and I will love to be part of your MarTech adoption and Marketing transformation journey. Feel free to share your experience on the topic of MarTech adoption and Marketing Enablement.

Feel free to reach out if you need guidance on putting together MarTech adoption strategy or establishing a Digital Marketing Academy.


During Customer Data Platform (CDP) and Data Lake discussions, I often come across wild assumptions on Data Lake’s ability to solve every problem while CDPs end up being heavily underestimated. In my opinion, neither CDP and nor Data Lake is replacement for each other but a perfect complement to each other. Data Lakes are a key source of data for CDPs while CDPs can help improve the quality and completeness of data in Data Lake. Another key platform in this mix especially for marketers is the Data Management Platform (DMP). I will like to spend some time to answer the DMP vs CDP vs Data Lake question.

Don’t think DMP vs CDP vs Data Lake but DMP+CDP+Data Lake.

If you don’t have the patience to read through all of this article on the difference of DMP vs CDP vs Data Lake, please skip directly to the summary section in the end.

DEFINITIONData Management Platform (DMP) collects anonymous web and digital data. It comprehends information about prospects psychographics and demographics.  

Manage segments of customers with anonymous profiles.

For broadening marketing reach by building segments, audience mining etc.

Capture targeted audience at the right time in the buying funnel through relevant messaging.

Better optimization programs and smarter media buying decisions can be taken based on audience analysis and latest campaigns.
Customer Data Platform (CDP) is a type of packaged software which creates a persistent, unified identifiable customer profile that is accessible to other systems. Data is pulled from multiple sources, anonymized, cleaned and combined with third party data, intent data etc. to create a single profile of a customer.  

CDP enables real time activation of omni-channel experience across. CDP data can be leveraged in real time to provide more personalized content and delivery over web, mobile, Email, ABM, Ads etc. CDP data is accessible by external systems and structured to support digital and marketing team needs for experience management, campaign management, marketing analysis and business intelligence  

CDP is always a hot storage meaning easily retrievable and live connected to like customer master. CDP doesn’t need technical skills to manage and operate.
A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

The key focus of Data Lake is to ensure that highly connected data is available to all enterprise systems and functions.

Data lake can have a combination of cold and hot storage. Cold storage for more older data like over 3 years.  Data Lake need very technical resources to build and operate it. Data Lakes don’t offer integration with last mile solutions like MarTech solutions.

Data Lake provides ability to understand what data is in the lake through crawling, cataloging, and indexing of data. It always ensures data assets are protected.

Data Lake allows to run analytics without the need to move data to a separate analytics system. Generate different types of insights including reporting on historical data and doing machine learning where models are built to forecast likely outcomes and suggest a range.

Different types of analytics on your data like SQL queries, big data analytics, full text search, real-time analytics, and machine learning are needed to uncover insights. You can create new business models based on historical data and new financial models based on customer behavior, product categories, market data, risks and opportunities
 USERSAdvertising Professionals Ad agencies Marketing (limited)Digital Marketing Customer Experience Sales (limited)Data scientists, Data developers, and Business analysts (using curated data)
DATA SOURCES & MANAGEMENTData is ingested   from various client and media sources like marketing analytics, CRM, ad-servers, publisher partners and point of sale (POS).
Data is also collected from mobile apps, client’s website, as well as other channels that use native apps.
It is then augmented and enriched with   3rd party vendor data; private data exchanges are established.
First Party Data:
Web Analytics
Advertising Data
Marketing Automation Data
Second Party Data
Third Party DataIntent Data
Marketing Lists
Device data
Data Lake can connect structured and unstructured data available in:
MDM (Master Data Management) systems
Commercial Data
Product Data
Multiple other critical backend IT and Data systems in an enterprise
Line of Business Applications

Summary and Recommendation on DMP vs CDP vs Data Lake


So how should we use this information on DMP vs CDP vs Data Lake and apply it to your business? DMP can take care of most of your needs if you are only focused on marketing segmentation and advertising. There are many mature products in the market that you can buy and start using immediately. If you have use cases broader than that (I hope so), then you must look into CDP and Data Lake. If you are a CDO, CMO or CIO reading this, you must look into CDP and Data Lake. CDP and Data Lake are both required by every organization as both provide solutions to different problems. While Data Lake brings the data from enterprise together and makes is useable immediately, CDP focused on doing the same for the use cases limited to teams focused on the customer side. These are primarily digital and marketing teams.

If you have a functional Data Lake, you should build a CDP (light) as data lakes are not built to solve last-mile use cases. Your digital and marketing experiences will struggle as you will not be able to utilize Data Lake to full potential.  CDP implementation will be lightweight and primarily focus on

  • Append digital and marketing specific data that is not available in Data Lake.
  • Create customer 360 and build segments for activation
  • Connect with last-mile experience and marketing systems to activate the data

If you only have CDP, you should look into building a Data Lake to solve bigger use cases and enable digital transformation in other areas like Sales, Customer Service, finance etc. Data is the blood for a Digital Transformation. 

If you don’t have both, you should start at least with CDP as those can build quickly and you can start hacking growth while you build data lakes that can take years in a large enterprise. As I mentioned above, you will still need a Data Lake. Plan to have that in the long term.

The question of DMP vs CDP vs Data Lake is not right as all these systems come together and help you enable transformation in the digital age that we all call Digital Transformation.

More suggested content from some experts in this space

Some additional content from the post on LinkedIn where I got some good feedback from experts in this field:

Most organizations are looking to add CDP to their MarTech stack as they are getting a data lake stood up, leading to parallel efforts and often times the 3rd party CDP (with speed to market) beating the in-house data lake build initiative. Then it gets to be buyer-beware as all CDPs aren't true and good CDPs. It's all about having laser-focused CDP use cases ready to deliver business value and knowing which vendor to partner with to maximize ROI. - Fauzia Chaudhry (Senior Manager, MarTech, Robert Half)
Today’s customer is on at least 5 or more connected device at any given point in time and with this device hopping the expectation is to have the same intimate moments of delightful and seamless experience on all of the channel of engagement and that is where CDP makes an immense impact, especially with privacy and regulations. - Raphy Mathias (Domain Information Officer, Toyota Financial Services)
CDPs are designed from the ground-up to solve both of these problems using AI to make sense of the data, and automation to active data into individual channels. As channels proliferate and customers move to Digital consumption modes, this combination of activation + automation is a must-have to grow Revenue without adding complexity and cost. - Shashi Upadhyay (EVP, Dun & Bradstreet)

Day 2 of MarTech Conference

Here are my tweet notes from Day 2 of Marketing Technology conference (MarTech) at Boston

Some of good reads from others who attended MarTech.