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.

Power lies in Product Adoption

Power of system is realized when users adopts the system. Number of subscriptions or units sold is a great metric to calculate revenue and initial success of a product but the true to success of the product is based on how well it is adopted. Once a product is well adopted, it confirms all aspects of its success like product objectives, customer impact, profitability, market share etc. As product managers we must focus on adoption as the key goal/OKR besides the normal financial metrics.

Most importantly, adoption doesn’t means it has to be a fancy solution. On a product demo from a vendor, I learnt that the most adopted feature of their financial planning tool for CFOs and their financial teams; is the ability to generate data loaded cubes in excel. Who will believe that in today’s world, but that is what finance teams want. Providing the solution that the customers need and continuous improvement is the key to product adoption.

DMP vs CDP vs DATA LAKE

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.

DATA MANAGEMENT   PLATFORM (DMP)CUSTOMER DATA   PLATFORM (CDP)DATA LAKE
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)
IT
Sales
Finance
HR
Marketing
Digital
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
CRM
Advertising Data
Marketing Automation Data
Second Party Data
Third Party DataIntent Data
Marketing Lists
Device data
Etc.
Data Lake can connect structured and unstructured data available in:
MDM (Master Data Management) systems
ERP CRM
Commercial Data
Product Data
Multiple other critical backend IT and Data systems in an enterprise
Line of Business Applications
DMP vs CDP vs DATA LAKE

Summary and Recommendation on DMP vs CDP vs Data Lake

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)

Problem Reframing

Reframe Problems Before Solving

I recently understood the importance of reframing the problem before looking into solutions. It helps in creating simple, effective and economical solution in far shorter time.

Problem Reframing
Image Source: HBR

Here are Seven practices for effective reframing of problem in few minutes that I learnt from HBR article:

  1. Establish legitimacy: It’s difficult to use reframing if you are the only person in the room who understands the matter. Share this article with your team https://hbr.org/2017/01/are-you-solving-the-right-problems
  2. Bring outsiders into the discussion: Someone who works with your team but not part of it. They will think differently and challenge the group’s thinking.
  3. People’s definition in writing: This helps in ensuring everyone have the same view and understanding of the problem.
  4. Ask what’s missing: This ensures the description of problem is accurate and complete.
  5. Consider multiple categories: Invite people to identify specifically what category of problem they think the group is facing.
  6. Analyze positive exceptions: Look to instances when the problem did not Occur, asking, what was different about that situation?
  7. Question the objective: Reframe by paying explicit attention to the objectives of the parties involved first clarifying and then challenging them.

What I learnt on Day 1 of MarTech Conference

Martech Conference at Boston turned out to be one of the best conference I have ever attended. Here is the recap of what I learnt at MarTech Conference in day 1. I think twitter is a very nice global notebook to take notes. This way you are not selfish to keep the good notes from MarTech to only yourself.