Artificial intelligence (AI) is growing in stature in the marketing realm. Marketers today rank AI as their #1 priority for investment, according to a recent State of Marketing Report published by Salesforce. The adoption of AI is staggering with about 84% of marketers reporting that they use AI in their customer acquisition and retention engines, that’s almost 300% growth in 2 years.
What exactly are marketers doing with AI? The usage of AI is crossing all barriers, from improved segmentation and personalisation, deeper insight, forecasting and process automation.
With the growth in advertising technology, riding on big-data driven AI, advertising companies reimagined the process of delivering digital ads. According to eMarketer, online ad sales rose from $60 billion in 2019 to an estimated $97 billion in 2022.
What do sales teams feel about the impact of AI on what they do? They would tell you-cautiously optimistic. According to latest research from Salesforce, 86% of sales reps view AI as having a positive impact on their future roles. However, 68% of the same reps had concerns as well. And as you would imagine, mostly concerning the very relevance of their existing jobs. 31% percent said technology might eventually negatively impact the art of selling, as AI driven optimisation of sales interactions replaces human-to-human relationships.
Five reasons to be happy about AI in advertising
While it is only natural to fear the unknown, there is reason to believe that AI will make the existing jobs of sales reps better. AI augments the sales process and leaves people to do what they do best which is to be human.
AI has the potential to level the playing field for both advertisers and publishers. It empowers sales teams, who don’t have the budgets that large corporations or media companies may have, with the tools they would like. AI-driven ad technology can help sales teams boost both the effectiveness of their messaging and their efficiency.
1. Improved data unity and synchronization
With the impending censure of third-party cookies in Apple and Chrome’s privacy policies, marketing professionals are increasingly seeking first-party data to power their marketing initiatives. Merkle’s 2021 Customer Engagement Report stated that first-party data was a strategic priority for a staggering 88% of the marketers. For publishers, first-party data is vital to building advertiser centric audiences; and for advertisers, first-party data is increasingly vital for targeted advertising.
And that’s where the challenge is. First-party data is not only complex, more often than not, it is fragmented and ill organised. According to Salesforce research, 64% of customers start their purchase process on one device and finish on another. With the proliferation of smartphones and devices, marketers have to deal with an average of 12 primary sources of customer data, an increase of 20% from 2020. And as one would expect, most of the data residing in these sources has inconsistent identity information, expired or outdated information, and unconventional taxonomy.
AI can be a great asset to a common Customer Data Platform to significantly improve identity matching. Algorithms can execute “fuzzy logic” on IDs and resolve or isolate discrepancies. AI can also ensure data consistency by mapping data from siloed systems to a common data model.
2. Better audience segmentation and discovery
Customers today prefer digital first, and they want relevant experiences. They want the messaging to be useful and timely in their digital lives. Delivering a personalised experience mandates the need for organised data as well as smart algorithms to discover customer segments and reveal their needs that may be impossible to do manually in real-time.
AI stands out at intelligent segmentation, discovering groups of customers and prospects with common attributes at a scale that is impossible for a human analyst to achieve. AI algos can sift through billions of records of customer data to identify meaningful patterns and segment audiences intelligently for more effective and targeted marketing programs.
3. Natural language shakes hands with technology
Natural-language processing (NLP) and image recognition are 2 areas which are extremely promising when it comes to AI. Chatbots have taken customer service to the next level with conversational customer service. Voice assistants such as Siri and Alexa are already changing the user experience whether we want to book a ticket or are in the mood to have Chinese. As voice recognition technology approaches 95%+ accuracy, voice navigation will become an intrinsic part of customer engagement.
Voice navigation is already built into call centre systems and used in analytics as well to some extent. However, AI based marketing technology is at the cusp of something never seen before. Imagine an ad sales rep never having to use a keypad again to make the right selection.
With less and less human clicks efficiency will improve significantly, and so will the efficacy of the whole process. And this will lead to ad reps spending less time in searching and updating and have more time to do what they do best – selling.
4. Back-end processes efficiency
We’ve already talked about how AI can help unify and harmonize data. It can also help sales teams become more efficient by prioritising their efforts, and allowing them to focus their time on key tasks while automating the mundane ones.
Lead Scoring was one of the first areas to benefit from AI technology. While there was resistance from sales teams initially in adopting the technology, and rightly so, with the uncertainty of the what the unknown will do to their current jobs. Questions such as – How can a software qualify a good lead better than me, were commonplace. But with technology integration into existing systems, many sales reps now use algorithms regularly to augment their priority their prospects. The same is true for determining next best actions based on interaction history and scheduling meetings.
AI can help ad sales teams match available inventory with “most likely to close” sales opportunities thereby significantly trimming the time spent on low-probability leads.
5. Improved measurement and optimisation
In the post pandemic period, digital ad spend has seen a meteoric rise. Brands now spend more than half of their advertising budgets on digital platforms. AI can help aggregate and analyse all that data seamlessly to help advertisers establish the impact of campaigns viz a viz desired outcomes, such as sales. Simply put, AI can help ad sales teams separate the noise and identify what works.
To measure the efficacy of multi-channel campaigns, one needs to harvest information from dozens of sources. And that’s not all. They also need to apply complex models to determine which aspects of the campaign, channels, devices, and tactics made an impact. AI-driven tools can help ad sales teams with automated recommendations to optimize campaigns based on the historical performance of ads.
AI excels at automating tedious tasks, and is also fantastic at sifting through huge amounts of data at unimaginable speed. Something humans can’t do. By allowing AI technology to do what it does best, we make the entire process of ad sales less artificial and more intelligent.
Girikon is a Gold Salesforce Consultant delivering value to customers across the globe for over a decade. Contact one of our experts to know how AI can help unlock the true value of your advertising sales.
PyTorch is an open source ML library used for developing and training deep learning models. The primary contributor to PyTorch is Facebook’s AI research group. PyTorch can be used with Python and C++. As one would expect, the Python interface is more sophisticated. PyTorch, backed by Facebook and supported by Amazon, Microsoft and Salesforce, is quite popular amongst developers and researchers.
Unlike other more popular neural network based deep learning frameworks such as TensorFlow, which use static computation graphs, PyTorch uses dynamic computation, which allows for greater flexibility when one wants to build complex architectures. PyTorch works very well with Python, and uses its core concepts like classes, structures and loops, and is therefore more intuitive to understand. When compared with TensorFlow, which has its own programming style, PyTorch is simpler to work with.
Why do we need PyTorch?
The PyTorch framework can be viewed as the future of deep learning. There are many deep learning frameworks accessible to developers today, with the more preferred frameworks being TensorFlow and PyTorch. PyTorch however offers more flexibility and computing power. For machine learning and AI developers, PyTorch is easier to learn and work with.
Here are some advantages of PyTorch:
1. Easy to Learn
PyTorch has the same structure as traditional programming which makes it more accessible to developers and enthusiasts. It has been documented very well and the developer community is continuously improving the documentation and support. Thereby making it easy to learn for programmers and non-programmers alike.
2. Developer Productivity
It works seamlessly with python, and with many powerful APIs can be easily deployed on Windows or Linux. Most of the tasks in PyTorch can be automated. Which means with just some basic programming skills, developers can easily boost their productivity.
3. Easy to Debug
PyTorch can use debugging tools of python. Since PyTorch creates a computational graph at runtime, developers can use PyCharm, the IDE from Python, for debugging.
4. Data Parallelism
PyTorch can assign computational tasks amongst multiple CPUs or GPUs. This is made possible with its data parallelism feature, which wraps around any module and allows parallel processing.
5. Useful Libraries
PyTorch is supported by a large community of developers and researchers who have built tools and libraries to extend the accessibility of PyTorch. This developer community contributes actively in developing computer vision, reinforcement learning, Natural Language Processing (NLP) for research and production. GPyTorch, BoTorch, and Allen NLP are some of the libraries used by PyTorch. This provides access to a powerful set of APIs that further extends the PyTorch framework.
Benefits of using PyTorch
1. Python-friendly. PyTorch was created keeping Python in mind (that’s why the prefix), as against other deep learning frameworks that were ported over to Python. PyTorch provides a hybrid front end enabling programmers to easily move most of the code from research to prototyping to execution for production.
2. Optimized for GPUs. PyTorch is optimized for GPUs to accelerate training cycles. PyTorch is supported by the largest cloud service providers: AWS currently supports the latest version of PyTorch. AWS includes its Deep Learning AMI (Amazon Machine Image) and is optimized for GPU. Microsoft also has plans to support PyTorch in Azure – their cloud service platform. PyTorch has a built-in feature of data parallelism, that allows developers to leverage multiple GPUs on leading cloud platforms.
3. Plethora of tools and libraries. PyTorch comes with a rich ecosystem of tools and libraries for extending its availability and potential. For instance, Torchvision, PyTorch’s built-in set of tools allows developers to work on large and complex image datasets. The PyTorch community of researchers across academia and industry, programmer and ML developers have created a rich ecosystem that provides tools, models, and libraries to extend PyTorch. The objective of this community is to support programmers, engineers and data scientists to further the application of deep learning with PyTorch.
5 ways in which AI apps can use PyTorch
With PyTorch, engineering teams can create deep learning predictive algorithms from data sets. For instance, developers can leverage historical housing data to predict future housing prices or use a manufacturing unit’s past production data to predict success rates of new parts. Other common uses of PyTorch include:
Image classification: PyTorch can be used to build complex neural network architectures called Convolutional Neural Networks (CNNs). These multilayer CNNs are fed thousands of images of a specific object, say a tree, and much like how our brains works, once the CNN is fed a data set of tree images, it can identify a new image of a tree it has never seen before. This application can be particularly useful in healthcare to detect illnesses or spot patterns, much faster than what the human eye can do. Recently a CNN was used in a study to detect skin cancer.
Handwriting recognition: Human handwriting has its inconsistencies as one moves across people and regions. Handwriting recognition involves interpreting the inconsistencies in human handwriting across people and languages.
Forecast time sequences: Another type of neural network is Recurrent Neural Networks (RNNs). They are designed for sequence modelling and are particularly useful for training an algorithm on past data. It can make predictions based on historical data, allowing it to make decisions based on the past. For instance, an airline operator can forecast the number of passengers it will have 3 months from now, based on the data from previous months.
Text generation: RNNs and PyTorch are also used for text generation. In text generation an AI model can be trained on a specific text to create its own output on its learning (for eg interpretation of poetry).
Style transfer: One of the most exciting and popular applications of PyTorch is a style transfer. It uses a set of deep learning algorithms to manipulate images and use the visual style of that image on another image to create a new set of images, combining the data of one with the style of another. For example, you can use your vacation album images, apply a style transfer app and make it look like a painting by a famous artist. And as you would expect, it can do the reverse as well. Convert paintings to look like contemporary photos.
AI is going to reshape many enterprise functions and how their respective teams work. And one of those areas is CRM. Salesforce, the world’s leading CRM platform is leading the way in embedding trusted AI into all their product offerings. As a Gold Salesforce Partner, Girikon is the preferred choice for many Salesforce customers across the globe. To know more about how AI can work for your business, contact us today.
TensorFlow is an open-source library created by the Google Brain team to build enterprise-grade machine learning algoriths. TensorFlow bundles together a host of machine learning models and algorithms and with the use of common programming frameworks, makes them useful. TensorFlow uses Python and JavaScript to build user friendly APIs for connecting with apps, and uses core C++ to execute the app functionalities.
While it is still early days for machine learning technology, it continues to evolve rapidly, introducing us to a new world of advanced algorithms and deep learning. Deep learning uses algorithms commonly referred to as Neural Networks. As the name suggests, they draw inspiration from our biological nervous systems, led by the brain, to process information. Deep learning algorithms enable computing devices to identify every single bit of data, establish what it means and learn patterns.
TensorFlow is a tool to develop deep learning models. It is an open-source AI library that uses data flow graphs to build learning models. With TensorFlow, programmers can build large-scale, multi-layered neural networks. TensorFlow is primarily used to perceive, understand, classify data and create predictive models.
Main Use Cases of TensorFlow
While TensorFlow can be used for many applications, here are 5 commonly used applications in the world of artificial intelligence.
Voice/Sound Recognition
One of the most popular use cases of TensorFlow is audio signal based applications. When fed appropriate data, neural networks can perceive and understand audio signals. These can be:
Voice recognition — primarily used in Internet of Things (IoT) applications, Automotive applications (Voice command based actions), Security (Authentication)
Voice search — Commonly used in Telecom and by mobile phone manufacturers
Sentiment Analysis — used in CRM applications
Flaw Detection (noise analysis) —Automotive and Aviation applications
The world is familiar with the common use case of voice-search and voice-activated assistants. This use case has been widely popularised by smartphone manufacturers and Mobile OS developers such as Apple’s Siri, Google Assistant and Microsoft Cortana.
Understanding and analyzing language is another widely used use case for Voice Recognition. Speech-to-text applications are used to extract and understand sound bites in larger audio files, and convert it into text.
CRM is another area were voice/sound based applications can be implemented to deliver a better and smarter customer service experience. Imagine a scenario where TensorFlow algorithms fill in for customer service reps, and guide customers to the right set of information much faster than an agent.
Text Based Applications
This is another commonly used application of TensorFlow. Text based applications for instance sentiment analysis can be used in CRM apps and Social Media for improving the customer or prospect experience, Threat Detection, used in Social Media and Government applications and Fraud Detection, used by Insurance, Finance companies are some common examples.
Language Detection is another popular use case of TensorFlow for text based applications.
We are quite familiar with Google Translate. More than 130 languages can be translated into each other using this service. An AI powered version of a translate engine can be used in common real world situations like translating heath diagnosis technical terminology or legal jargon in contracts into plain language.
Text Summarization
Google also came up with sequence-to-sequence learning, a technique for shorter text summarization. This can then be used to build headlines for news and articles. Another use case for TensorFlow popularised by Google is SmartReply. It automatically generates e-mail responses based on text recognition and contextual understanding.
Image Recognition
This use case is primarily used by Social Media and Smartphone Manufacturers. Facial Recognition, Image Search, Motion Detection, Computer Vision and Image Clustering are nowadays being deployed in Warehousing, Healthcare, Automotive, and Aviation Industries. Google Lens is another example where Image Recognition is being used to understand the content and context to help identify people and objects within images.
TensorFlow object recognition algorithms have the ability to identify and classify random objects within larger images. This has found use in engineering modelling applications such as creating 3D spaces from 2D images. Facebook’s Deep face is another example of photo tagging using image recognition technology. Deep learning technology can identify an object in an image never seen before by analyzing thousands of images with similar objects.
Healthcare Industry is also at the cusp of using Image Recognition for faster diagnosis. TensorFlow algorithms can process information and recognize patterns much faster than the human eye to spot illnesses and detect health problems faster than ever.
Time Series
TensorFlow Time Series algorithms is another method used today to establish patterns and forecasting of time series data. Meaningful statistics can be derived by these algos along with recommended actions. TensorFlow Time Series algorithms allow forecasting of generic time periods apart from generating alternative versions of the predicted time series.
A popular use case for Time Series algorithms is Recommendations. Time Series Recommendations has seen widespread usage amongst leading organizations such as Netflix, Google, Amazon, where they analyze and compare activity of millions of users to determine what a customer might wish to view or purchase. And with every interaction, while recording the activity of every action, these recommendations get even smarter. For instance, they throw up content what your family members or friends like or offer you a gift they might like.
Finance, Insurance, Government, Security and Threat detection, Predictive Analysis, Resource Planning and forecasting are some of the other use case scenarios of TensorFlow Time Series algorithms.
Video Detection
TensorFlow deep learning algorithms can also be used on video data. This is used in Motion Detection in Automotive and Aviation, Role based Gaming, Security and Threat Detection. Today, universities are doing deep research on Video Classification at a large scale to perceive, analyze, understand, classify video data. NASA is using TensorFlow algorithms to build a system for orbit classification and object clustering of asteroids. Consequently, they will be able to classify and predict near earth objects.
TensorFlow is an open-source framework, allowing developers the freedom to work on innovative and disruptive use cases, which will contribute further to Machine Learning technology.
Amongst many things, TensorFlow’s popularity is primarily due to the computational graph concept, automatic differentiation, and the adaptability of its python API structure. This makes TensorFlow more accessible to developers to solve real problems. Here are some advantages of TensorFlow.
1. Scalable
The TensorFlow library is well defined and structured. This means it works just as efficiently on a mobile device as on a powerful computer.
2. Open Source
The TensorFlow library is available free of cost. Anyone, anywhere can work on it and use it to solve problems.
3. Graphs
Tensorflow has a very powerful, inbuilt data visualization capability. This makes it easier for developers to work on neural networks.
4. Debugging
Tensorboard, which is a part of TensorFlow, allows easy debugging of code blocks. This reduces the need for combing through the whole code.
5. Parallelism
TensorFlow uses Central Processing Unit (CPU) and Graphics Processing Unit (GPU) for its functioning. Developers can use the architecture freely based on the problem they are trying to solve. A system uses GPU by default, which is why TensorFlow is sometimes referred to as a hardware acceleration library because it reduces memory usage.
6. Compatible
TensorFlow is compatible with popular programming languages like Python, C++, JavaScript, etc. This allows developers the freedom to work in an environment they are most comfortable with.
7. Architectural Support
The TensorFlow architecture uses Tensor Processing Unit (TPU). This makes computation faster than what one would get when using CPU and GPU. TPU models can be easily deployed on the cloud and work faster than CPU and GPU.
8. Library management
With the Google backing, TensorFlow is updated regularly with enhanced capability and flexibility with every release.
AI is already an intrinsic part of Salesforce, the world’s leading CRM platform. As a Gold Salesforce Partner, Girikon is the preferred choice for many Salesforce customers across the globe. To know more about how AI can work for your business, contact us today.