Top Artificial Intelligence Vendors : The market for Artificial Intelligence (AI) platforms will grow at a rate of 15% per year until 2022, according to Forrester statistics. Before analyzing the characteristics of the main Artificial Intelligence (AI) solutions applied to marketing and sales processes, I would like to put things in perspective.
I think we are witnessing a certain media bubble around the world of AI, which can lead to wrong conclusions and generate unrealistic expectations about this technology.
There is no doubt that this concept has become fashionable and is often being used more as a branding and marketing element than as a resource for process optimization and decision-making. Many of the big companies want to jump on the AI bandwagon, or at least make it appear to their shareholders, and thus gain an extra vote of confidence in the markets.
Machine Learning programs and algorithms can be divided into 2 large groups: supervised learning (we must enter the data and train them in each scenario and unsupervised learning. Both are used to create predictive models, which are capable of improving their performance, thanks to continuous and incremental exposure to large amounts of data.
The long-term goal is that these programs can learn from experience, and make better decisions, just as a human being would. The top Artificial Intelligence vendors listed in the article.
However, it is not a miracle technology that allows you to replace your team members with machines that make marketing and sales decisions, or any other business process.
Artificial Intelligence Vendors: Will It Replace Marketers?
What it is about is improving and optimizing existing processes, as well as helping to identify “insights”, to carry out better decision-making based on data. We must harness the power of technology to complement human skills.
This type of knowledge and patterns hidden in the data could not be discovered without the use of AI algorithms. In fact, I previously published an article collecting the findings of a study published by MIT on the application of machine learning to data-driven marketing or data-based marketing.
With that said, I am going to introduce you to the top four solutions on the market, as well as explain their advantages and limitations.
Main Platforms Of MLaaS (Machine Learning As A Service)
Machine Learning as a Service (MLaaS) is a concept that refers to the set of platforms in the cloud that cover the needs of digital infrastructure and data analytics -such as data processing and the evaluation of models-, and that generate results thanks to the integration with the IT infrastructure of your organization.
Amazon Web Services (AWS) has been one of the great promoters of AI solutions applied to the improvement of business processes, which are the most widespread in the market.
It proposes a pay-per-use model, benefiting from the entire Amazon infrastructure, which has become, in its own right, the de facto industry standard. It offers the widest range of services available on the market, the hiring of which can be scaled, thanks to the aforementioned pay-per-use model.
It is offered in two models:
- Amazon Machine Learning (Amazon ML) and
- Amazon SageMaker.
The first solution is one of the most automated on the market, as all data pre-processing operations are performed automatically, so users do not need extensive preparation. Obviously, this also limits the predictive model options.
In the case of needing to develop and deploy other models, as well as the computation of large databases, the alternative is Amazon SageMaker. This has the counterpart that it is required to have a team of data scientists behind, although their work will be significantly simplified.
Watson is the most advanced alternative on the market. No other platform has the IBM cognitive layer.
However, in order to extract all its performance, it is essential to have greater technical knowledge. In other words, it is required to have a data science team and data analysts.
It is primarily aimed at large companies, which also have the largest volumes of data to work with.
Salesforce Einstein: Artificial Intelligence for everyone
The philosophy on which Einstein is based is to make Artificial Intelligence available to any user, regardless of their knowledge of data analytics. After all, Salesforce was born as a tool for companies’ marketing and sales teams.
It integrates seamlessly within the entire Salesforce suite, making it an excellent resource for improving your customer acquisition processes.
On the other hand, its versatility is quite limited, not reaching the potential offered by IBM. However, Salesforce has found a simple solution for this, by developing an SDK interface with IBM’s Watson tool.
Microsoft Azure ML Studio
Finally, Microsoft’s solution is clearly inspired by Amazon’s own features and is aimed at its entire ecosystem of customers and developers.
Nearly all operations in Azure Machine Learning Studio (Azure ML Studio) must be completed manually, including data exploration, pre-processing, selection of analysis methods, and validation.
Despite the significant learning curve, the level of depth it provides is one of the highest on the market.
In addition, it presents greater flexibility in terms of “out of the box” algorithms and offers excellent synergies when combined with the entire Microsoft package and .NET technology, so it can be implemented very easily within your organization.
On the other hand, it provides an interesting “drag and drops” graphical interface that is missing in other solutions; although, it does not reach the ease of use of Einstein.
These are the best Machine Learning as a Service solution applied to Marketing and Sales. There are plenty of other vendors jostling for a foothold in this increasingly crowded industry.
Such variety can be confusing when choosing. After all, these solutions differ in multiple areas. What I want you to take away from this article is the true practical utility of AI in the field of marketing and sales, as well as that there is no single solution for all types of businesses.