Posts

The Value of the Human Mind – How Machine Learning is Helping Humans Win

/in /by

StrategyDriven Innovation Article | The Value of the Human Mind - How Machine Learning is Helping Humans Win

Overview

There is no doubt that we are living in the AI era. Artificial intelligence is at work all around us today. Even if we do not realize it, our thoughts and actions are training the technology to respond in the way we desire. Machine learning is one of the fundamental tasks of AI. Just as the name implies, the machines and platforms we use daily are learning from the consistent input we provide. Let’s look into ways that machine learning is helping to make our lives much easier.

What is Machine Learning?

Machine learning is an AI component that uses algorithms to find and apply data patterns. The process involves the input of data into a model that is used to predict an outcome. The more data that is input into the model, the “smarter” the machine application seems to get.

The data used can take on many forms, such as text numbers, images, videos, clicks, etc. If there is a way that the item can be stored, it can be applied to a machine-learning model. There are a variety of ways that machine-learning algorithms are incorporated. These various algorithms fit under three specific types of machine learning.

StrategyDriven Innovation Article | The Value of the Human Mind - How Machine Learning is Helping Humans Win

Types of Machine Learning

The three types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Deep learning is the refined form of machine learning we see used daily. Deep learning uses an algorithm to create what are called neural networks. Neural networks are loosely based on the neural networks of the human brain. Each of these types of neural networks fits in one of the three categories.

Supervised Learning

Supervised learning involves machine learning, where variables—called features—and labels are assigned to the model used. These features and labels are utilized to properly classify the data received. The algorithm can identify patterns based on predetermined features and labels.

An example of machine learning using the supervised model is a machine that can count coins of different denominations. If the weights (features) of nickels, dimes, and quarters (labels) are input into an algorithm, the model can predict the denominations of the coins based on knowing the weight (feature) of each. Another example of this is with a music streaming service that predicts the best choices of music to play based on the genre you routinely choose.

Unsupervised Learning

Unsupervised learning does not use predetermined features and labels. The model is set up to search for any patterns it can recognize. The process is much like a person collecting shells at the beach and later categorizing them based on their shapes. Since there are no labels in this process, there is a greater ability for the machine to analyze the data to locate hidden structures contained within it. Unsupervised learning has become popular among those in the cybersecurity community.

Reinforcement Learning

Behavior modification involves the use of reward and penalty to encourage or discourage specific activities. For instance, if a dog is being house trained, it will be rewarded when it does its business outside and scolded when it does so inside. Reinforcement learning uses these same feedback responses to train machines to learn.

The algorithm for reinforcement learning is based on a trial and error model. Large amounts of data are input into the model, and the machine is rewarded or penalized subsequent to whether the selections help or hinder the objective of the application. Reinforcement learning is seen with the training of robots for industrial automation.

StrategyDriven Innovation Article | The Value of the Human Mind - How Machine Learning is Helping Humans WinHow Machine Learning Helps Humans

We see machine learning at work in our everyday lives. From our search engine results to our ride-sharing apps, machine learning is front and center in the process.  What he have seen is that augmented intelligence enhances human’s intelligence. Let’s focus on some of the applications that use machine learning.

GPS has become a staple in the lives of all travelers. GPS uses machine learning to assist us in reaching our destination by using other users’ input and recognized patterns. Picture recognition used by Facebook is another form of machine learning that uses a supervised model. Ride-sharing apps use various machine learning models to predict destinations, estimate times, and determine pricing.

StrategyDriven Innovation Article | The Value of the Human Mind - How Machine Learning is Helping Humans Win

Conclusion

Once merely an element in sci-fi movies, AI has become a part of our daily experience. Whether we notice the presence of machine learning or not, our lives have been made notably simpler due to its role in developing applications designed to give humans the advantage. Hopefully, you can now recognize the ways that AI continues to benefit you every day.

Keeping Your Business Human In Today’s Digital World

/in /by

StrategyDriven Marketing and Sales Management Article | Digital Technology| Keeping Your Business Human In Today’s Digital WorldDigital technology has had a huge positive impact on business – software has helped to automate tasks that were once mundane and time-consuming, whilst the internet has helped to make the world more connected making it easier for niche businesses to find new customers.

The downside of all this is that business is starting to lose touch with its human side. Whilst many of us appreciate the convenience of modern technology, many of us still need human interaction both as a customer and an employee. Here are several ways in which you can inject a human element into your business whilst still using digital technology to its advantages.

Don’t over-rely on AI

There are many jobs that artificial intelligence is better at handling than us humans. This includes complex calculation tasks such as accounting and repetitive tasks such as building machinery.

Some tasks however are still performed much better by humans. These are jobs that involve creativity and emotion.

As this article at Harnham delves into, relying on AI for marketing can be damaging. Whilst analytics can uncover useful marketing strategies, there are times when you need to react emotionally to people’s demands.

Similarly, customer service still benefits from a human touch. As this article from Forbes suggests, human chatboxes are becoming a big problem – most customers find them irritating as they’re not able to deviate from their script when asked difficult questions and a lack of empathy towards customers’ frustration can only add to the anger.

As for using AI to help employees, it shouldn’t be used as a tool to replace in-person training. Many employees feel neglected when forced to learn everything via e-learning. Whilst it can be a useful supplement, relying wholly on digital training could demotivate new employees.

Show your face

Whilst the internet has helped to connect people, there are times when it can form a barrier. Long conversations with strangers can be had via chains of email, whilst we can buy products online without ever having to talk to a person. However, as this article at QuickBooks suggests, face-to-face interaction is still something we need in business to build trust.

In-person physical meetings may be the best way to do this, but they’re not always possible. Fortunately, there are ways to use technology to still show your face. Video communication tools can help to replace in-person meetings allowing you to still have a face-to-face meeting digitally. Meanwhile, it could be worth sharing photos of you and your staff on your website to help develop a human connection (you can even embed a video of you introducing yourself to clients).

Be playful with tech

Another way to stay human when using digital technology is to use this technology beyond it’s purely functional form. Many of are engaged by technology when it is used as a toy and many businesses have started to embrace this. Examples include Ikea’s use of AR to allow users to see what an item of furniture would look like in their home or Pizza Express’s delivery app that allows users to play a video game whilst they wait. Some may view this as gimmicks, but it can actually be a way of appealing to customers with technology whilst also showing a human appreciation for fun.

StrategyDriven Marketing and Sales Management Article | Digital Technology| Keeping Your Business Human In Today’s Digital World

Why AI-Driven Sales CRM is Leading the Way in Customer Relations

/in /by

StrategyDriven Customer Relationship Management Article | Artificial Intelligence | Why AI-Driven Sales CRM is Leading the Way in Customer RelationsOver the last several years, AI has become a popular trend for enhancing all areas of business. With the ability of the machine to learn how to perform tasks like forecasting, clustering, text and speech recognition, error corrections, database filling, and other activities that involve human intelligence, AI has become an invaluable tool for sales teams to understand and interpret the behaviours of customers and suggest products for these customers. One of the critical factors, according to SuperOffice, behind the growth of CRM software is its ability to allow businesses to access customer data in real-time. Here are several ways that AI-driven sales CRM is leading the way in customer relations.

Collecting and Filling Data

It is no secret that marketing and various CRM applications require substantial sets of qualified data. Many of today’s new AI-driven sales applications have made it much easier to capture the corporate data that allows users to create and fill in the new information to solutions, as well as cleaning the existing lists. With this new technology, the need for data entry is eliminated with Spiro.

Clustering Contact Details

With AI applications you can structure, clean, and analyze the sales data you collect. After a series of algorithmic executions and iterations, the application can effectively provide you with the best model along with a pattern, which will allow you to group your customers. After the data has been clustered, it can give you a list of all the customers included in each of the groups. It will consider new sales and provide updated reports to improve marketing strategies.

Suggest Products

When a specific product is considered, the AI-driven application will provide you with a list of the various products purchased in the past by the customer. An excellent example of this in action is Amazon. When a customer is shopping at the online retail shop, it provides them with a list of suggested products that they might want to purchase. For B2B companies, the product basket of all their customers is carefully analyzed to interpret details like business sectors, employee’s numbers, address, and the revenues. All of this increases customer relations because the company doesn’t have to waste time suggesting products that the customer won’t be interested in purchasing.

Forecasting and Pattern Recognition

AI-driven solutions play a critical role in the process of the forecasting of sales, regarding production. Through pattern recognition, the trends of the purchase of a product over the years can be determined. Also, the success rate of the product can also be predicted, which can save companies thousands in unused and unsold inventory and can help increase revenue by nearly 41%, according to the site, Big Contacts.

Highlight Inconsistencies

The ability of a sales team to close sales is dependent on the accuracy of the sales pipeline. An AI-drive CRM application can effortlessly highlight any inconsistencies within the sales pipeline so that it can quickly be addressed. With accurate reports and sales forecasts, companies won’t have to worry about the sales process being slowed down or stopped because of inaccuracies.

The most prominent advantage of AI-driven CRM solutions is the ability to efficiently analyze the data of the company, which can lead to an increase in sales, better customer satisfaction, and better responses to customers’ needs.