Traditional Customer Relationship Management (CRM) solutions don’t go deep enough when it comes to solving CRM problems today. To minimize your customer churn rate, you have to dig much deeper into this problem. Recommending the right products to your customers is important, but not nearly enough today to reduce churn rate.
Big Data is getting bigger
We are living in a hyper-connected world. In the world of digital interactions; from phone calls and page views to purchase – are adding to the never-ending long list of data. What’s more, with the arrival of Internet of Things (IoT), everything including inanimate objects such as cars, clothing, and refrigerators are generating more data by themselves every second.
But can this Big Data be useful in your businesses? The short answer is “Yes”.
Don’t try this at home!
How can we get any valuable strategic insights from our raw data? It’s not realistic to expect an organization to make sense out of terabytes unstructured data. First, you have to collect, process, analyze and visualize your raw data. Only then, will you be able to turn data into valuable information.
That being said, the data cleansing and data mining process is usually not for the faint-hearted. But, it has to be done; otherwise you cannot apply any machine learning algorithms to your data.
Artificial Intelligence (AI)
When data is analyzed and understood, it can help you increase your sales, develop better marketing strategies, and offer personalized and immediate services that your customers want. With artificial intelligence, you can turn your pre-processed data into a steadier insight stream you require to fulfill your many expectations.
Machine Learning (ML)
Machine Learning evolved from the computational learning theory in Artificial Intelligence. ML algorithms use statistical methods to make predictions. In business use, this is often referred to as predictive analytics.
Deep Learning (DL)
Deep Learning is a branch of Machine Learning. DL algorithms, Artificial Neural Networks (ANN), are inspired by neuroscience’s interpretation of the information processing patterns in our central nervous system.
There are many examples of (ML) and (DL) application. Deep Learning technologies are used in cancer diagnosis, streamlining product development; spam filtering, fraud detection, image recognition, natural language processing, improving cyber security, development of robots for manufacturing operations and self-driving cars.
The applications for artificial antelligence are endless. Our imagination sets the limit for what we can do. There are plenty of ways you can use artificial antelligence to add value to your enterprise.
Customer Service Problems
DTVS is developing and deploying highly effective machine learning and deep learning technologies to solve a variety of different client relationship management problems.
“65% of a company’s business comes from existing customers, and it costs five times as much to attract a new customer than to keep an existing one satisfied.” - source quoted as Gartner.
We can integrate and use deep learning algorithms to predict which customers are most likely to churn. Our DL solutions will provide you the insights to make optimal decisions when interacting with your customers and initiate preemptive actions to minimize churn.
Another major challenge is to predict the life time value for a customer. This is a common problem in companies with high customer acquisition costs.
Artificial Intelligence System Integration
The good news is that almost all key decisions can be supported by an integrated AI system. But, what is artificial intelligence system integration and how can we use it to take the engagement of our customers to another level?
For better understanding, think of artificial intelligence system integration as a software application. Deploying the application is more cost effective, faster and more accurate, which is contrary to what you would expect when doing the tasks manually. The result is better business prospects and more customer satisfaction.
To achieve high-growth, most companies should care about upselling products and services to existing customers. As mentioned earlier in this article, according to Gartner, 65% of a company’s business comes from existing customers. Thus, preventing churn should be the number one priority for any client relationship management systems.
CRM Use Cases:
Sentiment Analysis (Opinion Mining):
Our DL models can predict customer sentiment and behavior by tracking:
- - Causes for high churn likelihood
- - Levels of customer satisfaction
- - Trending support topics
- - Survey responses
- - Product reviews
- - Social media
- - Competitors
Learn more about our Sentiment Analysis services here.
ML & DL based recommendation engines will produce a list of recommendations to customers:
- - best product or service recommendations
- - best content recommendations
- - best promotional recommendations
Learn more about our Recommendation Systems here.
Natural Language Processing (NLP):
NLP can translate spoken languages to text or any other form for use as input to other systems. We can also do the reverse – translating the output of other systems to a spoken voice. NLP will also translate from one language to the other, or simply detect the language.
Natural Language Generation (NLG):
Image caption generators will construct natural language outputs (captions) from images. NLG will automatically describe the content of an image, mapping the meaning to English sentences.
Complex Event processing:
The algorithms can detect particular patterns (such as opportunities or threats) and initiate processes or actions accordingly.
Video and Image Analytics:
- - Scan unstructured data and look for common entities.
- - Scaling image and video analytics, using rules-based decision engines.
The Benefits of Deep Learning
Considering the many areas, you can now start seeing the potential benefits of deep learning technologies in your business. In addition to the above mentioned services and use cases, Deep Learning can also help you in many other areas in customer relationship management.
To mention only a few:
- - executing marketing tactics
- - predicting current customer value
- - predict customer lifetime value
- - optimizing prices dynamically
- - automate sales
- - most effective sales activity
- - improve customer services
- - price optimization for ad buys
- - forecasting demand
- - market response modeling
- - predicting advertising success
Contact us for more details.