Autonomous Customer Engagement can be defined as an engagement (either interactions or conversation) between a customer (human) and a machine. The machine is capable of engaging the customer, just like how human agents do. They create memories and hence can remember past events and co-relate it with current events. They learn, decide, predict and recommend just like the way humans do. And to an extent behave like sales agents, ensuring customer satisfaction, offering better deals and following up on purchases, with a goal to achieve higher customer retention and more revenue per customer. Continue reading →
AI is a simple science of being able to decide and carry out functions in real-time just like how human workers do
Just like the way human brain makes decisions, systems that can create a memory of patterns can quickly detect/recognize patterns from past memories, allowing them to make decisions which can be translated to predictive decisions, recommendations or the shortest path to the goal. Based on the status of these states, the system fires the next appropriate function, which could be an engagement with a customer or an insight to a marketer.
This simple mechanism allows the user to observe every step of the learning process, keeping the system transparent and agile. Without transparency, your AI investments could be a very risky proposition Continue reading →
How does the customer engagement system or the marketing automation platform know the past behavior or preferences of your customer? Does it have a memory? Is it making decisions based on past experiences?
To initiate true AI driven customer engagement, it is important that the system learn and create a memory file around each customer. This has to be constantly updated after every interaction so that memory consolidation and references are maintained, and accessible to any touch-point application in runtime.
The memory files will allow the machine to truly behave like a human: exhibiting relevance and context in its communication and driving customers towards their goal. These memories can be easily integrated with customer engagement applications to make them aware of customer states and preferences; helping them deliver highly effective communication and reducing the time to revenue.
This is exactly how Plumb5’s Memory Stack is designed to perform real-time contextual customer engagement. By ingesting individual customer data (created by the unification feature), Plumb5 extracts spatial and temporal patterns to arrive at states based on weight attribution, creating individualized memories. It also checks for similarities in other individual’s memories and creates collective memory states, before creating/updating the final memory file(output file).
The Plumb5 Unified Customer Stack, which forms the foundation to these memories, collects data from all data sources or data lake or data warehouse and creates a single customer file based on tags and unique identifiers. The Unified Stack, which is nothing but a centralized data repository of all individual customers (i.e., a transformed and harmonized dataset), has all the data parameters required to analyze/learn or make decisions.
The data repository contains each customer’s explicit or implicit data collected through touch-point applications. This individual stack, when extracted, presents the relationship between the customer and all other input parameters spanning products, channels, campaigns, feedback and other. On further extraction, we can find a relationship between the customer and associated factors from product interactions, or channel responses or preferences associated with the customer id, forming spatial relationships.
These relationships are converted to a set of tags with their own weights, which is used to understand the importance of the relationships in order to to prioritize the next best action. The weights can also be used to arrive at the nearest neighbor for firing a new event For the purpose of analysis, one can cluster customers based on these tags and their weights
This data, when laid out sequentially by time, presents the temporal pattern of the individual: allowing one to predict the next probable direction of the customer.
Both spatial and temporal relationship weights are combined to arrive at the net weight, which updates the final individual memory states. These memory states or patterns of a single individual are compared with other individual stacks and patterns to arrive at collective memory states; important for predicting recommendations or probable next actions based on similar profiles.
All of these states are consolidated into a single string (JSON) as memory states. On the collection of data from data sources, these memory states are updated at runtime using continuous-time Markov decision processes, allowing the string to keep updated information at all times. These strings can be consumed by customer engagement platforms for real-time contextual and relevant engagement.
These memory files will make it simpler for the proliferating customer touch-point space. With more businesses wanting to track, collect or engage customers using IoT devices, data needs to be organized for quick extraction of information and for runtime analysis and decision generation. The memory file will make it simpler for any such device that requires it to present quick relevant communications.
Currently, the robots deployed for customer service have no consolidated data about a customer and merely serve to answer generic questions. To take robotic customer service to the next level, these robots will need to have access to memories to make customer service far more contextual and relevant.
These memory files can also be integrated with NLP tags, where contextual conversation can be carried out at runtime, exhibiting to a complete AI based contextual experience for every customer or visitor.
It is evident that 99% of businesses, irrespective of big or small, is bleeding heavily due to data silos. The current environment demands huge dollars to be spent in order to maintain data, which covers paying for redundant data and isolated data due to data sources that don’t provide a common unique parameter for unification. Data grows on a daily basis and there is no escape from the cost incurred on growing redundancies and data silos. This sub-optimal approach not only costs a lot of money but also create gaps in achieving the desired results.
Problems associated with data silos can be many. For example, if you are facing the problem of higher customer acquisition costs, you can see that this is due to (a) inaccurate attribution models leading to ineffective ad spends and (b) lack of customer engagement or irrelevant targeting. Both these problems are resultants of data available across many data sources and lack of unique tags to merge them. So, the analysis carried out over individual data-sets results in wrong analysis, which results in low effectiveness and wasted resources. Continue reading →
Machine learning algorithms operate in a so-called black box — so the inputs and outputs are known, but how or why an algorithm makes the recommendation it does is not clear. Despite the AI media frenzy, companies and governments are concerned about the machine learning black box. The EU’s General Data Protection Regulation, which takes effect next year, includes a right to explanation clause, or an explanation of how a model made a decision.
The Explainable AI (XAI) program aims to create a suite of machine learning techniques that:
- Produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and
- Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.
New machine-learning systems will have the ability to explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future. The strategy for achieving that goal is to develop new or modified machine-learning techniques that will produce more explainable models.
Everyone knows that the real value of something can only be judged from practical experience and results; not from appearance or theory. And that’s exactly how one should measure any AI implementation – from the outcome of its application.
This is essential to clearing the hype around AI and focusing on methodologies that drive pure value. We believe that AI is the way forward and to substantiate, we’ve put together a case study to showcase how AI can bring true value to your business processes.
The Client: A global retail chain which sells products, both online and offline.
The Implementation: A behavior scoring model to generate machine driven recommendations, in order to resell/upsell products and drive revenue.
The Test: After six months of implementation, we ran up a test to understand how the logic fared. We filtered out data of all machine based recommendations that were triggered by specific customers, and see if they purchased the same product as recommended within a timespan of 0-5 days.
The below chart shows the outcome of the recommender logic.
A total of 184,712 users responded to the targeted recommendation
33,358 users purchased in less than 24 hours.
32,080 users purchased within 24-48 hrs of the recommendation being sent.
The machine had recommended about 2,000+ unique products to 20,000+ unique customers, based on their behavior. It generated revenues worth $14M in six months
This is proof that AI based Recommender Systems can make a big difference in the way businesses channelize their revenues and contextually engage their customers.
About Plumb5 Recommendations
Plumb5, which uses a context-based collaborative filtering technique, delivers highly effective recommendations with the high conversion rate to some of their retail and banking customers. The main contributor to the effectiveness of these recommendations is that Plumb5 works in real-time by ranking in run-time, a technique similar to the continuous-time Markov Decision process.
Click here to read more on how Plumb5 Recommendations work
With more businesses wanting to implement AI driven automation to improve operational efficiency and provide greater customer satisfaction, it is important to understand if the current data infrastructure is good enough to address these goals.
Increasing interest in AI has encouraged data engineers to come out with solutions which will help in automating processes such as machine driven customer engagement, operational workflows, Predictions and Solution Paths (Prescriptions), or even addressing unit economics. By automating business processes, we are not too far behind in providing a highly supervised environment to monitor and manage these machines.
However, to expect such a machine that can generate AI driven business decisions, a business has to carefully plan their data environment and make sure if the following factors are addressedTo solve the above challenges, it is evident that the business needs a unified data platform. Today, enterprise data environments are littered with data silos, and unifying this data is the biggest challenge which needs to be addressed first, as it forms the data foundation for every new innovation or learning that the business would want to experiment.
Plumb5 is a unified data platform that is designed keeping all the above conditions in mind. Plumb5 to its current capabilities can automate 65-70% of business processes paving the way for machine driven business operations.
Using the data connectors, data can be plugged into the learning network for real-time learning and intelligence. The weights defined by the learning network allowing the system to compute output states as soon as the input data is ingested into the network. The data “states” act as the markers for the automation engine to trigger relevant next actions.
If you would like to know more or would like to test it out on your data, you can contact the data team@plumb5 .