Everybody knows silos are bad for business

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 →

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Explainable AI: Visualizing and Interpreting Machine Learning

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.

Continue reading →

The Proof of the Pudding …

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.

 

The Results:
A total of 184,712 users responded to the recommendation, which is broken down by the number of days.
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 in excess of $200,000, every day!

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
The recommendation module connects to the unified data model on Plumb5, where data is primarily classified by users.  On every interaction, the algorithm creates and maintains an associated product list against each product, which gets updated after each relevant interaction, all in real-time. Read more about Plumb5 Recommender here

How does it work?

For a machine to personalize and drive the user to the desired goal path, it would need to match and deduce the next best event and target message that will drive him to the goal path. This computation needs to be done in real-time in order to personalize instantly when the user is active.

This real-time computation would require (a) appending current session behavior pattern to past behavioral pattern of the current user and (b) the deviation of the current user path to the most optimal goal paths in relation to a specific product.

With these parameters, the algorithm calculates the difference to arrive at the next closest pattern leading to the goal. As the user interacts, the algorithm appends the new action and re-adjusts to output the closest probable.

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The working of the algorithm can be visualized as per the diagram above, the pattern of user interactions is mapped from the initial event to the desired goal path. After analyzing a particular user’s interaction flow for a particular product, it can arrive at the most effective conversion pattern for a single product.

The area highlighted shows the most probable purchase paths for the given product, which is used to verify against with the current browsing pattern of the user. Now whenever the user’s pattern heads in a certain direction, the algorithm for matching patterns can quickly locate area coordinates and return the nearest event node to deduce possible pattern extensions.

These patterns are stored as strings and on every interaction, the string is updated which contains a list of associated products along with the association score, which gets updated after each relevant interaction, all in real-time.

This scoring is done after every interaction so that the updated score is available for recommendation at all times. The recommender outputs a string ([X101, P001{(P003, 0.966), (P004, 0.877), (P007, 0.746), (P002, 0.335), (P009, 0.200)}, where X101 is the user id, P001 is the product (product ID) and the product ids within the bracket are associated products with corresponding weights.

To ensure finer supervision, nested rules can be configured. For example, you can add a rule to exclude a product from the recommendation list if the product is already purchased by the user or exclude a product from the recommendation list if the user has an unresolved issue. This would allow marketers to fine tune their recommendation intelligence and make machine driven personalization more human. 

Is your business ready for AI?

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.

 

About Plumb5
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 .

Prism of Possibilities

Plumb5 integrated learning engine can provide businesses with a range of growth possibilities influencing top-line and bottom-line revenue growth. The AI engine which learns from the unified customer stack using segment-of-one customer data is able to address individual patterns and deliver personalization based on individual patterns. For a more global view, these similar individual patterns are grouped for users to view at a macro-segment level.

Listed are the aspects that are solved or optimized in order to ensure business profitability through AI and Automation. Continue reading →

Evolution of Data Model in Plumb5

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From generating real-time insights from data inputs from a couple of touch-points to automating business processes over holistic business data, the Plumb5 data model has come a long way. What started as a customer-as-the primary-key design for data collected from marketing touch-points is now scaled to incorporate every data parameter of the business, with customer key still in the center.

The data model design now lends itself to real-time learning over unified 1:1 customer data, which allows the machine to automate next steps in a business process using synthesized intelligence.

Reference links to understand real-time AI capabilities of the platform

  1. Real-time Pattern Detection 
  2. Real-time Recommendations
  3. Real-time Recommendations using NLP
  4. How Autosegmentation work?
  5. How Behavior Scoring works?
  6. Automation routines

 

Evaluating the right AI Platform for your business

As businesses are increasingly laying their focus on artificial intelligence solutions, it might be important to understand what goes into implementing these solutions.

Most business product and solutions have now taken to the tone of customer satisfaction, making every solution look the same. It’s only a matter of time, that they will all offer or at least speak about artificial intelligence. I would assume, it would become increasingly difficult for the businesses to understand the truth behind these systems. Continue reading →