The Proof of the Pudding …

…is in the eating! 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!

So, now you have the proof.
AI based Recommender Systems can make a big difference in the way businesses channelize their revenues and contextually engage their customers.

Read more about Plumb5 Recommender here

 

How Plumb5 Recommender works?

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.

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. 

Cross-domain visitor engagement

For businesses having multiple websites/apps, it could be meaningful to understand and engage their users on any of their brand websites. FMCG companies with dedicated websites for each brand or a hotel with websites for each property will have more opportunities to engage or cross-sell, whenever the user is active in any of their websites or apps.

Cross-domain visitor engagement allows businesses to engage visitors on any of their websites. Marketers can create user segments based on cumulative insights across domains and trigger engagements wherever the user is active.

 

Cross-domain tracking and dynamic tag management make it possible for engagement to merge sessions of two related sites pertaining to the same user, as a single session. Engagement can be triggered based on behavior recorded on the linked site.

 

The Plumb5 tracking script has linking features that allow the source domain to share relevant keys, where the destination domain can access it. The above screenshot shows the user’s click stream of related websites, sequenced by time stamp.

Plumb5 offers an easy interface to link all your domains for unified tracking without having to depend on engineers to integrate code. Using linked data, you can create custom segments spanning data across two or more sites and set global engagements that can fire on any targeted site.

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 .

Simplified Data Search

Plumb5 Machine Translation Algorithm can make a huge difference in the way we perceive data discovery. You can overlay this algorithm over any kind of data sets over which you can simply query in natural language to visualize insights across any data parameter.

The algorithm plays over a modular semantic model, which can be tweaked based on incoming datasets. Based on the incoming parameters, the algorithm will translate the natural language text to machine query, in order to identify the desired parameters. Based on the parameters selected, the model presents raw or computed outputs,  derived from rules of the model. Continue reading →

Conversational UI

Functional Bots for Business

A demo video showcasing Plumb5’s new conversational UI, where users can query to either generate ad-hoc reports or type commands to setup campaigns or other. The interface will allow users to chat or input a voice command to get reports, conduct campaigns, setup customer engagement, assign tasks, get approvals or perform other business tasks.

Continue reading →