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.

Customer Profitability
Using Unified Customer Data, it becomes easier to track spends and revenues of a single customer. Repeated customer campaigns add up to the customer spend and is extremely important to measure engagement costs to arrive at Net Profit Value of a single customer. This allows marketers to quickly analyze and re-strategize rules for spend parameters with regard to a particular customer, thereby optimizing customer spends, which contributes to bottom-line revenue.
Read more on how individual spends are monitored
Engagement Relevance
The Unified customer data reveals the entire journey of the customer across all channels (omnichannel). Using data and scores set by users, the machine can plot and predict possible behavioral patterns allowing the machine to target relevant campaigns and move users along the goal path quickly. Relevant targeting reduces conversion cycle which converts to lesser engagement spends and its associated time.
Click here to read about how scores are set
Lead Conversion
There is a big chasm between the money spent at advertising mediums and the actual people who convert at any touch-point like the website, mobile app or brand’s social page. Brands spend on offline and online Mediums with an objective to drive traffic to these touch-points. Most of these spends are unmeasured or measured at a macro-level which does present an accurate picture. The traffic that is generated needs to be converted at the touch-point in order to pursue sales conversion engagement. Today, a macro-level customer acquisition cost is arrived using parameters at a high level. The recurring engagement cost and the leakages might be missing which results in a bad marketing mix strategy.

A unified architecture in Plumb5 can iron out data obstacles with the help of pre-set tags for both advertising channels and brand engagement channels (email/website/app/social pages). This allows the unified customer data to stack data from all channels and create a journey all the way to the conversion milestone. This allows you to make sure that the incoming traffic are relevantly engaged based on session behavior and move them to an identifiable prospect segment, where further engagement to that user is addressed in lesser expensive channels like email, SMS or apps.
Click here to read more on streamling data from the first instance

Customer Retention
Using single customer data, the machine can deduce patterns and probable behavior which helps in automating repeat sales through periodic sales or cross-sell campaigns. Satisfaction predictors can help in strategizing campaigns that can improve customer satisfaction. These wins can contribute to the top-line growth of a business.
Read more about Recommendations and Cross-selling on Plumb5
Targeting Spends
Be it acquiring a new customer or converting an existing prospect or customer marketing for repeat sales, there is a constant spend on these engagements across channels. In today’s scenario, a reach (advertising) campaign carried out on Google Ad Network or Facebook or similar ad targeted sites accounts to the majority of customer acquisition cost. The objective of these ads are to generate traffic which might end up in a sale. It is important to realize that a sales happen over multiple sessions and just addressing the first session might not be a great way to expect conversions. It is important to measure an user’s journey to understand which ad channels, engagement and content strategies led to a conversion and apply the attribution model based on all these events.

Plumb5 Probabilistic Attribution Model scores each source channel, events, content containers to arrive at influencing scores to understand which channel is effective for an individual user. Using similar patterns, it is grouped to arrive at a marketing mix for users with similar patterns (segment). The model can clearly reveal dead marketing costs where the user can start focusing this budget towards a more productive channel. This can reduce your current CAC which contributes to bottom-line growth.
Click here to read more on Plumb5 Probabilistic Attribution

The patterns deduced from customer journey can be used to predict possible purchases, indicate possible drift patterns or mitigate customer churn risks, in order to strategize campaigns that build back customer confidence. The inbuilt path detection feature and recommendation features can be automated based on states to ensure timely engagement and extended relationship with customers

Read more on how patterns detected help in predictions

The Unified Architecture with integrated learning engine also contributes substantially to reduce your existing spends on data infrastructure and the resources necessary to manage this infrastructure.
Click here to read more on data infrastructure in Plumb5
Data Management
Business data is available in various forms and sizes. The amount of time and resource spend to just manage this data is huge. Plumb5 Unified Data Architecture connects all the isolated business data to a single semantic architecture and auto-organizes data using rules. Post this, data management, governance, and access functions can be configured using a simple interface reducing 70% costs on time and resource overheads.
Cost of Integration
Lack of Data-aware ecosystem requires constant data management. A data-aware model can reduce 85% time and costs. Plumb5 Data model is data-aware and any integration activity is a matter of few steps of wizard driven interactions to integrate any data source into the central unified architecture
Cost of IT Infrastructure
Due to silos, redundant data is prevalent. Plumb5 unified data environment takes away the duplicate data using the time-series sequencing rules on unified customer data. This feature can save up on space and processing power reducing infrastructure costs.
Cost of Human Resources
As most functions are automated or query driven, you can see most of the business Intelligence or operational Insights or customer engagement are driven by machines. AI-Driven automation can take out 20% resource overheads

These attributes of the platform can cumulatively bring in a visible difference with respect to performance metrics, allowing businesses to achieve more out of lesser investments, creating a robust technology model for sustenance and business longevity.

Understanding Plumb5 Learning Model

The Plumb5 Network can be explained in the following manner

(1)The inputs are stored as specified by the data-aware hierarchical semantic model which organizes the data into a tag assembly (patterns) with encoded weights (0,1). The objective is to achieve the formation of the top customer layer from the input layer.

The hierarchy of the data-aware semantic model can be viewed here

The input data collected from various data silos comes into the bottom level nodes. In generation mode, the bottom level nodes output the generated pattern of a given category. The top level usually has a single node that stores the most general categories (concepts) which determine, or are determined by, smaller concepts in the lower levels which are more restricted in time and space. When in inference mode, a node in each level interprets information coming in from its child nodes in the lower level as probabilities of the categories it has in memory.

(2) During processing, the new data collected is computed against relevant past cumulative score. The score is referred to a rule library supervised by business users, where the score range determines the state of the customer. Based on state activation, engagement rules are triggered to deliver pre-set dynamic templates to the customer. The responses of the engagement are collected as input and the network reruns the routine to accumulate new scores and its possibility to reach a new state.

The learning model allows the business to step up on decision accuracy using machine learning and intelligence.

The integrated NLP layer allows business users to query the AI machine in natural language and get answers, reports or even get tasks executed, all in real-time

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