Introduction To NLP Logix, LLC

Confidential and Proprietary

Introduction To NLP Logix, LLC

Ben WebsterData Scientist / Statistician, NLP Logix, LLC

[email protected]


Business and Definitions NLP Logix provides machine learning, predictive modeling, and

statistical analysis as a service for many industries. Our primary focus has historically been in marketing, client retention / customer churn modeling, and optimization tasks. We use cutting edge technologies such as neural networks, boosted trees, and support vector machines in order to leverage historical data to predict future events with unparalleled accuracy.

Definitions:Predictive Modeling: The area of data analytics concerned with forecasting probabilities and

trends

Big Data : Collections of data sets that become difficult to process using on-hand

database management tools or traditional data processing applications.

Machine Learning : The science of using computers to analyze big data to develop predictive

algorithms beyond the human element of time/labor and personal biases.


Putting the Model to Work

3. Production – We use your data to build the most accurate predictive model available, now let’s put it to work


Keeping the Pulse

Monitor the Models – Now that your model is working hard for you, we are working hard to make sure it is performing at the optimal level. We have integrated advanced reporting tools into our platform to monitor model performance and look for “regime change”

Example of why monitoring is extremely important:

If you built an automotive marketing predictive model in 2007 and never changed it, how effective would it be after 2008?

NLP Logix monitoring tools would have detected the market shift caused by the financial crisis and retrained the models to account for the “regime change”


Most clients require a model refit weekly. Some require nightly

Models tested using multiple criteria to determine which variables are changing and what this means

We are always hungry for new variables and new ways of expressing existing variables

Model Refit Frequency


Which customers are at risk of defection?

Which customers are potentially being underserved? (upsell potential)

Which customers should we focus on today?

Printed Circuit Board Manufacturer

UK based client—Customer Churn Model


Given all surrounding data points, what is the probability that a given customer’s purchase amount in the next 6 months will meet or exceed their purchase amount in the last 6 months?

What is the probability that a customer’s satisfaction with the manufacturer is declining? In a specialized industry, purchases don’t imply satisfaction.

Phrasing the Predictive Task


Probability of Growth


Low Probability of Growth— Just placed an unusually large order

or Orders have been consistently slowly

decliningor

They have been receiving late orders, or bad customer service



High Probability of Growth— Haven’t ordered in 6 months

or Consistently increasing order amount

and frequencyor

Increased contact for quotes, special requests, prototypes



Actionable Insights


Salesrep Scorecard

Sentiment analysis on email and salesrep notes data

Industry analysis for pursuing new business

Looking Forward


Verizon Wireless Sales / Service

Facilitate multi-level marketing campaign

Develop product lifecycle analysis

Create upsell prediction engine for POS system

Wireless Zone—Ponte Vedra


How many phones of specified type will be sold in a certain interval?

Who is most likely to purchase, given they receive a mailer?

Which customers are most valuable? (Generate the most revenue per year)

What is the most likely upsell opportunity?



Product Lifecycle


Identify Most Profitable Customers


Reduce the Mailing Footprint/ Costs

St. Johns County:Previous Mailers went to most of these zip codes

Customer Base across 3 most saturated zip codes

Targets of first new customer acquisition campaign


Targeted Mailers


Results of First Campaign


Use the results from marketing campaigns to drive upsell / cross sell opportunities at the point of sale

Begin analysis of results from email outreach to strengthen product suggestion engine

Looking Forward


Client Satisfaction Model—Currently Very Low Churn Which clients are less satisfied than

others?

Which clients would be receptive to upsells?

Which clients should we use for referrals?

Virtual Banking—Bank for Banks


Note: Customer Satisfaction scores are provided quarterly by all client banks (Red, Yellow, Green)

What is the expected use frequency for certain products for a bank this size?

What is the expected technical issue frequency for a bank this size?

What can we do to increase customer satisfaction levels for each client?



These data can be separated into 4 distinct categories

Institutional Information User Behavior and Entanglement Project Frequency and Technical Issues Client Support

Four “Pillars” of Data


Revenue Bauer Score Annual Survey Conference Attendance

These are fixed values which a Relationship Manager (RM) can not necessarily address, but which contribute significantly to the satisfaction

rating of a client, and may be indirectly influenced by other RM actions

Institutional Information


The number of users by category.

How many users are utilizing Online Banking? Mobile Banking? How does this compare to clients of comparable size and revenue?

Is the number of users for any specific service significantly increasing / decreasing?

As with Institutional Information, these values are fixed to an extent. An RM can not increase the number of users, but

could suggest projects that are more successful in comparable banks.

User Behavior and Entanglement


By combining the attributes of the bank which can not be changed by RM intervention we generate an expected satisfaction level by assessing all banks with similar attributes. We then consider all attributes including the client specific case and survey data to generate actual satisfaction scores.

Combining Fixed Variables—Generating an Expected Value


Project Frequency: Number of new projects per client per year. The average amount of time between new projects. Is this client more reluctant to begin new projects than similar clients?

Proportional Number of Issues: The number of project issues with respect to number of projects. Does a given client have more or less project issues than average client of comparable size?

Resolution Time: The average time taken to resolve a project issue. Is the average case age for the client similar to what would be expected for a client of comparable size?

Project and Development Frequency


Questions versus Problems—How often does the client call in with basic questions relative to the number of services they subscribe to, and how often do they require service?

Is the client getting the expected turn around time for problems, and are the frequency and type of problems to be expected?

Number of Fraud Reports—Time to resolve

Client Tech Support Satisfaction Surveys

Technical Support / Client Support


Model Output for each Client


1. Maintain an enterprise transaction system (point of sale, accounting, enterprise resource program etc.) Allows for easy access to data to build predictive models Allows for easy ability to deploy models into the customers work-flow

2. Single owner or closely held Active and focused leadership benefit the most from predictive modeling Provide consistent and iterative feedback to make the output more relevant

to business making the model better3. Limited IT resources

Provides for greater dependency on NLP Logix to deliver information Lessens barriers to sales and product delivery

4. Operate in a competitive market place Predictive modeling is a powerful competitive edge that is measurable. Once a company has adopted predictive modeling into their work-flow, it is

very difficult to leave5. Have a need for advanced analytics

Data scientists are in very high demand and low supply at this time. This trend appears to be accelerating, as the need for advanced analytics grows.

What we look for in a Client


Levels of the Enterprise Use of Data

Improved DecisionMaking

Increased Business Value

Standard/Adhoc Reports

Query Drill Down

Alerts

Statistical Analysis

Predictive Modeling

Optimization

Current enterprise data analytics systems


In most cases this becomes an iterative process. No models currently in production are as they were described in Phase 1 of the journey.

The end user will see the results then add additional variables that they hadn’t deemed pertinent until they saw the model in action.

Insight from one model always leads to inquiries about the next

Things to Remember


Questions??

Thanks!!

Introduction To NLP Logix, LLC

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