customer activation predictive model

Customer ActivationActivity Predictive Model

Customer ActivationFocus on Equity

Objectives1. To predict activity levels of each customer in the near future( (Current Model: 90 days)2. To profile customer activity over time (i.e., activity states with durations)3. To determine the recommendations to activate customers

Problem Dimensions• People

– Who are the people likely to be inactive in the next month?

• Activity State– What are the different states in customer life cycle?– What is the customer behaviour in a particular state?

• State Duration– How long would the customer will be in particular state?– What will be the transition time for a particular customer?

• Recommendation– What strategy will be effective to prohibit inactivity of a particular customer?– What strategy can bring customer back from inactive state to active state?

Analysis Process

Distributions:Inactive period behaviour, life cycle of customer

Comparative views:First time inactive vs. current inactive, inactive vs. active customer life cycle

ETL Merge Filter Visualize

Storage: ACMIIL (Trades)

Data Formats:Dates, categories, numeric value ranges, etc.

File Formats:Comma, Tilde, or Tab delimited

Customer types:Individual vs. Institutions, etc.

Transaction types:Buying/Selling,First time inactive, current inactive

Identifiers:Client Code, CommonClientCode

Timeline:Daily, Monthly

Aggregates:Counts, Sums of EQ buy, Sums of EQ sell

Activity Modelling - OutlineTrades

Data• Summary• Discovery

Model• Setup• Application• Code• Results• Setup - Next Steps• Application – Next Steps

Future Work

Data: Summary

Statistical measures (e.g., mean) errors– Units field has negative values– Too large or small values

Text data:Mis-matches

Numerical data: Unreal ranges

Numerical data:Spurious values

DQ Issues

Sizing for technology• ~7M EQ and ~1M DER trades per year• ~100k trading customers currently on

platform, and 1/3rd transacted in the last 6 months

Analysis caution• Data distributions highly skewed,

e.g., few high amount Txs by one or two individuals

Data: DiscoveryInactivity count

All clients inactive at least once for greater than 91 days

Inactivity Count

InsightsAll Clients have been inactive at least once

Freq

uenc

y

Data ParticularsData Duration 2012 Apr - 2015 SepEach Row Client-monthClient Category Individual and HUF# of Rows 366421# of Columns 31# of Unique Clients 49444

Data: DiscoveryAverage Inactive Duration

Average inactivity duration (days)

Freq

uenc

y

300 days

InsightsHistogram of Average inactivity duration gives maximum frequency at 300 days


Data: DiscoveryFirst Time Inactive vs. Currently Inactive

First time inactive

Currently inactive

Vintage (yrs) Vintage (yrs)

Freq

uenc

y

Freq

uenc

y5 yrs 7 yrs

InsightsCurrent inactive customers are a mix of first time inactive and other periods making it harder to study current inactivity alone => It brings about the need to study each activity level or state separately


Sum

Am

t. (s

old)

Sum

Am

t. (B

ough

t)Data: DiscoveryRandom customer 1: currently inactive(Tx Amount)

Trend Curve

Trend Curve

Data: DiscoveryRandom customer 1: currently Inactive (Tx Count)

Tx C

ount

(sol

d)Tx

Cou

nt (b

ough

t)

Trend Curve

Trend Curve

Data: DiscoveryRandom customer 2: currently active(Tx Amount)

Sum

Am

t. (s

old)

Sum

Am

t. (B

ough

t)

Trend Curve

Trend Curve

Data: DiscoveryRandom customer 2: currently active (Tx Count)

Tx C

ount

(sol

d)Tx

Cou

nt (b

ough

t)

Trend Curve

Trend Curve

Data: Discovery Insights

• All clients have been inactive (> 91 days inactivity) at least once• The most-likely inactivity duration is ~300 days, i.e., if customer becomes

inactive => there is a high chance of a long inactivity period• Customer behaviour is different before various inactive states• Each inactive state (i.e., first time or second time, etc.) need to be

modelled separately• There are different trend curves in a customer’s life cycle that each of

customers follow• The trend curves may be grouped together into a finite set of

representative trend curves• All the above may be modelled using a State-space approach• A simple binary approximation is the Logistic regression model

Test Data

Three Year Trade Data

60% Used for Training Model

20% Used for Validating Model

20% Used for Testing Model

Total Available Data

Training Data

Validation Data

Time

Acc Opening Date

1 1

First Time inactive Inactive

1

Active Period

Inactive Period

Inactivity: Defined as 0 transactions in consecutive 91 days

Hypothesis: Customer’s state can be predicted using transactions data

Logistic Regression Model

To find predictive variables To predict next state of the

customer

0 0 0

0

Data Set Creation

Model: Setup

Summary after training the model

Model Validation

Model Test

Model: Code View

Model: Application

0 0 1 0 0 0 1

0 0 0 0 1 0 1

Actual States

Predicted States

Inactive State miss

Active State miss

Actu

al

Predicted

Positive

Positi

ve

Negative

Neg

ative

a b

c d

a - True Positiveb - False Negativec - False Positived - True Negative

𝐻𝑎=𝑑𝑁 0

𝑀𝑎=𝑏𝑁0

𝐻 𝑖=𝑎𝑁1

𝑀 𝑖=𝑐𝑁 1

- Active state hit rate- Active state miss rate - Inactive state hit rate - Inactive state miss rate

Model: Results

= 0.01%

Correct Pre-dicted Active

State

Wrong Predicted Active State

0

5000

10000

15000

20000

25000

30000

35000

Correct Predicted Inctive State

Wrong Prdicted Inctive State

02000400060008000

100001200014000160001800020000

= 84.5%

Threshold = 0.25

Correct

Predicte

d Active

State

Wrong P

redict

ed Active

State

0100002000030000

= 60.8%



0

5000

10000

15000

20000

25000

= 93.1%

Threshold = 0.35

Correct Predicted Active State

Wrong Predicted Active State

0

10000

20000

30000

40000

50000

60000



0

5000

10000

15000

20000

25000

= 40.3%

= 0.0009%

Threshold = 0.50

- Active state miss rate - Inactive state hit rate

aa

ac

c

c

d

d db

b

b

Model: Application (next steps)Multi-period

Hypothesis:- Error rates can be decreased by taking into account multiple periods for predictions

0 0 1 0 0 0 1

0 0 0 0 1 0 1

Actual States

Predicted States

Model predicts 1

Check customer’s transaction in next 30

days

If Tx = 0

Model output is 0 Model output is 1

TrueFalse

1

Active Period

Inactive Period

0

Future…

State-space Model

active

inactive closed

On-boarded

Technical Model: State-space Model

• In the applied model we have taken only two states 0 for active and 1 for inactive• Between these active and inactive state a customer can transit into many different states as shown in the

state space model above

• By applying state space model the complete life cycle of a customeri. Previous state ii. Next state iii. Time he will be in a particular state iv. Behaviour of customer in a particular statev. Behaviour of customer just before transition,vi. Behaviour of customer before going off-board, etc., will be profiled

Discussions and Questions

Back-up Slides

Model: DiscoveryPredictive Variables

Model: Setup (next steps)Customer Sampling

For the current model, Training, validation and Testing dataset has been created by sampling on the basis of rows, where each row is a particular customer and aggregated transaction amounts on monthly basis.

We can create Training, validation and Testing dataset by sampling as per customer basis.

customer activation predictive model

Data & Analytics