This era of Big Data and digital marketing has created varied scopes and alternatives to spend investments, in order to maximize profit in business. However, identifying the right target group (TG) to invest the right resources emerges as a critical problem, for both acquisition and retention. Customer lifetime value (CLV) is a metric that may aid in such decisions, the calculation of which seems quite generic in traditional literature. This paper recognizes the potential of a large, user-specific transaction dataset from a retail business operating online and proposes an AI-powered CLV classifier. For this, we exploratorily mine customers’ buying tendencies from their geographic, monetary, chronological, categorical data using PostgreSQL and then, use those as input features to a TensorFlow-coded neural network (NN) in order to predict their profitability in different ranges. We further validate the features using statistical inference, the optimized feature set resulting from which is again passed through another NN, providing more crisp metrics in lesser computation. The training data is prepared by iterative thresholding on customers’ number of days ordered. An SQL-backed recommender, working on maximum buyers’ tendencies, is also proposed to cross-sell and up-sell customers in order to maximize the said CLV. The research indicates ways to filter out factors influencing CLV and endorses the applicability of modern soft computing in devising business-specific solutions by exploiting commonly available users’ databases.

1. A Soft Computing Based Customer Lifetime Value
Classifier for Digital Retail Businesses
Shithi Maitra*| Md. Rakib Ahamed | Md. Nazrul Islam |
MD Abdullah Al Nasim | Mohsena Ashraf

2. The Research Question: Customer Lifetime Value (CLV)
● CLV is a predictive estimation of net profit by a customer for a business venture [1].
● CLV indicates which customers could be:
○ up-sold, cross-sold, held loyal [2].
○ devised the right communication for [3].
○ invested upon for a greater profit margin [4].
● Resources are limited, whilst the user-base to drive growth seems infinite in this tech-dominated world.
● Against this backdrop,
○ Which potential/existing customers should be invested on?
○ Based on what parameters can one measure CLV?
● The study suggests means for both predicting and amplifying CLV:
○ It addresses a ternary classification problem, labeling customers as of their potential CLV range.
○ The paper suggests an SQL-based recommender, using users’ most frequent buying tendencies.
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3. CLV in Recent Literature
● The first worked examples of CLV were published in the 1988 book: Database Marketing [5].
● The greatly generic model performs on just 3 parameters:
○ a constant profit margin
○ a constant retention probability
○ a discount rate
● Past 4 years’ developments have been reviewed:
○ RFM-based, Retention-centric Approaches [7, 8, 9, 10, 11]
○ ML-based Approaches [12, 13, 14, 15]
○ Statistical, Heuristic-based Approaches [16, 17]
○ Approaches Focused on Datasets [18, 19, 20, 21]
● The presented solution omits misapplication of CLV [6] by avoiding:
○ Nominal CLV often (impractically) leaving out discounts
○ CLV being miscalculated on revenue instead of net profit
○ Regression causing inaccuracy due to soft computing
○ Various demographic groups producing varied CLV
○ CLV being dynamic, insusceptible to change with inputs
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4. Proposed
Method:
Workflo
w
fig: Followed
workflow for
proposed
estimation of CLV
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5. Proposed Method: Preprocessing on Global Superstore Data
fig: EDA visualization of customers’
returning tendency
fig: EDA visualization of delay in
shipping
fig: Balanced labeling of superstore
dataset on PostgreSQL
table: Choosing the right threshold for
reasonable coverage of user-base
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6. Proposed Method: Preprocessing on Global Superstore Data (cont.)
table: Initially hypothesized feature-set for the proposed prediction of CLV
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7. Proposed Method: Predictive Modeling on Global Superstore Data
fig: Neural network trained for classifying CLV
table: Tuned hyperparameters for optimum NN
performance
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8. Proposed Method:
Postprocessing
on
Results
fig: EDA visualization of best product
suggestions
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9. Experimental Results: k-Fold Cross Validations
table: Experimental results of all k = 10 CVs against each NN model
table: t-test results, verifying an effective improvement in training time and test accuracy, by
downsizing to 15 features from 22 features 9

10. Experimental Results: Learning Curve | Admissible & Inadmissible Features
fig: Gradual fall in cross-entropy
loss with epochs, with all
hyperparameters kept the same
for both models
table: Results
of ANOVA test
for both
admissible
and
inadmissible
continuous
features
table: Results of Chi-squared independence test for both admissible and
inadmissible categorical features 10

11. Concluding Remarks
The solution shown here is for an online retailer, with certain advantages (also, disadvantages) of
their own:
● The study endorses
○ hard computing for extracting features, business insights and suggesting the best deals.
○ soft computing for making predictions about profitability-CLV.
● An achievement of this work is, mining out significant features from mere transaction data.
● The solution can be explored for cases where
○ the deliverables too are electronic.
○ the deliverables are services in lieu of goods.
● Further research can improve the existing ~80% accuracy by augmentation or by mining
better features
● The solution can be implemented for CLV in terms of sales, revisits, minutes spent on
browsing etc.
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12. References
[1] P. Farris, N. Bendle, P. Pfeifer, and D. Reibstein, Key marketing metrics: the 50+ metrics every manager
needs to know. Pearson UK, 2017.
[2] V. Kumar and B. Rajan, “Customer lifetime value: What, how, and why,” in The Routledge Companion
to Strategic Marketing. Routledge, 2020, pp. 422–448.
[3] P. S. Fader, B. G. Hardie, and K. L. Lee, “Rfm and clv: Using isovalue curves for customer base analysis,”
Journal of marketing research, vol. 42, no. 4, pp. 415–430, 2005.
[4] G. Cokins, Performance management: Integrating strategy execution, methodologies, risk, and
analytics. John Wiley & Sons, 2009, vol. 21.
[5] R. Shaw and M. Stone, “Database marketing. gower: London,” 1988.
[6] E. A. ElHamd, H. Shamma, M. Saleh, and E. Elkhodary, “Customer engagement value: process,
limitations and future research,” Journal of Modelling in Management, 2021.
[7] X. Wang, T. Liu, and J. Miao, “A deep probabilistic model for customer lifetime value prediction,” arXiv
preprint arXiv:1912.07753, 2019.
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13. References
[8] T. Rathi and V. Ravi, “Customer lifetime value measurement using machine learning techniques,” in Artificial
Intelligence: Concepts, Methodologies, Tools, and Applications. IGI Global, 2017, pp. 3013–3022.
[9] M. B. A. H. Eid, R. Robin, N. Wierdak et al., “An empirical investigation of the factors affecting customer lifetime value,”
International Journal of Quality & Reliability Management, 2021.
[10] R. Riyanto and A. Azis, “Modelling customers lifetime value for noncontractual business,” International Journal of
Informatics and Information Systems, vol. 4, no. 1, pp. 55–62, 2021.
[11] R. Heldt, C. S. Silveira, and F. B. Luce, “Predicting customer value per product: From rfm to rfm/p,” Journal of Business
Research, 2019.
[12] R. Sifa, J. Runge, C. Bauckhage, and D. Klapper, “Customer lifetime value prediction in non-contractual freemium
settings: Chasing highvalue users using deep neural networks and smote,” in Proceedings of the 51st Hawaii International
Conference on System Sciences, 2018.
[13] P. Jasek, L. Vrana, L. Sperkova, Z. Smutny, and M. Kobulsky, “Comparative analysis of selected probabilistic customer
lifetime value models in online shopping,” Journal of Business Economics and Management, vol. 20, no. 3, pp. 398–423,
2019.
[14] T. T. Win and K. S. Bo, “Predicting customer class using customer lifetime value with random forest algorithm,” in 2020
International Conference on Advanced Information Technologies (ICAIT). IEEE, 2020, pp. 236–241.
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14. References
[15] E. Froberg and S. Rosengren, “Man (ager heuristics) vs. machine ¨ (learning): Automation for prediction of
customer value for brick-andmortar retailers,” Machine (Learning): Automation for Prediction of Customer Value for
Brick-and-Mortar Retailers (October 27, 2020), 2020.
[16] P. Jasek, L. Vrana, L. Sperkova, Z. Smutny, and M. Kobulsky, “Modeling and application of customer lifetime value
in online retail,” in Informatics, vol. 5, no. 1. Multidisciplinary Digital Publishing Institute, 2018, p. 2.
[17] A. Avinash, P. Sahu, and A. Pahari, “Big data analytics for customer lifetime value prediction,” Telecom Business
Review, vol. 12, no. 1, p. 46, 2019.
[18] B. P. Chamberlain, A. Cardoso, C. B. Liu, R. Pagliari, and M. P. Deisenroth, “Customer lifetime value prediction
using embeddings,” in Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and
data mining, 2017, pp. 1753–1762.
[19] G. Desirena, A. Diaz, J. Desirena, I. Moreno, and D. Garcia, “Maximizing customer lifetime value using stacked
neural networks: An insurance industry application,” in 2019 18th IEEE International Conference On Machine Learning
And Applications (ICMLA). IEEE, 2019, pp. 541–544.
[20] S. FEIZ, “The mediating role of customer lifetime value on customer relationship management and business
performance,” Ph.D. dissertation, Universiti Teknologi Malaysia, 2018.
[21] D. Bayanjargal, B. Davaasuren, and R. Enkhbat, “A numerical approach to the customer lifetime value,” iBusiness,
vol. 10, no. 2, pp. 85–91, 2018.
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