Customer Relationship Intelligence: Integrating CRM and MDM for Enhanced Customer Engagement

Tejasvi c. Addagada

cs.AISep 5, 2026

Customer Relationship Intelligence: Integrating CRM and MDM for Enhanced Customer Engagement

Authors: Tejasvi c. Addagada

Organizations: Enterprise AI Governance, Information Technology HDFC Bank, Mumbai, India

Abstract

This study examines how Customer Relationship Management (CRM), Master Data Management (MDM), and Customer Knowledge Management (CKM) jointly constitute a Customer Relationship Intelligence (CRI) framework for enhanced Customer Engagement (CE). A cross-sectional survey of 100 participants across retail, healthcare, IT, and telecommunications sectors was analysed using Spearman rho correlation and ordinal logistic regression (IBM SPSS). Bivariate correlations were weak and non-significant (r<0.19, p>0.06). Regression identified CRM (beta=0.717, p=0.002) and CKM (beta=0.581, p=0.009) as significant positive predictors of CE; MDM showed a positive but non-significant direct effect (beta=0.346, p=0.071). The model explained 20.5% of CE variance (Nagelkerke R^2=0.205). Parallel mediation analysis (Hayes PROCESS Model 4, 5,000 bootstrap samples) found no significant indirect effects of MDM on CE via CRM (IE=0.021, 95% BC CI [-0.072, 0.121]) or CKM (IE=0.032, 95% BC CI [-0.061, 0.126]); Hypothesis H4 was not supported. CRM and CKM emerge as the principal drivers of CE within the CRI framework, while MDM functions as a foundational data quality enabler whose strategic value is realised through its enabling effect on CRM execution and knowledge management. Findings should be treated as exploratory given the sample size and cross-sectional design. Future research should replicate with larger sector-specific samples and longitudinal designs, particularly in regulated BFSI contexts where MDM architecture is shaped by data governance mandates.

Figures & tables

Cronbach’s Alpha	N of Items
0.809	16

TABLE I: Reliability Statistics

Category	Group	Freq.	%
Age Group	18–24 Years	18	18
	25–34 Years	24	24
	35–44 Years	16	16
	45–54 Years	22	22
	55 and above	20	20
Gender	Male	47	47

TABLE II: Demographic Details (Percentages may not sum to 100 due to rounding or non-response on individual items)

	CRM	MDM	CKM	CRI
CRM (rho)	1.000	0.187	$-$ 0.024	0.141
$p$ (2-tail)	—	0.063	0.814	0.161
MDM (rho)	0.187	1.000	$-$ 0.016	0.137
$p$ (2-tail)	0.063	—	0.874	0.175
CKM (rho)	$-$ 0.024	$-$ 0.016	1.000	0.136
$p$ (2-tail)	0.814	0.874	—	0.178

TABLE III: Spearman’s Rho Correlation (

N=100

)

Model	$-2$ Log L	$\chi^{2}$	df	$p$
Intercept Only	185.640	—	—	—
Final	164.727	20.913	3	$<$ 0.001

TABLE IV: Model Fitting Information (CE)

	$\chi^{2}$	df	$p$
Pearson	197.018	165	0.045
Deviance	126.794	165	0.988

TABLE V: Goodness-of-Fit (CE)

Cox & Snell	Nagelkerke	McFadden
0.189	0.205	0.083

TABLE VI: Pseudo

R^{2}

(CE)

	$b$	SE	Wald	df	$p$	CI LL	CI UL
[CE=1]	1.802	1.296	1.933	1	0.164	$-$ 0.738	4.342
[CE=2]	3.312	1.226	7.300	1	0.007	0.909	5.714
[CE=3]	6.136	1.332	21.226	1	$<$ .001	3.526	8.746
[CE=4]	8.090	1.431	31.963	1	$<$ .001	5.286	10.895
CRM	0.717	0.227	9.996	1	0.002	0.273	1.162
MDM	0.346	0.191	3.262	1	0.071	$-$ 0.029	0.721

TABLE VII: Parameter Estimates (CE)

Model	$-2$ Log L	$\chi^{2}$	df	$p$
Intercept Only	198.064	—	—	—
Final	186.376	11.688	3	0.009

TABLE VIII: Model Fitting Information (BP)

	$\chi^{2}$	df	$p$
Pearson	195.567	165	0.052
Deviance	144.252	165	0.876

TABLE IX: Goodness-of-Fit (BP)

Cox & Snell	Nagelkerke	McFadden
0.110	0.118	0.043

TABLE X: Pseudo

R^{2}

(BP)

	$b$	SE	Wald	df	$p$	CI LL	CI UL
[BP=1]	0.382	1.255	0.093	1	0.761	$-$ 2.077	2.841
[BP=2]	2.259	1.165	3.756	1	0.053	$-$ 0.026	4.543
[BP=3]	4.399	1.223	12.935	1	$<$ .001	2.002	6.797
[BP=4]	6.214	1.299	22.896	1	$<$ .001	3.669	8.760
CRM	0.364	0.212	2.964	1	0.085	$-$ 0.050	0.779
MDM	0.341	0.185	3.392	1	0.066	$-$ 0.022	0.704

TABLE XI: Parameter Estimates (BP)

Bootstrapped indirect effects
Path	$b$	SE	CI LL	CI UL	Sig.
MDM $\to$ CRM ( $a_{1}$ )	0.045	0.101	$-$ 0.153	0.241	ns
MDM $\to$ CKM ( $a_{2}$ )	0.070	0.101	$-$ 0.128	0.268	ns
CRM $\to$ CE ( $b_{1}$ )	0.466	0.076	0.317	0.615	∗∗
CKM $\to$ CE ( $b_{2}$ )	0.463	0.076	0.314	0.612	∗∗
MDM $\to$ CE ( $c^{\prime}$ )	0.122	0.076	$-$ 0.027	0.271	ns

TABLE XII: Parallel Mediation: MDM(

X

)

\to

{CRM, CKM}(

M

)

\to

CE(

Y

). Bootstrap

N=5{,}000

, 95% BC CI.

{}^{**}p<.001

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Abstract

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