The Effects of Multichannel Shopping

Customer Spending, Customer Visit Frequency, and Customer Profitability

Ashish Kumar, Ram Bezawada, & Minakshi Trivedi (2018) — JACR

Motivation and Gaps

  • Multichannel Retail Proliferation: Multichannel strategies—offering products across offline stores, online channels, mail catalogs, etc.—are essential touch points to enhance consumer convenience.
  • The Core Puzzle: What customer-intrinsic and situational factors drive users to adopt multiple channels? How does multichannel shopping affect firm outcomes?
  • Research Gaps:
    1. Behavioral Duality: Prior research typically isolates stated attitudes (surveys) or actual purchases (scanner data). This study leverages both actual and stated behavioral data.
    2. Joint Consequence Modeling: Quantifies the consequences of multichannel shopping along three dimensions jointly—customer spending, visit frequency (loyalty), and customer profitability—controlling for potential endogeneity.

Main Contribution: Provides a comprehensive joint analysis of multichannel shopping drivers and performance metrics using dual-source panel data from a specialized Wine Retailer.

Two-Stage Research Design

  • Unique panel dataset of 225 households over 2 years (actual scanner transactions) matched with detailed survey psychographics.

Stage 1: Antecedents of Multichannel Shopping

  • Explores customer-intrinsic technology attitudes, shopping attitudes, online socialization, and marketing/communication variables driving the latent utility of multichannel adoption (\(U_h\)):

\[U_h = \alpha_{0h} + \beta_{tech} Technology_h + \beta_{shop} Shopping_h + \beta_{net} Socialization_h + \beta_{mix} MarketingMix_{ht} + \epsilon_h\]

Stage 2: Joint Consequences Model

  • A simultaneous 3-equation system modeling Customer Spending, Visit Frequency, and Customer Profitability.
  • Employs a Bayesian framework estimated via MCMC / Gibbs Sampling (50,000 iterations, 40,000 burn-in) to account for cross-equation correlations.

Stage 1 Results: Drivers and Frictions

  • GLM parameter estimates (Table 4) map the drivers and barriers to multichannel adoption:

Strongest Technology Drivers (+):

  • Technical Expertise (0.4615**): Facility with tech strongly drives channel adoption.
  • Internet Service Adoption (0.3543***): Active adoption of digital channels.
  • Internet Usage (0.0761***).

Shopping Attitude Frictions (-):

  • Deal Sensitivity (-2.0764*) and Shopping Enjoyment (-2.0364*): Single-channel brick-and-mortar loyalists prefer immediate product ownership and deal hunting in physical stores.
  • Shopping Convenience (0.1224**) has a positive effect.
  • Online Socialization (0.1692***) is a strong driver.

Demographic Heterogeneity: Education (0.0231**) and larger household sizes (0.0003*) encourage adoption, whereas older (-0.0269*) and female (-0.5496**) customers are more single-channel resistant.

Stage 2 Results: Consequences

  • Quantifies the significant positive effects of multichannel shopping across all three performance dimensions (Table 5 estimates):
Customer Response Metric Multichannel shopping Parameter Baseline Effect Model Explanatory Power (\(R^2\))
Customer Spending 0.0328* +3.28% spending increase 0.96
Customer Visit Frequency 1.8469* +1.85 visits / quarter 0.68
Customer Profitability 0.0004* +0.04% marginal profit growth 0.86
  • Unified Lift: Multichannel shoppers are fundamentally more valuable to the firm—they spend more, visit more frequently, and generate higher baseline profits.
  • Response Correlation: Positive cross-correlation between customer spending and profitability is highly significant (0.5723).

Stage 2 Results: Marketing Mix Trade-offs

The Profitability Dilemma of Promotions

  • Price: Expectedly hurts spending (-2.8116***) but strongly drives profitability (+0.3682***).
  • Promotions: Positively drive short-term spending (+3.2182***) but significantly erode overall profitability (-21.8924***).
  • Insight: Heavy deals drive channel traffic and sales volume but trigger profit leakage.

Communication Mix Drives Loyalty (Visits)

  • Marketing communications strongly drive visit frequency (customer loyalty):
    • Catalogs have the largest effect (1.6219***).
    • Emails have a highly significant positive effect (0.3815***).
    • Educational programs attended have a marginal effect (3.1239***).

Situational Lifts: Special occasion purchases dramatically increase both spending (0.0808***) and profitability (0.0731***).

Managerial Implications and Strategy

  • Segment-Targeted Marketing:
    • Leverage survey insights to identify high-potential technology-adoptive customers. Do not waste digital marketing budgets targeting technology-averse, highly deal-sensitive shoppers.
  • Coordinate Promotional Strategy:
    • Price cuts boost short-term multichannel spending but trigger profit erosion. Coordinate online/offline promotions to protect margins.
  • Customer Lifetime Value (CLV) Optimization:
    • Focus on retaining multichannel shoppers as their long-term customer tenure (0.0055***) is highly profitable.
    • Deploy traditional catalogs (1.6219***) and digital emails (0.3815***) strategically to drive store visit frequencies.

Citation: Kumar, A., Bezawada, R., & Trivedi, M. (2018). Journal of the Association for Consumer Research, 3(3), 294-307. DOI: 10.1086/698876