Retail

Which retail decision needs better evidence?

A retailer introduces store pickup and sees website conversion fall while store transactions rise. The digital team may conclude that the initiative failed; store leaders may claim success. Neither channel view establishes the overall effect. Customers may research online, buy in-store, collect an order and make an additional purchase—or simply move an existing transaction to a more costly fulfilment path.

Arcus’s central thesis is to evaluate the customer journey and total retail contribution together. Channel attribution should support that assessment, not substitute for it. An initiative can be useful despite lower sales in one channel, and unattractive despite a strong headline growth rate.

The right scope may be a customer study, a format or assortment decision, a pricing review or a focused operating diagnostic. Avoid treating every problem as a need for another platform or a faster delivery promise. The customer proposition and operational requirements should determine the investment.

What empirical research can—and cannot—tell leaders

Gallino and Moreno’s April 2014 Management Science study examined a retailer’s buy-online, pick-up-in-store implementation using proprietary data. It found an association with lower online sales and higher store sales and traffic, and analysed cross-selling and channel-shift explanations. Its direct management lesson is the limitation of evaluating an integrated initiative using one channel’s data. The historical retailer setting does not establish the effect or profitability of a current pickup proposal elsewhere.

A 2016 Journal of Retailing meta-analysis by Janakiraman, Syrdal and Freling distinguishes return-policy dimensions including time, money, effort, scope and exchange. The published summary reports that purchase and return responses depend on the dimension and study context. This is not evidence that any broadly “generous” policy will increase profit. Leaders still need their category’s return costs, resale recovery, customer response and applicable obligations.

These sources provide mechanisms and questions rather than current market forecasts. Arcus’s framework below is an original analytical synthesis. It should be tested against the retailer’s own data, customer research and operating conditions; it is not a validated forecasting model.

Illustrative decision: pickup growth or store labour displacement?

Consider a fictional retailer testing store pickup. Online orders grow, but employees now pick and pack during the same peak hours when they advise walk-in customers. Pickup customers value the service, while queues and missed assistance may reduce other purchases. A channel-only result can omit this displaced work.

Compare three explanations: the service creates additional demand, it mainly shifts existing customers from ordinary store shopping, or it creates demand but the peak-hour labour arrangement erodes the benefit. Examine combined sales, pickup completion, additional purchases, customer waiting and actual task time. Use a feasible comparison and document unrelated changes.

Options include a narrower pickup range, different collection windows, dedicated picking capacity or a different fulfilment location. Compare their customer effects and ongoing requirements before assuming that more volume will spread the cost. Spare capacity outside the peak is not equivalent to capacity at the time customers need it.

This scenario is illustrative, not an Arcus client result. It does not imply that pickup is generally attractive or unattractive. The useful decision is which service design fits the category, customer promise and available capacity.

What leaders should examine before scaling

Reconcile the customer, merchandising, finance and operations views of the initiative. Different incentives can encourage each team to optimize its own channel or budget while shifting work elsewhere. Agree who owns the combined customer outcome and contribution, including exceptions and returns.

Include implementation, integration, inventory preparation, staff training and ongoing service costs. Distinguish a time saving from a cash saving and a sales shift from incremental margin. Compare unit economics at realistic volumes and peaks; do not assume that average labour availability covers every location or service window.

Run a proportionate test with a baseline, relevant comparison and enough follow-up to observe returns and repeat behaviour. Establish definitions and data quality before reporting precision. Protect customer information and use appropriate access and privacy arrangements when linking journeys across channels.

Review availability, cancellations, customer experience, net contribution and workload together. A reliable narrower promise may be preferable to an ambitious service the retailer cannot consistently deliver. Document what would change the decision and who can authorize expansion or adjustment.

Discuss your retail decision

Tell Arcus the category or format, customer question, operating constraint and decision timeline. We can discuss research, strategy or operating support and the evidence needed to move forward. You can also ask about a complimentary presentation on a business problem your team would like to solve.

Related support includes strategy planning, market research, brand strategy, pricing, customer loyalty, marketing strategy, operations improvement and organizational transformation.

Historical grocery customer research and brand strategy case

The following account is retained from the earlier page. Its findings describe the historical study context, not current category demand or a quantified retention improvement.

Scope: The scope of work on this engagement included an attitude and usage study for a grocery retailer retailer. The market for frozen and dry foods has moved towards emerging ethnic cuisines linked to a premium for “ready to eat” foods, a move away from “ready to cook” and “cooking from scratch”. Three dynamic food segments were identified with extensive qualitative research.

Opportunity: Identify new growth vectors in the food industry for emerging cuisines.

Recommendations: Consumer preferences for types of food have changed significantly as a result of time constraints, introduction of new ideas by recent immigrants, frequent global travel andmore openess to experimentation. As a result, the market for froen and dry foods has moved towards emerging ethnic cuisines linked to a premium for “rready to eat” foods, a move away from “ready to cook” and “cooking from scratch”. Three dynamic food segments were identified with extensive qualitative and quantitative research.

Results: Following extensive qualitative research, a new brand platform and business strategy was identified. Thereafter, marketing concepts were tested with customers and prospects.

Existing related resources