What is the best way to conduct in-store experiments to test retail marketing strategies?

In retail marketing, defining a research question involves identifying a specific area of interest, such as consumer behavior or market trends, to guide the investigation. For example, a research question could focus on understanding the impact of social media marketing on purchasing decisions. Hypotheses are then formulated as testable statements that predict the relationship between variables, such as "increased social media engagement will lead to higher purchase intent." Both the research question and hypotheses serve as the foundation for designing and conducting studies to gain insights into consumer preferences and behaviors within the retail industry.

I beg to differ on this. In my opinion, even before we decide on research objective, we need to zero in on marketing decision(s) and if these are limited to a particular store or a particular type of stores or stores exhibiting same characteristics. This will help a researcher devise experiment design.

To do proper research in brick-and-mortar retail, you will need proper data insights. As more and more traditional retailers have known customers, analyzing their purchase behavior patterns would be extremely lucrative to come up with evidence-based hypotheses. Methods to making this happen: > Segment the customers - RFM or more advanced clustering method > Look for anomalies - like which products/brands/ shops have generated a higher purchase frequency / CLV > Come up with testing ideas from store managers / category managers - they might be identifying things that are not visible to the data/ retail teams. (We plan to do gamification in our tool (Omniconvert) to incentivize them to do this)

What will you change? This is an important factor Some ideas from my experience: Place Snickers bars near the checkout counter Reduce the real estate for toilet paper brands and increase the cosmetics real estate Find and validate 3 new vendors in the exotic products category Change the lighting in the wine and spirits area BOGO bundles in the cosmetic areas In-store signage for Heineken Taste experiment for the new vegan vendor Late afternoon discounts for precooked meals Limited-time discount codes that become available to be picked up at the entrance at 8 PM Photos with babies in the apparel area

If you have identifiable customers and they generate a significant amount of revenue, you will be in a great position to isolate the effect of that experiment. In other words: if you aim for a 15% uplift in Revenue by adding 2 new beer brands, how long should you be waiting if you want to add them only in 4 of your stores? Increasing the number of stores will lead to more orders and customers - therefore, less time to validate that experiment. Variables: > Stores being used in the treatment group vs control > Categories being tracked > Goals > Expected uplift > Time to run it > Confidence interval (aim for >95% to be on the safe side. The time to run that experiment is influenced by the minimum detectable effect (expected uplift)

Before launching your experiment, you have to make sure that the changes are consistent in the treatment group of stores. Some store managers who are responsible might think that this is a waste of time. So, make sure that they are implementing those changes. Our software (in beta now) allows you to run the experiments AND track the impact thanks to our customer analytics solution so that you understand the impact on purchase behavior AND customer feedback (NPS).

I am a STRONG believer in Customer Lifetime Value as a North Star Metric in retail (wrote an entire book about it) You might be running experiments that seem OK from a revenue perspective, but you end up with a slow decaying effect. One example: you earn more real-estate on your shelf by eliminating 2 beer brands. In the first 2 weeks, customers who bought those beer brands continued to buy from those stores. But bit by bit, they become irritated if they were loyal to that particular brand and migrate to a competitor that still carries that brand. That's why, you have to measure more metrics when it comes to experiments: Orders, customers, revenue - immediate transactional metrics, but also long-term metrics: - NPS, CSAT, CLV

Gone are the days of hefty investments in physical production and testing, before a full blown launch of a product. Today, in our dynamic marketplace overflowing with brands and innovations, agility is key. Virtual store testing offers a lifelike environment for swift decision-making, and companies across the globe are gravitating towards this to help with their decision making. From packaging to layout to impactful point-of-sale materials, these virtual simulations mirror real-world scenarios, providing ample validation for strategic choices.

Dentro do possível, algo o mais realista possível. Ou seja, evitar óculos de realidade virtual (VR), ensaios em estúdios de laboratórios de pesquisa comportamental, ou exercícios no computador com cenário tridimensional com navegação acionada com mouse. Ou seja, implementar os experimentos no mundo real, onde a pessoa faz suas compras. Que seja em uma loja. Mas numa loja real. Todo o resto - teste A/B, grupos controle, etc etc - são bem vindos e importantes. Mas acho mais importante ainda considerar, ainda mais em um mundo com tanta tecnologia, a implementação nO mundo real no ambiente no qual a pessoa está acostumada a comprar, sem fricções por causa de processo ou tendo que ensiná-las a navegar em um ambiente que não existe na prática.