Retail Testing Optimized in 6 Steps

Data-driven decision-making is core to the sustainability of business operations in the modern marketplace, which faces competition from every angle on every niche platform and sales channel. Testing and gathering data on any given topic has never been easier with the variety of tools available to business decision makers, but the effectiveness of any test comes down to proper strategy and implementation. In this article published by MarketDial, they summarize the process in a few easy-to-follow steps.

1. Thoroughly define your hypothesis in a clear and measurable way (the intent of your test and anticipated outcomes).
2. Identify the segments or variables that you wish to test, often one component of how you do business.
3. Set the parameters for your test, and determine how results will be measured and what level will provide confidence in the outcome.
4. Create a representative treatment set, also known as targeting criteria, that will be used and paired with the test objective in a way that reduces human bias.
5. Create a control group or baseline to compare your results with that effectively captures the sentiment before exposure to the test.
6. Conduct the test, gather and analyze the results, and organize results and insights into an actionable implementation plan.

Testing business changes and improvements has never been easier, but it all comes down to proper and disciplined test planning, execution, and analysis. It is a process that helps to validate improvements rather than implementing them first and realizing too late that there is a design flaw or the improvement does not resonate with customers—a mistake that may cost much more than you might spend on testing and verifiable success probabilities. Depending on the complexity of the information needed, you may want to consult with a data research agency (with experience in analyzing large data sets) that can guide the process, perform regression analysis of the data, and create an objective implementation plan without company-created biases.

The Consumer Goods Industry Digitally Transformed

The consumer goods industry has undergone a prolonged period of digital transformation, and over the last 20 years, the growth of e-commerce has steadily outpaced brick-and-mortar retailing. Noogata, a modular AI data science platform, has compiled a short list of key industry changes affecting businesses, and they demonstrate how these have increased the need for deeper data-driven solutions.

• The success of platform-based economies and marketplaces like Amazon, Walmart, or Etsy has created increasing competition from digital-savvy start-ups, and incumbents are faced with both increased competition and opportunity. The rise of platform marketplaces has increased the need to integrate multiple platform points of sale, with owned sites and brick-and-mortar channels.
• The direct-to-consumer sales model is now a primary distribution channel for many brands, as turnkey solutions such as Shopify have enabled e-commerce-ready sales points at a much lower entry cost. Many B2B producers have opened D2C channels to increase their understanding of their end customer and gathering of primary data.
• Brick-and-mortar will continue to have a large market share. According to Noogata, e-commerce still only represents 20% of total US retail market share, although there has been continuous growth over the last two decades. Brick-and-mortar should not be overlooked as a channel, as certain product offerings and brand experiences lend themselves to in-store settings, and a physical location provides a different level of customer service, as well as opportunities for hybrid sales models of purchase online, pickup in-store, etc.
• Digital media means that marketing can reach audiences in a much more precise and targeted way—and with nearly real-time trackable results. The greater degree of precision that is possible comes with a greater degree of complexity and the need for better strategy and audience understanding.

In Noogata’s perspective, all of these transformative trends in the consumer goods industry point to the need for AI data science solutions. But from the [B]RIGHT perspective, there is still the need for an overarching strategy and audience understanding that precedes the implementation of such tools. It should all start with a deep analysis of your brand positioning in relation to your audience. That will provide the solid basis upon which to build your overarching business strategy and to inform where to implement efficiency and automated tools.

(Editorial) Methodology: We help brands grow based on data, research, data analysis and synthesis, and a hefty dose of proven processes, tactics, and channel-specific strategies. | Objective: To distribute timely insights and information about tools, trends, and research and data strategies that work. | Sample size: n=the entirety of the Internet. | Audience: Smart brand marketers that understand the importance of using data and research to inform strategic decision-making.