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Learn How Retailers, Brands and Manufactures are Leveraging Predictive Analytics to Reduce Costs, Markdowns and Time to Market for their Retail Development Cycles
Predictive Analytics is being rapidly adopted by the retail industry as a more efficient and cost-effective way to make better product decisions.
At this point, we are all familiar with general analytic tools that report everything from sales history to website traffic. These tools organize the data of actions such as sales that have taken place in the past.
First Insight’s Predictive Analytics platform takes these data points, along with user-generated content - the Voice of your Customer - to provide forward-looking metrics yielding accurate demand and sales predictions for products that are still early in the development cycle.
Retail predictive analytics help you create and design better products, but they go far beyond general product recommendations. Here are just some of the additional insights that can be gained with the proper tools:
Each tool or platform in the retail predictive analytics industry has its own subset of technologies and methods for retail forecasting. Most tools rely on a combination of predictive algorithms and historical product performance data or feedback from customers. Some platforms go far beyond just previous product performance with complex methodologies of predictive technologies to provide astoundingly insightful outcomes.
We at First Insight pioneered early retail predictive analytics technologies and have the most mature platform that can function as a strong case study for advanced predictive technologies and methodologies. Each predictive retail analytics platform employs different tools, but well-formed products such as our InsightSuite platform combine the following:
Historic sales data is combined with information about product features, price points, regional sales performance, and a multitude of other factors that may impact sales. Mature predictive analytics tools have millions or even billions of points of data to draw upon in aid of predicting the performance of new retail products.
Voice of the Customer, or VoC, is a collection of direct feedback from customers or survey participants that is used to determine strong and weak selling points as well as overall interest in the product or offering. This user-generated feedback can also be used to gain insight into potential changes in design and/or features of a product that could add value, willingness to buy at various price points, insight for packaging and marketing, and interest based upon segments like geography.
These technologies are the keystone of predictive analytics. Similar to how the human mind works, machine learning and AI draw upon previous product performance data and the Voice of the Customer to anticipate what may happen in the future. That data is compiled and used to predict the sales performance of products in development.
A Bayesian model is a statistical model that relies on probability to fill in gaps in certainty. In the retail context, this means leveraging Machine Learning and AI to synthesize previous performance data and Voice of the Customer. Probabilistic outcomes derived from this data are used to improve a product before it goes to market and predict optimum criteria for improved sell-through.
In essence, Human Computational Modeling is the practice of predicting human behavior. Machine learning is leveraged to create behavioral models. In the retail industry, this boils down to creating models around purchase decisions and consumer preference. Human computational modeling touches all aspects of retail sales from setting an initial price to marketing strategies to markdown cadences.
For over ten years, First Insight has been the leader in experience management and digital product testing for retail. Now, we are sharing our success with more industries to help you deliver value to your customers and growth to your bottom line.
Many different outcomes can be derived from predictive analytics tools. Flexible platforms such as our InsightSuite can also be customized to derive the precise outcomes desired by a retailer. Below, we will outline some common outcomes that can be expected when using Ppredictive Aanalytics tools as a component of your retail product development strategy.
This is the most obvious and expected outcome that most retailers hope to receive from their predictive tools. From entry price points to regional pricing strategies, you should expect to receive deep insight into how to price your products.
Learn which products will sell, and which products will be left on the shelf. Identify products that resonate with specific niches and products with mass appeal. Avoid controversial and offensive products and identify offerings that will create a buzz and maximize sales for your brand.
Improving sales and setting effective price points will lead to reducing markdowns. If you have to markdown your products, predictive tools canshould help you set the proper markdown cadence.
Know what the perfect entry price point is for your retail offerings. Understand how to maximize full-price sales to reduce markdowns and improve sell-through. Predictive analytics tools with multi-national support can also strategically recommend different entry points for different regions.
In-store testing, focus groups and other forms of costly “real-world” product testing can be greatly reduced. This shortens the product development cycle, reduces production costs and waste, and provides design teams with more creative flexibility.
Mature retail predictive tools can greatly improve overall product success rates. You can eliminate poor concepts earlier, find the right price points, and spend more time investing in products predicted to succeed.
Design teams can flourish when empowered by predictive analytics. They have more latitude to take creative risks and VoC data can provide useful insights on how to improve a product after the initial concept has been developed.
Many retail Predictive Analytics tools provide rapid results unparalleled by traditional in-store testing. For example, our InsightSuite provides results in as little as 24-48 hours. In some cases, this may increase speed to market by up to 5 months.
By leveraging First Insight's retail analytics, rue21 has greatly improved their product success rates.
Learn how're brand leveraged predictive analytics to increase sell-through and margins
First Insight’s InsightSuite Software Platform utilizes two vital components, The Voice of the Customer and Predictive Analytics, to enable brands, manufacturers, retailers, and licensees to make better product decisions on new products by answering questions that include: