Retail Analytics

 

Generate a competitive advantage through the use of Analytics. 

Price Optimization

 

Together we can conduct cross product forecasting, create elasticity models, create optimization scenarios and build solutions to implement pricing decisions in real-time based on current market data. Below some potential applications:

  • Price Setting for products, portfolios and channels based on market conditions and target contribution margins.

  • Price Elasticity/Sensitivity to estimate the impact of pricing on sales volumes.

  • Dynamic Pricing to estimate price-change triggers based on customer behavior to maximize revenue/profitability.

Clustering

 

Together we can analyze your data to spot clusters and communities with similar buying habits and demographics; customize your offerings to local markets, rolling out different types of stores, products, pricing, marketing and even customer service strategies. Many different elements of your company can be customized, for example:

  • What is Being Sold: Merchandize Space and Assortment (division, category, department, classification, attributes like size and color, packaging design etc.), Pricing, Promotions and Marketing Programs (spending levels, media mix, major messages).

  • Where its Being Sold (Location): Geodemographics and Attitudes (population density, age, income, marital status, ethnicity, religion, lifestyle segment, psychographic).

Next Best Product

 

Through the use of a product recommendation engine understand the preferences and intent of each visitor and show the most relevant recommendation type and products in real time. Driven by models capturing life-event patterns, buying behavior, social media interactions, and other aspects, which customers need to be approached and on which channel. Here are some examples:

  • Event Based Marketing: Identify events that are key milestones to a customer. Events can take several forms, such as major life changes (getting married, having a baby) or interactions with the retailer (purchase of products).

  • Predictive Analytics: Understand your customer’s historical and transactional data to identify common behavior patterns before a certain event –like making a purchase, upgrading a service or leaving for a competitor. Based on these patterns, a predictive model can determine if the behavior matches those that would indicate a future purchase. Common applications include predictive scoring for customer value and market basket analysis to determine which products are often purchased together.

Marketing Mix

 

Quantify the potential value of all marketing inputs and identify marketing investments that are most likely to produce revenue growth.

  • Use predictive analytics to optimize future marketing investments to drive growth in sales, profits and share.

  • Balance short-term marketing and promotion tactics with long term brand building needs.

  • Optimize allocation of traditional media vs. digital media and determine the synergies between the two with marketing modeling.

  • Determine which media vehicles and campaigns are most effective at driving revenue, profits, share and consumer segments.

  • Quantify the ROI of improving marketing effectiveness in terms of sales and profits.

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