SPR Consulting - Predictive Retail Analysis

Intelligently predict which prospective customers are most likely to buy your goods and services based on data from past purchases. The Predictive Retail Analytics dashboard is fueled by Azure Machine Learning models that help take the guesswork out of target marketing and selling by enabling companies to quickly identify and respond to new opportunities.

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Predictive Retail Analytics

Wouldn’t it be nice to spend time and energy on the potential customers who are most likely to purchase your goods or services? 

SPR Consulting’s dashboard demonstrates how the data from millions of past purchases can intelligently determine which prospects are most likely to buy, allowing marketing and sales to develop targeted communications for each audience including:

• Personalized customer offerings

• Tailored customer experience

• Loyalty initiatives for high lifetime value customers

Develop a Customer Profile

Increase your understanding of your customer base by visualizing their attributes. View their characteristics at a high level or drill down into each category for additional information. This dashboard provides information about:

• If they purchased

• Gender

• Commuting distance to stores

• Year they purchased

• Geographical location

• Education

• Occupation

Gain Additional Customer Insights

Unlike traditional practices, which are more backward-looking in nature, predictive analytics approaches are focused on helping companies glean actionable intelligence based on historical data. Take a different look at your customer base by viewing the weighted distribution of a purchaser’s occupation, education, and average yearly income. The world map quickly shows the distribution of your customers’ yearly income by geographic location. This visualization also provides insight into if your customers are homeowners and their marital status.

Target New Prospects; Generate New Revenue

Using Azure ML, this solution takes the intelligence gathered from your past customer purchases and applies it to a prospective customer base. Users now have a snapshot of the individuals most likely to purchase. Users may then take action by developing a targeted and customized sales or marketing campaign based on a prospective customer’s income, location, education or occupation.

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