Retail & CPG

Increase Seachability and Conversion, Reduce Return: When your product information are listed accurately and completely, you immediately increase the searchability of your product. Customers will select yours over your competitors because they can see the aspects of the products they want! They will return less because they know what they are buying. Work with us to enrich your product data, and make is most accurate and complete!

Demand Forecasting: CPG datasets may be used to anticipate future product sales volumes by analyzing historical sales data, market trends, and external factors such as seasonality and promotions. By analyzing CPG statistics, merchants can precisely forecast demand variations, optimize inventory levels, and ensure enough stock availability to fulfill consumer demand while eliminating excess inventory and stockouts.

Merchandising Optimization: CPG datasets may help with merchandising decisions by offering insights into product performance, shelf location, and pricing strategies. Retailers may use sales data, customer behavior, and competition benchmarks to improve product placement, change price tactics, and create successful promotional programs that increase sales and profitability. This enables merchants to improve the entire shopping experience and maximize the efficacy of their merchandising efforts.

Customer Segmentation and Targeting: Marrying CPG databases with your purchase data, merchants may segment customers based on their purchase habits, preferences, and demographics, allowing for targeted marketing and tailored incentives. Retailers may identify high-value client categories, understand their specific wants and preferences, and personalize marketing messages and offers to each segment. This enables retailers to boost consumer engagement, loyalty, and lifetime value.

Comprehensive Food Data: work with us to discover a wealth of Food Data in the US market. For each SKUs, from UPC to pricing to all kind of food features, the data will power your business in the food industry.

Food Safety: Our Food Safety AI platform integrates with IoT devices, identifies commercial kitchen hazard, food safety issues through the supply chain, and insure utmost trust for our consumers.

Scalable Data Pipeline: We specialize in building Data Pipelines for retail, CPG and food data! From data acquisition to data cleaning, from feature extraction to feature engineering, from data labeling to data monitoring, let your customized pipeline do the heavy work for your business and AI!

Price and Promotion Management: With our price and promotional data and AI, price and promotional tactics may be managed centrally across all sales channels. Retailers may adjust prices, provide discounts, and start promotions concurrently across online and physical channels, assuring consistency and accuracy. This allows merchants to respond swiftly to market changes, develop dynamic pricing strategies, and optimize promotional activities in order to increase sales and profitability.

Merchandising and Product Assortment: Customer segmentation may help retailers discover which goods are popular in different categories. Retailers may adjust their product offerings to each segment's individual requirements and interests by assessing purchase history and preferences. For example, if one sector favours environmentally friendly products, the merchant might provide extra shelf space to them or emphasize them in marketing efforts.

Pricing Strategies: Customer segmentation enables merchants to apply dynamic pricing strategies depending on segment characteristics and behavior. High-value segments may be willing to pay higher costs for specific items or services, whereas price-sensitive segments may prefer discounts or promotions. Retailers may tailor pricing strategies to enhance sales and profitability in each area.

Inventory Management: Understanding consumer segmentation enables merchants to improve their inventory management procedures. By monitoring purchase frequency, seasonality, and product preferences, merchants may guarantee that they have the correct items on hand to suit the needs of various groups. This eliminates stockouts, superfluous inventory, and increases overall supply chain efficiency.

Customer Experience Personalization: Customer segmentation enables retailers to provide personalized shopping experiences both online and in-store. By leveraging data on preferences, browsing behavior, and past interactions, retailers can customize product recommendations, website layouts, and in-store displays to resonate with each segment. This enhances customer satisfaction, increases engagement, and fosters long-term loyalty.

Customer Retention and Churn Prediction: Customer segmentation helps retailers identify at-risk customers and proactively implement retention strategies. By analyzing factors such as purchase frequency, feedback, and engagement levels, retailers can predict churn and take targeted actions to prevent it. This might include offering personalized incentives, addressing customer concerns, or providing exceptional customer service to win back disengaged customers.

Market Expansion and Targeting New Segments: Retailers can use customer segmentation to identify new market opportunities and target previously untapped segments. By analyzing demographic data, psychographic profiles, and purchase behavior, retailers can identify segments with similar characteristics to their existing customer base and tailor their marketing efforts to attract them. This helps retailers expand their reach, increase market share, and drive growth.

Product Feedback and Improvement: Retailers may utilize sentiment analysis to evaluate consumer feedback from a variety of sources, including online reviews, social media, and customer surveys. Understanding client sentiment about certain products or services allows merchants to find areas for improvement, solve frequent pain points, and fine-tune their offers to better match customer expectations. This feedback loop enables merchants to improve product quality and consumer happiness.

Brand Reputation Management: Sentiment analysis helps retailers monitor and manage their brand reputation by tracking online mentions and sentiment towards their brand. By analyzing social media conversations, news articles, and customer reviews, retailers can identify potential PR crises or negative sentiment trends early on and take proactive measures to address them. This might involve responding to customer complaints, clarifying misinformation, or implementing damage control strategies to protect their brand image

Competitor Analysis: Sentiment analysis enables retailers to gain insights into the sentiment towards their competitors' products and services. By monitoring online discussions and reviews about competitors, retailers can identify strengths and weaknesses in competitors' offerings, uncover market gaps, and benchmark their own performance against competitors. This information informs competitive strategies, such as product differentiation, pricing adjustments, or marketing campaigns targeting competitor weaknesses.

Predictive Analytics for Demand Forecasting: Sentiment analysis can be used as input for predictive analytics models to forecast future demand for products or services. By analyzing sentiment trends and customer feedback, retailers can anticipate shifts in consumer preferences, identify emerging trends, and adjust their inventory levels and marketing strategies accordingly. This proactive approach helps retailers optimize stocking levels, minimize stockouts, and capitalize on market opportunities.

Personalized Email Marketing: Retailers can automate personalized email campaigns based on customer behavior, preferences, and purchase history. For example, sending targeted product recommendations, exclusive offers, or reminders for abandoned carts. This tailored approach increases email engagement rates, drives conversions, and enhances customer loyalty.

Automated Email Marketing Campaigns: Using automated email campaigns, businesses can nurture customer connections by offering relevant information, promotions, and product suggestions based on their behavior and preferences. Retailers may use customer data and segmentation to set up automated processes that send tailored emails at key stages in the customer experience, such as welcome emails, abandoned cart reminders, post-purchase follow-ups, and re-engagement efforts. This provides constant communication with customers and encourages repeat purchases, resulting in increased revenue and brand loyalty.

Social Media Engagement Automation: Automating social media engagement allows retailers to maintain a consistent presence across various social platforms and interact with customers in real-time. By scheduling posts, monitoring mentions and comments, and responding to inquiries and feedback, retailers can cultivate a community of engaged followers and build brand affinity. Automation tools can analyze social media data to identify trends, sentiment, and influencers, informing content strategies and enabling retailers to capitalize on opportunities for viral marketing and user-generated content.

Customer Retention Campaigns: Retailers can use automation to implement customer retention campaigns aimed at re-engaging inactive or lapsed customers. Automated workflows can trigger personalized communications, such as special offers, loyalty rewards, or feedback requests, to encourage repeat purchases and strengthen customer relationships.

Product Launches and Pre-orders: Retailers can automate campaigns to promote new product launches or pre-orders. Automated email sequences, social media teasers, and targeted ads can build anticipation, generate buzz, and drive pre-sale orders or early adopter sign-ups. Automation ensures timely communication and coordination of marketing efforts to maximize product visibility and sales upon launch.

Post-Purchase Follow-up: Automation enables retailers to automate post-purchase follow-up campaigns to gather feedback, provide order updates, and encourage customer reviews or referrals. Automated emails or messages can solicit feedback on the purchase experience, offer additional product recommendations, and thank customers for their support. This strengthens customer relationships, fosters brand advocacy, and encourages repeat business.

Predictive Analytics for consumer Insights: By using predictive analytics, businesses may anticipate consumer wants and behavior, allowing for proactive involvement and tailored suggestions. Retailers may forecast future purchase intent, identify high-value consumers, and segment customers based on their propensity to churn or respond to certain marketing efforts by evaluating previous transaction data, browsing activity, and demographic information. This allows companies to automate focused marketing activities including tailored product suggestions, loyalty program incentives, and reactivation campaigns, resulting in increased sales and retention.

Unified Customer Experience: By optimizing multi-channel operations, retailers can deliver a consistent customer experience across all touchpoints. This includes maintaining uniformity in branding, product assortment, price, promotions, and customer service across both physical and digital platforms. By combining data from many channels, businesses may acquire a comprehensive understanding of consumer behavior and preferences, allowing for personalized interactions and tailored suggestions regardless of channel.

Omni-channel Marketing: Retailers may optimize marketing efforts across numerous channels to target buyers at different stages in their purchasing cycle. This entails coordinating marketing campaigns, promotions, and message across physical locations, internet, social media platforms, email, and mobile applications. Retailers may use consumer data and segmentation to offer targeted and personalized marketing communications that appeal to individual tastes while driving engagement and conversions across channels.