CPG & Retail
With rapidly changing customer needs and increased competition, retail and CPG companies are facing challenges in using the vast customer data at their disposal and identifying the right kind of customer segmentation that would have led to increased loyalty and encouraged repeat purchase. Leaders in this space are viewing data science and real time analysis as critical to their business strategies as they enhance operational efficiencies by building customer insights across geographies and stores and aid in decision-making across the organization.
Machine learning insights are augmented with market data to personalize the shopping experience across the consumer journey, while also propelling faster-go-to-market by improving product design and testing. Real-time predictive & prescriptive analytics help the companies to be more responsive to the market dynamics, enable targeted marketing through micro-segmentation, forecasting trends and behaviors to better target the promotions. It helps companies to institutionalize data analytics and modeling methodologies into their decision-making ecosystem, helping them make better, informed and fact-based business decisions. Our offerings are designed to help retailers rethink, evolve and achieve their vision of an integrated analytics and insights-driven omni-channel ecosystem.
In today’s marketplace, actionable consumer insights are critical to unlock customer-centric marketing and merchandising. DecisionTree experts will help you get a more detailed understanding of customer needs and attitudes by developing detailed customer profiles, discovering new micro-segments and improve targeting high-value opportunities. We also help you to optimize your loyalty program by combining information from loyalty programs with data from external sources such as social media, mobile applications and in-store sensors to develop insightful personas and predict the customer’s next purchase.
With increasing complexity in CPG and retail, companies require in-depth insights to effectively manage and forecast future demand and performance; advanced pricing analytics tools can do that. The use of advanced pricing analytics will improve retail margins 2%–4% and grow sales 1%–2%. DecisionTree uses algorithm models to estimate the impact of pricing on sales volume, optimize the pricing of products/services based on revenue and profitability, price simulation to identify optimal pricing, estimate price-change triggers based on customer behavior and position products properly against the competition.
Marketing Insights and Sales Analytics
True ROI is the holy grail of any business and we will help you achieve that by targeting the right customer segment, optimizing marketing channels according to budgets and omni-channel programs. DecisionTree experts will help you add rigor to the company’s business planning and marketing resource allocation processes by building an AI-enabled live data platform, enabling faster decision-making in real time. This helps to provide a holistic view of conversion attribution across all marketing channels and optimize on media allocation.
Forecasting and Demand Planning
The ability to effectively forecast demand is critical to the success of a retailer as it leads to lower inventory costs, faster turnover cycles and allows retailers to quickly respond to trends. DecisionTree uses analytics to optimize forecasting for CPG and regulate the ordering cycle to align the inventory planning process to demand management data and reduce wastage. Forecasting demand peaks for new products is especially crucial for businesses such as consumer technology or fashion retail. Using web analytics and product attributes, we use machine learning to comb through indicators such as web page visits, social media, control groups and data sources to identify the demand for new products and also estimate the impact of cannibalization.
Merchandising and Store Insights
Visibility and product placement are key for retailers and CPG companies. Merchandising analytics enable planners to align their merchandising decisions with customer expectations. Our solutions help you optimize merchandise assortment and store-front layout, track new product performance, optimize markdowns and promotions, and improve margins. We also employ machine learning technique like store clustering, whose insights can be used to target demands of similar stores and drive new operational efficiencies, help you make the right merchandising and purchasing decisions, aid in category space allocation decisions, identify profitable stores and top-selling SKUs.
Social Listening and Digital Insights
To build a valuable brand, it is critical to listen to your audience. Social media, with its far reaching user base worldwide, provides unique customer data points. Our social listening tools can help your brand gather insights into your products, your brand’s influencers, the values it stands for and track customer sentiments. We can also help you find out the effectiveness of your communications and customer interactions on a broad range of digital marketing platforms. This will help you keep track of the conversations surrounding your brand and take appropriate action.
Personalized Campaigns and Response Modeling
We want to help you keep the shopper at the center of all your decisions. By using analytics-powered targeting and personalization, we can help you personalize promotions based on recommendations, aggregate insights from shopping behavior, campaign responses & social engagement, track and measure the effectiveness of every channel & campaign and identify the leading indicators of a consumer’s purchasing habits, thereby his future buying value. Digital personalization is the key to tailoring web experiences to visitors.
Using retail analytics on loyalty card data, a leading pharmacy retail company increased transactions by 15% and clocked 30% rise in average ticket size for loyalty card holders.
Sales at a leading US-based marketplace seller soared 1.5 times, while operating margins registered a 50% increase after changes were made to its pricing algorithm.
Retail giant registered 18% revenue growth by using analytics-driven targeted marketing.