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Big Data Analytics in Retail

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cloud Big Data and adaptive retail enterprise

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A quality connection to customers

Are you with your consumers, or one step behind? When they enter the store or visit your site, what do you know about them and how can you service them? What did they Tweet about you? With Hortonworks, you can be your customers’ first choice based on an enhanced ability to respond to consumers, serve up timely promotions based on their preferences, and improve service with faster orders.

Le potenzialità dei dati empirici per i retailer

Connected Data Platforms from Hortonworks dramatically reduce the cost of capturing, ingesting, storing and analyzing data. When integrated with existing systems and operations, retailers can make statistically confident observations on empirical retail data, rather than rolling the dice with customer panels, in-store surveys or focus groups to guess what drives sales.

Use Cases

Build a 360° View of the Customer

Retailers interact with customers across multiple channels, yet customer interaction and purchase data is often isolated in data siloes. Few retailers can accurately correlate eventual customer purchases with marketing campaigns and online browsing behavior.

Connected Data Platforms gives retailers a single view of customer behavior. It lets them store data longer and identify phases of the customer lifecycle. Analytics increase sales, reduce inventory expenses and retain the best customers.

Analyze Brand Sentiment

Enterprises lack a reliable way to track their brand health. It is difficult to analyze how advertising, competitor moves, product launches or news stories affect the brand. Internal brand studies can be slow, expensive and flawed.

Le piattaforme dati collegate permettono di ottenere un quadro veloce e imparziale delle opinioni su un brand espresse nei social media. I retailer possono analizzare il sentiment su Twitter, Facebook, LinkedIn o social media specifici di settori particolari. Con una comprensione migliore delle percezioni dei clienti, possono allinearsi ad esse attraverso comunicazioni, prodotti e promozioni specifiche.

Localizzare e personalizzare le promozioni

Retailers that can geo-locate their mobile subscribers can deliver localized and personalized promotions. This requires connections with both historical and real-time streaming data.

Con Apache Hadoop® e Apache NiFi i dati vengono raggruppati per creare promozioni localizzate e personalizzate e diffonderle attraverso i dispositivi mobili. I retailer possono sviluppare app per dispositivi mobili, attraverso cui comunicare ai clienti informazioni su eventi e vendite locali, allineate alle loro preferenze e al luogo in cui si trovano (anche con riferimento a un reparto particolare di un negozio specifico).

In previsione delle festività natalizie del 2013, Macy's ha lanciato un test in due dei suoi negozi monomarca con la tecnologia iBeacon di Apple. L'articolo descrive come, “in futuro, Macy's potrebbe essere in grado di inviare avvisi ai consumatori in base al reparto in cui si trovano, con la possibilità di segnalare una promozione sulle scarpe da ginnastica quando si trovano nel reparto scarpe, o addirittura consigliando prodotti interessanti nelle vicinanze.”

Optimize Websites

Online shoppers leave billions of clickstream data trails. Clickstream data can tell retailers the web pages customers visit and what they buy (or what they don’t buy) on their site. But at scale, the huge volume of unstructured weblogs is difficult to ingest, store, refine and analyze for insight. Storing web log data in relational databases is too expensive.

Apache Hadoop can store all web logs, for years, at a low cost. Web retailers use information in that data to understand user paths, do basket analysis, run A/B tests and prioritize site updates. This improves online conversions and increases revenue.

Optimize Store Layouts

In-store layout and product placement affect sales. Retailers often hire extraneous staff to make up for a sub-optimal layout (e.g. “Are you finding what you need?”). Brick-and-mortar stores lack “pre-cash register” data about what in-store shoppers do before they buy. In-store sensors, RFID tags & QR codes can fill that data gap, but they generate a lot of data.

Apache Hadoop can store that huge volume of unstructured sensor and location data. The intelligence allows retailers to reduce costs and simultaneously improve customer in-store satisfaction. This improves same-store sales and customer loyalty.