According to Harvard Business Review, 73% of customer journeys occur across multiple channels, and omnichannel customers spent 4% more on every in-store shopping occasion and 10% more online than single-channel customers.
Whereas the mentality five years ago focused on brick-and-mortar being replaced by cloud services, the current mentality focuses on using digital and physical channels as complements to differentiate and drive better customer engagement. As a result, businesses are in a race to shift from the “digital first” organization to the “data first” organization, using real-time data insights to directly affect customer engagement and create a 360-degree customer view across all channels.
In this new world, businesses need to translate massive volumes of complex data at unparalleled speed into omnichannel insight, with streaming data analysis, visual foresight and streamlined machine learning. As we see the growing importance of data across the omnichannel, a new mentality and foundation is required.
This new approach must weave data into a unified strategy that consolidates customer experiences and translates them into out-of-the-box strategies for engagement that drive category leadership and competitive differentiation, from Inventory and Supply Chain Management, to Customer 360 for Omnichannel Experiences, to Next-best Offer/Next-best Action Personalization.
To make the most of new types of data in order to understand your customer’s buying process in an omnichannel world, you must be able to analyze new types of data. Customers shop in-store, on the web and via social media channels. It’s your job to quickly correlate social data with point-of-sale systems, and even weather forecasts with wearable devices, in order to build an accurate picture of your real-time customers — and potential customers.
It’s the coveted customer 360, and you must be able to query massive, diverse datasets instantly to reach these prospects with offers that will keep them in your ecosystem.
A massive retail conglomerate in Asia needed to correlate buyer behavior, purchase history, facial recognition, demographic data, and visit behavior — across all of their brands and businesses. In order to consider a customer profile complete, they required detailed information like the following:
- Visited three of our brands in a specific geographic area
- Spendper dollars at each brand
- Has bought what specific products
- Typically visits at this time of day
- Visits correlate with sales/special offers
This requires analysis of a massive amount of complex data — much of it in real time. That demands a high-performance analytical engine to segment, slice and dice the customer data. But it’s well worth the investment in the technology. Once accomplished, the monetization potential on this detailed customer data is priceless.
A related realm is next-best offer personalization, with the aim to provide individually tailored recommendations and offers to customers in real time.
Artificial intelligence (AI) is the key to predicting and personalizing more accurately at speed. You must be able to drill down into various metrics to optimize the customer experience, and you must remove the bottlenecks to real-time data processing. AI can help you do that.
Geospatial database capabilities, on the other hand, are well-suited to tracking retail inventory in real time from a variety of sources, so you can get instant notifications of inventory sold, optimize stock replenishment and avoid out-of-stock situations. These capabilities make it easy to visualize inventory as it moves from location to location, achieving effective real-time inventory analysis and supply chain management. Such geospatial capabilities are only useful, though, if you are able to pose complex questions to diverse sets of data.
For example, one major big-box retailer needed to deploy a system that allowed for complex queries on very large datasets so that they could get real-time insights into their organic food inventory. By fusing all of their transaction data with inventory text descriptions, the retailer can then run advanced analytical queries across billions of rows of data that returns results in under a second, giving them the ability to understand operations at a much more granular level — in real time. Within just two queries, the retailer could understand where their highest organic food product sales were occurring throughout the nation.
In addition, the retailer can integrate, fuse and then make use of high-volume historical and live streaming data coming in from multiple sources. Sources include, for example, social feeds, traffic data, weather detail and demographic data. They can then serve on-demand queries, and perform on-the-fly geospatial analysis, even as the variables at play shift.
This retailer also wanted agile tracking of shipments to assist store managers in tracking inventory, arrival times, workforce planning and most especially perishable goods. Their farm to store model has tight timelines and even thinner profit margins, and the difference of a delivery day could mean the difference between profit and waste.
They rely on GPU-accelerated data analysis and geospatial technology to produce route optimization based on truck size, and on whether the cargo is perishable or contains hazardous materials. They also receive ETAs, notifications and custom location-based alerts.
Data is the glue across all channels, and companies will need to embrace business-differentiating data innovations, from artificial intelligence to using super-fast GPUs, that meet customers wherever they are.
Daniel Raskin is the Chief Marketing Officer at Kinetica, where he is responsible for leading all aspects of worldwide marketing. Raskin has approximately 20 years of experience building brands and driving product leadership. Prior to joining Kinetica, he was Vice President of Marketing and most recently served as Senior Vice President of Product Management at ForgeRock, a digital identity management company. Prior to that role, Raskin was Chief Identity Strategist at Sun Microsystems. He also has held senior executive positions at McGraw-Hill, NComputing, and Agari.