By Noel Yuhanna and Mike Gualtieri
Digital technologies empower today’s customers, disrupt every industry, and cause your executives to question their competitive strategies. In the age of the customer, the only sustainable competitive advantage you can have is the degree to which you know and engage with your individual customers.
But despite the importance of this new technology, progress has been slow for most firms.
Traditional approaches are insufficient because they don’t deliver what today’s customer expects — engagement — and lack granular and contextual customer data. Executives don’t find actionable meaning in the data, and fail to use personal data in customer experiences. Some companies — think Netflix Inc. with its Cinematch recommendation engine and Wegman’s Food Markets with its in-store mobile app — are already excelling at creating the engaging, individualized experiences around your products and services that today’s customer demands.
The key to success is implementing a multidimensional view that helps individualize and contextualize customer experiences, deliver new customer insights, and create new opportunities for businesses to deliver differentiated experiences. This requires a new IT architecture that can support faster insights, process larger amounts of data more quickly, enable predictive analytics, and support the integration of information from both inside and outside your four walls. Forrester defines a multidimensional view of the customer as:
A view of the customer that uses all of the available information about them — including information pertaining to psychographics, behaviors, social networks, smart devices, geolocation, and Internet usage — to deliver individualized and contextual products, services, and experiences.
The four key technology components of such an architecture are Big Data, predictive analytics, in-memory technologies, and data virtualization. The business benefits of these four technologies help overcome the gaps and limitations of traditional data management platforms to support real-time data integration, exploit new data sources, and speed the generation of predictive customer insights.
Big Data is critical, but is only one piece of your customer data management solution. We often hear from clients that they need a “Big Data strategy”. What they really need is a holistic strategy that includes Big Data, predictive analytics, in-memory, and data virtualization. Here’s what you need to know about each of these technologies:
Big Data Is The Fuel That Drives Your Customer Experience Engine
You must consider every shred of customer data available for analysis, as it may contain gems that you can use to individualize experiences. Traditional data management solutions and approaches have difficulty consolidating and processing the array of large and unstructured data sets that defines Big Data. To support a customer Big data platform, you need new technologies and architectures, including Hadoop, NoSQL databases, advanced enterprise data warehouses, and cloud analytic platforms.
Predictive Analytics Learns About The Individual Needs Of Your Customers
Predictive analytics uses machine-learning algorithms to dig deeper to find patterns that you can’t see using traditional BI tools. Big Data has breathed new life into predictive analytics, as more data can lead to better predictive models. Firms use these predictive models to anticipate what individual customers want, just as Pandora Media Inc.'s recommendations engine (a predictive model) provides personalized song playlists. Predictive analytics requires a breadth of tools and technologies to store, process, and access the volume, velocity, and variety of Big Data. Predictive analytics includes general-purpose Big Data predictive analytics solutions, industry- or domain-specific solutions, embedded solutions, database analytics, and consulting offerings.
In-Memory Data Solutions Delivers Value In Real-Time
Ever wonder why Google Inc. searches run so fast compared with accessing data in your business applications? Memory matters! Customer data stored and processed in memory helps create an opportunity to host predictive models and data needed upsell and cross-sell new products to a customer in real-time. Key technologies that can help deliver real-time customer experiences include in-memory platforms and event processing platforms.
Data Virtualization Is A Silo Crusher
Data silos are still a huge problem for most firms. Data virtualization integrates disparate customer data sources in real time or near real time to deliver a comprehensive multidimensional view of the customer to support personalization. Data virtualization can support all types of data: structured (text, relational data, and formatted data), semistructured (XML and files), and unstructured (emails, blogs, images, and video). It can integrate with Hadoop, NoSQL, and enterprise data warehouse platforms, and various on-premises sources such as packaged apps, custom apps, and mainframe apps. Data virtualization can also integrate with external sources: social platforms like Facebook Inc LinkedIn Corp. , and Twitter Inc.; software-as-a-service applications like those from Salesforce.com Inc. SAP AG ’s SuccessFactors, and SugarCRM; and marketplace data-as-a-service applications such as DataMarket Inc, Dun & Bradstreet Inc., Factual Inc., and Microsoft Corp. ’s Windows Azure Marketplace.
Your customers increasingly expect and deserve to have a personal relationship with you and the hundreds of firms in their lives. Companies that continuously ratchet up personalization will succeed. Those that don’t will be increasingly become strangers to their customers. This sounds bad — but there is good news. The world is flush — and getting flusher — with Big Data from cloud, mobile, and the Internet of Things. Firms that invest heavily in a holistic approach will be primed to take advantage of its customer data and use it as a differentiator.
Noel Yuhanna and Mike Gualtieri are principal analysts at Forrester Research Inc.