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MIT Technology Review

A platform economy is key when building your modern enterprise technology architecture

In order to meet customer demands for real-time insights and personalized experiences, organizations today must adopt a “platform mentality”—embracing technology that leverages data aimed at building brand loyalty.

Enterprises are investing significantly in their IT infrastructure to deliver the personalized experiences that customers increasingly demand. The average enterprise now has more than 400 custom applications. This increase has resulted in valuable customer data being siloed in each system with unique schemas and governance, making it nearly impossible to access, gain insights and leverage the data in a holistic, fast, and secure way that delivers business results.

A modern, scalable enterprise platform has a robust data pipeline, the notion of a real-time customer profile, and AI and machine learning (ML) at its core to deliver compelling experiences

In an age when customer experience management (CXM) is a mandate, companies need to build a comprehensive view of the customer. Data silos need to be broken to deliver the best customer experiences.

Great customer experiences increase brand loyalty
With the advent of big data, cloud computing and artificial intelligence (AI), there are unprecedented opportunities to deliver transformative experiences for customers. Enterprises are now competing in the “platform economy,” where online gathering places enable a wide range of activities, where companies provide experiences instead of access to proprietary goods and where brand loyalty is built through every interaction with customers.

Effectively using these interactions to increase brand loyalty is often an elusive goal. Seventy-two percent of global enterprise IT decision-makers say their data landscape is complex and difficult to navigate due to the variety and number of data sources. When I speak to executives, they understand the promise of what their data holds, yet 50 percent of them report that it’s inaccessible to a wide variety of stakeholders within their organization.

Platform first = customer first
To compete successfully in a platform economy, enterprises must adopt a “platform mentality.” This means that they need to invest in a platform that allows them to handle centralized as well as virtualized customer data. A platform facilitates collaboration across the enterprise, allowing data engineers, data scientists, IT, business analysts, citizen analysts and stakeholders to work on the data at the level they need. The platform should equip each role in the enterprise with the tools that make them most productive.

Adopting this mentality requires a shift in the way businesses think about their data. A modern, scalable enterprise platform has a robust data pipeline, the notion of a real-time customer profile, and AI and machine learning (ML) at its core to deliver compelling experiences. The platform gathers data globally and has capabilities of transforming it into a language that all applications can understand.

Developing this type of platform eliminates data silos and makes it available via application program interfaces (APIs) in a centralized manner via roles-based access controls. This approach requires that different areas of the organization must be willing to let data flow into the platform. There is clear strategic value in this type of platform mentality. When people working in different areas of an organization, and the data they manage, come together, everyone can serve the customer in an informed manner leading to better business performance.

Building a real-time customer profile is critical
Whether customer data comes from in-store transactions, web interactions or mobile moments, stitching it together to build a unified customer profile is the first challenge in meeting rising customer expectations. The second is evolving the customer profile as customers’ interactions with the business change in real time.

Today, customers have a multitude of ways in which they can interact with companies. For example, one might anonymously search for a new camera during the day. Later that evening, one might make the purchase from their mobile device using the store app. Every single interaction that one has with a company creates a data point – collected from different places at different times on different devices, some anonymous and some known.

Let’s fast forward a few days. The customer now wants to buy a new case and a lens for their camera but when they return to the company’s website, instead of seeing items to complement their purchase, they see an ad for the camera they already bought. That would not be the right experience for the customer.

A real-time customer profile, built using identity resolution across cookie IDs, mobile ID, and customer relationship management (CRM) ID, will tie their engagements together, improving each engagement by personalizing it around customer interests.

Without a platform capable of continually capturing and stitching together different data points for a single customer profile, it is impossible to understand a customer’s needs well enough to deliver the right experience at the right time. And, without a real-time customer profile, companies are working with stale data which might take days to process. By then, intent has changed, and the experience that’s delivered won’t match what the customer is likely to want.

AI and ML with pre-built intelligent services democratize customer insight
Processing the petabytes of data that companies have today is impossible without AI and ML. That’s why intelligent services are key aspects of the modern enterprise platform. By bringing customer data together, AI and ML can more quickly develop accurate next-best-action models. And, as new data is ingested into the platform, the models can be retrained, continually improving in real time.

The internal benefits of adopting AI and ML extend far beyond the data science department to teams throughout the enterprise and, most importantly, to customers. For example, customers benefit when an analysis of employee training and performance data identifies customer service areas in need of improvement. Or when a manufacturing company combines data from their enterprise resource planning and supply chain management systems to ensure their partners have the parts they need when they need them. Because of this, their customers receive their orders on time, every time.

With AI and ML fueling your enterprise technology platform, intelligent decision-making becomes democratized over the entire enterprise, allowing teams in every area of the business to discover insights and better serve customers.

AI and ML also make it possible to do advanced targeting and personalization at scale across the business, based on factors such as interest and behavior, as well as customers’ transactional, financial, and operational patterns. This real-time, accurate, predictive intelligence is crucial for any organization that wants to provide truly transformative customer experiences.

Today’s platform economy requires that companies meet and exceed customers’ ever-changing expectations. Those that adopt a platform mentality and embrace technology most capable of aggregating customer data and mining rich insights will serve a better, more contextualized customer experience and will be the ones that will win the hearts and minds of their customers, therefore building a loyal customer base.