Big Data and Analytics: Creating New Value

The massive amount of data available from connected devices creates an unprecedented opportunity for increased optimization of products and services — and, consequently, revenue. The deep investment in big data gathering and analytics is fueled by an ability to create added value for companies based on actionable business insights, and to create added value for consumers by providing lifestyle benefits.

While gathering and analyzing data comes at a substantial cost, the return can be great for businesses and impacts almost every facet of business operations.

Targeted and Timely Marketing

Big data’s marketing promise is companies working together on analytics for tailored marketing capabilities that reach the right people at the right time. Predictive analysis can provide more targeted advertising, on-device couponing, service reminders in vehicles or appliances, or more rewarding perks and programs based on actual usage trends. Through data-driven insights, brands can take advantage of cross-sell and upsell opportunities.

Other marketing and sales tactics include more targeted and timely offers to increase a brand’s ability to attract new customers, and to retain those they already have. Value-added services improve the customer experience in ways that garner greater customer loyalty.

Companies that know their customers better have a competitive advantage: They can deploy a highly targeted offer rapidly — often faster than their competitors. Media services such as Netflix and Pandora have disrupted their industries not just through their streaming models, but through the ways they have innovated personalized recommendations that significantly increase duration of engagement.

In the retail sector, near real-time analysis of both in-store and online purchasing behaviors leads to faster insights into demand shifts. Stores can adjust merchandise stock levels, pricing and bundled offers to take advantage of shifts before their competitors do.

Amazon and Walmart employ big data analytics to optimize their supply chains, resulting in improved product availability and delivery to the consumer. Retailers also use video analytics and data from consumers’ mobile devices to analyze in-store shopping patterns and adjust store layout to optimize traffic flow and product placement.

Anticipating Customer Service Needs

The volume and velocity of quality performance data, coupled with always-on connectivity, enables improved customer service. Machine-to-machine automation enables the real-time aggregation of performance data from thousands of devices. That in turn enables the rapid deployment of over-the-air application updates to address error codes and security threats, or to deliver enhanced services based on actual usage.

Convenient access to their account, service, and delivery information is becoming a standard customer expectation. When brands respond quickly to anticipate and manage customer expectations, that improves their experience.

Communication systems that enable the choice of mediums and provide automated or personalized messages for customer service or product updates reduce the number of in-bound customer service calls and increase overall satisfaction.

The expensive, time-consuming and often risky process of product research and development is enhanced when consumer data, quality-of-performance data, and predictive analytics can be integrated across marketing, product design, manufacturing and testing. By enabling concurrent engineering and advanced modeling of product usage, companies can streamline time to market significantly, while improving product quality and chances of adoption.

Revolutionizing Business Models

The availability of product usage data also is pushing new data-driven business models. Access to device data provides original equipment manufacturers the opportunity to transform a traditional relationship with their customers. They can shift it from a one-off sale to an ongoing relationship that can take advantage of upsell opportunities and offer services that generate recurring monthly or annual revenue streams.

Companies leveraging connectivity to introduce new business models include the following:

  • Uber’s simple but profound idea for connecting “I need a ride” people to “I’m looking for a fare” drivers resulted in a disruptive data-driven business that is revolutionizing public transportation.
  • In the insurance industry, access to actual driving data, such as speed, braking pressure and cornering behaviors allows insurers to create better risk profiles for individual policy premiums. Insurers like Progressive are using usage-based insurance premiums to lure the best drivers away from competitors.
  • Streaming music service Spotify leverages 600 Gigabytes of user-centric data per day to provide music recommendations or select the next song heard on its service.

While some companies collect and manage user and device data internally, a host of Data as a Service and Analytics as a Service companies have emerged. These companies collect and aggregate data from an array of mostly free — often social media — data sources, and deliver the data to B2B customers via an API or Web dashboard.

The sheer volume, real-time velocity, and variety of largely unstructured data being generated by smart home products make almost any data calculation a big data calculation. Companies are still trying to figure out how to translate all of this data into real business value, but the opportunity is there for applications in marketing, customer service and new business models.

Brad Russell is a research analyst at Parks Associates.

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