4 Ways to Transform Your Business Through Customer Analytics

Companies regularly collect reams of data from their customer interactions and operations. Increasingly, they are looking to build capabilitiesthat can synthesize this raw asset into actionable insights (a competency known as Data Analytics), dramatically improving operational performance, enabling promotion & product ‘mass customization’ and spawning new business models. Leveraging data, however, is easier said than done. Many companies do not have a data-analytics vision and, therefore, tend to underestimate its potential impact. Before investing in capabilities (the combination of talent, technology and math), managers should first consider how analytics can transform their business.

For select firms, data and the capabilities that manage it is a competitive differentiator, on par with other valuable assets like a brand. Recent academic research shows that companies that use DA to guide decision making are more productive and experience higher returns on equity than competitors that don’t. However, not all industries offer the same opportunities. Some sectors like entertainment, construction and services will have modest requirements for high performance analytics, given their market dynamics and structure,. Based on our consulting experience, we believe that companies in retail, manufacturing, banking, telecom, wholesale, and healthcare industries are best positioned to exploit the breakthrough opportunities provided by data analytics.

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Brian Ross, president of Precima, a part of the LoyaltyOne analytics solution is on the front lines of transformational DA. "We believe that today’s advantage is quickly becoming tomorrow’s necessity. The first step in this transformation starts at the top. The C-Suite has to establish the long-term vision and align the organization to build the capabilities, processes and tools." Strategy-minded leaders should consider the following four areas for breakthrough DA:

1. Optimizing operations

Powerful analytics can significantly improve operational performance, reduce cost and minimize risk. For example, collating supply chain data onto one integrated platform will allow manufacturers to better collaborate with suppliers during product development, reducing cost, shrinking development time and minimizing the risk of costly errors. In other cases, analytics can enable the deployment of self-optimizing manufacturing systems. McKinsey has written about impact of data analytics on the oil industry. Operational data from wells, pipelines, and mechanical systems can be collected and analyzed, feeding back real-time commands to control systems that adjust oil flows to optimize production and minimize downtimes. One major oil company has used this approach to cut operating and staffing costs by 10-25% while increasing production by 5%.

2. Transforming marketing & products

Real time data analytics enables companies to quickly customize products and promotional offers on the fly for different customer segments. As an example, retailers can track the behavior of individual customers through their usage patterns — both at their site, through social media and from location-specific smartphones — and predict their likely behavior in real time. Once they can predict behavior, retailers can better drive purchases by triggering customized offers, special discounts, or product bundles. In another example, McKinsey works with a personal-line insurer client who leverages DA to tailor insurance policies for each customer, using constantly updated profiles of customer risk, home asset value and changes in wealth.

According to Brian Ross, "Our most telling case studies today lie in enhanced one-to-one communications between vendor and customer. We have seen DA deliver impressive results of 90+% sales lifts, direct response rates as high as 87% and 4% retention gains."

3. Enhancing decision making

The capability to quickly process and synthesize large amounts of data opens up the possibility of using controlled experiments to test different scenarios around important investment, marketing and operational decisions. For example, Amazon assigns a number of their web page views to run experiments; they seek to understand which factors promote sales and drive higher user engagement. McDonald’s has equipped some restaurants with sensors that gathers operational data through tracking store traffic and ordering patterns. The gleaned insights are used to model the impact of variations in menus, restaurant designs, and training on sales and operational productivity.

4. Enabling new business models

Firms with world class analytics competencies have the opportunity to germinate totally new business models and services. McKinsey has worked with a global manufacturer that learned so much from analyzing its own data that it decided to create a new business doing similar work for other companies. This service business now outperforms the company’s manufacturing one. Information aggregation is another business model that can be spawned from analytics. Consider this: UPS regularly collects a mountain of data on shipment patterns, energy usage etc. on the estimated 3-5% of U.S. GDP they ship annually. This data could be mined, synthesized and then sold to organizations that provide economic forecasting services.

Within five years, analytics will be a game-changer for many companies. However,building capabilities will not be easy or inexpensive. Developing a bold vision of analytics; transformational impact is a good first step.

Mitchell Osak is managing director of Quanta Consulting Inc. Quanta has delivered a variety of winning strategy and organizational transformation consulting and educational solutions to global Fortune 1000 organizations. Mitchell can be reached at mosak@quantaconsulting.com