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Spot disruption patterns with advanced analytics
SOMETIMES disruption is at your doorstep and obvious - an analytics-enabled new business model threatening your business. More often than not, you have to look out for the signposts pointing to an "analytic disruptor" headed your way.
While predictions of this sort are never certain, three common patterns of disruption have emerged. They include 1) a radical lowering of cost; 2) a big jump forward in customer experience; and 3) new business models combining services, technologies and data in unexpected ways.
These are worth a closer look. While data analytics is not the only source of advantage, investors have richly rewarded the companies disrupting industries with analytics - companies like Domino's and Netflix - ascribing to them much higher valuation growth than that of even the best-performing among their large, incumbent competitors.
Radically lowering cost: What lies at the root of potential disruption involving cost and customer experience are often serious product, service or process deficiencies. Analytic disrupters turn these shortcomings into opportunities by addressing and solving them with advanced analytics.
In some cases, advanced analytics can improve low-yield or low-quality processes. For instance, it can optimise the routing of delivery services based on real-time traffic and forecasts, or improve sales force effectiveness using analytics to qualify leads as well as steer and monitor sales approaches.
The use of analytics has also helped improve utilisation and allocation of resources. Examples include car-sharing or workspace-sharing networks that use advanced analytics to enable economies of sharing and connectivity.
In other situations, the systematic use of advanced analytics has made highly routine processes and unnecessary activities or resources obsolete.
For example, automated restocking of coffee pods using a virtual pantry instead of grocery runs is made possible by coffee machines that track and learn from their owners' coffee consumption patterns. Other examples include preventive maintenance or preventive healthcare informed by analytics. These can replace future costly fixes or cures.
Each of these patterns significantly threatens the profits of traditional services businesses.
A big jump forward in customer experience: Data and analytics can significantly improve customer experience and boost customer advocacy of an existing product or service. This can involve enhancing products or services with a high degree of personalisation, making them easier to use, improving their quality and increasing responsiveness to customer issues.
Analytics-savvy players have shown that industries with limited customer understanding are susceptible to customer-experience disruption.
Advanced analytics can draw insights from data on customer interactions, product usage and social media, and flag changing customer behaviour, using methods such as the predictive Net Promoter Score for customer interactions or social listening.
There are opportunities as well to collect useful data, such as information about home appliance usage, which is currently only rarely collected or used to improve products. Sometimes this work involves better recognition of important feedback and learning from it. Industrial distributors' practice of maintaining large safety stocks of products also reflects a lack of awareness of changes in demand. But advanced analytics can inform these distributors.
A case in point is the use of advanced analytics by Chinese e-commerce giant Alibaba for its physical Hema supermarkets. Alibaba uses data on customer location and order history to make personalised recommendations on its app, and to provide a data-driven selection of food and products in its stores, which also act as distribution centres offering 30-minute delivery of online orders within the three-kilometre radius.
Advanced analytics also provides an opportunity to fix generic, impersonal or poor customer experience. Today, it is common practice to use very similar customer service processes to serve your most and least valuable customers without discrimination. Most customer service interactions require users to share information that companies already know about them. All users are treated the same way. But with advanced analytics, personalisation and robotic process automation can address such shortcomings.
Generic, untargeted pricing and a lack of pricing transparency are also weaknesses that advanced analytics can help address, enabling targeted or dynamic pricing that reflects real-time demand and supply. For instance, with the exception of car insurance, most insurance products are still priced based on a few simple demographic data points. This is ripe for disruption.
New business models combining services, technologies and data in unexpected combinations: Of the three patterns of disruption, new business models that combine services, technologies and data in unexpected ways have the biggest impact. They redefine the rules of competition and can disrupt the profitability of entire industries. They are also the hardest of the three to foresee.
These businesses disrupt competitive dynamics and profitability by combining services, technologies and data sources that may not even seem to be related, and in doing so, create a significant and discontinuous change in customer and business value - all powered by advanced analytics and new ways to collect and deploy data.
Familiar examples include how free smartphone apps combine up-to-date mapping data, traffic flows and real-time routing capabilities, thus challenging the established ways by which navigation companies Garmin and TomTom were competing.
Social networking platform LinkedIn has built, on its social media data, assets to support new recruiting services and executive search, challenging traditional players in those fields.
More recently, DataSpark, the big data and geo location analysis arm of telecom provider Singtel, has partnered with MobilityX, part of Singapore's public transportation system, to explore integrating mobile payments into urban transportation systems, improve service in underserved areas, and create transportation planning services for industry parks, hospitals and university campuses.
In the agricultural field, Monsanto is shifting to outcomes-based and new analytics services to avoid disruption by precision agriculture firms that might dramatically reduce demand for its seeds and pesticides.
Harnessing disruption: So, what are the early warning signs for leadership teams to detect when their business model is vulnerable to disruption caused by data analytics?
The best way to evaluate this threat is to understand the activity of data intermediaries and new information platforms that work with data broadly related to your business and your customers.
No organisation is immune. The disruptive force of advanced analytics affects even the most data-savvy companies.
For example, companies such as CloudHealth claim they can reduce an Amazon Web Services (AWS) bill by 10-20 per cent by uncovering over-provisioning and mismatched infrastructure. This threatens to slow the torrid growth in Amazon's profitable AWS cloud services.
Amazon, on the other hand, is challenging Google as the logical starting point for e-commerce searches. Having used analytics to massively improve its targeting and selection of both product and retailers, Amazon has now become the starting point for more than half of US consumers' e-commerce searches, according to some estimates.
Leadership teams can start to assess their own risk and opportunity by asking some fundamental questions: Do any of the signposts above describe what you see in your industry? What competitive moves are you witnessing?
Advanced analytics disruption leaves very little time to adapt. Bold strategies and determined leadership are the only ways to cope with it. How ready are you to transform and run a business to benefit from the disruptive opportunities of advanced analytics?
Will you be predator or prey?
- Rasmus Wegener is a global product leader and partner with Bain & Company's Advanced Analytics practice, based in the Atlanta office. Florian Hoppe is a partner in Bain's Singapore office.