You're a visionary. You see brilliant opportunities, but can already hear the naysayers: "Great idea, but will it be lucrative? "
The naysayers are too often correct. A new business segment that looks like a winner might not be profitable, cost effective or sustainable in the long term. Even when a company accesses the talent of experts, the results can disappoint. Using big data for business growth, however, can help ensure that your organization's strategy hits close to the mark.
When determining whether to move forward with a business growth opportunity, it pays to have a data-backed long term perspective. Considerations might include: What profit margins can be sustained? Will costs remain in line? How can we offset costs? What additional risks are we taking on? Will the initial market size reflect long-term size? What impact will future competitors exert? These are questions that should be asked — and not just by creativity-tamping skeptics.
One potential tool is big data, which offers a perspective on information technology that has taken off in the last decade or so. The grouping of very large data sets reveals patterns, trends and associations that can be especially helpful in gauging cost effective business opportunities. Big data uses three major characteristics as a tool: volume, velocity and variety.
Big Data Volume
Big data's volume delivers a more precise understanding of customers, costs of growth and risk. Currently economics, energy and population dynamics are fields that are actively exploiting big data volume. By way of example, American Express used extremely sophisticated predictive models to examine their Australian market, according to a report by the technology company Pivotal. Tired of "hindsight reporting," according to the report, AmEx used a model with 115 variables to predict which credit accounts in that market were likely to defect and close, with the goal of influencing any outcome that could negatively impact the company.
Big Data Velocity
A sister revolution, the Internet of Things (IoT) is bringing increased velocity to big data. Progressive Insurance, for instance, made waves in the automobile insurance market by introducing a small device called Snapshot®, which users can plug into their car each time they drive, and it collects data from the vehicle in one-second intervals. In turn, Progressive rewards safe driving with better insurance rates. In a 2014 article, "How auto insurer Progressive collected 10 billion miles of driving data from its customers," ZDNet reported Progressive had amassed 10 billion miles of driving data from its customers, and the company didn't stop there: It went on to integrate GPS data into Snapshot®. By virtue of multiple-sensor platforms (e.g., the smart grid, smart health monitoring, smart cars, even smart products that self-report on usage) the velocity of big data accumulation will only increase.
Big Data Variety
Big data allows room for opportunism. Having massive sets of data may just beg the question: What other data sources can be obtained to add value to the data sets already in use? What about leveraging data from hiring websites to understand talent and hiring patterns in locations where you may be considering expansion?
For example, a maker of diapers wanted to develop an ad campaign that reached frequent diaper-purchasers, with the goal of directly translating the ad into sales. The company ran the ad through an online search engine, but soon noticed that the campaign — which featured an extremely cute baby — was delivering clicks from many more visitors than seemed plausible, given the limited size of the diaper-consuming public. Rather than give up on the leads gained, the company then used social media sites and other sources of demographic data to more closely hone in on its target market: people who were actually interested in buying diapers, not just looking at cute babies.
This is the goal of an emerging specialization: customer profitability analytics. Using a variety of big data sets can be the key to aligning product and service offerings with specific market segments.
Knowledge Driven by Big Data
Astute business unit and product managers with access to big data resources – which is, granted, a major assumption – can leverage them to better inform both ongoing monitoring processes as well as future growth forecasts. That might include:
- Building products, IoT objects and services that collect data about themselves
- Accessing market meta-indicators provided by sources outside the company
- Examining margin growth using big data analytics such as Bayesian models, neural nets or deep learning
- Crowdsourcing product ideas, feature innovations or new pricing models.
Organizations considering expansion should offer both their "ideas" team and their skeptics a steady diet of big data for business growth, whether developed internally or merged with third party information. The resulting course of action will more closely align with market needs and company objectives, thereby improving the chances for effective growth. Properly situated in an enterprise, big data can both nurture visionaries, and give those whose job it is to be cautious the necessary information to steer growth and innovation successfully.