Big Data in Big Companies: 5 Examples of Successful Projects
Big companies have more data at their disposal than ever before. Few would argue that they don’t feel this data has value. Big data is not a new concept, but extracting useful insights and turning those into actions is easier said than done.
One of the many challenges, between companies recognising that tapping into big data would be useful and implementing an actionable plan, is the variety of quality of data sources. Some companies, such as a payment or credit card company, will have mountains of data to analyze.
Data is not always a high enough quality to be useful. Too much data is unstructured, messy or simply doesn’t contain enough value to be helpful. Unstructured external data could be useful, but again, the volumes large companies are exposed to is enormous. For big companies, they want to do something, and they have the resources, but as the chief science officer of AIG, Murli Buluswar, said to McKinsey, “Initially, it largely ends up being imagination and inertia.”
Despite these challenges, some big companies around the world have started to benefit from analysing internal and external big data sources. Here are five examples of successful projects from companies around the world.
#1: Major US Wireless Provider
Recordings of voice conversations in customer service centres weren’t always easy to analyze at scale. There is a difference between a manager listening to a few calls when conducting a weekly quality appraisal, or assessing a complaint, and trying to extract data from hundreds or thousands of calls.
However, one major US wireless provider wanted to know why so many people called into the customer service centres after making payments. When the data was analysed, it showed that the majority of calls were very similar queries and although short in duration, caused call volume spikes that negatively impacted other key performance indicators (KPIs). The data showed that customers wanted reassurance that when payments were late that service wouldn’t be interrupted.
Once the company understood this, customers were given information through other channels to confirm that service would not be interrupted and calls dropped dramatically.
#2: Wellmark Blue Cross and Blue Shield
As you can imagine, the largest health insurance provider in Iowa and South Dakota, with over 2 million members, will have a lot of data spread across the company.
Wellmark was also having problems with large call volumes at their customer service centres. A rudimentary analysis of calls showed that six percent of their customers accounted for 50% of the calls that came in. So they needed to understand why these customers, many of them elderly, were calling so often.
After they deployed a new big data solution, they were able to untangle common reasons for calling and turn the answers into a wide range of materials they could use to give customers that ensured call volumes reduced.
#3: GE Digital
General Electric (GE) is a multinational industrial conglomerate with 330,000 staff, dozens of divisions and revenues over $120 billion (2016 figures). It has also been one of the fastest large companies to embrace digital transformation and big data, earning it the chance to exploit emerging market opportunities and new technologies quicker than slower-moving competitors.
GE’s CEO, Jeff Immlet, is eager to “digitalize the industrial space”, creating, what GE is calling, the “Factory 3.0”, which brings together data, software and hardware.
One of the many ways they are doing this is optimising the supply chain. As Vince Campisi, chief information officer at GE Software explains in McKinsey: “We’ve been able to take over 60 different silos of information related to direct-material purchasing, leverage analytics to look at new relationships, and use machine learning to identify tremendous amounts of efficiency in how we procure direct materials that go into our product.”
#4: Weather Channel
In America, the Weather Channel enjoys sufficient viewer figures to provide advertisers with an effective way to reach a wide range of demographics. Advertisers can also reach customers through apps and Location FX and Weather FX, two crucial data platforms in the large and fragmented American advertising landscape.
The Weather Channel went into partnership with Pantene, a popular shampoo brand, and Walgreens, a nationwide retail chain. Using data they collected, Pantene and Walgreens were able to target female consumers with adverts to buy shampoo and other Pantene products that would reduce frizz when humidity spiked. Branded as “haircast”, this generated 10% extra sales of Pantene products in Walgreens in July and August 2016.
Another campaign involved a pizza chain that sent location-based advertising when the weather dropped to encourage people to buy pizza, which also resulted in higher sales.
Worldwide, Starbucks has over 23,700 stores with a presence in 74 countries. But no other country in the world has as many Starbucks as America, with the current count exceeding 13,300 retail locations.
In some cities, you can walk or drive between one and another and your coffee will still be hot. They are everywhere. And that is no accident. Starbucks doesn’t open stores close to other stores in the hope that one will take customers away from the other. Starbucks has benefited from big data insights to assess traffic patterns, foot traffic and other data to understand where they should open new locations. They have even created a planning and development application to leverage big data more effectively when assessing where to open a store next.
With the right – most relevant, useful – data, a structured approach to analysis, internal or external data experts, and fit for purpose technology, big companies are starting to make enormous practical gains from big data.