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Can big data transform customer service?

Can big data transform customer service?
October 4, 2013in Business2013-10-042014-10-01businessen
Organizations are falling over themselves to mine value from the terabytes of data that they collect from customers, machines and the environment. Real Times investigates whether big data can tell businesses more about their customers to guide both product development and customer support.
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Organizations are falling over themselves to mine value from the terabytes of data that they collect from customers, machines and the environment. Real Times investigates whether big data can tell businesses more about their customers to guide both product development and customer support. Ultimately, it may even be able to tell them how much their customers are worth.

With these potential benefits, it’s no surprise that a report from analyst Gartner found that 64% of organizations were either planning or investing in big data tools and technology in 2013. But it added that deployments were still at an early stage as enterprises assessed what the technology could do for them.

Customer experience is the most popular area of investigation in the Gartner survey, with 55% of respondents looking into that. “For big data, 2013 is the year of experimentation and early deployment,”  said Frank Buytendijk, research vice president at Gartner. “Adoption is still at the early stages with less than 8% of all respondents indicating their organization has deployed big data solutions. Twenty percent are piloting and experimenting, 18% are developing a strategy, 19% are knowledge gathering, while the remainder has no plans or don't know.”

building a clear plan

This measured approach is recommended, because success is much more likely if you have a clear idea of what you are seeking – even if the questions change as you gain more insight. Forrester analyst Martha Bennett says that one of the biggest reasons that big data projects fail is because organizations are not starting their project with a question.

“Successes don’t start with a data scientist or other expert simply ‘exploring’ the data or running random algorithms,” she cautions. “Sure, such techniques will yield results; but without context, there’s no way of telling whether these are noise or signal.” It could be simply looking for a goal such as improved customer service or customer profitability and building questions out of that.

improving customer service

One of the most important insights big data offers is that it shrinks what is unknowable about the customer. “Almost everything is knowable,” explains Gareth Herschel, Research Director at Gartner. “The number of things we don’t know but cannot know is shrinking, because there is a lot of information out there that we can collect.”

Knowing more about the customer means you can have a much more personal interaction with them. Big data and analysis is what makes the customer experience of market leaders like Amazon so impressive. Customers can receive personalized service according to their background, loyalty, mood and type of call.

providing offers in real time

Hershel cited an example of a European mobile operator that used big data to understand the current mind-set of a calling customer. The system could determine the customer’s sentiment from mining social media and previous interactions, and provide the customer offers in real-time. These took into account the context of the contact, their likelihood of churning and expected profitability.

“Because the agents knows everything about the customer and product, they are able to smoothly transition from a service situation into a sales situation,” explains Hershel. The key to the success of this interaction is providing real-time contextual information at the point of contact.

feeding product development

Another interesting example cited by Gartner related to US FMCG company General Mills. It used big data tools to analyze all its customer support interactions to identify production problems and potential opportunities for new products.

This analysis had picked up a lot of enquiries around ingredients – specifically around gluten-free products. Customer support handed this information to marketing and product development resulting in a gluten-free product, Rice Chex. General Mills was able launch this product to market very successfully with minimal marketing spend.

calculating customer value

Perhaps the most important question big data can answer, is how much are my customers worth? This value is not just calculated in how many products your customers buy or are planning to buy. It also needs to take into account their complete value – whether this is supporting other customers, being advocates for your products, or offering insight on product design.

“Customers are your most credible and persuasive marketing and sales resources – much more knowledgeable about buyers’ needs, and much less expensive than the resources you’re probably currently using to grow your business. Big Data needs to capture this reality and focus your organization on it,” writes Bill Lee is president of the Customer Reference Forum, Executive Director of the Summit on Customer Engagement, in the Harvard Business Review.

So how to get started? Gartner recommends the following steps for overhauling customer service with big data:

  1. find 10 examples of big data best practice for customer insight in your industry
  2. focus on customer processes where big data enables customer innovation
  3. identify three main opportunities for big data projects
  4. identify what other parts of the organization needs to be involved in the initiative
  5. build the business case for delivering actionable customer insights
  6. build a joint business and IT team that can make this project a reality
  7. hire in necessary skills. Evaluate and choose necessary technologies
  8. deliver pilot. Show business value and iterate

read more about Big Data in Real Times

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