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Customer service improved by big data

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In an increasingly global and commoditised world, differentiating on product and price has become a challenge to say the least. The fact is that any product, solution or service your organisation can deliver, no matter how innovative at the time, can and will be copied and offered by someone else in short order. The key then to attracting, and perhaps more importantly retaining, customers, lies in delivering enhanced value and a better customer experience, says Gary Allemann, MD of Master Data Management.

Improving the customer experience requires that organisations utilise all available data from all customer touch points and channels, and this in turn requires big data. However, simply analysing the data is not sufficient, as poor quality data will lead to poor and inaccurate insight. Ultimately, it is the combination of big data and data quality that will drive improvements to the customer experience, enabling organisations to differentiate based on service levels, which is fast becoming one of the only avenues for competitive advantage.

For any organisation of any size across any industry segment, the customer experience is one of the most important factors that can be controlled, manipulated and improved. Should a customer have a bad experience with your business, the chances that they will become a repeat customer are slim to none. Considering that maintaining existing customers is easier than winning new ones, this is a serious problem. In fact, according to CMSWire, the United States loses approximately $83-billion a year on defections and abandoned purchases due to bad experiences.

In addition, in a connected, consumer-driven world, there can be no ‘sweeping bad experiences under the rug’. There are simply too many channels for word of mouth to spread, and social media is king when it comes to disgruntled consumers spreading stories about bad experiences far and wide. The influence of such bad publicity can be catastrophic for businesses, not only resulting in losing the customer who had a bad experience, but also thousands of potentially new ones. In fact, according to Ed Thompson, an analyst at market research firm Gartner: “Between 5% and 10% of companies truly have a customer culture at their core, but the rest have been forced to care because all other means of differentiation have been eroded over time. That’s why it is currently a hot topic and has been very high on CEO agendas for the last three years or so.”

Delivering an excellent customer experience is one of the best ways of ensuring customers become repeat business. Great experiences are likely to be shared with family, friends and networks, and given the power of word of mouth (or social media), this can be a powerful tool in competitive differentiation and improving the bottom line. However, the customer experience is a continually moving target, as customer needs change, evolve and adapt. Therefore, achieving improvements to the customer experience requires a sound and in-depth understanding of the customer, their needs, requirements and preferences, on an on-going basis. This in turn requires organisations to be able to compile, analyse and create insight from all available information about the customer. In other words, it requires big data.

Big data helps organisations better understand their customer, bringing together all touch points and communication channels for analysis to ensure organisations are delivering the optimal customer experience and adapting as customer needs change. The key is agility – the ability to identify areas for improvement or innovation quickly and deliver on this fast.

However, improving the customer experience requires not just more data, but quality data.

On the one hand companies are under pressure to deliver customer analytics quickly in order to stay ahead of the competition. Gartner says that building open source big data analytics solutions from scratch can take 18 months or more – can you afford to give your competition an 18 month head start? More than 65% of the time can be allocated to so-called data munging – consolidating, integrating and cleansing various sources until they are of sufficient quality to be used.

On the other hand, poor quality data results in poor quality analytics, which in turn delivers inaccurate insights that can be detrimental to decision-making ability.

Manually reviewing big data for quality and consistency, however, is a time consuming task that does not support the required levels of agility. Ultimately, improvements to the customer service require investment not only into big data solutions, but also data quality tools for big data.

In a consumer-driven, commoditised world, poor customer service can have serious negative implications for any organisation. Not only does the immediate lost revenue of an unhappy customer impact the bottom line, the reputational damage caused can have far-reaching consequences. Improving the customer experience requires intelligent insight into the customers themselves – just one more reason to invest in big data and data quality solutions.