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July 20, 2015

From Big Data to Big Win: Solve an actual business need

The world of Big Data is changing dramatically right before our eyes – from the amount of data being produced to the way in which it’s structured and used.

By Cbr Rolling Blog

According to CSC, data production will be 44 times greater in 2020 than it was in 2009. This trend is echoed by Salesforce CEO, Marc Benioff who declared at Dreamforce 2014 that, "There’s going to be 10 times more mobile data by 2020, 19 times more unstructured data, and 50 times more product data by 2020." Whatever shape Big Data goes by and however much there is, it presents both an enormous challenge and an incredible business opportunity.

When looking at the challenge of Big Data, the kneejerk inclination has been to focus on the ‘Big’ and positioning it as largely an infrastructure concern for the CIO. This is not surprising as the majority of vendor and analyst messaging has framed it as such when trumpeting the features of Big Data solutions.

To be sure, these solutions are absolutely necessary when it is an infrastructure investment to prepare your data for future use. However that is only part of the story. If businesses want to harness Big Data to drive revenues – and make it a Big Win – the technology needs to fit a defined business need, rather than be a solution in search of a problem.

The way to a Big Win is to focus on the end result

Turning Big Data into a Big Win starts by identifying what wins look like across the organisation – not just for CIOs. It’s important to understand that every customer-facing department now has a stake in Big Data, and every senior role – be it the CSO, CRO, CXO or CMO – can lead on procuring a solution. In fact, with a new proliferation of start-ups and point-solution providers, pretty any seniority level in the organisation can buy a Big Data solution.

It no longer has to be an IBM-level of investment, with a 12 month deployment time. Tools such as predictive lead scoring, content intelligence, and social media analytics can be trialled at a low cost, and scaled as early business results prove their worth.

Perhaps your organisation’s Big Win is in Sales: improving deal cycle velocity by enabling your SDRs to identify and pre-empt sales-ready prospects based on the behavioural signals from their content consumption. Perhaps your Big Win is in Customer Service: improving your Net Promoter Score by providing teams with in-call propositions and recommendations based on each customer’s revealed interests. #

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Perhaps your Big Win is in Marketing: analysing digital body language across owned marketing channels and social media, in order to understand and engage more effectively.

Whatever it is, the ability to collect vast amounts of data on individual customers – their content consumption habits, their preferences, their interactions with the company – and then analyse those data sets for predictive behaviour is a powerful way to provide a better experience at every level of the org chart. These insights can be used to optimise interactions with both your existing customers and new ones coming into your call centre or your website or your office.

Making Big Data a Big Win depends on analysis

There are many ways that Big Data can be analysed and parsed to make it useful and actionable for actual business needs. Let’s take some examples from our home territory – marketing:

Descriptive: These types of analytics describes the data you already have. If applied to your marketing assets, this might include the topics and themes contained within each piece of content, or the metadata descriptors (size, date created, file type, etc) for each piece of content. If customers interact with this content in digital channels, descriptive analytics could also include how many page views or click-throughs have been generated in a certain time frame.

Predictive: This analyses customer behaviour across multiple data points in real time to perform predictive segmentation and adapt the communication experience. The most common use of this in marketing is for predictive lead scoring – allowing you to filter low-probability leads and prioritise the best with rich data to profile the value and context of sales prospects.

Prescriptive: This kind of analytics displays the concrete steps you should take with your marketing strategy. If you’re using content marketing, prescriptive analytics can be used to optimise your content strategy against a known ‘best content strategy fit’. For example, showing you how much more content you need to create and which particular topic it should be about to optimally meet your audience’s needs.

Depending on what constitutes a ‘Big Win’ for your organisation, either descriptive, predictive or prescriptive analytics (or more likely a combination of all three!) might be the best fit for you. Identifying your most critical business needs will inform the lens you use to analyse your data and the type of analytics solution you use to do it.


By Andrew Davies, CMO and Co-Founder of idio, a provider of content intelligence.

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