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The Business Case for Big Data


Big Data Use Cases

Sometimes use cases can drive points home. In reality, Big Data use cases are as varied as big data itself. Nonetheless, here are some use cases I’ve seen or implemented which may stimulate your creative thinking and better enable you to apply these concepts to your own business.

Customer Sentiment — Customer sentiment is being expressed for every company, product and service in existence over multiple social channels at an increasing rate. Using social monitoring and text mining tools, there’s a compelling opportunity to analyze what prospects and customers think about each of your products or services, as well as what they think about each of your competitors’ products or services, and correlate this sentiment analysis to sales efforts, product mix, marketing spend, advertising expense, loyalty programs, market share, customer share, competitor programs and specific cost and profit measures. This type of correlation is powerful in manipulating company operating decisions to influence customer behaviors with predictive responses. Taking this a step further, there’s also an opportunity to correlate customer sentiment analysis with broad economic factors, specific market indicators, competitor moves or other factors that may uncover patterns that permit companies to model changes for improved customer consumption and company performance. It’s become abundantly clear that companies which do not track customer sentiment are losing customers to companies that do, and who are manipulating their business models or product offerings to capitalize on that customer sentiment.

Customer Experience (CX) — Big Data offers an opportunity to tap into the many internal and external customer interaction and behavioral data points to detect, measure and improve the desired but illusive objective of consistent and rewarding Customer Experience success. Big Data can access and bring together what are normally siloed data repositories housing many types of semi-structured and unstructured data in order to capture the information necessary to achieve a complete view of the customer experience, link this information to demographic, cultural and other preferences, and leverage the information for improved customer services delivery (order processing, product delivery, invoicing, customer support, renewals, etc.), increased revenue objectives (i.e. up-sell, cross-sell and customer share) and decreased customer churn.

Predictive analytics — IMHO, predictive analytics driven by Big Data may quite possibly be single biggest opportunity for business growth initiatives. Leveraging broader data sets to improve visibility and confidence for strategy development, new product introductions, increased geo presence and other business development decisions which incur investment, long periods of roll-out, risk and predicted payback can aid decisions which directly impact top line revenues.

There's no business segment that can't benefit from improved decision making, but here are some line of business examples for business managers to consider.

Marketers are using Big Data to better forecast what products to sell to what customers and when, and how to bundle products to increase sales. Marketers are sifting through external data to determine what products correlate to different customer segments for increased sales conversions. They’re also using big data such as comparable and competitor price lists and market acceptance rates to calculate pricing elasticity for their own products and services. Marketers are using social graph analysis to implement influencer marketing campaigns with some big results. In this scenario, social media data is mined to show which customers or even non-customers have the most influence over others inside social circles. This helps marketers leverage those influencers in a unique way and with results that generally cost less and perform much better than traditional advertising methods.

Sales managers are analyzing website and social media data to identify products and services frequently viewed (i.e. measured by volume of page reads, long page durations and low exit points) but not as frequently purchased in order to uncover the barriers standing in the way of purchase, and unlock otherwise hidden sales opportunities. They also using Big Data to reduce customer churn by combining internal data from customer service systems or help desks with external behavioral data from social streams in public or semi-public venues to detect customer dissatisfaction, predict customer churn and implement remediation measures.

Customer service managers are extracting unstructured data from social networks and social streams to better predict product defects based on consumption rates, usage patterns and geographies, and how product defects occur or accelerate when used with other products. They’re also using this information to take proactive actions and implement remediation measures, sometimes in advance of the defects occurring. By analyzing customer complaints (tweets, SNS/SMS, etc.) along with the volume, trending and responses to those complaints, extracting anomalies or patterns, and comparing the data to defect signs or product complications, proactive action can be taken while costs to repair both products and reputation are low. Some Call Center managers are even tapping into customer service call recordings (using customer profiles, keywords and sentiment analysis) to quickly detect product deficiencies early; not when the period-end reports are compiled and read two weeks later.

Human Capital Management professionals are better optimizing human resource assignments based on environmental factors, customer market trends and their employees’ online social profiles. In addition to cost optimization, many are also seeing results which include increased staff engagement, productivity and performance. In my discussion with the President of SHL, she describes how comapnies can apply big data to permit business leaders to compare and benchmark talent in their companies across staff, groups and even other companies.

Retail organizations are using store security videos to understand in-store customer traffic patterns (which can also be done with IC/RFID tags on carts) and determine how changes to in-store configuration can impact revenues. They are also correlating this information with Point of Sale (POS) and weather data to understand how environmental conditions impact product positioning, promotions and sales.

Risk managers and similar risk management professionals are using more external data sources to uncover correlations and patterns which lead to intelligence that reduces risk and moves management closer toward continuous risk management.

Finance is becoming a more recognized data and decision making enabler to the rest of the business. Finance staff are generally analytical thinkers and financial applications are often the only information systems that tie all other data to profit and loss measures. Finance has the ability to link all other Big Data elements to measures such as customer profitability, Customer Lifetime Value (CLV), product margins and other data sets which link to financial outcomes and therefore must be a part of any Big Data initiative.

Online recommendation engines, fraud detection, risk modeling, research and development and so on, the possibilities of applying more data for improved decision making are endless.

Next - Big Data Risks and ROI >>

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The Business Case for Big Data



Sales managers are analyzing website and social media data to identify products and services frequently viewed but not as frequently purchased in order to uncover the barriers standing in the way of purchase, and unlock otherwise hidden sales opportunities.

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