The definitive Big Data analytics checklist for smart marketers posted on Mon, January 28 2013 in Analytics, Big Data, Data Scientists, Data Warehousing

Convinced about the potential of Big Data but looking for concrete examples of how it can help you - as a marketer – to gain a 360 view of consumers, up the customer experience and drive revenue? Well here are five goodies that Big Data tools have to offer to woe and keep clients.


Recommendation engines are perhaps the best-known, specific Big Data analytics-driven tool. They are all about customer micro-segmentation and improving their experience. The best-known examples are, of course, Amazon’s use of real-time, item-based, collaborative filtering (IBCF) to fuel its ‘Frequently bought together’ and ‘Customers who bought this item also bought’ features and LinkedIn suggesting ‘People you may know’ or ‘Companies you may want to follow’. Amazon generates about 20% more revenue via its recommendations. That could be your company.

Sentiment analysis

Using real-time mapping, web data extraction ‘brand listening’ techniques and sophisticated text mining tools can allow you to follow conversations on social media sites, filter them for relevance and process the content using natural language algorithms. This sentiment analysis enables you to determine how consumers really feel about your company, brand or services, from a macro-level down to individual user sentiment.

These insights can even be integrated with retail point of sales (POS) data from tills to better understand how emotions about the brand translate into actual sales or, when matched to news stories and weather conditions, to enhance B2C communication. They enable you to target people who already like your brand individually in-store or with relevant digital marketing content.

Call center monitoring & reporting

Companies that work with call centers can activate Big Data analytics to monitor and report the calls with fully automated solutions. These convert voice into text and allow calls to be ranked in different categories such as ‛operator friendliness’ or cross-selling and up-selling opportunities. Solutions like these offer obvious implications for performance management, quality monitoring, risk and compliance optimization, sales effectiveness and service satisfaction. Some even offer the additional benefit of being able to look at e-mail correspondence and online chat.

In-store behavior analysis

Big Data insights bridge the online-offline dichotomy. They provide retailers with the possibility to mine their databases in real time whenever a customer is browsing their store and extract (from past purchases, clickstream behavior or activities on the company’s Facebook page) how he should best be approached or what he is willing to pay.

Some of the leading-edge retailers apply Big Data technologies to analyze video streams from their in-store camera systems and create mappings of customer foot traffic throughout the stores. This Big Data stream is then merged with sales data which allows retailers to develop new applications that help optimize product placement in the store.

Reducing churn

Big Data tools tell you when customers are making negative comments about you online, when they have partially switched to the competition or when their shopping basket has changed in content or size. They allow vendors to take preventive action, before the damage is done. For instance, you could use speech analytics to monitor the conversations of customers who were terminating their accounts and identify other at-risk customers by comparing their discourse with those tell-tale keywords and phrases.

If you want to know more, read Greenplum’s in-depth booklet ‘Analyzing Customer Behavior‘.

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