In the present information age, due to data overload, marketers are often ignoring simple basics of the game. There is too much focus on big data and its ensuing analysis so marketers are actually ignoring the critical individuals. On the surface, technology and the gadgets used appear very useful, but the clutter is taking away far too much time for marketers to sieve through critical business intelligence. As a result, loyal customers of brands are getting shut down by the noise of the masses. Customer retention is a comparatively a far less expensive process than new customer acquisition, so the focus must shift back to servicing the loyal existing service or product user.

Source:http://customerthink.com/satisfying-your-customer-success-teams-with-big-data-analytics/

Data scientists are inseparable with modern business but they must be guided by more holistic strategy. A common fallacy is that marketers or managers are not aware of how to ask data from such scientists. There exist some better ways to ask the usual questions. It must be pondered what question to ask as the business analytics performed may have far-reaching repercussions. Questions posed must be specific and actionable. Also what kind of data needed is important. Cerner requires data from the US Department of Health and Human Services as it supplies health care solutions. Similarly, i-Medicare selects policies based on data from Centers for Medicare and Medicaid Services. Thus only relevant data must be taken out though it needs to be processes as not all desired information may be contained in raw data. Public data may also be useful in some circumstances. Private lending data has great vintage value due to apt comparisons to be made. Another question is how to obtain the data with what all being the sources. Facebook’s perfectly legal experiment to assess customer emotions was not well received by the public leading to a lawsuit even. Also with enormous data warehousing now done, a lot of it unfortunately is junk. In fact ninety five percent of the data worldwide is unstructured which makes it very difficult to process. Also a lot of it is inaccurate. Most importantly, data scientists and their implementers must make use of the KISS principle. The Model must never get too complicated.

Source:https://hbr.org/2016/11/better-questions-to-ask-your-data-scientists?referral=00563&cm_mmc=email-_-newsletter-_-daily_alert-_-alert_date&utm_source=newsletter_daily_alert&utm_medium=email&utm_campaign=alert_date&spMailingID=15983803&spUserID=OTY0OTMwNTk5NwS2&spJobID=904362140&spReportId=OTA0MzYyMTQwS0

With Netflix putting a lot of traditional video rental retail stores’ business into disarray, a few loyal customers felt uncertain by the changes. This could be out of a lack of trust on the new players, being inconvenient to them or simply lack of access. So in stepped Red Box which leveraged the rise of the vending technology to recycle the older business in its new avatar. Many such vending machines now interface intelligently with customers with them selling a range of products from custom- baked pizzas to smartphones and premium cosmetics. Such interfaces produce huge amounts of Big Data which brands then use to understand the market better. Even in rural markets, the likes of Sephoria and Best Buy are dispersing certified organic produce.

Source:http://innovationexcellence.com/blog/2016/11/22/welcome-to-the-new-era-of-automation/

With the advent of big data, there is enormous amount of information to be gleaned. Yet studies suggest that in spite of the vast sums available, even in data wise well endowed firms only a fifth of employees are actually using business intelligence reports. This is primarily due to a lack of data literacy. So a lot of organizations are trying to develop ways to solve this conundrum. One of those is using graphics and charts. While pictures have been known to be easier to understand than wordy scripts, that may not be accurate when it comes to complex charts or graphs which a lot of people do not have the necessary orientation to grasp. This gap is fortunately reducing thanks to usage of Artificial Intelligence (AI) especially where Natural Language Generation (NLG) can take place. This is allowing bots of predict human behaviour to act accordingly. The Alexa interface can actually make conversation. Examples abound from various industries such as inventory management in grocer shops, financial analysis firms, trainings at call centres that have leveraged such bots that develop intelligence based on human conversations. These use the embedded analytics to solve real problems.

Source:https://hbr.org/2016/11/bots-that-can-talk-will-help-us-get-more-value-from-analytics?referral=00563&cm_mmc=email-_-newsletter-_-daily_alert-_-alert_date&utm_source=newsletter_daily_alert&utm_medium=email&utm_campaign=alert_date&spMailingID=15976846&spUserID=OTY0OTMwNTk5NwS2&spJobID=904287451&spReportId=OTA0Mjg3NDUxS0

Big Data has changed the way the business world works. The enormous amount of data now available makes it easier for companies to process granular bits of information but at the same time makes the entire process more complex. It allows several other laggards to catch up to industry leaders in far less time than was earlier possible. To put things into context, Wal-Mart handles in excess of a million transactions every hour, overall business transactions every day will rise to above four hundred and fifty billion by 2020, volume of business data doubles every fifteen months or so and more than five billion people are using mobile phones in some capacity worldwide. This has led to business disruptions with innovative companies such as Amazon, Google, Facebook, Airbnb and Uber transforming to industry leaders. Companies that have leveraged this data to conduct business analytics have managed to identify trends and work accordingly, giving them unbridled work advantage. However, in order to be part of this transformation, a cultural shift needs to take place. Professionals must get acquainted to handling such data or using tools such as Hadoop to process the same. Organizations need to be aligned to this new reality so that change management and coordination can be seamless.

Source:http://www.forbes.com/sites/ciocentral/2016/11/08/another-side-of-big-data-big-data-for-social-good-2/#77e18c9f6663

Imbibing business analytics into work can make wholesale changes into the marketing culture. First of all it helps in breaking past the traditional status quo. Instead of relying on gut instinct, one can take help of real time data to take informed decisions. Story-telling is an art that is increasingly being used by marketers to highlight achievements or important events with easy recall value. Business insights gleaned through data allows the team to uncover more such stories as granular information can be sought. Analytics also allows the marketers to study a variety of metrics. Some of those metrics could be aspects such as – emails sent, open rate, opt-out rate, click-through rate or conversion rate. Adjustments to existing plans can also be made seamlessly if analytics is clearly integrated with the work pattern.

Source:https://hbr.org/2016/10/how-an-analytics-mindset-changes-marketing-culture

Not doing anything is certainly not an option, yet over analysis may also not be a business solution. With the onslaught of Big Data, companies are conducting high level number crunching to deduce the figures. Yet not all trends which come out need be accurate. There exists a major gap between raw data and actual business insights. A lot of so called insights may simply be coincidences. That is why business leaders have such difficulties in gauging genuine insights about their customers. Most of the trends captured are simply rehashes of demographic or psychographic analysis. The co-relation may hardly exist between a person’s ethnicity and the newspaper read for example. Answers to genuine questions may also be multiple. The likes of Apple and Google have reached a stage where their innovations are questioned by the market. Yet even such giants struggle to process the raw data they extract to form coherent business intelligence.

source:http://www.forbes.com/sites/rogertrapp/2016/10/27/why-leaders-face-a-tough-job-finding-what-customers-really-want/#6a27123703ca