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There are now several ways in which predictive analytics is being used to improve the overall customer experience. One is obviously in predicting customer needs using business analytics to run through entire history of purchases, demographics, gender and other such aspects. The next is to provide instant product or service feedback. The third is to identify the risks on flight takers, or those who may shift to other competitor brands. A more appropriate pricing model may also be deduced thanks to the business intelligence captured. Staffing decisions may also be taken accordingly, on whether to increase the manpower or not. The service model may also be rectified on a preemptive style, rather than only when the feedback gets really bad, out in the open. The data captured can likewise be used to conduct a real- time marketing. The future can thus be saved by using this form of predictive analytics.

Source:https://www.forbes.com/sites/blakemorgan/2019/01/16/7-kinds-of-predictive-analytics-for-customer-experience/#141bf1004223

Uploaded Date:22 January 2019

Growth is now in the digital age being measured very differently, from what it was earlier, thanks to the rise of business analytics. Digital disruption, rising customer expectations and increased competition have upped the game for the respective industry incumbents. The winners of today are those that can spot the hyper- granular opportunities. This concept was first discussed more than a decade back in the landmark book The Granularity of Growth written by partners at McKinsey & Company- Sven Smit and Patrick Viguerie- as well as MerdadBagai, who was then with Alchemy Growth Partners. A strategy to beat the odds now has to be devised by all market players. The market leaders now need to ace the Growth Pass. This includes the concepts of precision, scale, speed and agility.

Source:https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/reinventing-growth

Uploaded Date:15 January 2019

The role of the marketer has changed. The process of marketing now involves the use of descriptive business analytics, and not the descriptive method as was earlier the case. This transition is not always smooth, but some methods have been identified to make sure that happens. First of all, the entire data warehousing must take place at a central location, rather than split out. In order to have the best metrics between conversion and churn, a complete 360- degree real- time view of the consumer has to be put in place. Personalization is now the holy grail, as marketers need to bring in the personal touch across the purchase journey. In particular, this has brought in the best of results in the ABM (Account- Based Marketing) space.

Source:https://www.forbes.com/sites/forbescommunicationscouncil/2018/12/11/transitioning-from-descriptive-to-predictive-analytics-in-your-marketing/#599a30133528

Uploaded Date:31st December 2018

In order to boost sales, Microsoft applies a data- driven approach. Companies have been using people analytics or the behavioral data of their employees to understand what goes into cracking the right sales pitch. Business transformation or reorganization does not always benefit, as existing networks will then get disrupted. The LinkedIn Sales Navigator can be used to further improve the existing networks. The business accounts which show the promise of high growth, need to handled differently. Microsoft also invests in people relations, as this invariably boosts customer satisfaction. Using data- driven business analytics, the customer satisfaction or the lack of it, can now be predicted with a certain level of accuracy. To make this entire effort a success, the participation of the executive level is extremely crucial. The data warehousing once done, must remain free to use and not be constrained by the presence of silos. Proper investment must be made to recruit the right people in data science, analytics and data engineering.

Source:https://hbr.org/2018/12/a-look-into-microsofts-data-driven-approach-to-improving-sales

Uploaded Date:28 December 2018

The word Mar Tech of course is an amalgamation of the words marketing and technology. This Mar Tech has a genuine requirement for Digital Asset Management (DAM). Digital assets include pictures, videos, logos, graphics, PDFs, documents, audio files, illustrations, animations and slide decks. The requirement stated is in order to follow the brand’s guidelines as stated during the very inception. It will also enable space not to get cluttered by unimportant files, which in turn will boost the advanced search capabilities. Content duplication can also be avoided, improving the depth of data warehousing being practiced. In turn, it will lead to better control over the content. Plugins and integrations can then be made available. It can work to optimize the CMS, marketing automation tools, file sharing on cloud, publishing and project management. It will also help optimize social media content, thus boosting digital marketing efforts towards the right direction. All of this will then point towards a culture of collaboration.

Source:https://www.martechadvisor.com/articles/asset-management/need-for-digital-asset-management-dam-martech/

Uploaded Date:03 December 2018

The start of modern data science can be traced back to Google search ranking optimization and for LinkedIn recommendations. But now data science is used across all fields. Yet, the terms data science and data scientist aren’t that well understood. One of the key tasks for data scientists is to build a solid foundation on which business analytics may be performed. Machine learning pipelines and personalized data-backed products then need to be worked on. Contrary to the usual perception, data science is now being performed in several industries beyond tech, such as healthcare, travel bookings, restaurants, ride-sharing and much more. More than tall promises such as artificial intelligence or autonomous cars, it is about facilitating the daily processes. The skills needed by data scientists is also constantly evolving. At present for instance, a disproportionate amount of time is getting spent on cleaning and optimizing the data. Specialization is getting increasingly important. Data science is now being worked on in three broad areas which are- business intelligence, decision science and machine learning. A major challenge right now and only expected to expand is the ethics in the field. Privacy and data protection are two of the crucial aspects where ethics could get challenged.

Source:https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists

Uploaded Date:27 November 2018

Seven ways have been identified for making best use in marketing of business analytics. The first such method is through chatbots. The next one is by providing personalized content. This is now possible because analytics tools can process granular bits of information to understand the key characteristics of individual users. This will also specify the digital marketing campaign as specific ads can be cropped up for particular audiences. The same logic will apply on social media as well. Another related area of massive help to sales professionals is lead scoring. It helps identify which leads and the sources which bring maximum value. Personal Identifier Information (PII) can help with detecting fraud and improving cybersecurity. Speech analytics can be a part of this to track call transcripts. Once the right keywords have been identified, marketers can always integrate the top twenty words into the CRM. This helps alignment between the sales process and the CRM. This is of much value to call centre managers in particular.

Source:http://www.bandt.com.au/opinion/seven-ways-apply-speech-analytics-marketing

Uploaded Date:23 November 2018

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