Predictive analysis is now getting more popular as most B2B companies are using it to expand their businesses. But what is important here is to target the right people/accounts. The best way to do this to look at the CRM (customer relationship management) but there is a dearth of optimal databases. To expand their databases, companies are coming up with new marketing ideas, prompting people to view their websites, generating leads and opportunities. But this method may be tedious and costly. Thus we can say that predictive analysis should be used to identify appropriate a/c targets as well as increase the no. of contracts from cost effective marketing programs. To read more:

http://marketingland.com/predictive-data-abm-move-account-lists-account-contacts-181446

In the task of predictive analysis, predicting the unknown itself is a challenging problem. Moreover, the entry of an unknown variable in the equation makes the task all the more troublesome. Summary-level data are generally inaccurate and lack deep insights, because of which sometimes such unknown variables manage to creep in. Buyer life cycles generally vary in length in spite of which analysts generally tend to work with smaller cycles, which is dangerous because sometimes important marketing decisions are taken based on flawed information. B2Bs are also depending on real-time insights and are scrapping linear prediction models. It is noticed that, combining Big Data with traditional CRM information is also not sufficient because data science involves lot of research and experimentation. Hence we can conclude that predictive analysis derives its success from data governance and collection. Read more at: http://www.marketingprofs.com/opinions/2016/30118/predictive-analytics-has-a-scaling-problem-and-bad-data-is-to-blame

Customer service is the most effective part of any company. It not only helps to improve customer retention rate, but also helps to understand new market trends. Big data analytics and business intelligence play a crucial role here. Simple web crawling into a social site helps to understand the on-going sentiment and current market trend. Different social responses, video hits, ads and newsletters help to optimize the search results. In other word this process facilitates smother recommendation system and customer centred predictive modelling. To make a better prediction we can use –

1. Connectors help to integrate diverse data sets in different formats

2. Lightweight search facilitates even non-technical data analysis

3. Queries optimizes the search dynamics

4. Enterprise search also scores with its semantic search, which means that the context is recognized and included in the analysis and structuring of the data.

Using all these parameters business enterprises can design a better profitability model.

To read, follow: http://www.computerweekly.com/blogs/Data-Matters/2016/02/search-driven-business-intelligence-intelligence-for-a-customer-centric-business-world.html