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Monday, February 19, 2018
Home Big Data & Analytics Preparing the CRM for predictive analysis

Preparing the CRM for predictive analysis


Access to data is not enough for a company to maintain its competitive advantage. By Imad Alabed, senior director of Pivotal & Knova.

Barbecues in the garden are a summer classic. One of the key ingredients to good outdoor food is the weather. Some people see a clear sky and then prepare the barbecue. Others, for their part, believe that joint pain is a sign of rain. Thanks to the advances in meteorology, most people open their weather forecast app to make sure the forecast is not rain while preparing the meat. We are all happier when we are assured of a positive outcome of our effort.

In the past, many companies relied on their instinct and past experience; but now the market demands that business decisions be based on facts and figures; not in hunch. In a recent Aberdeen Group survey on business intelligence and analysis, 46% of respondents indicated that competitive pressures require them to be data-driven .  The ability to translate historical trends and real-time data into actionable information paves the way for performance improvements.

Attention to the future

Access to data is not enough for a company to maintain its competitive advantage. Employees of any level of responsibility should be able to take action based on available information.  Aberdeen defines predictive analytics as a technology that allows companies to analyze structured and unstructured data to reveal trends and correlations, as well as to predict the likelihood of certain customer behaviors. Customer Relationship Management (CRM) solutions are the ideal complement to predictive analytics, as the company can maximize sales opportunities and improve the productivity of its account managers. Making the wrong decision at the wrong time can be expensive; you need to be able to predict ‘what’ and ‘where’.

In addition to improving business relationships and ensuring the provision of a high quality service, companies must know their customers and have historical data from buyers to form a clear vision of the true interest of the customer. The combination of predictive analytics and social CRM offers even greater potential to meet current and potential customers. Profile information, publications and click histories can be used to create more complete customer profiles, thus obtaining more accurate analysis. A greater and deeper knowledge of constantly changing consumer trends allows companies to enrich market knowledge and achieve greater customer satisfaction. Definitely,

The combination of predictive analytics and social CRM offers even greater potential to meet current and potential customers.

Preparation of ‘crystal ball’

The great amount of information and the speed at which it circulates are two of the biggest challenges that the companies face. According to the Aberdeen survey, 96% of companies suffer from ineffective use of data. One of the aspects of predictive analysis that intimidates potential users is the accuracy of the data on which the findings are based. To provide the best analysis, work data must be prepared correctly. This step is so important that some analysts spend more than three-quarters of their time preparing data for analysis. The automation of the preparation of the data facilitates that the users maintain the control of the same, reducing the computer load.

The inaccuracy of the data is not the only factor that can spoil a forecast; sometimes the information is scattered in so many locations and in so many formats that it is not possible to consolidate it. Companies must also integrate the data into a unified customer view across all systems to increase the accuracy and relevance of the data being analyzed. Companies that use analytical dashboards are 42% more likely to have standardized data coming from multiple channels, ensuring a proper software integration. In addition to ‘clean’ data, predictive analysis must have access to multiple data sources, since it ‘learns’ with each new data source. At the same time, it is important to avoid incorporating too many sources relatively quickly. A suitable approach is to start the project with a smaller amount of consolidated data, which allows for a quick return on investment, and then to grow with data from new sources. This ensures continued growth in predictive analytics.


Although CRM solutions already collect large amounts of information, predictive analysis allows data to be obtained at a deeper level. CRM solutions with predictive analytics provide real-time practical information that improves the decisions that every day sales, operations, marketing and executive teams must make.

Most CRM systems are extremely flexible and offer data models that are easy to modify or extend. This flexibility ensures that CRM is able to adapt to the changing requirements of the data. However, over the years, many companies have not given adequate attention to data control. Prepare the way for predictive analysis by initiating a data cleansing activity.