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The huge mass of data in circulation from diverse sources represents a wealth of knowledge and potential. Those who tap into it can see what is happening in their market. By using Big Data as a tool to support Integrated Marketing Management, companies can centralise the analysis of all the activities in their marketing mix, to augment ROI through multichannel analysis, better measurement of campaign effectiveness, and optimisation of resources and assets. Database data (personal details, traceability, loyalty cards, etc.) is integrated with data from purchase transactions and interactions with the brands across multiple channels, and the classic customer profile is linked to a social identifier to build a fuller picture of the customer and offer a more consistent and personalised experience. In short, it is a holistic analytical approach.
At the analysis stage, the first step is to identify and build a set of KPIs (Key Performance Indicators). These serve to measure the customer experience and identify the correlation between marketing campaigns and the results achieved, both by type and volume of sales and by interest generated. After the data is collected, an algorithm is developed to estimate the campaign’s effect on sales, taking into account the period, the execution channel and the target market. It is also vital to anticipate all the potential sources of KPIs for measuring Customer Experience, namely:
In light of the diversity of the data and the need for deep analysis, in terms of both data granularity and handling the historical volume, Reply has focused on Big-Data technology as the most effective tool for supporting Integrated Marketing Management.
To enable the Marketing Department to analyse the data, the system that implements the Business Intelligence logic must:
The data and the campaigns’ results must then be assessed against the KPIs devised at the analysis stage, to enable the overall variation in the “audience” and in the business volume to be measured by period, execution channel and campaign market. In addition, a campaign’s impact on sales can be estimated via dedicated algorithms that analyse historical trends from previous years to adjust the calculation for seasonal factors in the data.