A rising number of organizations use analytics to gain a competitive advantage and innovate. A sparking factor of this move is more effective use of analytics to improve customer engagement. Building on customers’ feedback and wider data, organizations are able to increase the customers’ satisfaction, which in turn become more willing to provide feedback, and thus triggering the same chain of effects. To get there, successful organizations put at work several factors, including the use of a wide range of data sources, well-developed core analytics capabilities, and integration of AI.
As the MIT Sloan Management Review points out in its 2018 Data & Analytics Global Executive Study and Research Report, the following trends are to be noted :
- Competitive advantage from analytics continues to grow;
- Analytics is driving customer engagement;
- Analytically mature organizations use more data sources to engage customers;
- Sharing data can improve influence with customers and other groups such as vendors, government agencies, and even competitors.
Knowing this, you might want to get on these trends, and if you are looking for a way to start turning data into customer engagement, you might be wondering where and how to start developing your business’ capacity to provide data-driven insights.
To determine the relative analytics proficiency of an organization, the MIT Sloan Management Review developed the Analytics Core Index.  Although this might seem to you as very theoretical, this index breaks down, in more academic words, the steps to allow your business to trigger the chain effect we mentioned in the first paragraph.
Unsurprisingly, organizations with the highest scores on the Analytics Core Index successfully turn analytics into customer engagement, and they start with developing the 3 following capabilities: ingesting data, analyzing the data, and applying insights.
When ingesting data, you will want to capture, aggregate, and integrate data from multiple sources to garner new customer insights and deepen your relationships. Best examples in practice would possibly be a mix of loyalty programs, reward cards, code-enabled offers for transaction tracking, newsletters for leads and prospects identification, physical information packets, web registrations, cookie-backed online customer profile building, making the latter available for your call centers and PoS, establishing an updated customer lists, and/or purchased demographic data.
Moving quickly from this data to customer insights will require some form of automation of your data analysis, although it is important to put emphasis on distinguishing between descriptive analytics, predictive analytics, and prescriptive analytics. Since the utility of adding more data sources has many limits, the analysis step will be the one where you will want to invest in a proper resource such as an industry appropriate software and/or an experienced analyst. Unless you are up for the challenge as much of analyzing customer data requires some advanced knowledge of mathematics and statistics. To get you started, some common customer segmentation schemes include demographic, geographic behavioral, customer profitability, psychographic segmentation, or any other that could help you gain the needed customer insight. 
Lastly, applying insights should be done by first disseminating the data insights, and incorporating the analytics into an automated process. And keep in mind why you are doing this; customer insights are at the heart of improving the customer experience, and making it easier to attract the ones you don’t.
While most organizations report success with analytics in creating competitive advantage and engaging with customers, rarely do they suggest that these benefits are easy to achieve. And be aware that problem a lot of companies run into is turning their customer insights into a palpable improvement in experience, partly because insights often address the problem, but fail to provide the solution. Even if incorporating many data sources, moving quickly from data to decision and aligning incentives remains essentially quite a process, your creativity as to developing solutions based on insight will be determinant on the success. To help you go forward with your data and to provide some insights to how use this knowledge to better engage your customers, we put together an ebook on the subject. Take also note that using data to deliver more tailored offerings increases the need for better data security, as well as strong data governance practices to ensure that customer data is used in accordance to international regulations.
 https://sloanreview.mit.edu/projects/using-analytics-to-improve-customer-engagement/  https://www.slideshare.net/mitsmr/turning-data-into-customer-engagement-88431955  https://www.dummies.com/business/marketing/data-driven-marketing/11-ways-to-capture-customer-data-for-your-data-driven-marketing-campaign/  https://www.dummies.com/business/marketing/data-driven-marketing/data-driven-marketing-for-dummies-cheat-sheet/