Today's businesses are rapidly awakening to the fact that they are sitting on huge wellsprings of data, and so they are rushing to integrate analytics and intelligence into their CRM operations.

It's easy to see why. Effective data collection and analysis are essential for better customer relationships.

A 2018 MIT Sloan study, Using Analytics to Improve Customer Engagement, found that 59% of respondents (up from 51% in 2015) recognize that analytics are key to gaining a competitive advantage for their organizations. And as an example of effective data use, the report notes Mall of America's use of free Wi-Fi access hotspots to gather data on foot traffic patterns, time spent in particular locations, and visitor counts during mall events.

Data + Insights: Here's the Deal

Using insights gleaned from data to create a single, unified picture of each customer gives an organization the information it needs to make decisions on how to acquire, interact with, and retain customers by improving offers, messaging, and customer experience. Analyzing data can segment buyers and predict customer behavior to better tailor marketing and sales activities and develop more efficient customer acquisition strategies.

In short, data + insights eliminates guesswork and hunches and backs every decision and marketing and sales activity with solid figures and metrics.

But getting to this stage requires more than just acquiring basic contact information. Ultimately, you need to transform the organizational culture and capabilities to generate trust in big data to fully benefit from the power of analytics.

In the meantime, though, here's a short-term road to success so you can generate long-term buy in.

First you have the right questions. Then you input the data, the raw material. That will lead you to the information—more contextual processed data. Finally, you get to insights, the conclusions drawn as a result of analyzing the information.

Take the following steps to turn raw data into actionable insights.

Step One: Getting the Right Strategy

Mountains of digital data are generated every day. At a time when data-driven decisions are being made at almost every enterprise level, how can you make sure the data you collect and analyze actually brings value to the organization?

• Ask and answer this question (if you can; it's trickier than you think): What is the overall strategic business problem you're trying to solve?
• Understand how you're going to use the data by determining the project goals, specific needs to be addressed, and how and by whom the data will be used.

Step Two: Getting the Right Data

• Check your data sources, whether market research, internal data, or external data. Know each source's benefits and flaws, and ways to overcome such limitations.
• Ensure that you are using technology that delivers the most up-to-date data possible (as a point of reference, consider how much turnover your company has had in the past six months).
• Get IT or your data science team's help in identifying the most critical data to use in analytics.
• Work with them to ensure the data is "clean"—that is, it's correct and structured, and the outliers have been eliminated to be usable by reflecting frequency, geography, metrics, etc. Check and recheck for even small data errors that might impact the processing.
• Once you have the "raw" data possibilities from the general data science team, you need to confirm that it's the right data—i.e., necessary to reach your marketing goals. Work closely with Marketing Ops and Sales Ops to ensure that the chosen data will deliver the actionable customer intelligence you are trying to derive.
• Don't forget the details, including filtering, sorting data by importance, summarizing data, and bringing it to life through visuals.

Step Three: Getting the Right Actionable Intelligence From the Data

• Perform the three type of analytics: descriptive (summary of the data), predictive (trend lines, sentiment analysis), and prescriptive (actionable insights for optimal decisions).
• Make sure that the data is accompanied by context to provide the correct insight.
• Generate insights that are specific, clearly communicated, relevant to the person who is receiving it, and closely aligned with business objectives and strategies.
• Focus on trends, instead of individual data elements, so you won't miss broad changes in movements or direction.
• Get powerful insights by looking for strong correlations between variables.
• Ask other analysts for their perspectives. Having other eyes on the same data could generate unique insights.
• Play devil's advocate to scrutinize data from various angles.
• Get the right data results to the right people. For example, C-level officers would need high-level insights and clean data on, say, global prices or market trends, while marketing teams require individual customer metrics and impact on marketing efforts.

* * *

From achieving true personalization to predicting behavioral patterns and creating ongoing conversations, improving customer engagement all starts with this process.

May the data be with you.