CRM data stores contain extremely rich, multi-faceted, and interconnected datasets that are invaluable sources of information on a company’s markets, customers, and sales and marketing operations. These latent treasure troves of data just need to be teased out with the appropriate analyses, but it is extremely important that these analyses be set up as correctly and as optimally as possible from the start.

Step 1: Investigating the Ins and Outs of Your CRM Data

The first step is to survey the landscape by getting full access to your CRM system. You should get a good rundown of how your sales and marketing teams have been using the data.

Next step here is to make sure you understand the pathologies of the data, itself — the number and causes of corrupted records, duplicates, missing values, censored values, etc. Any sufficiently large dataset will contain errors and issues, and CRM data is particularly susceptible because it is mostly manually entered, and is a mixture of structured and unstructured data.
 
Step 2: Grouping the Data Together

To do anything meaningful with the data, you need to get it together in an easily accessible place — for example, as a series of spreadsheets, a set of interconnected tables, or a whole relational database that allows you to run queries and reports.

Not only will you need to extract the raw data from your CRM, you’ll also need to deal with the data issues that crop up through the standardization of format and normalization of data values. You can use Excel or R software for this purpose.

a. Defining the Units of Analysis

It includes the grouping of data into market segments. In each market segment, the customers are supposed to have similar needs and buying behavior, so you can develop “ideal customer” profiles.

b. Conducting an Exploratory Analysis
Once you have gotten all the data in place, you will want to start doing some data profiling and initial exploratory analysis of the data you have pulled together in order to a) develop a sense of the data; and b) detect any potential issue early on.

An exploratory analysis can be done simply using the basic functions in Excel such as AVERAGE(), STDEV(), MAX(), MIN(), MEDIAN(). These functions are flexible enough to ignore blank or erroneous data points, and so are easily applied without requiring a lot of preparatory work. Doing light exploratory analysis will also enable you to detect any systematic issue with the data and see what valuable information you might need to append to the data using external sources.

Step 3: Mapping Out Your Analyses

After that arduous step of getting your data pulled together and set up for the big analysis, you’re closer to uncovering a hidden treasure trove of customer insights.

First, you need to work on mapping out the analytical steps you are going to perform. Because there are millions of potential analyses you can do on any sufficiently rich datasets, it is important that you limit your focus strictly to the analyses that will contribute to your objectives. Therefore, you should start first by defining the kind of conclusions that you want to get and work backward from the expected results to the analyses that will provide those results.

Setting the Foundation of Your Analysis: Identifying Two Basic Components
This is generally in the form of “analysis of X over Y”. Eg: Trends over time or productivity across the sales team. This is usually the most common of data analysis
Common Types of analyses you can do with CRM Data. For reference, I’ve provided a sample analysis that can be done with CRM data.

Step 4: Executing the Analyses and Interpreting the Results

Once you have fully fleshed out your plan and properly prepared, you are finally ready to dive in and start analyzing the data with your powerful toolsets of statistical models and data manipulation scripts.

At this point, you can set up templates and automation steps so that when you redo the analysis with a subset of the data or an entirely different dataset, you do not have to start from scratch. For each analysis, think of the ultimate answers that you are looking for, and optimize your calculation steps so that you get exactly those answers — no more, no less.

The less data you touch, the fewer the steps you carry out, the less error prone your results are, and the easier it is to replicate your work.

For each completed analysis, try to stay focused on the evidence that you are trying to get and the hypothesis you are trying to prove or disprove so that you can sharply evaluate the results and avoid getting bogged down in analysis paralysis.

Step 5: Visually Presenting Your Insights

To really make an impact with your analysis, you will need to pull together all of the disparate results, and most importantly, connect the dots together to form a holistic view and a consistent narrative of the insights that arise.

 

Shaleen Choudhary

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