CRM analytics gets you closer to the customer

Successful analysis provides a complete view of the customer, using quantitative analysis.

DATA MINING AND ANALYSIS

CRM analytics gets you closer to the customer
Successful data mining and analysis provides a complete view of the customer, allowing businesses to make informed decisions based on quantitative analysis.

By Christine M. Campbell, Assistant News Editor

As companies seek ways to increase revenue, they are beginning to realize that merely having customer data stored in a data warehouse is not enough. Businesses are discovering that to achieve a return on their investments, they need to analyze the data to get close to their customers -- and close sales.

Assistant News Editor Christine M. Campbell recently spoke with Dan Vesset, senior analyst at IDC, as to why companies are attracted to data mining and analysis, also known as CRM analytics. This process, according to Vesset, allows companies to look for buying trends and patterns. Yet integrating this data into a data warehouse can be challenging, he said.

What attracts companies to data mining and analysis?

Companies can take this data that has been captured . . . and look for trends and patterns and insight into this data and act upon it to make their company more profitable. So you're moving from cost cutting and efficiency to potentially having value at the top line and bottom line, as well.

How can data mining tools extract CRM data more effectively?

In general, data analysis is done in the context of a data warehousing project. Companies extract the data from their CRM systems and load it into a data warehouse. They can also add to their internal CRM data external demographics data.

Then, in the data warehouse, the data is aggregated and prepared for further analysis. Usually, companies then use end-user reporting tools or multi-dimensional analysis tools that allow end users to view the data, to drill into it and look at the data from different dimensions for analysis.

What are some of the best practices in data mining and analysis?

To a large extent, the best practices have come from marketing because of the iterative nature of marketing. Companies might send e-mail campaigns, or they might put a banner ad online. Then, they track the results of the campaign and analyze the data. Based on that analysis, they might adjust their next marketing campaign so they can look at a certain demographic group of prospects that responded to the campaign.

Do you have any examples of that?

Companies that do have online stores, like Lands' End will do an online campaign, and then analyze that data. Based on that, they would adjust the way they interact with their customers. But also in that case, they have a catalog business, so they try to combine the data from their catalog business and online business so that they can have a complete view of the customer.

What are some of the roadblocks to successful data mining and analysis?

The most challenging thing is getting the data out of operational systems and into a data warehouse. There's a significant effort in making sure the data is standardized and cleansed before it is loaded into a data warehouse. We've found that 75% of the data warehousing effort goes into this initial stage of data integration. It's an area that has been less automated than some of the other tasks of data analysis.

It's always a challenge. The more data sources you have, the more challenging it becomes. . . . It's also challenging, if you have a global organization, to bring the data together in a central point from different subsidiaries or business units. That's probably the biggest roadblock to successful implementation. Once the data is in the data warehouse, it's fairly straightforward to aggregate it and present it to the end users.

How can successful data mining and analysis help achieve profitability and ROI?

It can help companies who have traditionally relied on the gut feeling of their managers and have not based their decisions on quantitative analysis. If you're analyzing data, . . . you can find out who are your more profitable customers and focus on them, rather than trying to address the whole customer population. Often, it's 20% of your customers that bring in 80% of the profits, so you might focus on those. You can only find out what those 20% are by doing some analysis of what they bought, how much they spent with you, how much effort went into servicing those customers after the sales.

If you move beyond CRM, you can analyze your product quality: If you look at warranty information, for example, for which products require the most service and which suppliers supply these products that had problems.

What kind of infrastructure is needed?

There are several parts to data warehousing. You have software for generating the data warehouse. This includes ETL tools -- extract, transfer, load. The second part is the data warehouse management tools. Oracle is the biggest company there, so are IBM and Microsoft. . . . The more well known vendors (for generation of data) are Informatica and Ardent. For information access tools, the vendors there are Business Objects and Cognos. Those are the three major software pieces that are needed for data analysis.

What trends should we expect to see in data mining and analysis?

There will be more automation between the transaction processing and analytical tools, so the analysis can be fed back into the transaction systems to act upon the analysis and modeling that has been done.

I think there's going to be more integration of various data sources. As e-commerce grows, we'll see more integration of online clickstream data with internal, offline CRM data.

MORE ON THIS TOPIC: 
SearchCRM is laden with links on CRM analytics.
Go to SearchDatabase for the latest on data mining and data warehousing.


This was first published in April 2001

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