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Optimizing the Stages of the Customer Life Cycle (Part 2 of 4)

Examining opportunities in each stage of the customer life cycle proves the potential benefits of a customer analytics program.

An enterprise can best benefit from a customer analytics program by examining opportunities for improvement at each stage of the customer life cycle.

Enhancing the company's position during each of those stages ultimately leads to decreased costs, increased revenues, and continued growth. It can be done by looking at each of the stages, considering the interactions with the prospect or customer, and determining what actionable insights can be used to speed a process, reduce its risks, or result in a better outcome.

You should ensure that your team members understand the different customer interactions at each stage as well as what the desired outcomes are.

Let's look at the initial stage of the customer life cycle: awareness. During this stage, two key drivers might motivate the company's actions: make prospects aware of the problem that needs to be addressed, and make sure each prospect is aware of the company's product as a solution.

These are typical marketing department tasks, often associated with lead generation. The overall objective of lead generation is to stream prospects into the sales funnel, and specific measures of success include the number of identified prospects and the number of qualified prospects, where "qualified" includes a number of criteria (such as having interest in the product, the budget to make the purchase, and the authority to make the decision, and a purchase expected within a specific time frame, to name a few).

The company's business goal in the awareness stage could be stated as: increase the size of the pool of qualified prospects because that creates the greatest opportunity for engagement and subsequent customer acquisition.

That specific goal can be divided into more discrete measures (which allow the marketing team to assess where those measures are not being satisfactorily met), then you can analyze your opportunities for improvement. To continue this example, increasing the pool of qualified prospects actually implies two sub-goals: increasing the number of prospects and improving the precision in determining whether those prospects are qualified.

Further division leads to more specific areas for improvement, and we can apply different kinds of analyses and interpretations in order to target our improvement efforts in these areas, as shown in Table 1.

Area for Improvement

Example Techniques

Prospects are not aware of the need

Analyze the success of marketing campaigns in relation to identified and qualified prospects and increase focus on successful campaign models

Analyze visitors to company website, track visits to increased interest, and use results to improve marketing content

Not enough prospects identified

Identify geographic locations with highest number of identified prospects and target other similar locations

Analyze the media through which prospects are identified and increase attention through those channels

Too few qualified prospects

Analyze the stages of the engagement process by marketing campaign and streamline marketing campaigns based on performance

Identify geographic locations with highest number of qualified prospects and target other similar locations

Identify characteristics of qualified customers and target locations with similar populations

Table 1: Example analyses for improving customer awareness.

The example techniques described tend towards location analytics, prospect profiling, and clustering to identify desired characteristics, and this is just the beginning of your improvement process.

Each time you identify an area for improvement further investigation may reveal increasingly precise metrics. Measuring these exposes specific business problems and provides a way of assessing their relative criticality.

The ability to select specific problems, target specific improvements, and choose a combination of analytic techniques for improvement establishes the motivation for designing and building an analytics technology environment.

Other articles in this series:

Part 1: Analytics for Customer Engagement

Part 3: Planning for Customer Analytics

Part 4: Managing Customer Data for Analytics

 

About the Author

David Loshin is a recognized thought leader in the areas of data quality and governance, master data management, and business intelligence. David is a prolific author regarding BI best practices via the expert channel at BeyeNETWORK and numerous books on BI and data quality. His valuable MDM insights can be found in his book, Master Data Management, which has been endorsed by data management industry leaders.


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