Customer Analytics

"Customer analytics can provide the brains to match the marketing system's brawn."

Customer analytics utilizes behavioral data to identify unique segments in a customer base that can help the business act upon. Information obtained through customer analytics is often used to segment markets, in direct marketing to customers, predicate analysis, or even to guide future product and services offered by the business.

Customer analytics can help organizations answer who, what, when, where, and why questions, i.e., what channel should I communicate through? When is the best time to target this person, and why would they be receptive to this message?

In the most basic sense, customer analytics is made possible by combining elements of business intelligence (software such as IBM’s cognos, SAP’s Lumira and Business Object’s suite, and Qlik’s QlikView, amongst a whole host of others) with predictive analytics solutions like SAP’s and SAS’s suite of analytical tools, as well as R, Python, WEKA, etc., etc.

In IBM’s Achieving Customer Loyalty with Customer Analytics, IBM argues that customer analytics can uncover “patterns and trends in customer behavior and sentiment hidden among different types of customer data such as transactions, demographics, social media, survey and interactions.” “The results of the analysis are then used to predict future outcomes so businesses can make smarter decisions and act more effectively." Results from these models can then be presented back to the business users in easily digestible dashboards and scorecards. “Self-learning predictive models ensure that each new iteration of customer analytics insight and the business decisions it drives become more accurate and effective,” argues IBM.

Customer analytics can also help determine which of a casino’s advertising campaign or advertising partner’s pages have the highest landing rates, as well as show conversion rates for all of a casino company’s advertising and marketing budgets. Mobile analytics can also display how many visitors downloaded material from a site, which can help in factoring a company’s advertising and marketing budgets. And, finally, mobile analytics can display which pages have the highest exit rates. With this type of analysis, marketers can rapidly adjust marketing campaigns to exploit the most effective ones and, conversely, trim the non-performing ones.

The biggest problem with any analytics procedure is filtering out the noise associated with the data. Without clean data, the trends, patterns, and other insights hidden in the raw data are lost through aggregation and filtering. Organizations need an unstructured place to put all kinds of big data in its pure form, rather than in a more structured data warehousing environment. This is because what might be considered just “noise” in the raw data from one perspective could be full of important “signals” from a more knowledgeable perspective. Discovery, including what-if analysis, is an important part of customer analytics because users in marketing and other functions do not always know what they are looking for in the data and must try different types of analysis to produce the insight needed. Among the frequent targets for analysis are the following:

  • Understanding sentiment drivers.
  • Identifying characteristics for better segmentation.
  • Measuring the organization’s share of voice and brand reputation compared with the competition.
  • Determining the effectiveness of marketing touches and messages in buying behavior, i.e., attribution analysis.
  • Using predictive analytics on social media to discover patterns and anticipate customers’ problems with products and/or services.

The marketing department is clearly the main department where customer analytics should be used. Marketing departments are becoming increasingly qualitative and  in need of data-driven decision-making; gut feelings aren't good enough in today's complicated and fast-moving world. Customer analytics can be a very effective tool for micro-targeting customers with customized marketing offers and promotions as well as for cross-selling or up-selling customers. Unwanted marketing campaigns can annoy customers, thereby eroding loyalty and, potentially, hurting sales. Even worse, unwanted marketing campaigns can give customers the impression that the organization cares little for them, which might make them seek out a competitor. 

In its Achieving Customer Loyalty with Customer Analytics, IBM argues that customer analytics can provide businesses with the ability to:

  • Analyze all data types to gain a 360-degree view of each individual customer.
  • Employ advanced algorithms that uncover relevant patterns and causal relationships that impact customer satisfaction and loyalty.
  • Build predictive models that anticipate future outcomes.
  • Learn from every customer interaction and apply lessons to future interactions and strategies.
  • Deploy customer insights to decision-makers and front line systems.
  • Improve sales forecasting and help minimize sales cycles.
  • Measure and report on marketing performance.

 

Intelligencia can help corporations understand their customers in a uniquely personal way. Contact us to find out more. 

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