CRM analytics is often acknowledged as a type of business intelligence that most often provides historical reporting in the form of dashboards, operational reports, sales forecasts and/or account segmentation among others. While historical reporting, queries, and data visualization is highly useful in illustrating what has occurred and in certain instances can be used to perform “what if” planning scenarios, it is not based on advanced analytical techniques such as predictive modeling. In addition, CRM analytics is often based on customer information residing in a system or multiple CRM systems of record, thus, financial information and purchase history along with other relevant customer information may be lacking unless it is ported into a customer information hub or business intelligence application for analysis. (Click here for more on “Realizing the Downstream Benefits of Effective Customer Data Management”)
In contrast, customer analytics is often utilized for in-the-moment or near-real time decision support for marketing, merchandising, Ecommerce, customer service and support, and sales when analysis of multivariate dimensions are required. For example, multiple dimensions such as time-frame (duration), product/service purchase history, industry, company size, household income, education level, geography and purchase amount (market-basket), can be used to define certain performance metrics such as customer lifetime value or limits on customer acquisition costs. In addition, trade promotions, customer service guidance, fraud alerts, customer identity authentication, and affinity marketing along with cross-sell/up-sell offers are largely dependent on advanced analytical techniques. In short, customer analytics combined with appropriate algorithmic models, business process rules and workflows has the potential to enhance engagement by being contextually relevant, in the moment and in the channel of customer preference.
Customer Information Lives Throughout and Beyond the Enterprise
In today’s Omni-channel, customer-obsessed marketplace, businesses need to embrace the reality that customer information does not only reside in a CRM system–it lives throughout and beyond the enterprise. The proliferation of customer interaction channels serve as a catalyst for investment in multi-channel software solutions. From mobile phones, tablets, computers, in store, catalogue, SMS, social networks, and online forums, customers are able to interact with businesses when they want, how they want and for any reason. Converting both unstructured (contextual) and structured (data) information into actionable insight and responding to customers according to their preferences with customized information is a challenge for most organizations. However, enabling technologies based upon advanced analytics combined with customer engagement solutions such as digital marketing, contact center, sales and/or Ecommerce solutions may provide support for overcoming this challenge.
Five Primary Types of Advanced Analytical Techniques
Through the lens of customer engagement, the practical applications of these five types of techniques should be viewed as:
- Descriptive-Who is the customer?
- Diagnostic-Why are they engaging with your brand?
- Predictive-What are they likely to want?
- Prescriptive-How best should your brand engagement with them?
- Cognitive-When the customer engages with your brand, your brand knows why, what they likely want, and how best to engage with them based on numerous previous experiences.
There are software vendors that offer solutions with templates and features that facilitate creating specific types of models such as customer retention for retail and telecommunications, or identity fraud for banking or medical claims management. Operationalizing analytics by embedding models within customer engagement workflows facilitates brand consistency and optimizes personalization through automation. Evaluate how, and if this might shorten time to value when implementing customer analytics programs.
- Enterprise customer analytics differs most from CRM analytics in that five primary types of advanced analytical techniques are utilized in addition to business reporting such as historical, operational, sales forecasting, and/or account segmentation;
- Enterprise customer analytics is dependent on numerous information sources from multiple channels, legacy systems of record, devices, cloud, and streaming data–inclusive of interactional and transactional information as well as externally purchased third-party data;
- Expertise is advanced analytics combined with well-defined goals, an operationally executable plan, clear definitions for measurable performance metrics and cross functional resources able to execute on this plan are critical to success.
For business return on investment justification, case studies and a Checklist for Success see our latest study: “How Customer Analytics & Insights Enrich Journey Design Processes: Benchmarks, Best Practices & GalaxyTM Vendor Evaluations.” ©2015-2016 Hypatia Research Group. All rights reserved. No part of this research study may be re-purposed, distributed, translated or published in any format without the express written consent of the Hypatia Research Group, LLC and its management. Permission to link to this research must be requested in writing. For advisory services or assistance with vendor selection, requirements gathering or business process mapping, contact Research@HypatiaResearch.com.