Case study: How to understand your customers using Microsoft Customer Insights

The challenge

If you work for a large organisation in either the public or the private sector, you have customers. Chances are that your organisation has made a pledge in its Annual Report that you are taking steps to ‘do more’ with your customer data, so you can understand them better. It’s a noble endeavour, but it can also be a devilish one with traps and pitfalls along the way.

A Stellar customer recently engaged Stellar as they embarked on this journey to develop a deeper understanding of their customers. They also wanted to improve the targeting​ of relevant offers to deliver a more tailored customer experience. In this process, the scope and opportunity also included a re-look at the relevant internal processes and improved tracking of returns from marketing spend.

The Stellar solution

The starting point for this solution was to first understand the customer. This involved using a range of internal and external behavioural and transactional data sources to create a unified view of each customer and the typical interactions they had with the product and service channels.

Fortunately, the client in question uses a range of Microsoft products and services. So, this was a logical fit for Microsoft’s Dynamics 365 suite integrated with Microsoft Customer Insights™.

This approach sought to answer some fundamental questions around buying habits, product selection, geographic distribution and other external indicators. Cost analysis, campaign design and implementation planning were also needed to put the new insights to the test.

Key Questions

Action Taken

  • Can I use external data sets and transactional information to define my customers and what interests them?
  • Use behavioural and financial data to identify customer’s profile and value segments, and their motivators
  • What is the range of trade-offs we should test and how can we use the results downstream?
  • Set up and run what-if scenarios across the range of campaign offers
  • How do we implement campaigns quickly and accurately?
  • Establish cross channel tests and measure results with minimal IT involvement
  • How can Machine Learning improve these services?
  • Looks at predictive analytics around the next best offer, potential churn and retention of customers based on transaction patterns and external data

In conjunction with the in-house team, Stellar was able to take these actions and deliver them to their executive quickly.

Customer Insights

Using Customer Insights™ Stellar was able to develop meaningful, robust customer segments along with workable campaigns that can be purposefully designed around the customer’s own, easily defined segments.

In addition to validating customer and campaign definitions, the other essential benefit of this test and learn approach is that it provides much-needed performance data for the next generation of campaigns, with a customer’s actual needs and wants as the primary driver.

As a result of using Customer Insights™ our customer can:

  • Improve targeting, resulting in higher conversion rates and improved campaign profitability
  • Improve the customer experience to encourage loyalty and boost retention metrics
  • Replace legacy internal processes with more customer-centric interactions
  • Link customer actions to campaigns, improving measurement and enabling better attribution of future marketing spend
  • Easily develop and share visualisations and performance metrics with internal stakeholders and increase accessibility using a single integration point – Dynamics 365

The future

In addition to these significant advancements, by tapping into customer data, organisational knowledge is improved, and new opportunities are brought to light.

To extend this capability, machine learning models are embedded within the Customer Insights solution and can be viewed and modelled directly through Dynamics 365.

The result is that in near real-time, an analyst, without any knowledge of computer code can update customer propensity models. These predictions can be communicated to field service agents who can then make contact and increase the likelihood of sales growth and retention.

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