Customer Intelligence: Analytics That Brings Actionable Customer Insights

Senior Business Analyst and BI Consultant, ScienceSoft

Published:
6 min read

Editor’s note: Maryna explains what customer intelligence is and how it helps gain customer insight to advance your position on the market. Read on to learn how to develop and improve your customer intelligence solution for the purpose and feel free to consider ScienceSoft’s business intelligence consulting services to get professional assistance.

Nowadays, when great customer experience is considered the primary brand differentiator, the lack of a deep understanding of changing customer needs or flexibility to quickly meet them most certainly means losing the competition. If you have found yourself in either of the above two situations, it’s time to take some real action. In my practice, I usually offer those of ScienceSoft’s clients who experience similar difficulties a customer intelligence solution as a cure. Below, I will tell you why consumer intelligence is so relevant and how a properly tuned customer intelligence solution will help cater to your customers due to fast and well-grounded business decisions.

customer intelligence

How customer intelligence transforms your company

Here is the explanation of customer data analytics that I think helps quickly get its gist. It is gathering and analyzing data related to customers to get deeper insights into their behavior and further employ these findings in the decision-making.

The key benefits customer data analytics brings to the table are:

Every customer touchpoint provides companies with an opportunity to understand consumer needs and deliver a personalized experience. If you want to see how it works on a real-life example, check ScienceSoft’s project for a multibusiness corporation. There, ScienceSoft helped the client create a 360-degree customer view across communication channels to use this information further for customer loyalty fostering.

The necessity to quickly understand and even predict the market has evolved into an everyday business challenge. Customer intelligence allows analyzing customer behavior under various circumstances and delivering accurate forecasts and actionable near-real-time recommendations for marketing and sales due to the use of elaborate machine-learning algorithms.

Sales efficiency is tightly bound to customer intelligence: by analyzing customer data and feedback, companies can identify effective and failing sales tactics, enhance customer journeys and, consequently, increase sales. To see how advanced analytics technologies may improve sales efficiency, read about our project, where ScienceSoft’s data scientists employed statistical methods and algorithms to historical sales data of a European dairy manufacturer. As a result, the company saw the underlying reason for the gap between their plan and fact sales performance and received an accurate sales forecast.

You can get more information on the value of customer data analytics in the article written by my colleague Alex Bekker, Head of ScienceSoft’s data analytics department.

Do these benefits seem out of reach?

ScienceSoft’s experts are ready to help you fully leverage customer analytics capabilities by developing or improving your customer intelligence platform.

ScienceSoft’s approach to developing or upgrading a customer intelligence solution

When implementing or tuning a customer intelligence solution, you should ensure high user-adoption and avoid negative ROI. For that, I recommend leveraging agile customer intelligence analytics with the following steps:

  • Study the needs of departments (marketing, sales, customer service, etc.) and find out how client intelligence may satisfy the hottest ones.
  • Develop an analytical solution or upgrade the existing one accordingly and work hard on its adoption in collaboration with department managers to perform effective ad-hoc optimization.
  • After the solution's capabilities are fully leveraged, and its users witness some tangible outcomes, such as a higher conversion rate or a successful product launch, it’s time to iterate. For example, upgrade the solution with self-service analytics. At this stage, users are ready to appreciate the flexibility of such tools as Power BI by Microsoft or Tableau by Salesforce.
  • The next step is the implementation of advanced analytics capabilities – machine learning, data science, etc. to drive more value to the organization through root-cause analysis and accurate forecasting.

3 focus areas in your customer intelligence journey

Now that you know how to foster customer intelligence in your company, here are some points to pay special attention to in order to ensure that your solution is effective:

1. Define and integrate the required data sources

Before developing or upgrading the solution, you should make sure that you have all the data necessary for your customer intelligence insights. For that, I recommend sticking to a simple algorithm I use in my work:

  • Establish what kinds of insights your company is looking for. They can be answers to the following questions: What influences the purchase decision? What items are frequently bought together? How to reinforce customer loyalty?
  • Try to understand whether the IT solutions your company uses (CRM, customer service tools, accounting software, etc.) have the data to answer your questions.
  • Define the data sources you can resort to for the required data, work out a roadmap for getting access to them and integrating them with your customer analytics solution’s architecture.

2. Focus on data quality

Pay special attention to setting up proper data quality management procedures not to compromise your customer intelligence solution with messy customer data. To improve data quality, you’ll have to assign the responsible personnel to perform such tasks as removing and preventing duplicate data, setting up rules for data records, managing the quality of metadata, etc.

In case your customer intelligence solution deals with big data, the data quality procedures will differ a bit. As the requirements for big data quality vary from task to task, data experts usually have to set up satisfactory data quality thresholds for every particular situation.

3. Mind the sensitivity of customer data

While developing your customer intelligence solution, I advise you to ensure that such measures as data anonymization and encryption are employed in it, user access rights and user authentication mechanisms are set up before the solution launch.

If you already have a customer analytics solution in place, it will be forethoughtful to check it regularly with vulnerability assessment and penetration testing. These measures will help you identify possible security weaknesses and handle them before sensitive customer data is exposed to actual threats.

It’s high time you knew your customers!

From my own experience, I may say that customer intelligence is a surefire way to understanding who your customers are and what they want. It facilitates creating a business strategy that resonates with your target audience so that you can quickly understand customers’ changing needs and react to those changes. However, as you may have noticed, both developing and tuning a customer intelligence solution may require more effort than you’d think.

To ensure customer intelligence ROI, you may always resort to a professional consultancy, who will help you define your business objectives, design your customer intelligence implementation or evolution strategy and assist you with the transformation into a customer-centered company. If you feel in need of help with any of these or other customer intelligence tasks, reach out to me at any time.

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