We are all customers of different services. Imagine you are facing a problem with a product/ service and want to talk to customer service. You explain the issue but they are reluctant to understand or help. You would probably lose trust in the company and stop interacting with the same brand. 32% of customers say that they would walk away after one bad experience according to studies.
To minimize bad user experiences, businesses need to adopt customer intelligence. Customer intelligence enables businesses to analyze customer interactions and provide actionable insights to enhance customer journeys.
Some customer intelligence insights may require the business to make difficult trade-offs. For example, Sheena Iyengar’s research reveals that customers make inferior choices with choice overload. Companies can use this to shape customer choices towards more profitable options in complex purchases by first overloading customers with choices. While this can increase profitability, it can lead to abandoned sales funnels and customer dissatisfaction. Customer intelligence can help businesses manage such trade-offs and look at customer experience and business results in a holistic way.
What is customer intelligence (CI)?
Customer intelligence, also called voice of customer or customer analytics, is the process of collecting and analyzing customer interaction data to gain insights about customer behavior. Customer intelligence includes the use of technologies such as feedback management, social media monitoring, Natural Language Processing (NLP) as well as other analytics and data management technologies.
The purpose of CI is to improve customer interactions by personalization and other customer experience improvements.
Why is customer intelligence important?
My mentor in management consulting used to say that customer success is sales, I couldn’t agree more. Happier customers make sales, one of the most difficult parts of B2B much easier.
Customer experience is becoming the key factor for a business to distinguish itself among increasing competition. Though 84% of marketing executives agree on the importance of customer experience and prioritize customer-centric strategies, only 14% of them report that they have strong capabilities in that area.
The more you know customers, the better you can interact with them. From the healthcare (personalized care) to the retail (customer journey maps) industry, businesses can benefit from customer intelligence.
You can also check out list of customer services to find the option that best suits your business needs.
How to build successful customer intelligence?
The customer intelligence process contains a collection of customer feedback or interaction data via multiple channels, such as email, websites, phone calls, mobile apps, text messages from various messaging apps and paper forms. Types of data collected from customers can be categorized into three classes:
- Direct feedback provided to the business is the feedback that customers give on purpose to evaluate the experience of their customer journey with the business. Surveys and complaints are varieties of direct feedback. An important share of direct feedback, unless it is incentivized, tends to be the result of extremely positive or negative customer reactions. While it is not representative, it is good for identifying issues and best practices
- Direct feedback provided to review aggregators takes place when customers have the intention to provide feedback to the business but they do not provide it directly to the business. Instead, they share it with 3rd parties. Prevalence of this type of feedback can suggest either that the business is encouraging such feedback or that customers do not trust that the business will take action based on their feedback.
- Indirect feedback is the feedback provided when customers reveal what they think about the company. Social networks, and text or speech complaints in customer service interactions are examples of indirect feedback.
- Inferred feedback includes data collected from all enterprise processes associated with customers. Purchase history, cookies, location are some examples. Process mining is an emerging technology to understand customer interactions in detail and identify pain points and improvement areas. Learn how process mining improves customer journey.
The second step of customer intelligence is the analysis of the collected data. Businesses should adopt data analytics tools to analyze customer feedback so that they can divide customers into groups based on similar patterns of feedback data. Analytics algorithms and technologies that enable customer intelligence include customer behavior modeling, customer lifetime value forecasting, dynamic micro-segmentation, predictive customer analytics, and machine learning.
After the analysis phase, it is time to share these actionable insights with the organization. Dashboards, alerts, customer journey maps help sharing customer intelligence insights.
Insights should also drive changes in business, the results of analytics need to be operationalized with personalization engines, improved service improvement workflows, and other capabilities to guide future marketing and customer service actions.
What are the benefits of customer intelligence?
Customer intelligence provides behavioral segmentation and enables businesses to deliver personalized services for each segment. Thanks to this customer-centric strategy, businesses:
- Improve sales efficiency/marketing ROI: Personalized product recommendations increase conversions by 150% and average order value by 50%.
- Enhance customer loyalty via customer service effectiveness: Better understanding customers reduce the customer retention challenge for businesses.
- Make data-driven decisions: After collecting and analyzing customer data, taking action based on insights you gained from data is the next step.
Which types of data go into customer intelligence?
- Transactional data: The purchase history of customers includes the key data points that inform personalization engines.
- Behavioral data: This data type shows what customers do on your website. Businesses collect behavioral data with different techniques such as
- Heat maps
- Market research surveys
- Demographic data: Demographics reveal who your customer is in. It contains information about gender, age, marital status, education, location, and employment. While demographic data was an important input to product decisions in the past, transactional and behavioral data tends to be a better indicator of user’s preferences.
- Psychographics data: Psychographics is an emerging approach informed by psychology to understand why people make certain decisions. It reveals information about what triggers customers to buy by looking at the interests, opinions, and activities of the customer.
What are customer intelligence use cases?
It is hard to list all customer intelligence use cases but we provided some examples below:
This one is the most common use of customer intelligence. Behavioral segmentation divides users into different groups who have a similar behavioral pattern. Users may have the same lifecycle stage, previously purchased particular products, or have similar reactions to messages.
Modeling user flows on-site
User flow is the path taken by a prototypical user on a website or app to complete a task. The user flow takes them from their entry point through a set of steps towards a final action, such as purchasing a product. With content intelligence, businesses can monitor users’ movements through their journey. This monitoring enables businesses to model user flows on-site and identify improvements to optimize the user flows.
Geo-targeting is one of the simplest ways to customize messages or offers to customers. For example, food delivery apps use your location to offer the closest restaurants to you or when you write “hospitals” or “ATM” in the search query of navigation apps, search engines deliver you sorted results based on your location.
Personalization has numerous use cases that you can explore in our dedicated article on the topic.
Based on behavioral segments, businesses can send specific messages or offers that are customized to known preferences or buying patterns of these customer segments. Personalized emails are also a use case of account-based marketing (ABM) in B2B.
What are customer intelligence case studies?
- Challenge: A telecommunication industry has been losing its market share to a competitor due to the lack of a unified view of its customers’ problems. The company wanted to launch different campaigns to meet the needs of the different segments.
- Solution: The firm segmented its large customer base into different groups by using customer intelligence1. This helped the company design a tailored growth strategy for different groups.
- Results: In the first year of partnership, the telecom company exceeded its growth target by 11% and grew its networked services business by 17%. Along with the accelerated growth of the business, the company boosted its sales by 30% with improved account planning.
From automotive industry
- Challenge: After Mercedes-Benz USA launched a redesigned website, they realized that its build-your-own vehicle tool was not as easy to use as web developers had thought of.
- Solution: They used survey technology that enables customers to provide real-time feedback to Mercedes3. The company identified how site visitors were using the tool and what they thought of it.
- Results: With that knowledge, Mercedes optimized their website and made it more user-friendly. In the end, the enhanced customer experience improved ROI.
You can check out our data-driven list of sales intelligence software vendors.
If you still have questions about customer intelligence, we would like to help:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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