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Customer Intelligence: Features, Integrations, Benefits and Tools

Customer Intelligence Solutions - ScienceSoft

Since 2005, ScienceSoft has rendered business intelligence consulting services to help companies implement customer intelligence for improved customer relationships and streamlined operational and strategic decision-making.

Customer Intelligence in Brief

Customer intelligence solutions collect and analyze customer data to help companies gain a deeper understanding of customers’ needs and behavior, personalize the customer experience, improve customer journeys, and drive sales.

Core functionality for customer intelligence software: customer data consolidation and management, customer data analysis, customer segmentation, and customer data reporting.

Important integrations: CRM, marketing automation software, customer service software, an ecommerce platform, a point-of-sale system.

Customer Intelligence Software: A Core Feature Set

The feature set of a particular customer intelligence solution depends on the industry, specificity of a company’s operations, its operational and strategic goals, a data maturity level across the company, and much more. Below, ScienceSoft outlines the core features of a consumer intelligence solution.

Customer data collection and management

  • Ingesting customer data from internal data sources (CRM, ecommerce software, a point-of-sale system, etc.) and external data sources (social media, product review sites, public databases with customer surveys or demographics data, etc.).
  • Support for dynamic customer data collection to enable near real-time data analysis.
  • Customer data cleansing and enrichment (detecting and updating or removing missing, false or irrelevant data).
  • Customer data standardization and unification (identifying and deduplicating redundant data).
  • Combining data on each customer from diverse channels (webpage, telephone, social media, live chat, etc.) for an omnichannel customer view.

Customer analytics

  • Online analytical processing (OLAP) to roll up, drill down, slice and dice customer data.
  • Pre-built data mining models to identify unusual patterns and trends within huge customer data sets.
  • Support for various types of customer data analysis:
    • Customer satisfaction assessment.
    • Profitability analysis of individual customers and customer segments (grouped by the number of transactions, revenues, average transaction size, etc.).
    • Analysis of customer buying frequency and customer engagement.
    • Analyzing the sensitivity of certain customer segments or individual customers to pricing or promotional changes.
    • Analyzing customer navigational and behavioral patterns to estimate preference for particular product/service types and categories.
    • Calculating customer lifetime value.
    • Customer acquisition analysis (cost per acquisition, cost per lead, return on ad spend, return on new customers, etc.).
    • Customer churn analysis to elicit behavioral patterns leading to churn and plan preventive actions.
    • Customer retention analysis (customer renewal rate analysis, retention costs analysis, customer engagement analysis, etc.).
  • Pre-built ML models for:
    • Customer behavior modeling (converting, churning, spending more, spending less, etc.).
    • Customer choice modeling to identify how particular customers/customer segments respond to competing products/service offers.
    • Identifying the next-best actions across various customer touchpoints.

Customer segmentation

  • Segmenting customers based on various geographic, demographic, behavioral, transactional and psychographic criteria.
  • Support for multiple customer segmentation approaches: RFM segmentation, segmenting customers according to their lifetime value, profitability, etc.
  • Dynamic customer micro-segmentation based on real-time data updates (e.g., dividing general customer segments according to their buyer stage) for personalized marketing.
  • Pre-built ML models for discovering new customer segments, high-value segments, etc.

Customer data reporting

  • Pre-built reports for different types of customer intelligence users (C-suite, department heads, middle managers, sales reps, etc.).
  • Interactive dashboards with the drill-down, drill-through and filtering capabilities.
  • Embedded reporting capability to export customer insights into business applications used by sales teams, marketing teams, etc.
  • Self-service reporting capabilities to enable business users to create custom reports and dashboards.
  • Scheduled (on predefined periods or triggered by events) and ad hoc reporting capabilities.
  • Mobile reporting capabilities.

Important Integrations for Customer Intelligence Solutions

ScienceSoft recommends integrating customer intelligence software with all data sources holding relevant customer information to maximize its efficiency. Below we list the common go-to integration options.

Customer relationship management (CRM) software

  • To enable comprehensive customer segmentation.
  • To analyze customer engagement and determine which products/services are required the most.
  • To improve sales efforts with ML-based recommendations (e.g., product recommendations generated by analyzing customer purchase history, customer sentiment) and maximize the long-term value of every customer.

Marketing automation software

  • To measure the success of marketing efforts on various parameters.
  • To provide ML-based recommendations for personalized marketing campaigns and optimal marketing actions (e.g., starting an email campaign for particular customer segments).

Customer service software

  • To determine customer and product experience gaps.
  • To analyze customer service requests together with customer behavior to create and optimize customer retention strategies for particular customer segments.
  • To analyze customer sentiment to optimize product/service portfolio and enhance customer loyalty.

Ecommerce platform

  • To track metrics like the conversion rate by devices (PC/tablet/mobile), average order value, revenue and product/service preferences by customer segment, advertising channel performance, return on ad spend, etc.

Point of sale system

  • To define customers’ preferred payment methods and purchase time.
  • To identify popular product/service bundles.

Integrations for Customer Intelligence Solutions - ScienceSoft

What Ensures the Success of Customer Intelligence Solutions

Based on our 33 years in data analytics and 17 years in business intelligence, ScienceSoft defined a number of factors that should be covered to ensure the success of the solution.

Robust data security

For ensured safety of customer data (personal data, financial data, etc.) and compliance with regulatory requirements (GDPR, HIPAA, etc.), the solution should offer fine-grained access control, customer data anonymization, dynamic data masking, and end-to-end data encryption.

Automated pre-configured reports

To deliver timely relevant KPIs (conversion rate, CLTV, CSAT, churn rate, loyalty rate, net promoter score, etc.) to end users (sales reps, marketing teams, content managers, etc.).

Embedded analytics capabilities

To integrate analytics content (customer analytics reports, dashboards with relevant KPI suites, etc.) directly into business applications (CRM, marketing software, etc.) for prompt usage by non-technical users.

AI and ML capabilities

To analyze customer data from disparate sources at high volume and speed, make recommendations on next-best sales actions or ways to personalize customer interactions, enable customer behavior modeling, and much more.

Cost Factors and Benefits

Factors determining the implementation cost of a customer intelligence solution:

  • Number of required internal and external data sources (CRM, social media, website, etc.) and customer data complexity (structured, semi-structured, unstructured, real-time, etc.).
  • Customer data volume.
  • Complexity of customer data cleansing.
  • Complexity of customer data analysis, ML and AI capabilities.
  • Data security and compliance requirements.
  • User training, if necessary.

Key financial outcomes

  • Increased

    conversions by 150% and average order value by 50% due to personalized product recommendations

  • Lowered

    new customer acquisition costs due to optimized marketing efforts

  • Increased

    sales force effectiveness due to targeting qualified prospects

  • Increased

    sales to existing customers due to precise cross- and up-selling targeting

  • Reduced

    customer churn and increased customer loyalty due to the enhanced customer experience

  • Tools to Build a Customer Intelligence Solution: ScienceSoft’s Choice

    In our customer intelligence solution projects, we frequently rely on the following techs:

    Microsoft Power BI

    Best for

    Flexible customer analytics in B2C and B2B sales.

    Description

    • Pre-built integrations for 120+ data sources for a 360-degree customer view.
    • AI-based customer data preparation and analytics with the Power BI dataflows and Power BI Quick Insights features.
    • Machine learning modeling to anticipate customer demand, predict customer churn and customer response to marketing campaigns, identify next-best product recommendation, etc.
    • Customer data discovery with pre-built and custom visuals.
    • Securing customer data with data sensitivity labeling, end-to-end data encryption, and real-time data access monitoring.
    • Available as a SaaS option running in the Azure cloud or as an on-premises option in Power BI Report Server.
    • Mobile capabilities.

    DEMO: Watch our Power BI demo.

    Pricing

    • Power BI Desktop – free.
    • Power BI Pro - $9.99/user/month.
    • Power BI Premium - $4,995/dedicated storage and compute resources/month, $20/user/month.

    Salesforce Interaction Studio

    Best for

    Digital customer experience analysis and optimization.

    Description

    • Native integrations with SFMC, Service/Sales Cloud, Commerce Cloud, Pardot, Tableau, Datorama, and Einstein Analytics.
    • Integration with third-party data sources with the help of ETL, API, or Interaction Studio Gears.
    • Consolidating customer data across online and offline customer touchpoints into a Unified Customer Profile (UCP).
    • Support for real-time customer segmentation.
    • Tracking visitors at every touchpoint on the website/mobile app in real time.
    • Monitoring digital customer behavior under various marketing activities/offerings.
    • Triggering personalized messages with recommendations on the most relevant products/product categories, content, etc. based on customer behavior.

    Pricing

    Prices are available by direct request to Salesforce.

    Looker

    Best for

    Ecommerce analytics.

    Description

    • Integrating with corporate applications via open APIs.
    • Unifying customer data across various channels for analysis and customer segmentation.
    • Real-time customer analytics capabilities.
    • Tracking ecommerce KPIs (revenue, shopping cart abandonment, conversion rate, etc.).
    • Tracking the behavior of best customers vs. one-time buyers to design new promotions and marketing tactics.
    • Embedding customer analytics content into websites, portals, apps, etc.
    • Web-based only.

    Pricing

    Prices are available by direct request to Looker.

    When Opt For a Custom Customer Intelligence Solution

    ScienceSoft recommends developing custom customer intelligence software if your company:

    Has specific requirements to the customer intelligence solution (gathering data from a large number of diverse data sources, support for pre-built reports for different user groups, a particular data refresh rate, etc.), which are not covered with the basic functionality of packaged solutions.

    Needs a customer intelligence solution, which allows for quick system evolution (for example, adding new functional modules to address the newly arisen business needs).

    Consider Professional Services for Customer Intelligence Software Implementation

    With 17-year experience in building data warehouse and BI solutions, ScienceSoft designs and implements tailored customer intelligence solutions for companies to get a 360-degree customer view, personalize customer journeys, increase customer loyalty and decrease customer churn.

    Customer intelligence consulting

    • Analysis of customer intelligence needs.
    • Conceptualization of a customer intelligence solution.
    • Solution architecture design and tech stack selection.
    • Planning of solution implementation (risk management planning, defining KPIs for measuring customer intelligence software quality, etc.).
    • Business case creation, including cost estimation, time budget estimates.
    Go for customer intelligence consulting

    Customer intelligence software implementation

    • Customer intelligence needs analysis and elicitation of software requirements.
    • Customer intelligence solution conceptualization.
    • Solution architecture design and tech stack selection.
    • Customer intelligence solution development.
    • Solution integration with relevant software (ecommerce software, CRM, POS, etc.).
    • Customer intelligence software quality assurance.
    • After-launch support and optimization.
    Go for customer intelligence implementation

    About ScienceSoft

    ScienceSoft is an IT consulting and software development company headquartered in McKinney, Texas. With 17-year experience in BI consulting and development services, we deliver reliable customer intelligence solutions to translate raw customer data into actionable insights for optimized marketing, sales, product/service development, and customer services processes. Being ISO 9001 and ISO 27001 certified, ScienceSoft relies on a mature quality management system and guarantees that cooperation with us does not pose any risks to our customers’ data security.