Customer Data Analytics
Core Functionality, Integrations, and Required Tools
Having 34 years of experience in data analytics, ScienceSoft advises on, implements and supports custom and platform-based customer analytics solutions.
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Customer Analytics: Summary
Customer analytics is the process of collecting and analyzing customer data to understand customers’ needs, price sensitivity, doubts in choosing products/services, etc., and develop targeted marketing and sales activities.
Note: Customer analytics is often used interchangeably with customer analysis, but the former is a broader term. While customer analytics is about collecting data and applying analytics techniques to it, customer analysis is the process of examining the insights and deciding on the best strategy to act upon them.
Key features of customer analytics software to have: customer data collection and management, customer segmentation, customer behavior analytics and prediction, advanced analytics for customer relationship management.
Important integrations to set up: CRM, an ecommerce platform, a point of sale (POS) system, social media accounts, marketing software.
4 types of customer data for analysis
Transactional data
Analysis of such data as product/service purchases, returns, reservations, and more gives insights into customers’ spending habits, payment method preference, the share of wallet, etc.
Data about service/product use
Understanding product/service utilization patterns (by analyzing log data, IoT data, requests from customer support service, etc.) helps businesses create a better customer experience and enhance a product/service portfolio.
Web behavior data
Analysis of pages opened by customers, how they scroll down the page, places where they click on purchase/return, etc. It helps identify key points in a customer journey (where customers get stuck/leave/convert), assess the performance of specific pages, and tailor them accordingly.
Data from customer-created texts
Analysis of customers’ online reviews or social media posts about a product/service helps identify customer preferences, reveal customer complaints and problems to solve and recognize customer attitude to products/services in particular and company brand in general.
4 Types of Analytics for Customer Data
ScienceSoft lists four types of customer data analytics to help you define which option suits the nature of the customer analysis problem you need to solve.
Descriptive customer analytics
Discovering what has happened or what is currently happening in business by analyzing historical and real-time customer-related data.
Example: Revealing a drop in sales last month, an increase in the customer attrition rate.
Diagnostic customer analytics
Finding roots of a business problem by analyzing internal and external customer-related data with such techniques as data discovery, data mining, etc.
Example: Drilling down on sales and gross profit to define the reason for missing the net profit target.
Predictive customer analytics
Making predictions and forecasts using data mining and machine learning techniques on historical and real-time customer data.
Example: Estimation of how much revenue can be gained if customers are offered a discount for their next purchase upon certain conditions.
Prescriptive customer analytics
Suggesting the best possible course of action based on the predictive analytics’ estimation of possible future events.
Example: An analytics system recommends offering a 5% coupon to a particular customer segment to get the maximum revenue increase from it.
We always start with designing a data analytics strategy that outlines the right mix of analytics types for customer data that a particular business needs and allows incrementally implementing these types, from the simplest to more advanced ones.
The Functional Scope of Customer Analytics Solutions
ScienceSoft’s data analytics experts specializing in customer analysis have compiled a set of features for an efficient customer analytics solution:
Customer data collection and management
- Automatic collection of customer data across different customer touchpoints (sales channels, social media, customer surveys, customer service centers, etc.) and transaction channels (online and offline stores, marketplaces) for analysis and segmentation.
- Consolidation of customer profiles across various data source systems to get a 360° view of each customer.
Customer segmentation
- Segmenting customers based on geographic criteria (location, time zone, language, etc.).
- Demographic customer segmentation (gender, age, income, marital status, etc.).
- Behavior-based segmenting of customers (purchasing patterns, attributes sought, etc.)
- Segmenting customers based on psychographic behavior (lifestyle habits, interests, personal opinions and beliefs, etc.).
- Automated dynamic customer segmentation based on the pre-defined segmentation criteria.
Customer behavior analytics and prediction
- Customer data mining and customer behavior modeling to predict customer response to marketing campaigns, brand and category adoption, customer movement across segments, etc.
- Customer behavior analysis across a sales funnel to identify customer experience bottlenecks.
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Customer service analytics is to extract meaningful information from call center logs, customer emails, feedback, and social media interactions to identify service gaps and improve accordingly.
- Customer satisfaction analysis to define key factors that influence customer satisfaction rates, customer service experiences, sales/marketing campaign success, etc.
- Customer churn analysis.
- Customer response analysis to assess customer acquisition/retention policies and strategies effectiveness.
- Customer sentiment analysis to define factors critical for conversion (e.g., delivery and customer service terms).
- Customer life span prediction with micro-segmentation and predictive modeling based on customer purchase history, demographics, etc.
- Customizable reporting dashboards, e.g., for specific customer segments.
Advanced analytics for customer relationship management
Pre-built and custom ML models for:
- Recommending marketing/sales campaigns for specific customer segments, cross-sell and upsell campaigns, etc.
- Identifying customers that are most likely to churn, key factors that influence customer decisions, key churn drivers, etc. for developing highly customized customer acquisition/retention strategies.
- Identifying optimal (most attractive and profitable) store locations.
- Identifying the most profitable customers and predicting customer lifetime value for designing new pricing strategies.
Customer Analytics Illustrated
How to Analyze Customer Data
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Define the objective
Customer data can be analyzed for various purposes, e.g., to segment customers, understand their preferences, predict behavior. Defining the analysis objective helps determine what data to collect and what analytics techniques to apply.
2
Collect relevant data
Customer data can arrive from internal sources (e.g., a CRM, an ecommerce platform, marketing software) and external sources (e.g., social media, reviews websites). The correct choice of data sources and their smooth integration will condition the relevance and timeliness of the insights.
3
Establish data quality management
Dealing with incomplete, duplicated, and otherwise 'dirty' data, data quality management lays the foundation for accurate analytical insights.
4
Analyze the data
With properly chosen data analytics techniques (e.g., time series analysis, cohort analysis, predictive modeling) and easy-to-navigate dashboards, the output of customer data analytics makes it easy to draw conclusions (e.g., on dependencies and patterns) and put them into action.
5
Monitor and refine
Since customer behavior trends are not static, it’s essential to ensure continuous customer data monitoring. Such an approach makes it possible to spot changes and timely adjust marketing and sales strategies, as well as the applied analytics techniques.
Consider Professional Customer Analytics Implementation Services
With 34-year expertise in data analytics, ScienceSoft helps companies implement cost-effective customer analytics solutions to consolidate customer data into centralized storage, segment and analyze customers to target them with relevant offers and maximize customer lifetime value.
Customer analytics consulting
- Customer analytics needs analysis.
- Customer analytics solution conceptualization.
- For platform-based solutions: choosing a fitting customer analytics platform and laying out the customization scope.
- For custom solutions: outlining the optimal customer analytics solution architecture and tech stack.
- Customer analytics solution implementation planning (milestones, risk management planning, defining KPIs for measuring customer analytics software quality, etc.).
- Business case creation including cost estimation, time budget estimates.
Customer analytics software implementation
- Customer analytics needs analysis and drawing up customer analytics software requirements.
- Customer analytics solution conceptualization.
- For custom solutions: customer analytics software architecture design.
- Customer analytics software development/customization.
- Customer analytics software quality assurance.
- After-launch support and optimization.
Customer Analytics Solution: Valuable Integrations
CRM software
Core integration
Automated transfer of CRM data (online chat sessions, phone calls, purchase records, emails, surveys, etc.) to a customer analytics solution to build customer knowledge and discover new sales opportunities. Automated transfer of analytics insights to CRM to be used by marketing, sales, and other customer-facing teams. For example, email analytics results (analysis of what emails customers opened, what links were clicked on, etc.) help define the level of customer engagement and focus the sales efforts accordingly.
Ecommerce platform
Core integration
Automatic loading of ecommerce data (user behavior data, product data, etc.) to a customer analytics solution helps companies track metrics like ecommerce site traffic, conversion rate by devices (PC/tablet/mobile), average order value, revenue by customer group, advertising channel performance, return on ad spend, etc. This data helps determine issues breaking the conversion funnel, track the performance of marketing campaigns, and more.
Point of sale system
Core integration
Point of sale systems feed customer analysis software with transactional data. When analyzed, this data provides insights into customers’ preferences about products/services, payment methods, and purchase time and helps recognize popular product/service bundles.
Social media accounts
Social media accounts supply customer analytics software with customer sentiment on product/service/brand/etc. Analysis of such data allows companies to assess the success of marketing/sales campaigns, measure brand awareness, identify unmet customer needs ahead of competitors, spot emerging consumer trends, and much more.
Marketing software
With automated loading of insights from customer analytics software (data on the most profitable customer segments, the most/least popular products/services and product bundles, key churn drivers, etc.) to marketing software, marketing teams can create personalized marketing campaigns for attracting and converting profitable customers at scale.
Factors Determining the Success of Your Customer Analytics
Below you may find the proven success factors, which ScienceSoft’s experts stick to when working on customer analytics projects:
Well-established data governance
Comprehensive security (data anonymization, end-to-end data encryption, fine-grained access control, data masking, etc.) and data quality management framework (customer data profiling, cleansing and enrichment, master data management, etc.) ensure the safety of customer data under analysis, compliance with relevant regulations (GDPR, HIPAA, etc.), and the quality of analytics insights.
Powerful reporting and visualization capabilities
To easily track the most relevant KPIs (conversion rate, CLTV, CSAT, churn rate, loyalty rate, net promoter score, etc.), visualize customer interactions across various touchpoints, collaborate, and share key analytics findings with colleagues.
Advanced analytics capabilities
To get more accurate insights into customer behavior, discover root causes of customer churn, design highly personalized customer retention campaigns, enable automatic generation of next best offers for customers and action points for sales and marketing specialists, etc.
Customer analytics benefits
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Customer Analytics: Success Stories by ScienceSoft
Customer Data Analytics Solution
- A big data management platform to aggregate data from 10+ sources.
- 30-dimension ROLAP cubes for regular and ad-hoc reporting to enable:
- User engagement assessment.
- User behavior trend identification.
- User behavior forecasting, etc.
Analytics Solution for Customer and Retail Analysis
- Data hub and data warehouse to ingest and store data from 15 data sources.
- An analytical server with 5 OLAP cubes and about 60 dimensions overall.
- 90+ reports to analyze customers’ behavior and shopping preferences, track engagement of online store visitors as well as track the stock level in real time and assess employee performance.
Tools to Build a Customer Analytics Solution: ScienceSoft’s Choice
Microsoft Power BI
Features
- Integrating and analyzing customer data from 120+ pre-built data sources for a 360-degree customer view, customer segmentation and profiling.
- Integrating with Azure Data Lake Storage Gen 2 to enable collaboration on customer data across Power BI and Azure data services.
- AI-based customer data preparation and analytics with the Power BI data flows 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.
- Role-based collaborating on reports, dashboards and datasets with Power BI workspaces.
- 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.
Watch our Power BI demo.
Best for
Flexible analytics of all customer interaction channels and customer lifecycle stages in B2C and B2B sales.
Pricing
- Power BI Desktop – free,
- Power BI Pro - $9.99/user/month,
- Power BI Premium - $20/user/month, $4,995/dedicated storage and compute resources/month
Salesforce Interaction Studio
Features
- Tracking visitors with unique tracking IDs at every touchpoint on the website/mobile app in real time.
- Identifying individuals across digital channels, matching anonymous and known users to named profiles, tailoring customer identity-matching policies.
- Storing customer data across various sources and consolidating customer profiles across various customer touchpoints (online and offline stores, interactions with call center agents, in-store/in-branch associates, at ATMs, etc.).
- Automated cataloging of all products and content for ML-powered product recommendations for customers.
- Real-time customer segmentation and AI-driven next best offer/action.
- Monitoring of customer digital behavior under various marketing activities/offerings.
- Triggering personalized messages and recommendations on the most relevant products, content, categories, etc. based on customer behavior.
Best for
Digital experience analysis and optimization.
Pricing
Prices are available by direct request to Salesforce.
Looker
Features
- Unifying customer data from customer touchpoints and across various channels for analysis and segmentation.
- Tracking customer KPIs (revenue, shopping cart abandonment, conversion rates, etc.).
- Identifying consumer trends and updating prices based on predictive modeling of supply and demand.
- Tracking the behavior of best customers vs. one-time buyers to design new promotions and marketing tactics.
- Automatic identification of top performers in product categories.
- Configurable alerting capabilities (e.g., setting up rules for receiving email alerts when product restocking is needed).
Best for
Ecommerce analytics
Pricing
Prices are available by direct request to Looker.
When to Opt for Custom Customer Analytics Software
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ScienceSoft recommends custom customer analytics software development if your company:
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Cost Factors and ROI of a Customer Data Analytics Solution
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Factors determining the implementation cost of a customer analytics solution:
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Major financial outcomes of implementing customer analytics:
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About ScienceSoft
ScienceSoft is an IT consulting and software development company headquartered in McKinney, TX. We provide business intelligence consulting services to help our clients turn customer data into insights beneficial for sales, marketing, product development, and customer service. Being ISO 9001 and ISO 27001 certified, we rely on a mature quality management system and guarantee cooperation with us does not pose any risks to our customers’ data security.