How to Use BI Solutions for a Customer-Centric Strategy

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Companies have long understood the importance of Big Data and how leveraging it can lead to success.

Data-driven companies were the first to discover how to extract value from customer data – information that pertains not just to customer profiling based on preferences, behaviors, and socio-demographic data, but also to their purchasing potential, lifetime value (e.g., by measuring Life Time Value), and the impact of emerging consumption models (such as customers who choose sustainability as a new lifestyle).

Business Intelligence solutions have entered this scenario, becoming essential tools not only for the strategic and operational management of the business but also for driving a transformation that enhances customer relationships, ultimately benefiting the consumer and increasing company profits.

Customer Centricity: the Customer at the Center

Thanks to customer data, companies can better understand their customers – their needs, purchasing habits, conversion barriers, and how to meet their demands to achieve business objectives more effectively and quickly. This involves shifting from a product-centric view, where everything revolves around product development, to a customer-centric strategy, where the customer is at the heart of the entire business strategy (even product development is subordinated to consumer satisfaction).

It’s not just about acquiring more new customers but maximizing the value of existing ones. This is where customer relationship management plays a fundamental role. The Customer Lifetime Value (CLV) is a very useful metric in this regard: after spending so much to acquire a customer, why not maximize the economic return by making the relationship long-lasting and profitable for both parties in the long term?

Considering that acquiring a new customer costs 6 to 7 times more than retaining an existing one, it’s not surprising that increasing the customer retention rate by just 5% can generate a profitability increase ranging from 20% to 95%.

This explains customer centricity: a set of strategies that guide business decisions by placing the customer at the center, where every decision is evaluated based on its impact on the life (and consumption choices) of the customers.

Everything revolves around three main factors:

  1. Identifying the most valuable customers
  2. Improving the customer experience
  3. Investing in the customer relationship

The customer relationship thus becomes a crucial aspect for customer-centric companies, regardless of their size (from multinational corporations to SMEs). This drives the need to measure and improve the customer experience, going beyond what CRM systems allow at an operational level. Business Intelligence perfectly meets this need by integrating data from different sources and functions, revealing new opportunities for improvement and growth. Let’s see how.

Managing Customer Data with Business Intelligence

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Customer data comprises the information and data that an organization collects, processes, and maintains about its customers or potential customers. This data includes a wide range of information useful for enhancing the customer relationship and overall experience. Examples include identification data, demographic data, behavioral data, transactional data, feedback and reviews, interaction or web browsing data, social media engagement data, as well as preferences, interests, and geolocation.

Many of this information is hidden within a company’s information assets (some easy to access, others less so), inside the CRM (Customer Relationship Management), a software focused on managing customer relationships, aimed at organizing, monitoring, and improving customer interactions, managing sales information, customer service, and communications.

Then there are the new frontiers of AI, such as Chatbots for virtual assistance, popularized by the explosion of software like ChatGPT: how can this vast amount of information be transformed into something truly useful for the business?

Moreover, putting the customer at the center also means aligning the more operational aspects of different business functions—inventory, human resources, product development, etc.—around the customer. To understand the economic impact of operational and strategic decisions based on customer satisfaction, it is also necessary to access information hidden in the company’s ERP (Enterprise Resource Planning).

We could say that companies possess these valuable data internally, which, when taken individually, are important, but their maximum potential in terms of value is only realized when they are meaningfully related to each other to support business decision-making.

Transforming Customer Data into KPIs

To measure the success of a decision, it is essential to identify indicators that make its effects measurable. The KPIs (Key Performance Indicators) of a customer-centric strategy serve precisely this purpose: to evaluate the degree of customer satisfaction and engagement. Here are the most well-known:

  • Customer Satisfaction Score (CSAT): Measures overall customer satisfaction with a product, service, or specific interaction, typically through a single-response survey (e.g., from 1 to 5).
  • Net Promoter Score (NPS): Indicates the likelihood of customers recommending the company or product to others, based on a question like “On a scale of 0 to 10, how likely are you to recommend [company/product] to friends or colleagues?”
  • Customer Retention Rate: Calculates the percentage of customers the company has retained out of the total number of customers over a specific period, helping to evaluate the effectiveness of retention strategies.
  • Customer Churn Rate: Represents the percentage of customers who have terminated their relationship with the company or stopped using a product/service over a specific period. A low churn rate indicates higher customer loyalty.
  • Customer Lifetime Value (CLV): Calculates the projected financial value a customer can generate during their entire relationship with the company, helping to assess the long-term importance of the customer.
  • Repeat Purchase Rate: Measures the frequency with which customers make repeat purchases, indicating how often customers return to make new purchases.
  • Customer Effort Score (CES): Assesses the effort a customer must exert to get assistance or resolve a problem, helping to improve business processes to reduce the required effort.
  • Customer Feedback Volume and Sentiment: Monitors the quantity and tone of customer feedback across different platforms or BI tools that enable machine learning algorithms and natural language processing (NLP) for text analysis. Analyzing positive, negative, and neutral feedback helps identify areas for improvement.
  • Customer Journey Conversion Rate: Measures the percentage of customers who have successfully completed a specific journey, such as registration, purchase, or account upgrade.
  • Cross-sell and Upsell Rate: Evaluates the percentage of customers who accept offers for related (cross-selling) or higher-level (upselling) products.

Individually, these are useful indicators for measuring a parameter, but to understand the impact that their variation (positive or negative) can have on the business, a further step is needed.

Transforming KPIs into Strategic Decisions

Some of the listed KPIs are autonomously served by individual software: CRM, marketing automation systems (e.g., HubSpot, Marketo, or Salesforce Marketing Cloud), Customer Experience (CX) management software (e.g., Medallia, Qualtrics, and CustomerSure), Social Media Management platforms, Customer Support software, Sales Automation tools (e.g., Salesforce, Zoho CRM, and Microsoft Dynamics 365), or email marketing tools.

Others are more complex, resulting from advanced analyses conducted using BI and Advanced Analytics tools. Platforms like Tableau, Power BI, QlikView, and Dataiku enable the extraction of meaningful insights from customer data: beyond reports, they allow the creation of interactive dashboards to enable users to make data-driven decisions.

The most significant transformation occurs when the BI solution is tailored to the company’s business characteristics and the customer journey:

  • Associating and analyzing customer data from various sources, such as transactions, social media interactions, customer feedback, chatbot usage, and demographic data, allows companies to identify customer behavior patterns and preferences (including unconscious or unexpressed ones) to optimize offers and marketing strategies to the fullest (e.g., correct segmentation and campaign optimization).
  • Enabling Sentiment Analysis to identify common issues, preferences, and suggestions to improve products, services, and customer support.
  • Uncovering unknown friction points with consumers.
  • Enabling information sharing within the team so that everyone is aligned with the same goals.

Improving Customer Relationships in Retail

Here are three concrete examples of how Business Intelligence solutions are already being used in the retail sector to enhance customer relationships:

  1. Purchase Behavior Analysis: using Business Intelligence tools, a retail company can analyze customer transaction data, identify purchasing patterns, and discover related or complementary products that customers frequently buy together. This information can be used to suggest similar products or offer special discounts on complementary products, thus enhancing the customer’s shopping experience.

  2. Personalized Loyalty Programs: the same system can analyze customer loyalty program data, such as previous purchases, product preferences, and purchase frequency, integrating them with other information. The insights gained can be used to create personalized offers for customers, such as specific discounts on preferred products or invitations to special events, improving customer engagement and encouraging repeat purchases.

  3. Inventory Management and Demand Forecasting: by analyzing historical sales data, it is possible to predict future product demand. These analyses can be integrated with information on seasonal consumption or previous points to create more accurate scenarios. This way, it is possible to optimize inventory (and the entire supply chain), ensuring products are available when customers truly need them, for optimal inventory management (avoiding understocking, overstocking, and stock-outs) and improving the customer experience.

Improving customer relationships is a strategic objective essential for any company as it brings both short-term and long-term benefits, including increased sales, customer loyalty, cost reduction, and improved company reputation.

Identifying the right Business Intelligence and Advanced Analytics solution is the first step toward moving in this direction.

We realize Business Intelligence & Advanced Analytics solutions to transform simple data into information of freat strategic value.

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