BPM Analytics

What is customer analytics and why does it matter for businesses?

Successful businesses throughout history have realised the importance of knowing and understanding their customers. Organised market research can provide them with insights into their target audience and assess the market viability of goods and services. Modern businesses are highly data centric and collect useful customer data at every interaction. With customer analytics, you can examine this information to better understand your customers and identify what influences their buying decisions. This blog explains what customer analytics is, why companies need to conduct more effective customer analysis, the benefits it delivers, the customer data it draws on and the steps to run a customer analytics project.

What is customer analytics?

Customer analytics is the structured examination of customers’ behaviour to glean insights into market trends. Customer analytics aims to recognise and predict customers’ needs by understanding their buying decisions from past interactions and transactions and adapting the business to meet evolving requirements and alleviate pain points. Simply put, it is the study of customers’ past to help an organisation serve them better in the future. Customer analysis marketing is crucial to any leading business’ overall marketing strategy. Employing customer analytics can enhance how your organisation interacts with its customers in several ways, including:

• Speaking the customer’s language when outlining communications and marketing materials that directly address their objectives
• Creating marketing campaigns that target specific demographics
• Understanding which marketing channels work best for your intended audience and investing in them accordingly
• Improving products and services based on customer reviews
• Building long-lasting customer relationships and enhancing customer loyalty

Customer analytics, customer analysis and consumer analytics are often used interchangeably, but there is a useful distinction. Customer analysis is the exercise of working out who your customers are and what they need, typically as a section of a marketing plan. Customer analytics is the ongoing, data-driven discipline that makes that analysis measurable and repeatable across every interaction, using customer data analytics on transactions, behaviour and feedback rather than one-off research. Consumer analytics applies the same methods to end consumers in business-to-consumer (B2C) markets. The four types of customer analytics (descriptive, diagnostic, predictive and prescriptive) are covered in a separate Infosys BPM blog.

Why companies need to conduct more effective customer analysis

Companies need to conduct more effective customer analysis because customer behaviour now changes faster than annual planning cycles, and decisions made on assumptions rather than evidence are expensive to reverse. Four reasons stand out:

• Customer expectations shift quickly. Buying patterns, preferred channels and tolerance for friction change within months, not years. Analysis run once at the start of a plan is out of date by the time the plan is executed; effective customer analysis is continuous.
• Attrition shows up in the data before it shows up in revenue. Falling purchase frequency, rising support contacts and declining engagement are visible weeks before a customer leaves. Organisations that analyse these signals can intervene while the relationship can still be saved.
• Marketing and service budgets need evidence. When every channel and campaign competes for the same budget, the teams that can show which customers respond to what, and at what cost, win the allocation argument.
• Customer data is now a regulated asset. Privacy laws such as the EU General Data Protection Regulation (GDPR), in force since May 2018, require a lawful basis and a stated purpose for processing personal data. Disciplined, purpose-led customer analysis is therefore a compliance requirement as well as a commercial one.

Benefits of customer analytics

Let’s take a closer look at the core benefits of scrutinising and acting on customer experience data analytics.

• Customer retention:

It costs less for businesses to satisfy existing customers than to acquire new ones. If your organisation is facing high customer attrition rates, customer analysis can help highlight common factors that were responsible for customer loss. With this data, you can undertake corrective measures.

• Customer engagement:

  Knowing your clientele makes it possible to create customised experiences that align with their buying habits. Crafting such tailor-made experiences requires in-depth knowledge of customer behaviour and their immediate needs. Staying aware of the difficulties and frustrations they experience equips an organisation with the tools to improve the experience and appropriately engage with customers.

• Boost sales and profits:

  Identifying and tracking key customer analytics metrics enable businesses to recognise high-value customers and engage with them to bolster relationships through special offers or upsell relevant products and services. Knowing where your profitable customers are, understanding their needs, and focusing your marketing activities on those customers are sure-fire ways to boost revenue.

• Validate expenses:

  Business decisions based on data-driven insights are hard to fault. If detailed customer data analyses indicate the need to invest in a marketing activity or upgrade a certain customer-facing process, you have the evidence to justify and validate the expenses associated with these actions.

What customer data does customer analytics use?

Customer data analytics draws on five kinds of data, and the most useful insights come from combining them rather than analysing any one in isolation:

• Behavioural data: website and app activity, content viewed, features used, cart and checkout actions.
• Transactional data: purchase history, order value, frequency, returns and payment method.
• Demographic and firmographic data: age, location and income band, and for business customers, industry, size and role.
• Attitudinal data: survey responses, reviews, Net Promoter Score (NPS) and customer satisfaction (CSAT) feedback, complaint themes.
• Operational and service data: support tickets, response and resolution times, channel used, repeat contacts.

How to run a customer analytics project: Eight steps

A systemised approach to collecting, categorising, managing, and analysing customer data helps draw accurate and actionable conclusions to add business value. The steps to executing a successful customer analytics project are as follows:

• Segment your existing customers based on geographic location, age, socio-economic status, the media they consume, and how they interact with your products or services.
• Know your customers better by conducting surveys, customer interviews, and focus group discussions.
• Analyse the existing feedback provided when customers are happy with a product or a service, need help, or wish to make a complaint. Such feedback helps understand customer issues and objectives.
• Collaborate to make the most of customer knowledge. Various teams gain different insights from their interactions with customers, and clear communication within the organisation is important to compile these insights into usable, structured data.
• Leverage analytics for accurate insights into customer behaviour, buying habits, client profitability, and more.
• Collate all the available data into buyer personas to clearly understand current and prospective customers and help identify your ideal customer.
• Collaborate and share customer analysis results and buyer personas across your organisation. Customer support, marketing, product, and sales teams can benefit from detailed customer research results and incorporate the findings into their processes.
• Decide which customer segments and buyer personas are immediate priorities and start working on them.

Customer analytics examples by industry

The same discipline produces different decisions depending on the sector:

• Retail and e-commerce: basket and browsing data identify which products are bought together and where shoppers abandon checkout, feeding personalised recommendations and targeted win-back offers.
• Banking and financial services: transaction patterns and channel usage flag customers likely to close accounts or move balances, so relationship teams can act before the customer does; the same data supports fraud and credit-risk models.
• Telecommunications: usage, billing and support-contact data combine into churn scores that decide which subscribers receive a retention offer, and which offer.
• Business-to-business (B2B) services: account health scores built from usage, support and renewal history tell account managers where to spend their time, and which accounts are ready for an upsell conversation.

How can Infosys BPM help?

Thorough and effective customer analytics can greatly impact your business, and getting it right means collaborating with an experienced data analytics partner. Infosys BPM offers a slew of Marketing and Customer Analytics Services that include solutions for tracking and analysing consumer data, covering descriptive, diagnostic, predictive and prescriptive analytics, with churn prediction, next-product-to-buy and share-of-wallet solutions built to improve customer lifetime value, reduce churn and lift campaign effectiveness. Reach out to know more about how your organisation can benefit from leveraging customer data.

Frequently asked questions

What metrics does customer analytics track?

The most common customer analytics metrics are customer lifetime value (CLV), customer acquisition cost (CAC), retention and churn rates, purchase frequency, average order value and satisfaction measures such as Net Promoter Score (NPS) or customer satisfaction (CSAT). Which ones matter depends on the goal: a retention programme watches churn rate and CLV, while an acquisition campaign watches CAC and conversion rate.

Is consumer analytics the same as customer analytics?

In practice the two terms are used interchangeably and the methods are the same. Where a distinction is drawn, consumer analytics refers to studying end consumers in business-to-consumer (B2C) markets, often using panel, survey and market-level data, while customer analytics covers any organisation’s own customers, including business accounts, using first-party transaction and interaction data.

What are the main challenges in implementing customer analytics?

The recurring obstacles are fragmented data held in separate systems (sales, marketing, support and finance each keep their own view of the customer), inconsistent data quality, privacy and consent rules that limit how data can be combined, and a gap between producing an insight and acting on it. A single agreed customer record, clear data governance and named ownership of each insight address most of these.

Can customer analytics be outsourced?

Yes. Many organisations run customer analytics through a business process management (BPM) partner that builds the customer data foundation, runs segmentation and predictive models such as churn and next-product-to-buy, and delivers insight into marketing, sales and service teams. The organisation keeps ownership of its customer data and strategy while the partner provides the analytics talent, tooling and scale.