Customer Analytics: How to Use It to Unlock Business Growth

Customer Analytics: How to Use It to Unlock Business Growth

What is Customer Analytics? 

Customer analytics is the powerful and clear investigation of customer information, and behavior with the objective to find out, attract and reach the relevant or suitable prospect.

It uses raw data to identify and understand customers’ needs and satisfaction.  

One main goal behind using the best customer analytics is to identify and retain the most profitable customers.

These analytics are for advanced technologies including segmentation, data visualization, information management, and predictive modeling.

This process involves the collection and analysis of customer behavioral data across a wide range of devices and channels.

Importance of Customer Analytics

A company uses customer behavior analytics in different strategies such as pricing, promotion, and management. These strategies won’t provide better results without understanding customer behavior.

The company collects customers’ data from different communication channels and uses predictive analytics to predict their behavior.

This concept further acts as a platform for creating different strategies like customer acquisition, customer retention, and customer engagement.

Customer acquisition: After doing research on customer behavior, prepare result-oriented marketing, and sales plans that fit the relevant customer.

You can reduce marketing costs by targeting the relevant and right customers. Increase the conversion ratio by creating personalized email marketing campaigns.     

Customer retention: You can use predictive customer analytics after conducting research on the churned customers.

Form the proper strategies to make them proactively engage with your product or service to prevent more churn.  

Customer engagement: Personalized experience plays a key role in effective customer engagement.

You can make them engage with your product by studying their challenges, pain points, and queries, and trying to provide solutions to all of these.

This customer engagement analytics will strengthen customer relationships.  

Customer analytics involves 3 different steps. These are:

1. Data Collection
2. Data Validation
3. Data Analysis  

Data Collection: – This process involves the collection of data from different sources of consumer interaction. These sources cover:

• Website analytics

There is a lot more information you can get from website visitors.

Different tools including Mixpanel, Google Analytics, Optimizely, and CrazyEgg provide you with valuable information. 

• Customer service calls

With the aid of customer service calls, you can learn what kind of queries your customers have in their minds.

Based on this qualitative data, you can do an in-detailed behavior analysis. 

• Customer feedback

This idea will provide direct information about customers’ sentiment about your brand and based on this you can measure different metrics including net promoter score and customer satisfaction.

You can conduct regular surveys to get customer feedback. 

Data Validation: Proper data validation is required for all the data. This is done to ensure the accuracy of your data.    

Data Analysis: Before you proceed with this process, keep customer personas in place.

By comparing customer use cases with collected data, you are able to create predictive models specifically for your business.

This gives a clear idea of how customers identify your product, what they search for, which features attract them most, and why they leave. 

In addition to these data, it is possible to analyze other details like customers’ job profiles, their age, location, sex, income, and marital status.

After collecting all this data, you can use various technologies like ML, and AI for data purposes to find underlying patterns within such data. 

How Does Customer Analytics Help Businesses Grow and Improve?

The use of customer journey analytics tools can impact different processes including pricing, promotion, management, etc.

Businesses can make use of this data to prepare customer-oriented strategies and get a 360-degree view of the customer.

This analysis will help businesses come up with correct acquisition, retention, and engagement plans for customers.

Hence, it is said that customer analytics will change the customer decision-making process. 

Increased brand awareness: A powerful brand is purely dependent on an effective customer experience strategy. 

Enhanced customer engagement: Customer engagement rate can be increased by offering relevant products and showing personalized ads to your customers.

You can use customer analytics software to identify their needs.  

Enhanced sales and revenue: You can reach the right audience using a data-driven approach, which helps in getting more clicks, and increasing conversion rates. 

Reduce customer acquisition costs: Getting more new customers won’t cost you much if you have sufficient data related to customer intelligence, preferences, etc. 

Top 3 Reasons Why Businesses Use Customer Analytics

It offers various benefits for almost all businesses:

Following Customer Safety: Customer safety has become the first priority for almost all businesses after the Covid-19 pandemic.

However, meeting the safety demands of customers has become challenging when businesses are doing many things to maintain operations. 

These analytics can help the company address the most common concerns of customers:

1. Monitor crowds
2. Clean and disinfect floors, and surfaces
3. Promote a social distancing, and safety culture in the workplace
4. Display necessary warnings, and signage
5. Maintain necessary temperatures

Improving impulse purchases: According to Ombori, “An impulse purchase is a sudden buying decision that happens prior to purchasing a product”.

Impulse buying is another method for companies to boost revenue. They can use customer journey analytics software to study the user behavior behind unplanned purchases and plan effectively. 

Increasing business operations: This technique also increases overall business operations.

1. Positive feedback from customer
2. Enhanced staffing management
3. Improved conversion rates
4. Reduction in wait times
5. Measure marketing efforts
6. Identify product demand using POS systems
7. Enhanced customer appreciation

Why Does Your SaaS Business Need Customer Analytics?

The SaaS market is growing rapidly, implementing modern technologies, and showing higher growth potential.

SaaS customer analytics is how SaaS businesses monitor and track data to make business decisions.

Let’s describe why your SaaS business needs customer analytics:

Identify growth blockers

A major benefit of using customer analytics tools in SaaS companies is that you can discover restrictions that prevent the business from achieving growth.

Is it possible to do this using an analytical dashboard? Yes, if it shows accurate metrics.

It’s also crucial to track the correlation between metrics and make decisions based on the results.

Also, identifying what correlates with increased sales helps in focusing more on resources so that they can improve elements of your business to achieve success.

Monitor growth dynamics

Monitoring business performance is one of the most important things SaaS companies can do.

Monthly or quarterly growth figures across major business metrics can indicate how the business is performing or signal that a strategy needs to be changed.

A perfectly managed SaaS analytics solution transforms gigabytes of sales and marketing information into an easily understandable SaaS dashboard that indicates all the major metrics.

SaaS businesses can track monthly, quarterly, and yearly changes in sales volume, churn rate, profits, and client growth.

See the performance of new strategies

Customer analytics for SaaS enable CEOs and business leaders to monitor the performance of major business decisions.

They can compare the performance of their email campaigns to determine the total number of monthly or yearly conversions. 

Compare the performance of sales after implementing a new strategy for sales or marketing. This would be challenging; you’d need to look for monthly growth comparisons with the previous periods.

Like this, you’re evaluating if the planned strategy is working better than the earlier one. 

Predict revenue possibilities

Data analytics has a special feature that is essential for startups to forecast profit possibilities in the future.

You can check by looking at the present average monthly revenue to predict the approximate revenue for the next 6–12 months.

This provides you with a rough calculation of a budget for the campaign that tells you when to cut spending and when to increase it.

Any forecast based on past data is just a projection. Numerous elements come into play to smoothly run a business.

How Do You Implement Customer Data Analytics? 

Every business uses the right tools to gather different types of data, but it’s difficult to understand which is the most important for your business.

So, instead of considering all of your customer data, it’s better to segment your data and give high priority to information related to your ideal customers. 

Keep the following 4 important factors in mind before implementing customer data analytics strategy:

Understand your customers: Customer journey mapping helps you understand who your customer is, what he/she prefers to buy, which mode of purchase attracts her/him, and how he/she interacts. 

Define your expected outcomes: Prior to data collection, you should have a clear idea of what are you trying to achieve, and what problems you are going to solve for your company or sales team.

These goals will take your analysis to the next level. 

Collect suitable data: No matter whether you are getting data from online forms, website browsings, blog comments, and email surveys but organize it systematically in a single dashboard.

In order to collect, and make proper use of data, organizations must collect it in a large quantities.

They may conduct surveys, and user research, and collect user data either through their app or website.

In the case of apps, businesses may have to define activities where data is captured through user actions like login, logout, submitting the details, etc. 

Give importance to data security:  A data breach can damage your business reputation, and customers don’t trust you, which leads to a negative impact on your profits.

Hence, always ensure your customers that their data is safe and only used wherever necessary.   

With websites, the majority of the customer analytics platforms indirectly collect all customer data.

The advanced feature of such platforms is they have a “central repository” for storing enough data. The goal of the central repository is to combine all data sources into a single view of your visitor. 

6 Challenges of Customer Analytics

Marketing yields better results if it’s done with a proper strategy in today’s data-driven world.

Customer analytics is the best choice for the marketing team to obtain much-needed insights while focusing on deadlines, but certain challenges impact the organization’s growth. 

Manual operations fatigue

Data cleaning, transformation, integration, and implementation of fresh analytics must be executed accurately.

Sometimes, your data analytics team automates some major tasks, but other teams still follow manual processes to perform certain tasks at regular intervals.

These manual processes are imperfect, boring, and time-consuming. However, manual procedures can result in high employee turnover.

The majority of the managers observed that the best-performing data analytics team members work hard because they have to frequently perform manual data processing.

Manual procedures impact the productivity of the whole data team in multiple ways. 

Bad data damages good reports

Bad data harms the hard work done by the whole data analytics team.

If business analysts or others frequently see data that is incorrect in analytics reports, they might decide not to respect or trust the performance or calibre of the data team. 

Data errors

No matter whether the data you receive comes from internal or external sources, there will be inaccuracies in the data. These inaccuracies can prevent your data operations from running smoothly.

Errors may be due to duplicate data or missing details about individuals. If the new algorithm doesn’t work as planned, it could lead to data errors. It is not easy to track data errors and resolve them immediately.

Improper optimization of data formats

Data present in operational systems is improperly structured in a format that lends itself to the perfect generation of analytics.

A database well-optimized for data analytics is structured to improve reads and aggregations. The schema of an analytics database must be effortlessly understandable by humans. 

The goal isn’t constant

Sales and marketing teams are expecting clients for a data analytics team. Their demands change frequently.

They demand faster responses, and sales and marketing teams keep generating frequent requests despite the best deliverability of the analytics team. 

The data analytics team doesn’t have clarity on what they are expecting. Sales and marketing people are not perfect at data analytics.

They don’t understand what insights can be achieved until someone from the data team explains or shows them.

In some cases, they don’t get a clear idea of what they need until and unless they view it in production.

Usually, sales and marketing professionals don’t have a proper plan for what they need for the next week, next quarter, or the upcoming year.

Eliminating data silos

While chasing business goals, businesses communicate with clients across all channels and gather huge quantities of data such as orders, returns, page views, downloads, social media, clicks, metrics, audio logs, and deliveries.

Later, this data can be integrated with third-party market, demographic, and psychographic data.

Whole data is gathered in different CRM, marketing automation, call center platforms, ERP, and MRP systems. But there is no communication between these two systems.

They are used on many software platforms and in a wide range of databases. They use multiple technologies and APIs.

Accessing this huge amount of data is a challenging task that requires multiple skills, and it’s difficult to find a person who can perform these tasks.

Combining data from these origins becomes a complicated task. 

Sales and marketing teams need immediate answers, whereas the data analytics team needs to work closely with IT to manage operational systems, strategize and incorporate architectural modifications, and build, test, or deploy fresh analytics.

This process is lengthy, complicated, and subject to multiple blockages.

What are the 5 Customer Analytics Categories?

Customer analytics are powerful tools for companies to better understand customers and change services.

It is proven that businesses that implement analytics report 110% higher ROI. You can use customer data analytics to track what customers did and what actions you should take to target them in the future. 

Listed below are 5 categories of customer analytics. By understanding each of these customer analytics categories you can tackle various challenges in the market:

Descriptive Analytics: This type of analytics includes data perfectly. Usually, organizations collect data in structured, semi-structured, or unstructured formats.

This data may not be easy to understand, so it’s important to make it understandable for customer success analysts.

With the help of descriptive analytics, an organization can understand the past, and enable result-oriented action plans for the future.  

 This technology provides different information including:

1. Browsing history of the customer
2. Customer information
3. Purchase history
4. Transactional behaviors and
5. CSAT information

Diagnostic Analytics: This technology uses customer behavior data to analyze the problems related to customer behaviors. 

Predictive Analytics: Organizations will look at both past and present data to identify the future opportunities of any event.

Predictive customer analytics will predict the performance of an organization to predict many outcomes.  

Prescriptive Analytics: It helps in personalizing the user experience and highlights what products yield better results at what time.

This enhances the prediction of the possibilities of converting or churning.

Segmentation Analytics: With this, you can improve personalized marketing efforts, content, and communication.

Further, you can understand user behavior by creating sub-segments.  

Customer Analytics Use Cases

Customer analytics integrated with ML algorithms and predictive analytics can help companies enhance revenue growth, user engagement, and loyalty. 

Some major use cases of customer analytics involve:

Sentiment analysis

Measure the audience’s mood to enhance service quality, upsell or cross-sell, or stop the interaction.

Lead qualification

Allocate valid and correct lead scores to target those audiences who are interested in purchasing and bring lifelong loyalty. 

Fraud detection

Fraud identification and prevention is a crucial use case of customer analytics, especially for financial institutions.

By examining customer information and behavior, companies can detect fraudulent or suspicious activities and avoid possible losses.

In the banking domain, forging checks, fraudulent amount transfers, and unauthorized use of credit cards are common.

Furthermore, banks look into audience profiles to identify the people who have been involved in such activities. 

Quality assurance 

Quality assurance is very important to the smooth running of your company’s operations and the quality of customer service.

Poor quality control can reduce the level of customer satisfaction, resulting in a reduction in market share and revenue.

Ineffective production increases warranty issues, repairs, and customer complaints.

Companies take advantage of customer analytics services to get insights associated with possible quality issues and trends.

Industry-specific Customer Analytics

Analytics plays a major role in all industries where companies try to enhance the online customer experience and drive conversion. 

Customer analytics in banking

Banks use analytics to ensure better performance and make major decisions.

Data sets will have a huge impact on regions such as fraud, NPA monitoring, measuring value, and compliance. 

Customer analytics in real estate 

Customer intelligence analytics can help in collecting data associated with your clients and developing a client profile for expanding your real estate company.

Making wise decisions on client retention, acquisition, and client purchasing habits is possible with the use of data. 

Customer analytics in retail

The retail sector uses customer behavior analytics to discover the target market and guarantee that the offers are relevant based on indicators. 

Customer analytics in eCommerce

Online stores use eCommerce analytics to find famous services or products and ensure that high-value audiences receive incentives.     

What is one of the Benefits of Customer Analytics for Sales and Marketing?

A majority of sales teams with perfect sales strategies often deliver below-average results.

It is just because of their poor strategies. To achieve successful results, you have to conduct customer analysis in a detailed manner. 

Let’s see some ways where customer analysis can benefit the sales and marketing teams:

Exposes the behavior of customers

If a client browses your website, they visit at least a single page for a specific reason.

Knowing the reason behind their visit gives you a clear idea of how to sell a product or your service to them. On a larger scale, it assists you in segmentation and personalized messaging.   

This is because every customer has different needs.

For example, if you’re selling insurance policies, your customers are most likely to get one or two requests for vehicle insurance, home insurance, and health insurance.

In that case, you cannot send a vehicle insurance email to someone interested in health insurance.

The goal of customer analysis is to understand the interests of every person by looking at the available data. 

Furthermore, with sufficient data available in one place, you can analyze the interactions of your customers with your brand.

This gives more clarity on which methods are better suited for what types of audiences.  

Ensures better customer targeting 

Marketing campaigns on LinkedIn, email, Facebook, and YouTube can be missed or hit if not done properly.

By looking at the sales pipeline containing customer information, you can analyze which sales copy brought them to your website, which product is generating more visitors, where the majority of your audiences are from, etc. 

Retains more audiences

According to a study, approximately 62% of companies are spending resources to satisfy the demands of their audiences. This tells you the importance of customer retention over acquisition. 

A recent study from Accenture reveals that 41% of audiences move to other businesses because they are not satisfied with the level of personalization of the service offered. 

Only a few audiences give negative feedback about services instead of shifting to other businesses.

Using customer analytics tools, you can understand the problems of audiences, their interests, and their preferred mode of communication. 

A CRM makes customer analysis easier 

CRMs are the most important tools that need to be used by the sales team. Executing customer analysis is not difficult using CRM.

The majority of CRM platforms provide the option to categorize leads based on their priority level and interactions.

Like this, you can customize your communication with leads and clients.

The CRM has a built-in feature to conduct detailed analysis of customers, demographics, lead origins, page visits, and so on.

These details are usually presented in a simple-to-understand dashboard that provides all major information related to your present users and prospects.

Boosting Retail and eCommerce Sales with Customer Analytics

The online browsing of eCommerce and retail businesses produces thousands of data volumes in a minute.

Audiences are utilizing multiple benefits of these businesses, making life smoother for the customers in the market. 

All products offered by retailers like apparel, furniture, food, and appliances provide door-to-door service, ensuring a better buyer experience.

A smoother experience encourages audiences to spend extra amounts, allowing eCommerce firms to produce sufficient revenue by offering customized experiences in the industry.

The success of eCommerce businesses depends on how they deal with customer preferences. The eCommerce customer analytics help them grow their customers. 

Retailers are slowly shifting to eCommerce for their items. They are making better decisions using customer analytics.

By implementing customer analytics in eCommerce, they can achieve increased sales.

These companies use eCommerce analytics to predict their client behavior, and address their demands, enabling them to deliver services and products that exceed their clients’ expectations. 

With both AI and advanced analytics, eCommerce marketers are investing properly in their marketing campaigns with the aid of business intelligence and data science teams.

Through better product recommendations, cross-selling, and targeted advertising, businesses can boost their user experiences.

Conclusion:

Customer Analytics plays a vital role in understanding users and nurturing customer relationships. If your business uses the customer data properly, it will achieve the success it anticipates. It provides valuable insights which help businesses to have a stronger relationship with their loyal customers.