How Customized BI Solutions Improve Customer Relationships
Providing a powerful customer experience is key to building a successful company and a sustainable revenue base. The experience potential customers have when interacting with your brand shapes how they feel and is often the determining factor in whether they purchase or remain loyal. One recent study showed that 88% of buyers say experience matters as much as the products or services a company sells.
Developing strategies to personalize the customer experience is crucial. Deloitte research shows that brands that are leaders in personalization have 150% greater customer loyalty than companies that have poor personalization. Yet, more than half of consumer say that personalization efforts feel “off-target” and don’t match their needs or preferences.
How do you make sure personalization efforts work efficiently and at scale? The answer lies in robust business intelligence grounded in data-driven strategies.
Meeting Customer Expectations
Customers today expect businesses to understand their wants and needs. That becomes increasingly challenging as consumer expectations are constantly evolving, competition is becoming more advanced, and more data is available. To be effective, companies need to personalize their approach to build lasting customer relationships.
Personalization leverages data, analytics, AI, and automation to send highly contextualized communication to specific customers wherever they interact with your brand. This goes further than simple segmentation, but drills down to the unique differences in small groups of customers to achieve deeper levels of personalization. However, this requires a deep understanding of your customers and the right BI tools to manage the data to produce actionable insights.
Benefits of Customer Personalization
When you can meet these customer expectations, there are significant benefits. McKinsey studies show a direct correlation between growth and performance for companies that deliver better personalization strategies — as much as a 40% faster revenue growth rate than competitors. The flip side is just as important. 76% of consumers say they are frustrated when they don’t get the level of personalization they expect from the brands or businesses they choose.
Personalization produces:
- Improved customer engagement
- Higher conversion rates
- Increased customer satisfaction
- Better customer loyalty
- More repeat purchases
Business Intelligence Tools Power Personalization
BI tools manage and analyze the data to enable personalization throughout the buyer journey. There are several key steps to unlock the power of personalization, starting with your data collection and BI platform.
Managing Your Data Ecosystem
Your personalization strategy will only be as good as the data you have. This requires the right foundation for data collection and analysis. Today’s marketers are often overwhelmed with data from disparate sources, including:
- First-party data: Directly from customers through owned channels like a website or mobile app.
- Second-party data: Acquired from another organization that collects that data directly from customers.
- Third-party data: Collected and sold by data aggregators who do not interact with customers directly.
Data must be aggregated across channels with strict governance policies to provide a comprehensive view. Unfortunately, many companies still have significant challenges with siloed data that is locked in various programs and platforms. Effective personalization requires a holistic view of your data across ERP, CRM, websites, social platforms, and sales channels. The best BI tools can combine multiple data sources into a dashboard to provide you with a comprehensive view of customer engagements for a single source of truth.
Understanding Your Customers
The first step in personalizing customer experiences is using BI to collect and analyze customer data. This includes gathering information on demographics, psychographics, buying behaviors, product preferences, communication choices, and more.
This enables companies to build out customer segments and common attributes to build detailed customer profiles. Data analysis will uncover patterns to inform personalization strategies.
Data-Driven Personalization
With these insights in hand, companies can personalize content across channels and touchpoints, tailored to segments and individuals. Some examples include:
- Targeted promotions, offers, and incentives
- Personalized product recommendations
- Curated product bundles
- Dynamic website, landing page, or product content
- Predictive analytics to anticipate customer needs
- Hyper-personalized marketing for email or social campaigns
Optimizing The Customer Journey
Business intelligence also plays a critical role in identifying customer challenges and pain points. By analyzing customer data and interactions, companies can gain deep insights into the customer journey.
Here’s an example: By analyzing data, companies may uncover that customers are struggling to complete online purchases and abandoning their carts. Data can help determine at which stage in the process they are dropping off. With this intelligence, companies can map the customer journey to detail where the disconnect occurs and provide personalized interactions at the right point in the buying process to help improve conversions.
Adding personalized support at key friction points can help anticipate and resolve concerns.
Measuring Personalization Impact
To refine strategies, BI dashboards track metrics like engagement rates, customer satisfaction, retention, referrals, and revenue growth. By tying these to personalization efforts, companies see the impact of their efforts and drive continuous optimization for improvement.
With data at the heart of your personalization strategy, you can treat each customer uniquely to optimize interactions and drive conversions.
How Businesses Can Use Customer Data to Create Personalized Experiences
With the proper foundation, businesses can leverage customer data in a wide variety of ways to create a personalized experience. Examples include:
Personalized Recommendations
Sophisticated algorithms analyze consumer data to determine what a particular shopper may be interested in based on their unique characteristics and behaviors. It’s such intelligence that drives Amazon’s recommendation engine, which is responsible for more than a third of sales, or Netflix’s personalized recommendation algorithm that drives 80% of viewing on the streaming platform.
Customized Messaging
Understanding detailed customer data also allows companies to segment their audiences and tailor messaging to resonate better with distinct customer groups.
For example, customers who regularly order diapers and baby products from an online retailer can receive targeted promotions for similar products that are age-appropriate.
Customer messaging can then be adapted based on variables like customer age, gender, past transactions, browsing trends, cart abandonment rate, favorite payment methods, and more. This creates opportunities to tailor emails, text messages, ads, and other communications to customers’ unique characteristics for better reception rather than relying on a generic, one-size-fits-all messaging strategy.
Predictive Analytics
Advanced analytics programs now enable an even deeper understanding of customers by using AI to predict their likely future interests based on existing data. Instead of simply responding to what customers have already done, data and machine learning give companies the power to anticipate what someone may want ahead of time.
This allows for extremely timely suggestions and personalized offers catered to an individual. For instance, predictive algorithms might identify that customers order products regularly, such as printer ink or other office supplies, and present customized offers to trigger reorders.
The ability to calibrate promotions based on consumer data helps ensure marketing spend is optimized at the one-to-one level.
Personalized Loyalty Programs
Data from BI analysis can also help personalize loyalty programs with customized rewards levels, point systems, and targeted incentives for each subscriber. Data on past purchases, store visits, browser sessions, and demographic details allows companies to divide up loyal customers into premium tiers. For example:
- Big spenders would automatically unlock VIP perks, early access to sales, or special treatment.
- Brand evangelists on social media can get points for shares and referrals.
- Loyalty promotions can recognized when customers have birthdays, anniversaries, or other special occasions.
Each touchpoint can be individualized to make loyal customers feel uniquely valued and catered to for their behaviors rather than taking a one-size-fits-all approach.
Improved Customer Service
94% of customers say they are more likely to make repeat purchases after a positive customer service experience. Yet, nearly three-quarters of consumers say they’ve had a bad customer service experience. This experience can be a differentiator.
BI tools help power the customer data service teams need to be effective. Access to this information helps human agents address issues faster, answer questions accurately, troubleshoot problems unique to that shopper’s history, and address prior experiences. This dramatically improves interactions and makes it easier for support teams to personalize interactions.
Wyn Enterprise: BI Tools for Customer Personalization at Scale
Wyn Enterprise is a powerful business intelligence tool that empowers your users with self-service BI to uncover insights to personalize customer experiences. Enabling embedded BI, you have access to customer data within the tools you already use. Wyn Enterprise’s unique server-based licensing approach also allows you to scale without adding additional cost. There are no per-used fees and no data limitations.
Wyn Enterprise empowers you to build a data-driven culture to personalize interactions and track the impact of your business strategy. Request your personalized demo or sign up for a free trial today.